\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "5 6 0 3 \n",
+ "6 7 0 1 \n",
+ "7 8 0 3 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "10 11 1 3 \n",
+ "11 12 1 1 \n",
+ "12 13 0 3 \n",
+ "13 14 0 3 \n",
+ "14 15 0 3 \n",
+ "15 16 1 2 \n",
+ "16 17 0 3 \n",
+ "17 18 1 2 \n",
+ "18 19 0 3 \n",
+ "19 20 1 3 \n",
+ "20 21 0 2 \n",
+ "21 22 1 2 \n",
+ "22 23 1 3 \n",
+ "23 24 1 1 \n",
+ "24 25 0 3 \n",
+ "25 26 1 3 \n",
+ "26 27 0 3 \n",
+ "27 28 0 1 \n",
+ "28 29 1 3 \n",
+ "29 30 0 3 \n",
+ ".. ... ... ... \n",
+ "50 51 0 3 \n",
+ "51 52 0 3 \n",
+ "52 53 1 1 \n",
+ "53 54 1 2 \n",
+ "54 55 0 1 \n",
+ "55 56 1 1 \n",
+ "56 57 1 2 \n",
+ "57 58 0 3 \n",
+ "58 59 1 2 \n",
+ "59 60 0 3 \n",
+ "60 61 0 3 \n",
+ "61 62 1 1 \n",
+ "62 63 0 1 \n",
+ "63 64 0 3 \n",
+ "64 65 0 1 \n",
+ "65 66 1 3 \n",
+ "66 67 1 2 \n",
+ "67 68 0 3 \n",
+ "68 69 1 3 \n",
+ "69 70 0 3 \n",
+ "70 71 0 2 \n",
+ "71 72 0 3 \n",
+ "72 73 0 2 \n",
+ "73 74 0 3 \n",
+ "74 75 1 3 \n",
+ "75 76 0 3 \n",
+ "76 77 0 3 \n",
+ "77 78 0 3 \n",
+ "78 79 1 2 \n",
+ "79 80 1 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.00 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.00 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 \n",
+ "4 Allen, Mr. William Henry male 35.00 0 \n",
+ "5 Moran, Mr. James male NaN 0 \n",
+ "6 McCarthy, Mr. Timothy J male 54.00 0 \n",
+ "7 Palsson, Master. Gosta Leonard male 2.00 3 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 \n",
+ "10 Sandstrom, Miss. Marguerite Rut female 4.00 1 \n",
+ "11 Bonnell, Miss. Elizabeth female 58.00 0 \n",
+ "12 Saundercock, Mr. William Henry male 20.00 0 \n",
+ "13 Andersson, Mr. Anders Johan male 39.00 1 \n",
+ "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 \n",
+ "15 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 \n",
+ "16 Rice, Master. Eugene male 2.00 4 \n",
+ "17 Williams, Mr. Charles Eugene male NaN 0 \n",
+ "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.00 1 \n",
+ "19 Masselmani, Mrs. Fatima female NaN 0 \n",
+ "20 Fynney, Mr. Joseph J male 35.00 0 \n",
+ "21 Beesley, Mr. Lawrence male 34.00 0 \n",
+ "22 McGowan, Miss. Anna \"Annie\" female 15.00 0 \n",
+ "23 Sloper, Mr. William Thompson male 28.00 0 \n",
+ "24 Palsson, Miss. Torborg Danira female 8.00 3 \n",
+ "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.00 1 \n",
+ "26 Emir, Mr. Farred Chehab male NaN 0 \n",
+ "27 Fortune, Mr. Charles Alexander male 19.00 3 \n",
+ "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n",
+ "29 Todoroff, Mr. Lalio male NaN 0 \n",
+ ".. ... ... ... ... \n",
+ "50 Panula, Master. Juha Niilo male 7.00 4 \n",
+ "51 Nosworthy, Mr. Richard Cater male 21.00 0 \n",
+ "52 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 \n",
+ "53 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... female 29.00 1 \n",
+ "54 Ostby, Mr. Engelhart Cornelius male 65.00 0 \n",
+ "55 Woolner, Mr. Hugh male NaN 0 \n",
+ "56 Rugg, Miss. Emily female 21.00 0 \n",
+ "57 Novel, Mr. Mansouer male 28.50 0 \n",
+ "58 West, Miss. Constance Mirium female 5.00 1 \n",
+ "59 Goodwin, Master. William Frederick male 11.00 5 \n",
+ "60 Sirayanian, Mr. Orsen male 22.00 0 \n",
+ "61 Icard, Miss. Amelie female 38.00 0 \n",
+ "62 Harris, Mr. Henry Birkhardt male 45.00 1 \n",
+ "63 Skoog, Master. Harald male 4.00 3 \n",
+ "64 Stewart, Mr. Albert A male NaN 0 \n",
+ "65 Moubarek, Master. Gerios male NaN 1 \n",
+ "66 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 \n",
+ "67 Crease, Mr. Ernest James male 19.00 0 \n",
+ "68 Andersson, Miss. Erna Alexandra female 17.00 4 \n",
+ "69 Kink, Mr. Vincenz male 26.00 2 \n",
+ "70 Jenkin, Mr. Stephen Curnow male 32.00 0 \n",
+ "71 Goodwin, Miss. Lillian Amy female 16.00 5 \n",
+ "72 Hood, Mr. Ambrose Jr male 21.00 0 \n",
+ "73 Chronopoulos, Mr. Apostolos male 26.00 1 \n",
+ "74 Bing, Mr. Lee male 32.00 0 \n",
+ "75 Moen, Mr. Sigurd Hansen male 25.00 0 \n",
+ "76 Staneff, Mr. Ivan male NaN 0 \n",
+ "77 Moutal, Mr. Rahamin Haim male NaN 0 \n",
+ "78 Caldwell, Master. Alden Gates male 0.83 0 \n",
+ "79 Dowdell, Miss. Elizabeth female 30.00 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S \n",
+ "5 0 330877 8.4583 NaN Q \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "7 1 349909 21.0750 NaN S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C \n",
+ "10 1 PP 9549 16.7000 G6 S \n",
+ "11 0 113783 26.5500 C103 S \n",
+ "12 0 A/5. 2151 8.0500 NaN S \n",
+ "13 5 347082 31.2750 NaN S \n",
+ "14 0 350406 7.8542 NaN S \n",
+ "15 0 248706 16.0000 NaN S \n",
+ "16 1 382652 29.1250 NaN Q \n",
+ "17 0 244373 13.0000 NaN S \n",
+ "18 0 345763 18.0000 NaN S \n",
+ "19 0 2649 7.2250 NaN C \n",
+ "20 0 239865 26.0000 NaN S \n",
+ "21 0 248698 13.0000 D56 S \n",
+ "22 0 330923 8.0292 NaN Q \n",
+ "23 0 113788 35.5000 A6 S \n",
+ "24 1 349909 21.0750 NaN S \n",
+ "25 5 347077 31.3875 NaN S \n",
+ "26 0 2631 7.2250 NaN C \n",
+ "27 2 19950 263.0000 C23 C25 C27 S \n",
+ "28 0 330959 7.8792 NaN Q \n",
+ "29 0 349216 7.8958 NaN S \n",
+ ".. ... ... ... ... ... \n",
+ "50 1 3101295 39.6875 NaN S \n",
+ "51 0 A/4. 39886 7.8000 NaN S \n",
+ "52 0 PC 17572 76.7292 D33 C \n",
+ "53 0 2926 26.0000 NaN S \n",
+ "54 1 113509 61.9792 B30 C \n",
+ "55 0 19947 35.5000 C52 S \n",
+ "56 0 C.A. 31026 10.5000 NaN S \n",
+ "57 0 2697 7.2292 NaN C \n",
+ "58 2 C.A. 34651 27.7500 NaN S \n",
+ "59 2 CA 2144 46.9000 NaN S \n",
+ "60 0 2669 7.2292 NaN C \n",
+ "61 0 113572 80.0000 B28 NaN \n",
+ "62 0 36973 83.4750 C83 S \n",
+ "63 2 347088 27.9000 NaN S \n",
+ "64 0 PC 17605 27.7208 NaN C \n",
+ "65 1 2661 15.2458 NaN C \n",
+ "66 0 C.A. 29395 10.5000 F33 S \n",
+ "67 0 S.P. 3464 8.1583 NaN S \n",
+ "68 2 3101281 7.9250 NaN S \n",
+ "69 0 315151 8.6625 NaN S \n",
+ "70 0 C.A. 33111 10.5000 NaN S \n",
+ "71 2 CA 2144 46.9000 NaN S \n",
+ "72 0 S.O.C. 14879 73.5000 NaN S \n",
+ "73 0 2680 14.4542 NaN C \n",
+ "74 0 1601 56.4958 NaN S \n",
+ "75 0 348123 7.6500 F G73 S \n",
+ "76 0 349208 7.8958 NaN S \n",
+ "77 0 374746 8.0500 NaN S \n",
+ "78 2 248738 29.0000 NaN S \n",
+ "79 0 364516 12.4750 NaN S \n",
+ "\n",
+ "[80 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(80)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Dictionary\n",
+ "- Survived: \t0 = No, 1 = Yes \n",
+ "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd \t\n",
+ "- sibsp:\t# of siblings / spouses aboard the Titanic \t\n",
+ "- parch:\t# of parents / children aboard the Titanic \t\n",
+ "- ticket:\tTicket number\t\n",
+ "- cabin:\tCabin number\t\n",
+ "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "_cell_guid": "5ebc1e0e-2b5a-4d92-98e0-defa019d4439",
+ "_uuid": "1892fbb34b26d775d1c428fdb7b6254449286b28"
+ },
+ "source": [
+ "**Total rows and columns**\n",
+ "\n",
+ "We can see that there are 891 rows and 12 columns in our training dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.00 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.00 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.00 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.00 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "Moran, Mr. James | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "McCarthy, Mr. Timothy J | \n", + "male | \n", + "54.00 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "S | \n", + "
| 7 | \n", + "8 | \n", + "0 | \n", + "3 | \n", + "Palsson, Master. Gosta Leonard | \n", + "male | \n", + "2.00 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | \n", + "female | \n", + "27.00 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "S | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "Nasser, Mrs. Nicholas (Adele Achem) | \n", + "female | \n", + "14.00 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "C | \n", + "
| 10 | \n", + "11 | \n", + "1 | \n", + "3 | \n", + "Sandstrom, Miss. Marguerite Rut | \n", + "female | \n", + "4.00 | \n", + "1 | \n", + "1 | \n", + "PP 9549 | \n", + "16.7000 | \n", + "G6 | \n", + "S | \n", + "
| 11 | \n", + "12 | \n", + "1 | \n", + "1 | \n", + "Bonnell, Miss. Elizabeth | \n", + "female | \n", + "58.00 | \n", + "0 | \n", + "0 | \n", + "113783 | \n", + "26.5500 | \n", + "C103 | \n", + "S | \n", + "
| 12 | \n", + "13 | \n", + "0 | \n", + "3 | \n", + "Saundercock, Mr. William Henry | \n", + "male | \n", + "20.00 | \n", + "0 | \n", + "0 | \n", + "A/5. 2151 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 13 | \n", + "14 | \n", + "0 | \n", + "3 | \n", + "Andersson, Mr. Anders Johan | \n", + "male | \n", + "39.00 | \n", + "1 | \n", + "5 | \n", + "347082 | \n", + "31.2750 | \n", + "NaN | \n", + "S | \n", + "
| 14 | \n", + "15 | \n", + "0 | \n", + "3 | \n", + "Vestrom, Miss. Hulda Amanda Adolfina | \n", + "female | \n", + "14.00 | \n", + "0 | \n", + "0 | \n", + "350406 | \n", + "7.8542 | \n", + "NaN | \n", + "S | \n", + "
| 15 | \n", + "16 | \n", + "1 | \n", + "2 | \n", + "Hewlett, Mrs. (Mary D Kingcome) | \n", + "female | \n", + "55.00 | \n", + "0 | \n", + "0 | \n", + "248706 | \n", + "16.0000 | \n", + "NaN | \n", + "S | \n", + "
| 16 | \n", + "17 | \n", + "0 | \n", + "3 | \n", + "Rice, Master. Eugene | \n", + "male | \n", + "2.00 | \n", + "4 | \n", + "1 | \n", + "382652 | \n", + "29.1250 | \n", + "NaN | \n", + "Q | \n", + "
| 17 | \n", + "18 | \n", + "1 | \n", + "2 | \n", + "Williams, Mr. Charles Eugene | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "244373 | \n", + "13.0000 | \n", + "NaN | \n", + "S | \n", + "
| 18 | \n", + "19 | \n", + "0 | \n", + "3 | \n", + "Vander Planke, Mrs. Julius (Emelia Maria Vande... | \n", + "female | \n", + "31.00 | \n", + "1 | \n", + "0 | \n", + "345763 | \n", + "18.0000 | \n", + "NaN | \n", + "S | \n", + "
| 19 | \n", + "20 | \n", + "1 | \n", + "3 | \n", + "Masselmani, Mrs. Fatima | \n", + "female | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "2649 | \n", + "7.2250 | \n", + "NaN | \n", + "C | \n", + "
| 20 | \n", + "21 | \n", + "0 | \n", + "2 | \n", + "Fynney, Mr. Joseph J | \n", + "male | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "239865 | \n", + "26.0000 | \n", + "NaN | \n", + "S | \n", + "
| 21 | \n", + "22 | \n", + "1 | \n", + "2 | \n", + "Beesley, Mr. Lawrence | \n", + "male | \n", + "34.00 | \n", + "0 | \n", + "0 | \n", + "248698 | \n", + "13.0000 | \n", + "D56 | \n", + "S | \n", + "
| 22 | \n", + "23 | \n", + "1 | \n", + "3 | \n", + "McGowan, Miss. Anna \"Annie\" | \n", + "female | \n", + "15.00 | \n", + "0 | \n", + "0 | \n", + "330923 | \n", + "8.0292 | \n", + "NaN | \n", + "Q | \n", + "
| 23 | \n", + "24 | \n", + "1 | \n", + "1 | \n", + "Sloper, Mr. William Thompson | \n", + "male | \n", + "28.00 | \n", + "0 | \n", + "0 | \n", + "113788 | \n", + "35.5000 | \n", + "A6 | \n", + "S | \n", + "
| 24 | \n", + "25 | \n", + "0 | \n", + "3 | \n", + "Palsson, Miss. Torborg Danira | \n", + "female | \n", + "8.00 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 25 | \n", + "26 | \n", + "1 | \n", + "3 | \n", + "Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... | \n", + "female | \n", + "38.00 | \n", + "1 | \n", + "5 | \n", + "347077 | \n", + "31.3875 | \n", + "NaN | \n", + "S | \n", + "
| 26 | \n", + "27 | \n", + "0 | \n", + "3 | \n", + "Emir, Mr. Farred Chehab | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "2631 | \n", + "7.2250 | \n", + "NaN | \n", + "C | \n", + "
| 27 | \n", + "28 | \n", + "0 | \n", + "1 | \n", + "Fortune, Mr. Charles Alexander | \n", + "male | \n", + "19.00 | \n", + "3 | \n", + "2 | \n", + "19950 | \n", + "263.0000 | \n", + "C23 C25 C27 | \n", + "S | \n", + "
| 28 | \n", + "29 | \n", + "1 | \n", + "3 | \n", + "O'Dwyer, Miss. Ellen \"Nellie\" | \n", + "female | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330959 | \n", + "7.8792 | \n", + "NaN | \n", + "Q | \n", + "
| 29 | \n", + "30 | \n", + "0 | \n", + "3 | \n", + "Todoroff, Mr. Lalio | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "349216 | \n", + "7.8958 | \n", + "NaN | \n", + "S | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 50 | \n", + "51 | \n", + "0 | \n", + "3 | \n", + "Panula, Master. Juha Niilo | \n", + "male | \n", + "7.00 | \n", + "4 | \n", + "1 | \n", + "3101295 | \n", + "39.6875 | \n", + "NaN | \n", + "S | \n", + "
| 51 | \n", + "52 | \n", + "0 | \n", + "3 | \n", + "Nosworthy, Mr. Richard Cater | \n", + "male | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "A/4. 39886 | \n", + "7.8000 | \n", + "NaN | \n", + "S | \n", + "
| 52 | \n", + "53 | \n", + "1 | \n", + "1 | \n", + "Harper, Mrs. Henry Sleeper (Myna Haxtun) | \n", + "female | \n", + "49.00 | \n", + "1 | \n", + "0 | \n", + "PC 17572 | \n", + "76.7292 | \n", + "D33 | \n", + "C | \n", + "
| 53 | \n", + "54 | \n", + "1 | \n", + "2 | \n", + "Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... | \n", + "female | \n", + "29.00 | \n", + "1 | \n", + "0 | \n", + "2926 | \n", + "26.0000 | \n", + "NaN | \n", + "S | \n", + "
| 54 | \n", + "55 | \n", + "0 | \n", + "1 | \n", + "Ostby, Mr. Engelhart Cornelius | \n", + "male | \n", + "65.00 | \n", + "0 | \n", + "1 | \n", + "113509 | \n", + "61.9792 | \n", + "B30 | \n", + "C | \n", + "
| 55 | \n", + "56 | \n", + "1 | \n", + "1 | \n", + "Woolner, Mr. Hugh | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "19947 | \n", + "35.5000 | \n", + "C52 | \n", + "S | \n", + "
| 56 | \n", + "57 | \n", + "1 | \n", + "2 | \n", + "Rugg, Miss. Emily | \n", + "female | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 31026 | \n", + "10.5000 | \n", + "NaN | \n", + "S | \n", + "
| 57 | \n", + "58 | \n", + "0 | \n", + "3 | \n", + "Novel, Mr. Mansouer | \n", + "male | \n", + "28.50 | \n", + "0 | \n", + "0 | \n", + "2697 | \n", + "7.2292 | \n", + "NaN | \n", + "C | \n", + "
| 58 | \n", + "59 | \n", + "1 | \n", + "2 | \n", + "West, Miss. Constance Mirium | \n", + "female | \n", + "5.00 | \n", + "1 | \n", + "2 | \n", + "C.A. 34651 | \n", + "27.7500 | \n", + "NaN | \n", + "S | \n", + "
| 59 | \n", + "60 | \n", + "0 | \n", + "3 | \n", + "Goodwin, Master. William Frederick | \n", + "male | \n", + "11.00 | \n", + "5 | \n", + "2 | \n", + "CA 2144 | \n", + "46.9000 | \n", + "NaN | \n", + "S | \n", + "
| 60 | \n", + "61 | \n", + "0 | \n", + "3 | \n", + "Sirayanian, Mr. Orsen | \n", + "male | \n", + "22.00 | \n", + "0 | \n", + "0 | \n", + "2669 | \n", + "7.2292 | \n", + "NaN | \n", + "C | \n", + "
| 61 | \n", + "62 | \n", + "1 | \n", + "1 | \n", + "Icard, Miss. Amelie | \n", + "female | \n", + "38.00 | \n", + "0 | \n", + "0 | \n", + "113572 | \n", + "80.0000 | \n", + "B28 | \n", + "NaN | \n", + "
| 62 | \n", + "63 | \n", + "0 | \n", + "1 | \n", + "Harris, Mr. Henry Birkhardt | \n", + "male | \n", + "45.00 | \n", + "1 | \n", + "0 | \n", + "36973 | \n", + "83.4750 | \n", + "C83 | \n", + "S | \n", + "
| 63 | \n", + "64 | \n", + "0 | \n", + "3 | \n", + "Skoog, Master. Harald | \n", + "male | \n", + "4.00 | \n", + "3 | \n", + "2 | \n", + "347088 | \n", + "27.9000 | \n", + "NaN | \n", + "S | \n", + "
| 64 | \n", + "65 | \n", + "0 | \n", + "1 | \n", + "Stewart, Mr. Albert A | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "PC 17605 | \n", + "27.7208 | \n", + "NaN | \n", + "C | \n", + "
| 65 | \n", + "66 | \n", + "1 | \n", + "3 | \n", + "Moubarek, Master. Gerios | \n", + "male | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "2661 | \n", + "15.2458 | \n", + "NaN | \n", + "C | \n", + "
| 66 | \n", + "67 | \n", + "1 | \n", + "2 | \n", + "Nye, Mrs. (Elizabeth Ramell) | \n", + "female | \n", + "29.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 29395 | \n", + "10.5000 | \n", + "F33 | \n", + "S | \n", + "
| 67 | \n", + "68 | \n", + "0 | \n", + "3 | \n", + "Crease, Mr. Ernest James | \n", + "male | \n", + "19.00 | \n", + "0 | \n", + "0 | \n", + "S.P. 3464 | \n", + "8.1583 | \n", + "NaN | \n", + "S | \n", + "
| 68 | \n", + "69 | \n", + "1 | \n", + "3 | \n", + "Andersson, Miss. Erna Alexandra | \n", + "female | \n", + "17.00 | \n", + "4 | \n", + "2 | \n", + "3101281 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 69 | \n", + "70 | \n", + "0 | \n", + "3 | \n", + "Kink, Mr. Vincenz | \n", + "male | \n", + "26.00 | \n", + "2 | \n", + "0 | \n", + "315151 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "
| 70 | \n", + "71 | \n", + "0 | \n", + "2 | \n", + "Jenkin, Mr. Stephen Curnow | \n", + "male | \n", + "32.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 33111 | \n", + "10.5000 | \n", + "NaN | \n", + "S | \n", + "
| 71 | \n", + "72 | \n", + "0 | \n", + "3 | \n", + "Goodwin, Miss. Lillian Amy | \n", + "female | \n", + "16.00 | \n", + "5 | \n", + "2 | \n", + "CA 2144 | \n", + "46.9000 | \n", + "NaN | \n", + "S | \n", + "
| 72 | \n", + "73 | \n", + "0 | \n", + "2 | \n", + "Hood, Mr. Ambrose Jr | \n", + "male | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "S.O.C. 14879 | \n", + "73.5000 | \n", + "NaN | \n", + "S | \n", + "
| 73 | \n", + "74 | \n", + "0 | \n", + "3 | \n", + "Chronopoulos, Mr. Apostolos | \n", + "male | \n", + "26.00 | \n", + "1 | \n", + "0 | \n", + "2680 | \n", + "14.4542 | \n", + "NaN | \n", + "C | \n", + "
| 74 | \n", + "75 | \n", + "1 | \n", + "3 | \n", + "Bing, Mr. Lee | \n", + "male | \n", + "32.00 | \n", + "0 | \n", + "0 | \n", + "1601 | \n", + "56.4958 | \n", + "NaN | \n", + "S | \n", + "
| 75 | \n", + "76 | \n", + "0 | \n", + "3 | \n", + "Moen, Mr. Sigurd Hansen | \n", + "male | \n", + "25.00 | \n", + "0 | \n", + "0 | \n", + "348123 | \n", + "7.6500 | \n", + "F G73 | \n", + "S | \n", + "
| 76 | \n", + "77 | \n", + "0 | \n", + "3 | \n", + "Staneff, Mr. Ivan | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "349208 | \n", + "7.8958 | \n", + "NaN | \n", + "S | \n", + "
| 77 | \n", + "78 | \n", + "0 | \n", + "3 | \n", + "Moutal, Mr. Rahamin Haim | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "374746 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 78 | \n", + "79 | \n", + "1 | \n", + "2 | \n", + "Caldwell, Master. Alden Gates | \n", + "male | \n", + "0.83 | \n", + "0 | \n", + "2 | \n", + "248738 | \n", + "29.0000 | \n", + "NaN | \n", + "S | \n", + "
| 79 | \n", + "80 | \n", + "1 | \n", + "3 | \n", + "Dowdell, Miss. Elizabeth | \n", + "female | \n", + "30.00 | \n", + "0 | \n", + "0 | \n", + "364516 | \n", + "12.4750 | \n", + "NaN | \n", + "S | \n", + "
80 rows × 12 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Pclass Name Sex \\\n",
+ "0 892 3 Kelly, Mr. James male \n",
+ "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
+ "2 894 2 Myles, Mr. Thomas Francis male \n",
+ "3 895 3 Wirz, Mr. Albert male \n",
+ "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "0 34.5 0 0 330911 7.8292 NaN Q \n",
+ "1 47.0 1 0 363272 7.0000 NaN S \n",
+ "2 62.0 0 0 240276 9.6875 NaN Q \n",
+ "3 27.0 0 0 315154 8.6625 NaN S \n",
+ "4 22.0 1 1 3101298 12.2875 NaN S "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "_cell_guid": "ed1e7849-d1b6-490d-b86b-9ca71dfafc7d",
+ "_uuid": "5a641beccf0e555dfd7b9a53a17188ea6edef95b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(891, 12)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(418, 11)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "_cell_guid": "418b8a69-f2aa-442d-8f45-fa8887190938",
+ "_uuid": "4ee2591110660a4a16b3da7a7530f0945e121b46"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "Kelly, Mr. James | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "Wilkes, Mrs. James (Ellen Needs) | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "Myles, Mr. Thomas Francis | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "Wirz, Mr. Albert | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "Hirvonen, Mrs. Alexander (Helga E Lindqvist) | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1 how titanic sank?\n",
+ "sank from the bow of the ship where third class rooms located \n",
+ "conclusion, Pclass is key feature for classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
![](\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\"/)
"
+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "5 6 0 3 \n",
+ "6 7 0 1 \n",
+ "7 8 0 3 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "5 Moran, Mr. James male NaN 0 \n",
+ "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
+ "7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S \n",
+ "5 0 330877 8.4583 NaN Q \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "7 1 349909 21.0750 NaN S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.2 Name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "train_test_data = [train, test] # combining train and test dataset\n",
+ "\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Mr 517\n",
+ "Miss 182\n",
+ "Mrs 125\n",
+ "Master 40\n",
+ "Dr 7\n",
+ "Rev 6\n",
+ "Col 2\n",
+ "Major 2\n",
+ "Mlle 2\n",
+ "Countess 1\n",
+ "Ms 1\n",
+ "Lady 1\n",
+ "Jonkheer 1\n",
+ "Don 1\n",
+ "Mme 1\n",
+ "Capt 1\n",
+ "Sir 1\n",
+ "Name: Title, dtype: int64"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train['Title'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Mr 240\n",
+ "Miss 78\n",
+ "Mrs 72\n",
+ "Master 21\n",
+ "Col 2\n",
+ "Rev 2\n",
+ "Dona 1\n",
+ "Ms 1\n",
+ "Dr 1\n",
+ "Name: Title, dtype: int64"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test['Title'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Title map\n",
+ "Mr : 0 \n",
+ "Miss : 1 \n",
+ "Mrs: 2 \n",
+ "Others: 3\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
+ " \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
+ " \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Title'] = dataset['Title'].map(title_mapping)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "Moran, Mr. James | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "McCarthy, Mr. Timothy J | \n", + "male | \n", + "54.0 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "S | \n", + "
| 7 | \n", + "8 | \n", + "0 | \n", + "3 | \n", + "Palsson, Master. Gosta Leonard | \n", + "male | \n", + "2.0 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | \n", + "female | \n", + "27.0 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "S | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "Nasser, Mrs. Nicholas (Adele Achem) | \n", + "female | \n", + "14.0 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "C | \n", + "
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+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
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+ "data": {
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+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
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| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "Kelly, Mr. James | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "Wilkes, Mrs. James (Ellen Needs) | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "Myles, Mr. Thomas Francis | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "Wirz, Mr. Albert | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "Hirvonen, Mrs. Alexander (Helga E Lindqvist) | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch \\\n",
+ "0 1 0 3 male 22.0 1 0 \n",
+ "1 2 1 1 female 38.0 1 0 \n",
+ "2 3 1 3 female 26.0 0 0 \n",
+ "3 4 1 1 female 35.0 1 0 \n",
+ "4 5 0 3 male 35.0 0 0 \n",
+ "\n",
+ " Ticket Fare Cabin Embarked Title \n",
+ "0 A/5 21171 7.2500 NaN S 0 \n",
+ "1 PC 17599 71.2833 C85 C 2 \n",
+ "2 STON/O2. 3101282 7.9250 NaN S 1 \n",
+ "3 113803 53.1000 C123 S 2 \n",
+ "4 373450 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Pclass Sex Age SibSp Parch Ticket Fare Cabin \\\n",
+ "0 892 3 male 34.5 0 0 330911 7.8292 NaN \n",
+ "1 893 3 female 47.0 1 0 363272 7.0000 NaN \n",
+ "2 894 2 male 62.0 0 0 240276 9.6875 NaN \n",
+ "3 895 3 male 27.0 0 0 315154 8.6625 NaN \n",
+ "4 896 3 female 22.0 1 1 3101298 12.2875 NaN \n",
+ "\n",
+ " Embarked Title \n",
+ "0 Q 0 \n",
+ "1 S 2 \n",
+ "2 Q 0 \n",
+ "3 S 0 \n",
+ "4 S 2 "
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.3 Sex\n",
+ "\n",
+ "male: 0\n",
+ "female: 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "sex_mapping = {\"male\": 0, \"female\": 1}\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 22.00 1 0 A/5 21171 \n",
+ "1 2 1 1 1 38.00 1 0 PC 17599 \n",
+ "2 3 1 3 1 26.00 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 35.00 1 0 113803 \n",
+ "4 5 0 3 0 35.00 0 0 373450 \n",
+ "5 6 0 3 0 NaN 0 0 330877 \n",
+ "6 7 0 1 0 54.00 0 0 17463 \n",
+ "7 8 0 3 0 2.00 3 1 349909 \n",
+ "8 9 1 3 1 27.00 0 2 347742 \n",
+ "9 10 1 2 1 14.00 1 0 237736 \n",
+ "10 11 1 3 1 4.00 1 1 PP 9549 \n",
+ "11 12 1 1 1 58.00 0 0 113783 \n",
+ "12 13 0 3 0 20.00 0 0 A/5. 2151 \n",
+ "13 14 0 3 0 39.00 1 5 347082 \n",
+ "14 15 0 3 1 14.00 0 0 350406 \n",
+ "15 16 1 2 1 55.00 0 0 248706 \n",
+ "16 17 0 3 0 2.00 4 1 382652 \n",
+ "17 18 1 2 0 NaN 0 0 244373 \n",
+ "18 19 0 3 1 31.00 1 0 345763 \n",
+ "19 20 1 3 1 NaN 0 0 2649 \n",
+ "20 21 0 2 0 35.00 0 0 239865 \n",
+ "21 22 1 2 0 34.00 0 0 248698 \n",
+ "22 23 1 3 1 15.00 0 0 330923 \n",
+ "23 24 1 1 0 28.00 0 0 113788 \n",
+ "24 25 0 3 1 8.00 3 1 349909 \n",
+ "25 26 1 3 1 38.00 1 5 347077 \n",
+ "26 27 0 3 0 NaN 0 0 2631 \n",
+ "27 28 0 1 0 19.00 3 2 19950 \n",
+ "28 29 1 3 1 NaN 0 0 330959 \n",
+ "29 30 0 3 0 NaN 0 0 349216 \n",
+ ".. ... ... ... ... ... ... ... ... \n",
+ "70 71 0 2 0 32.00 0 0 C.A. 33111 \n",
+ "71 72 0 3 1 16.00 5 2 CA 2144 \n",
+ "72 73 0 2 0 21.00 0 0 S.O.C. 14879 \n",
+ "73 74 0 3 0 26.00 1 0 2680 \n",
+ "74 75 1 3 0 32.00 0 0 1601 \n",
+ "75 76 0 3 0 25.00 0 0 348123 \n",
+ "76 77 0 3 0 NaN 0 0 349208 \n",
+ "77 78 0 3 0 NaN 0 0 374746 \n",
+ "78 79 1 2 0 0.83 0 2 248738 \n",
+ "79 80 1 3 1 30.00 0 0 364516 \n",
+ "80 81 0 3 0 22.00 0 0 345767 \n",
+ "81 82 1 3 0 29.00 0 0 345779 \n",
+ "82 83 1 3 1 NaN 0 0 330932 \n",
+ "83 84 0 1 0 28.00 0 0 113059 \n",
+ "84 85 1 2 1 17.00 0 0 SO/C 14885 \n",
+ "85 86 1 3 1 33.00 3 0 3101278 \n",
+ "86 87 0 3 0 16.00 1 3 W./C. 6608 \n",
+ "87 88 0 3 0 NaN 0 0 SOTON/OQ 392086 \n",
+ "88 89 1 1 1 23.00 3 2 19950 \n",
+ "89 90 0 3 0 24.00 0 0 343275 \n",
+ "90 91 0 3 0 29.00 0 0 343276 \n",
+ "91 92 0 3 0 20.00 0 0 347466 \n",
+ "92 93 0 1 0 46.00 1 0 W.E.P. 5734 \n",
+ "93 94 0 3 0 26.00 1 2 C.A. 2315 \n",
+ "94 95 0 3 0 59.00 0 0 364500 \n",
+ "95 96 0 3 0 NaN 0 0 374910 \n",
+ "96 97 0 1 0 71.00 0 0 PC 17754 \n",
+ "97 98 1 1 0 23.00 0 1 PC 17759 \n",
+ "98 99 1 2 1 34.00 0 1 231919 \n",
+ "99 100 0 2 0 34.00 1 0 244367 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 \n",
+ "5 8.4583 NaN Q 0 \n",
+ "6 51.8625 E46 S 0 \n",
+ "7 21.0750 NaN S 3 \n",
+ "8 11.1333 NaN S 2 \n",
+ "9 30.0708 NaN C 2 \n",
+ "10 16.7000 G6 S 1 \n",
+ "11 26.5500 C103 S 1 \n",
+ "12 8.0500 NaN S 0 \n",
+ "13 31.2750 NaN S 0 \n",
+ "14 7.8542 NaN S 1 \n",
+ "15 16.0000 NaN S 2 \n",
+ "16 29.1250 NaN Q 3 \n",
+ "17 13.0000 NaN S 0 \n",
+ "18 18.0000 NaN S 2 \n",
+ "19 7.2250 NaN C 2 \n",
+ "20 26.0000 NaN S 0 \n",
+ "21 13.0000 D56 S 0 \n",
+ "22 8.0292 NaN Q 1 \n",
+ "23 35.5000 A6 S 0 \n",
+ "24 21.0750 NaN S 1 \n",
+ "25 31.3875 NaN S 2 \n",
+ "26 7.2250 NaN C 0 \n",
+ "27 263.0000 C23 C25 C27 S 0 \n",
+ "28 7.8792 NaN Q 1 \n",
+ "29 7.8958 NaN S 0 \n",
+ ".. ... ... ... ... \n",
+ "70 10.5000 NaN S 0 \n",
+ "71 46.9000 NaN S 1 \n",
+ "72 73.5000 NaN S 0 \n",
+ "73 14.4542 NaN C 0 \n",
+ "74 56.4958 NaN S 0 \n",
+ "75 7.6500 F G73 S 0 \n",
+ "76 7.8958 NaN S 0 \n",
+ "77 8.0500 NaN S 0 \n",
+ "78 29.0000 NaN S 3 \n",
+ "79 12.4750 NaN S 1 \n",
+ "80 9.0000 NaN S 0 \n",
+ "81 9.5000 NaN S 0 \n",
+ "82 7.7875 NaN Q 1 \n",
+ "83 47.1000 NaN S 0 \n",
+ "84 10.5000 NaN S 1 \n",
+ "85 15.8500 NaN S 2 \n",
+ "86 34.3750 NaN S 0 \n",
+ "87 8.0500 NaN S 0 \n",
+ "88 263.0000 C23 C25 C27 S 1 \n",
+ "89 8.0500 NaN S 0 \n",
+ "90 8.0500 NaN S 0 \n",
+ "91 7.8542 NaN S 0 \n",
+ "92 61.1750 E31 S 0 \n",
+ "93 20.5750 NaN S 0 \n",
+ "94 7.2500 NaN S 0 \n",
+ "95 8.0500 NaN S 0 \n",
+ "96 34.6542 A5 C 0 \n",
+ "97 63.3583 D10 D12 C 0 \n",
+ "98 23.0000 NaN S 2 \n",
+ "99 26.0000 NaN S 0 \n",
+ "\n",
+ "[100 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
+ "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
+ "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 30.0\n",
+ "1 35.0\n",
+ "2 21.0\n",
+ "3 35.0\n",
+ "4 30.0\n",
+ "5 30.0\n",
+ "6 30.0\n",
+ "7 9.0\n",
+ "8 35.0\n",
+ "9 35.0\n",
+ "10 21.0\n",
+ "11 21.0\n",
+ "12 30.0\n",
+ "13 30.0\n",
+ "14 21.0\n",
+ "15 35.0\n",
+ "16 9.0\n",
+ "17 30.0\n",
+ "18 35.0\n",
+ "19 35.0\n",
+ "20 30.0\n",
+ "21 30.0\n",
+ "22 21.0\n",
+ "23 30.0\n",
+ "24 21.0\n",
+ "25 35.0\n",
+ "26 30.0\n",
+ "27 30.0\n",
+ "28 21.0\n",
+ "29 30.0\n",
+ " ... \n",
+ "861 30.0\n",
+ "862 35.0\n",
+ "863 21.0\n",
+ "864 30.0\n",
+ "865 35.0\n",
+ "866 21.0\n",
+ "867 30.0\n",
+ "868 30.0\n",
+ "869 9.0\n",
+ "870 30.0\n",
+ "871 35.0\n",
+ "872 30.0\n",
+ "873 30.0\n",
+ "874 35.0\n",
+ "875 21.0\n",
+ "876 30.0\n",
+ "877 30.0\n",
+ "878 30.0\n",
+ "879 35.0\n",
+ "880 35.0\n",
+ "881 30.0\n",
+ "882 21.0\n",
+ "883 30.0\n",
+ "884 30.0\n",
+ "885 35.0\n",
+ "886 9.0\n",
+ "887 21.0\n",
+ "888 21.0\n",
+ "889 30.0\n",
+ "890 30.0\n",
+ "Name: Age, Length: 891, dtype: float64"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(30)\n",
+ "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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GN2nSJB544AGeffZZ/u3f/o0ZM2bwzW9+87zr+v1+fvrTn1JcXMy3vvUtamtr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\nlGVZ+JaPb/v0eTOmfEyYBOOBMapOCI8VqnGF4XA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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "22.00 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "38.00 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "26.00 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "35.00 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
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100 rows × 12 columns
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.6 Fare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "5 6 0 3 0 2.0 0 0 330877 \n",
+ "6 7 0 1 0 3.0 0 0 17463 \n",
+ "7 8 0 3 0 0.0 3 1 349909 \n",
+ "8 9 1 3 1 2.0 0 2 347742 \n",
+ "9 10 1 2 1 0.0 1 0 237736 \n",
+ "10 11 1 3 1 0.0 1 1 PP 9549 \n",
+ "11 12 1 1 1 3.0 0 0 113783 \n",
+ "12 13 0 3 0 1.0 0 0 A/5. 2151 \n",
+ "13 14 0 3 0 3.0 1 5 347082 \n",
+ "14 15 0 3 1 0.0 0 0 350406 \n",
+ "15 16 1 2 1 3.0 0 0 248706 \n",
+ "16 17 0 3 0 0.0 4 1 382652 \n",
+ "17 18 1 2 0 2.0 0 0 244373 \n",
+ "18 19 0 3 1 2.0 1 0 345763 \n",
+ "19 20 1 3 1 2.0 0 0 2649 \n",
+ "20 21 0 2 0 2.0 0 0 239865 \n",
+ "21 22 1 2 0 2.0 0 0 248698 \n",
+ "22 23 1 3 1 0.0 0 0 330923 \n",
+ "23 24 1 1 0 2.0 0 0 113788 \n",
+ "24 25 0 3 1 0.0 3 1 349909 \n",
+ "25 26 1 3 1 3.0 1 5 347077 \n",
+ "26 27 0 3 0 2.0 0 0 2631 \n",
+ "27 28 0 1 0 1.0 3 2 19950 \n",
+ "28 29 1 3 1 1.0 0 0 330959 \n",
+ "29 30 0 3 0 2.0 0 0 349216 \n",
+ "30 31 0 1 0 3.0 0 0 PC 17601 \n",
+ "31 32 1 1 1 2.0 1 0 PC 17569 \n",
+ "32 33 1 3 1 1.0 0 0 335677 \n",
+ "33 34 0 2 0 4.0 0 0 C.A. 24579 \n",
+ "34 35 0 1 0 2.0 1 0 PC 17604 \n",
+ "35 36 0 1 0 3.0 1 0 113789 \n",
+ "36 37 1 3 0 2.0 0 0 2677 \n",
+ "37 38 0 3 0 1.0 0 0 A./5. 2152 \n",
+ "38 39 0 3 1 1.0 2 0 345764 \n",
+ "39 40 1 3 1 0.0 1 0 2651 \n",
+ "40 41 0 3 1 3.0 1 0 7546 \n",
+ "41 42 0 2 1 2.0 1 0 11668 \n",
+ "42 43 0 3 0 2.0 0 0 349253 \n",
+ "43 44 1 2 1 0.0 1 2 SC/Paris 2123 \n",
+ "44 45 1 3 1 1.0 0 0 330958 \n",
+ "45 46 0 3 0 2.0 0 0 S.C./A.4. 23567 \n",
+ "46 47 0 3 0 2.0 1 0 370371 \n",
+ "47 48 1 3 1 1.0 0 0 14311 \n",
+ "48 49 0 3 0 2.0 2 0 2662 \n",
+ "49 50 0 3 1 1.0 1 0 349237 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN 0 0 \n",
+ "1 71.2833 C85 1 2 \n",
+ "2 7.9250 NaN 0 1 \n",
+ "3 53.1000 C123 0 2 \n",
+ "4 8.0500 NaN 0 0 \n",
+ "5 8.4583 NaN 2 0 \n",
+ "6 51.8625 E46 0 0 \n",
+ "7 21.0750 NaN 0 3 \n",
+ "8 11.1333 NaN 0 2 \n",
+ "9 30.0708 NaN 1 2 \n",
+ "10 16.7000 G6 0 1 \n",
+ "11 26.5500 C103 0 1 \n",
+ "12 8.0500 NaN 0 0 \n",
+ "13 31.2750 NaN 0 0 \n",
+ "14 7.8542 NaN 0 1 \n",
+ "15 16.0000 NaN 0 2 \n",
+ "16 29.1250 NaN 2 3 \n",
+ "17 13.0000 NaN 0 0 \n",
+ "18 18.0000 NaN 0 2 \n",
+ "19 7.2250 NaN 1 2 \n",
+ "20 26.0000 NaN 0 0 \n",
+ "21 13.0000 D56 0 0 \n",
+ "22 8.0292 NaN 2 1 \n",
+ "23 35.5000 A6 0 0 \n",
+ "24 21.0750 NaN 0 1 \n",
+ "25 31.3875 NaN 0 2 \n",
+ "26 7.2250 NaN 1 0 \n",
+ "27 263.0000 C23 C25 C27 0 0 \n",
+ "28 7.8792 NaN 2 1 \n",
+ "29 7.8958 NaN 0 0 \n",
+ "30 27.7208 NaN 1 3 \n",
+ "31 146.5208 B78 1 2 \n",
+ "32 7.7500 NaN 2 1 \n",
+ "33 10.5000 NaN 0 0 \n",
+ "34 82.1708 NaN 1 0 \n",
+ "35 52.0000 NaN 0 0 \n",
+ "36 7.2292 NaN 1 0 \n",
+ "37 8.0500 NaN 0 0 \n",
+ "38 18.0000 NaN 0 1 \n",
+ "39 11.2417 NaN 1 1 \n",
+ "40 9.4750 NaN 0 2 \n",
+ "41 21.0000 NaN 0 2 \n",
+ "42 7.8958 NaN 1 0 \n",
+ "43 41.5792 NaN 1 1 \n",
+ "44 7.8792 NaN 2 1 \n",
+ "45 8.0500 NaN 0 0 \n",
+ "46 15.5000 NaN 2 0 \n",
+ "47 7.7500 NaN 2 1 \n",
+ "48 21.6792 NaN 1 0 \n",
+ "49 17.8000 NaN 0 2 "
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# fill missing Fare with median fare for each Pclass\n",
+ "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
+ "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
+ "train.head(50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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H2yiesTUBJk8McuCQS23mQwqIIiIiIiIyIvUaDjdu3Egmk2HdunXccccdrFmz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\nbeS/TKyu39Xybzfr+LkZVo3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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "1 | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "0 | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "2 | \n", + "0 | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "0 | \n", + "0 | \n", + "
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| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "1 | \n", + "2 | \n", + "
| 10 | \n", + "11 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "1 | \n", + "PP 9549 | \n", + "16.7000 | \n", + "G6 | \n", + "0 | \n", + "1 | \n", + "
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| 18 | \n", + "19 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "345763 | \n", + "18.0000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 19 | \n", + "20 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2649 | \n", + "7.2250 | \n", + "NaN | \n", + "1 | \n", + "2 | \n", + "
| 20 | \n", + "21 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "239865 | \n", + "26.0000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 21 | \n", + "22 | \n", + "1 | \n", + "2 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "248698 | \n", + "13.0000 | \n", + "D56 | \n", + "0 | \n", + "0 | \n", + "
| 22 | \n", + "23 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "0 | \n", + "0 | \n", + "330923 | \n", + "8.0292 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 23 | \n", + "24 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "113788 | \n", + "35.5000 | \n", + "A6 | \n", + "0 | \n", + "0 | \n", + "
| 24 | \n", + "25 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 25 | \n", + "26 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "5 | \n", + "347077 | \n", + "31.3875 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 26 | \n", + "27 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2631 | \n", + "7.2250 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 27 | \n", + "28 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1.0 | \n", + "3 | \n", + "2 | \n", + "19950 | \n", + "263.0000 | \n", + "C23 C25 C27 | \n", + "0 | \n", + "0 | \n", + "
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| 29 | \n", + "30 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "349216 | \n", + "7.8958 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 30 | \n", + "31 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "0 | \n", + "0 | \n", + "PC 17601 | \n", + "27.7208 | \n", + "NaN | \n", + "1 | \n", + "3 | \n", + "
| 31 | \n", + "32 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "PC 17569 | \n", + "146.5208 | \n", + "B78 | \n", + "1 | \n", + "2 | \n", + "
| 32 | \n", + "33 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "335677 | \n", + "7.7500 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 33 | \n", + "34 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "4.0 | \n", + "0 | \n", + "0 | \n", + "C.A. 24579 | \n", + "10.5000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 34 | \n", + "35 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "PC 17604 | \n", + "82.1708 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 35 | \n", + "36 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "113789 | \n", + "52.0000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 36 | \n", + "37 | \n", + "1 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2677 | \n", + "7.2292 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 37 | \n", + "38 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "A./5. 2152 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 38 | \n", + "39 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "2 | \n", + "0 | \n", + "345764 | \n", + "18.0000 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 39 | \n", + "40 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "0 | \n", + "2651 | \n", + "11.2417 | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "
| 40 | \n", + "41 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "7546 | \n", + "9.4750 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 41 | \n", + "42 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "11668 | \n", + "21.0000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 42 | \n", + "43 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "349253 | \n", + "7.8958 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 43 | \n", + "44 | \n", + "1 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "2 | \n", + "SC/Paris 2123 | \n", + "41.5792 | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "
| 44 | \n", + "45 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "330958 | \n", + "7.8792 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 45 | \n", + "46 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "S.C./A.4. 23567 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 46 | \n", + "47 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "370371 | \n", + "15.5000 | \n", + "NaN | \n", + "2 | \n", + "0 | \n", + "
| 47 | \n", + "48 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "14311 | \n", + "7.7500 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 48 | \n", + "49 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "2 | \n", + "0 | \n", + "2662 | \n", + "21.6792 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 49 | \n", + "50 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "349237 | \n", + "17.8000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 0.0 NaN 0 0 \n",
+ "1 2.0 C85 1 2 \n",
+ "2 0.0 NaN 0 1 \n",
+ "3 2.0 C123 0 2 \n",
+ "4 0.0 NaN 0 0 "
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.7 Cabin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "C23 C25 C27 4\n",
+ "G6 4\n",
+ "B96 B98 4\n",
+ "D 3\n",
+ "C22 C26 3\n",
+ "E101 3\n",
+ "F2 3\n",
+ "F33 3\n",
+ "B57 B59 B63 B66 2\n",
+ "C68 2\n",
+ "B58 B60 2\n",
+ "E121 2\n",
+ "D20 2\n",
+ "E8 2\n",
+ "E44 2\n",
+ "B77 2\n",
+ "C65 2\n",
+ "D26 2\n",
+ "E24 2\n",
+ "E25 2\n",
+ "B20 2\n",
+ "C93 2\n",
+ "D33 2\n",
+ "E67 2\n",
+ "D35 2\n",
+ "D36 2\n",
+ "C52 2\n",
+ "F4 2\n",
+ "C125 2\n",
+ "C124 2\n",
+ " ..\n",
+ "F G63 1\n",
+ "A6 1\n",
+ "D45 1\n",
+ "D6 1\n",
+ "D56 1\n",
+ "C101 1\n",
+ "C54 1\n",
+ "D28 1\n",
+ "D37 1\n",
+ "B102 1\n",
+ "D30 1\n",
+ "E17 1\n",
+ "E58 1\n",
+ "F E69 1\n",
+ "D10 D12 1\n",
+ "E50 1\n",
+ "A14 1\n",
+ "C91 1\n",
+ "A16 1\n",
+ "B38 1\n",
+ "B39 1\n",
+ "C95 1\n",
+ "B78 1\n",
+ "B79 1\n",
+ "C99 1\n",
+ "B37 1\n",
+ "A19 1\n",
+ "E12 1\n",
+ "A7 1\n",
+ "D15 1\n",
+ "Name: Cabin, Length: 147, dtype: int64"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.Cabin.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "for dataset in train_test_data:\n",
+ " dataset['Cabin'] = dataset['Cabin'].str[:1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "C85 | \n", + "1 | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "C123 | \n", + "0 | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title FamilySize \n",
+ "0 0.0 2.0 0 0 0.4 \n",
+ "1 2.0 0.8 1 2 0.4 \n",
+ "2 0.0 2.0 0 1 0.0 \n",
+ "3 2.0 0.8 0 2 0.4 \n",
+ "4 0.0 2.0 0 0 0.0 "
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title FamilySize \n",
+ "0 0.0 2.0 0 0 0.4 \n",
+ "1 2.0 0.8 1 2 0.4 \n",
+ "2 0.0 2.0 0 1 0.0 \n",
+ "3 2.0 0.8 0 2 0.4 \n",
+ "4 0.0 2.0 0 0 0.0 "
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
+ "train = train.drop(features_drop, axis=1)\n",
+ "test = test.drop(features_drop, axis=1)\n",
+ "train = train.drop(['PassengerId'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((891, 8), (891,))"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_data = train.drop('Survived', axis=1)\n",
+ "target = train['Survived']\n",
+ "\n",
+ "train_data.shape, target.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Pclass Sex Age Fare Cabin Embarked Title FamilySize\n",
+ "0 3 0 1.0 0.0 2.0 0 0 0.4\n",
+ "1 1 1 3.0 2.0 0.8 1 2 0.4\n",
+ "2 3 1 1.0 0.0 2.0 0 1 0.0\n",
+ "3 1 1 2.0 2.0 0.8 0 2 0.4\n",
+ "4 3 0 2.0 0.0 2.0 0 0 0.0\n",
+ "5 3 0 2.0 0.0 2.0 2 0 0.0\n",
+ "6 1 0 3.0 2.0 1.6 0 0 0.0\n",
+ "7 3 0 0.0 1.0 2.0 0 3 1.6\n",
+ "8 3 1 2.0 0.0 2.0 0 2 0.8\n",
+ "9 2 1 0.0 2.0 1.8 1 2 0.4"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_data.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Modelling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Importing Classifier Modules\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.naive_bayes import GaussianNB\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | Pclass | \n", + "Sex | \n", + "Age | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
| 5 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "2 | \n", + "0 | \n", + "0.0 | \n", + "
| 6 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "2.0 | \n", + "1.6 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
| 7 | \n", + "3 | \n", + "0 | \n", + "0.0 | \n", + "1.0 | \n", + "2.0 | \n", + "0 | \n", + "3 | \n", + "1.6 | \n", + "
| 8 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "2 | \n", + "0.8 | \n", + "
| 9 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "2.0 | \n", + "1.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived\n",
+ "0 892 0\n",
+ "1 893 1\n",
+ "2 894 0\n",
+ "3 895 0\n",
+ "4 896 1"
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission = pd.read_csv('submission.csv')\n",
+ "submission.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## References\n",
+ "\n",
+ "This notebook is created by learning from the following notebooks:\n",
+ "\n",
+ "- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\n",
+ "- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\n",
+ "- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\n",
+ "- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/data/titanic/images/titanic-disaster.jpg b/data/titanic/images/titanic-disaster.jpg
new file mode 100644
index 0000000..bd3daf7
Binary files /dev/null and b/data/titanic/images/titanic-disaster.jpg differ
diff --git a/data/titanic/input/gender_submission.csv b/data/titanic/input/gender_submission.csv
new file mode 100644
index 0000000..80bbbd8
--- /dev/null
+++ b/data/titanic/input/gender_submission.csv
@@ -0,0 +1,419 @@
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diff --git a/data/titanic/input/test.csv b/data/titanic/input/test.csv
new file mode 100644
index 0000000..2ed7ef4
--- /dev/null
+++ b/data/titanic/input/test.csv
@@ -0,0 +1,419 @@
+PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
+893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
+894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
+895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
+896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S
+897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S
+898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q
+899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S
+900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C
+901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S
+902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S
+903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S
+904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S
+905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S
+906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S
+907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C
+908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q
+909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C
+910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S
+911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C
+912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C
+913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S
+914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S
+915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C
+916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C
+917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S
+918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C
+919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C
+920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S
+921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C
+922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S
+923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S
+924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S
+925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S
+926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C
+927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C
+928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S
+929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S
+930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S
+931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S
+932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C
+933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S
+934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S
+935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S
+936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S
+937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S
+938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C
+939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q
+940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C
+941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S
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diff --git a/data/titanic/input/train.csv b/data/titanic/input/train.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/data/titanic/input/train.csv
@@ -0,0 +1,892 @@
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diff --git a/data/titanic/titanic-solution.ipynb b/data/titanic/titanic-solution.ipynb
new file mode 100644
index 0000000..95e2ca4
--- /dev/null
+++ b/data/titanic/titanic-solution.ipynb
@@ -0,0 +1,7397 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "_cell_guid": "6a09d4fb-60c5-4f45-b844-8c788a50c543",
+ "_uuid": "8e892e637f005dd61ec7dcb95865e52f3de2a77f"
+ },
+ "source": [
+ "# Titanic: Machine Learning from Disaster\n",
+ "### Predict survival on the Titanic\n",
+ "- Defining the problem statement\n",
+ "- Collecting the data\n",
+ "- Exploratory data analysis\n",
+ "- Feature engineering\n",
+ "- Modelling\n",
+ "- Testing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "_cell_guid": "4af5e83d-7fd8-4a61-bf26-9583cb6d3476",
+ "_uuid": "65d04d276a8983f62a49261f6e94a02b281dbcc9"
+ },
+ "source": [
+ "## 1. Defining the problem statement\n",
+ "Complete the analysis of what sorts of people were likely to survive. \n",
+ "In particular, we ask you to apply the tools of machine learning to predict which passengers survived the Titanic tragedy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "
|---|---|---|
| 0 | \n", + "892 | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "1 | \n", + "
| 2 | \n", + "894 | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "1 | \n", + "
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"
+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "5 6 0 3 \n",
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+ "23 24 1 1 \n",
+ "24 25 0 3 \n",
+ "25 26 1 3 \n",
+ "26 27 0 3 \n",
+ "27 28 0 1 \n",
+ "28 29 1 3 \n",
+ "29 30 0 3 \n",
+ ".. ... ... ... \n",
+ "50 51 0 3 \n",
+ "51 52 0 3 \n",
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+ "53 54 1 2 \n",
+ "54 55 0 1 \n",
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+ "58 59 1 2 \n",
+ "59 60 0 3 \n",
+ "60 61 0 3 \n",
+ "61 62 1 1 \n",
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+ "63 64 0 3 \n",
+ "64 65 0 1 \n",
+ "65 66 1 3 \n",
+ "66 67 1 2 \n",
+ "67 68 0 3 \n",
+ "68 69 1 3 \n",
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+ "70 71 0 2 \n",
+ "71 72 0 3 \n",
+ "72 73 0 2 \n",
+ "73 74 0 3 \n",
+ "74 75 1 3 \n",
+ "75 76 0 3 \n",
+ "76 77 0 3 \n",
+ "77 78 0 3 \n",
+ "78 79 1 2 \n",
+ "79 80 1 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.00 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.00 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 \n",
+ "4 Allen, Mr. William Henry male 35.00 0 \n",
+ "5 Moran, Mr. James male NaN 0 \n",
+ "6 McCarthy, Mr. Timothy J male 54.00 0 \n",
+ "7 Palsson, Master. Gosta Leonard male 2.00 3 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 \n",
+ "10 Sandstrom, Miss. Marguerite Rut female 4.00 1 \n",
+ "11 Bonnell, Miss. Elizabeth female 58.00 0 \n",
+ "12 Saundercock, Mr. William Henry male 20.00 0 \n",
+ "13 Andersson, Mr. Anders Johan male 39.00 1 \n",
+ "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 \n",
+ "15 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 \n",
+ "16 Rice, Master. Eugene male 2.00 4 \n",
+ "17 Williams, Mr. Charles Eugene male NaN 0 \n",
+ "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.00 1 \n",
+ "19 Masselmani, Mrs. Fatima female NaN 0 \n",
+ "20 Fynney, Mr. Joseph J male 35.00 0 \n",
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+ "24 Palsson, Miss. Torborg Danira female 8.00 3 \n",
+ "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.00 1 \n",
+ "26 Emir, Mr. Farred Chehab male NaN 0 \n",
+ "27 Fortune, Mr. Charles Alexander male 19.00 3 \n",
+ "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n",
+ "29 Todoroff, Mr. Lalio male NaN 0 \n",
+ ".. ... ... ... ... \n",
+ "50 Panula, Master. Juha Niilo male 7.00 4 \n",
+ "51 Nosworthy, Mr. Richard Cater male 21.00 0 \n",
+ "52 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 \n",
+ "53 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... female 29.00 1 \n",
+ "54 Ostby, Mr. Engelhart Cornelius male 65.00 0 \n",
+ "55 Woolner, Mr. Hugh male NaN 0 \n",
+ "56 Rugg, Miss. Emily female 21.00 0 \n",
+ "57 Novel, Mr. Mansouer male 28.50 0 \n",
+ "58 West, Miss. Constance Mirium female 5.00 1 \n",
+ "59 Goodwin, Master. William Frederick male 11.00 5 \n",
+ "60 Sirayanian, Mr. Orsen male 22.00 0 \n",
+ "61 Icard, Miss. Amelie female 38.00 0 \n",
+ "62 Harris, Mr. Henry Birkhardt male 45.00 1 \n",
+ "63 Skoog, Master. Harald male 4.00 3 \n",
+ "64 Stewart, Mr. Albert A male NaN 0 \n",
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+ "66 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 \n",
+ "67 Crease, Mr. Ernest James male 19.00 0 \n",
+ "68 Andersson, Miss. Erna Alexandra female 17.00 4 \n",
+ "69 Kink, Mr. Vincenz male 26.00 2 \n",
+ "70 Jenkin, Mr. Stephen Curnow male 32.00 0 \n",
+ "71 Goodwin, Miss. Lillian Amy female 16.00 5 \n",
+ "72 Hood, Mr. Ambrose Jr male 21.00 0 \n",
+ "73 Chronopoulos, Mr. Apostolos male 26.00 1 \n",
+ "74 Bing, Mr. Lee male 32.00 0 \n",
+ "75 Moen, Mr. Sigurd Hansen male 25.00 0 \n",
+ "76 Staneff, Mr. Ivan male NaN 0 \n",
+ "77 Moutal, Mr. Rahamin Haim male NaN 0 \n",
+ "78 Caldwell, Master. Alden Gates male 0.83 0 \n",
+ "79 Dowdell, Miss. Elizabeth female 30.00 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S \n",
+ "5 0 330877 8.4583 NaN Q \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "7 1 349909 21.0750 NaN S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C \n",
+ "10 1 PP 9549 16.7000 G6 S \n",
+ "11 0 113783 26.5500 C103 S \n",
+ "12 0 A/5. 2151 8.0500 NaN S \n",
+ "13 5 347082 31.2750 NaN S \n",
+ "14 0 350406 7.8542 NaN S \n",
+ "15 0 248706 16.0000 NaN S \n",
+ "16 1 382652 29.1250 NaN Q \n",
+ "17 0 244373 13.0000 NaN S \n",
+ "18 0 345763 18.0000 NaN S \n",
+ "19 0 2649 7.2250 NaN C \n",
+ "20 0 239865 26.0000 NaN S \n",
+ "21 0 248698 13.0000 D56 S \n",
+ "22 0 330923 8.0292 NaN Q \n",
+ "23 0 113788 35.5000 A6 S \n",
+ "24 1 349909 21.0750 NaN S \n",
+ "25 5 347077 31.3875 NaN S \n",
+ "26 0 2631 7.2250 NaN C \n",
+ "27 2 19950 263.0000 C23 C25 C27 S \n",
+ "28 0 330959 7.8792 NaN Q \n",
+ "29 0 349216 7.8958 NaN S \n",
+ ".. ... ... ... ... ... \n",
+ "50 1 3101295 39.6875 NaN S \n",
+ "51 0 A/4. 39886 7.8000 NaN S \n",
+ "52 0 PC 17572 76.7292 D33 C \n",
+ "53 0 2926 26.0000 NaN S \n",
+ "54 1 113509 61.9792 B30 C \n",
+ "55 0 19947 35.5000 C52 S \n",
+ "56 0 C.A. 31026 10.5000 NaN S \n",
+ "57 0 2697 7.2292 NaN C \n",
+ "58 2 C.A. 34651 27.7500 NaN S \n",
+ "59 2 CA 2144 46.9000 NaN S \n",
+ "60 0 2669 7.2292 NaN C \n",
+ "61 0 113572 80.0000 B28 NaN \n",
+ "62 0 36973 83.4750 C83 S \n",
+ "63 2 347088 27.9000 NaN S \n",
+ "64 0 PC 17605 27.7208 NaN C \n",
+ "65 1 2661 15.2458 NaN C \n",
+ "66 0 C.A. 29395 10.5000 F33 S \n",
+ "67 0 S.P. 3464 8.1583 NaN S \n",
+ "68 2 3101281 7.9250 NaN S \n",
+ "69 0 315151 8.6625 NaN S \n",
+ "70 0 C.A. 33111 10.5000 NaN S \n",
+ "71 2 CA 2144 46.9000 NaN S \n",
+ "72 0 S.O.C. 14879 73.5000 NaN S \n",
+ "73 0 2680 14.4542 NaN C \n",
+ "74 0 1601 56.4958 NaN S \n",
+ "75 0 348123 7.6500 F G73 S \n",
+ "76 0 349208 7.8958 NaN S \n",
+ "77 0 374746 8.0500 NaN S \n",
+ "78 2 248738 29.0000 NaN S \n",
+ "79 0 364516 12.4750 NaN S \n",
+ "\n",
+ "[80 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(80)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data Dictionary\n",
+ "- Survived: \t0 = No, 1 = Yes \n",
+ "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd \t\n",
+ "- sibsp:\t# of siblings / spouses aboard the Titanic \t\n",
+ "- parch:\t# of parents / children aboard the Titanic \t\n",
+ "- ticket:\tTicket number\t\n",
+ "- cabin:\tCabin number\t\n",
+ "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "_cell_guid": "5ebc1e0e-2b5a-4d92-98e0-defa019d4439",
+ "_uuid": "1892fbb34b26d775d1c428fdb7b6254449286b28"
+ },
+ "source": [
+ "**Total rows and columns**\n",
+ "\n",
+ "We can see that there are 891 rows and 12 columns in our training dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.00 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.00 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.00 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.00 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "Moran, Mr. James | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "McCarthy, Mr. Timothy J | \n", + "male | \n", + "54.00 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "S | \n", + "
| 7 | \n", + "8 | \n", + "0 | \n", + "3 | \n", + "Palsson, Master. Gosta Leonard | \n", + "male | \n", + "2.00 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | \n", + "female | \n", + "27.00 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "S | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "Nasser, Mrs. Nicholas (Adele Achem) | \n", + "female | \n", + "14.00 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "C | \n", + "
| 10 | \n", + "11 | \n", + "1 | \n", + "3 | \n", + "Sandstrom, Miss. Marguerite Rut | \n", + "female | \n", + "4.00 | \n", + "1 | \n", + "1 | \n", + "PP 9549 | \n", + "16.7000 | \n", + "G6 | \n", + "S | \n", + "
| 11 | \n", + "12 | \n", + "1 | \n", + "1 | \n", + "Bonnell, Miss. Elizabeth | \n", + "female | \n", + "58.00 | \n", + "0 | \n", + "0 | \n", + "113783 | \n", + "26.5500 | \n", + "C103 | \n", + "S | \n", + "
| 12 | \n", + "13 | \n", + "0 | \n", + "3 | \n", + "Saundercock, Mr. William Henry | \n", + "male | \n", + "20.00 | \n", + "0 | \n", + "0 | \n", + "A/5. 2151 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 13 | \n", + "14 | \n", + "0 | \n", + "3 | \n", + "Andersson, Mr. Anders Johan | \n", + "male | \n", + "39.00 | \n", + "1 | \n", + "5 | \n", + "347082 | \n", + "31.2750 | \n", + "NaN | \n", + "S | \n", + "
| 14 | \n", + "15 | \n", + "0 | \n", + "3 | \n", + "Vestrom, Miss. Hulda Amanda Adolfina | \n", + "female | \n", + "14.00 | \n", + "0 | \n", + "0 | \n", + "350406 | \n", + "7.8542 | \n", + "NaN | \n", + "S | \n", + "
| 15 | \n", + "16 | \n", + "1 | \n", + "2 | \n", + "Hewlett, Mrs. (Mary D Kingcome) | \n", + "female | \n", + "55.00 | \n", + "0 | \n", + "0 | \n", + "248706 | \n", + "16.0000 | \n", + "NaN | \n", + "S | \n", + "
| 16 | \n", + "17 | \n", + "0 | \n", + "3 | \n", + "Rice, Master. Eugene | \n", + "male | \n", + "2.00 | \n", + "4 | \n", + "1 | \n", + "382652 | \n", + "29.1250 | \n", + "NaN | \n", + "Q | \n", + "
| 17 | \n", + "18 | \n", + "1 | \n", + "2 | \n", + "Williams, Mr. Charles Eugene | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "244373 | \n", + "13.0000 | \n", + "NaN | \n", + "S | \n", + "
| 18 | \n", + "19 | \n", + "0 | \n", + "3 | \n", + "Vander Planke, Mrs. Julius (Emelia Maria Vande... | \n", + "female | \n", + "31.00 | \n", + "1 | \n", + "0 | \n", + "345763 | \n", + "18.0000 | \n", + "NaN | \n", + "S | \n", + "
| 19 | \n", + "20 | \n", + "1 | \n", + "3 | \n", + "Masselmani, Mrs. Fatima | \n", + "female | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "2649 | \n", + "7.2250 | \n", + "NaN | \n", + "C | \n", + "
| 20 | \n", + "21 | \n", + "0 | \n", + "2 | \n", + "Fynney, Mr. Joseph J | \n", + "male | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "239865 | \n", + "26.0000 | \n", + "NaN | \n", + "S | \n", + "
| 21 | \n", + "22 | \n", + "1 | \n", + "2 | \n", + "Beesley, Mr. Lawrence | \n", + "male | \n", + "34.00 | \n", + "0 | \n", + "0 | \n", + "248698 | \n", + "13.0000 | \n", + "D56 | \n", + "S | \n", + "
| 22 | \n", + "23 | \n", + "1 | \n", + "3 | \n", + "McGowan, Miss. Anna \"Annie\" | \n", + "female | \n", + "15.00 | \n", + "0 | \n", + "0 | \n", + "330923 | \n", + "8.0292 | \n", + "NaN | \n", + "Q | \n", + "
| 23 | \n", + "24 | \n", + "1 | \n", + "1 | \n", + "Sloper, Mr. William Thompson | \n", + "male | \n", + "28.00 | \n", + "0 | \n", + "0 | \n", + "113788 | \n", + "35.5000 | \n", + "A6 | \n", + "S | \n", + "
| 24 | \n", + "25 | \n", + "0 | \n", + "3 | \n", + "Palsson, Miss. Torborg Danira | \n", + "female | \n", + "8.00 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 25 | \n", + "26 | \n", + "1 | \n", + "3 | \n", + "Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... | \n", + "female | \n", + "38.00 | \n", + "1 | \n", + "5 | \n", + "347077 | \n", + "31.3875 | \n", + "NaN | \n", + "S | \n", + "
| 26 | \n", + "27 | \n", + "0 | \n", + "3 | \n", + "Emir, Mr. Farred Chehab | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "2631 | \n", + "7.2250 | \n", + "NaN | \n", + "C | \n", + "
| 27 | \n", + "28 | \n", + "0 | \n", + "1 | \n", + "Fortune, Mr. Charles Alexander | \n", + "male | \n", + "19.00 | \n", + "3 | \n", + "2 | \n", + "19950 | \n", + "263.0000 | \n", + "C23 C25 C27 | \n", + "S | \n", + "
| 28 | \n", + "29 | \n", + "1 | \n", + "3 | \n", + "O'Dwyer, Miss. Ellen \"Nellie\" | \n", + "female | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330959 | \n", + "7.8792 | \n", + "NaN | \n", + "Q | \n", + "
| 29 | \n", + "30 | \n", + "0 | \n", + "3 | \n", + "Todoroff, Mr. Lalio | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "349216 | \n", + "7.8958 | \n", + "NaN | \n", + "S | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 50 | \n", + "51 | \n", + "0 | \n", + "3 | \n", + "Panula, Master. Juha Niilo | \n", + "male | \n", + "7.00 | \n", + "4 | \n", + "1 | \n", + "3101295 | \n", + "39.6875 | \n", + "NaN | \n", + "S | \n", + "
| 51 | \n", + "52 | \n", + "0 | \n", + "3 | \n", + "Nosworthy, Mr. Richard Cater | \n", + "male | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "A/4. 39886 | \n", + "7.8000 | \n", + "NaN | \n", + "S | \n", + "
| 52 | \n", + "53 | \n", + "1 | \n", + "1 | \n", + "Harper, Mrs. Henry Sleeper (Myna Haxtun) | \n", + "female | \n", + "49.00 | \n", + "1 | \n", + "0 | \n", + "PC 17572 | \n", + "76.7292 | \n", + "D33 | \n", + "C | \n", + "
| 53 | \n", + "54 | \n", + "1 | \n", + "2 | \n", + "Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... | \n", + "female | \n", + "29.00 | \n", + "1 | \n", + "0 | \n", + "2926 | \n", + "26.0000 | \n", + "NaN | \n", + "S | \n", + "
| 54 | \n", + "55 | \n", + "0 | \n", + "1 | \n", + "Ostby, Mr. Engelhart Cornelius | \n", + "male | \n", + "65.00 | \n", + "0 | \n", + "1 | \n", + "113509 | \n", + "61.9792 | \n", + "B30 | \n", + "C | \n", + "
| 55 | \n", + "56 | \n", + "1 | \n", + "1 | \n", + "Woolner, Mr. Hugh | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "19947 | \n", + "35.5000 | \n", + "C52 | \n", + "S | \n", + "
| 56 | \n", + "57 | \n", + "1 | \n", + "2 | \n", + "Rugg, Miss. Emily | \n", + "female | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 31026 | \n", + "10.5000 | \n", + "NaN | \n", + "S | \n", + "
| 57 | \n", + "58 | \n", + "0 | \n", + "3 | \n", + "Novel, Mr. Mansouer | \n", + "male | \n", + "28.50 | \n", + "0 | \n", + "0 | \n", + "2697 | \n", + "7.2292 | \n", + "NaN | \n", + "C | \n", + "
| 58 | \n", + "59 | \n", + "1 | \n", + "2 | \n", + "West, Miss. Constance Mirium | \n", + "female | \n", + "5.00 | \n", + "1 | \n", + "2 | \n", + "C.A. 34651 | \n", + "27.7500 | \n", + "NaN | \n", + "S | \n", + "
| 59 | \n", + "60 | \n", + "0 | \n", + "3 | \n", + "Goodwin, Master. William Frederick | \n", + "male | \n", + "11.00 | \n", + "5 | \n", + "2 | \n", + "CA 2144 | \n", + "46.9000 | \n", + "NaN | \n", + "S | \n", + "
| 60 | \n", + "61 | \n", + "0 | \n", + "3 | \n", + "Sirayanian, Mr. Orsen | \n", + "male | \n", + "22.00 | \n", + "0 | \n", + "0 | \n", + "2669 | \n", + "7.2292 | \n", + "NaN | \n", + "C | \n", + "
| 61 | \n", + "62 | \n", + "1 | \n", + "1 | \n", + "Icard, Miss. Amelie | \n", + "female | \n", + "38.00 | \n", + "0 | \n", + "0 | \n", + "113572 | \n", + "80.0000 | \n", + "B28 | \n", + "NaN | \n", + "
| 62 | \n", + "63 | \n", + "0 | \n", + "1 | \n", + "Harris, Mr. Henry Birkhardt | \n", + "male | \n", + "45.00 | \n", + "1 | \n", + "0 | \n", + "36973 | \n", + "83.4750 | \n", + "C83 | \n", + "S | \n", + "
| 63 | \n", + "64 | \n", + "0 | \n", + "3 | \n", + "Skoog, Master. Harald | \n", + "male | \n", + "4.00 | \n", + "3 | \n", + "2 | \n", + "347088 | \n", + "27.9000 | \n", + "NaN | \n", + "S | \n", + "
| 64 | \n", + "65 | \n", + "0 | \n", + "1 | \n", + "Stewart, Mr. Albert A | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "PC 17605 | \n", + "27.7208 | \n", + "NaN | \n", + "C | \n", + "
| 65 | \n", + "66 | \n", + "1 | \n", + "3 | \n", + "Moubarek, Master. Gerios | \n", + "male | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "2661 | \n", + "15.2458 | \n", + "NaN | \n", + "C | \n", + "
| 66 | \n", + "67 | \n", + "1 | \n", + "2 | \n", + "Nye, Mrs. (Elizabeth Ramell) | \n", + "female | \n", + "29.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 29395 | \n", + "10.5000 | \n", + "F33 | \n", + "S | \n", + "
| 67 | \n", + "68 | \n", + "0 | \n", + "3 | \n", + "Crease, Mr. Ernest James | \n", + "male | \n", + "19.00 | \n", + "0 | \n", + "0 | \n", + "S.P. 3464 | \n", + "8.1583 | \n", + "NaN | \n", + "S | \n", + "
| 68 | \n", + "69 | \n", + "1 | \n", + "3 | \n", + "Andersson, Miss. Erna Alexandra | \n", + "female | \n", + "17.00 | \n", + "4 | \n", + "2 | \n", + "3101281 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 69 | \n", + "70 | \n", + "0 | \n", + "3 | \n", + "Kink, Mr. Vincenz | \n", + "male | \n", + "26.00 | \n", + "2 | \n", + "0 | \n", + "315151 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "
| 70 | \n", + "71 | \n", + "0 | \n", + "2 | \n", + "Jenkin, Mr. Stephen Curnow | \n", + "male | \n", + "32.00 | \n", + "0 | \n", + "0 | \n", + "C.A. 33111 | \n", + "10.5000 | \n", + "NaN | \n", + "S | \n", + "
| 71 | \n", + "72 | \n", + "0 | \n", + "3 | \n", + "Goodwin, Miss. Lillian Amy | \n", + "female | \n", + "16.00 | \n", + "5 | \n", + "2 | \n", + "CA 2144 | \n", + "46.9000 | \n", + "NaN | \n", + "S | \n", + "
| 72 | \n", + "73 | \n", + "0 | \n", + "2 | \n", + "Hood, Mr. Ambrose Jr | \n", + "male | \n", + "21.00 | \n", + "0 | \n", + "0 | \n", + "S.O.C. 14879 | \n", + "73.5000 | \n", + "NaN | \n", + "S | \n", + "
| 73 | \n", + "74 | \n", + "0 | \n", + "3 | \n", + "Chronopoulos, Mr. Apostolos | \n", + "male | \n", + "26.00 | \n", + "1 | \n", + "0 | \n", + "2680 | \n", + "14.4542 | \n", + "NaN | \n", + "C | \n", + "
| 74 | \n", + "75 | \n", + "1 | \n", + "3 | \n", + "Bing, Mr. Lee | \n", + "male | \n", + "32.00 | \n", + "0 | \n", + "0 | \n", + "1601 | \n", + "56.4958 | \n", + "NaN | \n", + "S | \n", + "
| 75 | \n", + "76 | \n", + "0 | \n", + "3 | \n", + "Moen, Mr. Sigurd Hansen | \n", + "male | \n", + "25.00 | \n", + "0 | \n", + "0 | \n", + "348123 | \n", + "7.6500 | \n", + "F G73 | \n", + "S | \n", + "
| 76 | \n", + "77 | \n", + "0 | \n", + "3 | \n", + "Staneff, Mr. Ivan | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "349208 | \n", + "7.8958 | \n", + "NaN | \n", + "S | \n", + "
| 77 | \n", + "78 | \n", + "0 | \n", + "3 | \n", + "Moutal, Mr. Rahamin Haim | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "374746 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 78 | \n", + "79 | \n", + "1 | \n", + "2 | \n", + "Caldwell, Master. Alden Gates | \n", + "male | \n", + "0.83 | \n", + "0 | \n", + "2 | \n", + "248738 | \n", + "29.0000 | \n", + "NaN | \n", + "S | \n", + "
| 79 | \n", + "80 | \n", + "1 | \n", + "3 | \n", + "Dowdell, Miss. Elizabeth | \n", + "female | \n", + "30.00 | \n", + "0 | \n", + "0 | \n", + "364516 | \n", + "12.4750 | \n", + "NaN | \n", + "S | \n", + "
80 rows × 12 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Pclass Name Sex \\\n",
+ "0 892 3 Kelly, Mr. James male \n",
+ "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
+ "2 894 2 Myles, Mr. Thomas Francis male \n",
+ "3 895 3 Wirz, Mr. Albert male \n",
+ "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "0 34.5 0 0 330911 7.8292 NaN Q \n",
+ "1 47.0 1 0 363272 7.0000 NaN S \n",
+ "2 62.0 0 0 240276 9.6875 NaN Q \n",
+ "3 27.0 0 0 315154 8.6625 NaN S \n",
+ "4 22.0 1 1 3101298 12.2875 NaN S "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "_cell_guid": "ed1e7849-d1b6-490d-b86b-9ca71dfafc7d",
+ "_uuid": "5a641beccf0e555dfd7b9a53a17188ea6edef95b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(891, 12)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(418, 11)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "_cell_guid": "418b8a69-f2aa-442d-8f45-fa8887190938",
+ "_uuid": "4ee2591110660a4a16b3da7a7530f0945e121b46"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "Kelly, Mr. James | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "Wilkes, Mrs. James (Ellen Needs) | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "Myles, Mr. Thomas Francis | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "Wirz, Mr. Albert | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "Hirvonen, Mrs. Alexander (Helga E Lindqvist) | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1 how titanic sank?\n",
+ "sank from the bow of the ship where third class rooms located \n",
+ "conclusion, Pclass is key feature for classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
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+ ],
+ "text/plain": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "5 6 0 3 \n",
+ "6 7 0 1 \n",
+ "7 8 0 3 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "5 Moran, Mr. James male NaN 0 \n",
+ "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
+ "7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S \n",
+ "5 0 330877 8.4583 NaN Q \n",
+ "6 0 17463 51.8625 E46 S \n",
+ "7 1 349909 21.0750 NaN S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.2 Name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "train_test_data = [train, test] # combining train and test dataset\n",
+ "\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Mr 517\n",
+ "Miss 182\n",
+ "Mrs 125\n",
+ "Master 40\n",
+ "Dr 7\n",
+ "Rev 6\n",
+ "Col 2\n",
+ "Major 2\n",
+ "Mlle 2\n",
+ "Countess 1\n",
+ "Ms 1\n",
+ "Lady 1\n",
+ "Jonkheer 1\n",
+ "Don 1\n",
+ "Mme 1\n",
+ "Capt 1\n",
+ "Sir 1\n",
+ "Name: Title, dtype: int64"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train['Title'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Mr 240\n",
+ "Miss 78\n",
+ "Mrs 72\n",
+ "Master 21\n",
+ "Col 2\n",
+ "Rev 2\n",
+ "Dona 1\n",
+ "Ms 1\n",
+ "Dr 1\n",
+ "Name: Title, dtype: int64"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test['Title'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Title map\n",
+ "Mr : 0 \n",
+ "Miss : 1 \n",
+ "Mrs: 2 \n",
+ "Others: 3\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
+ " \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
+ " \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Title'] = dataset['Title'].map(title_mapping)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "Cumings, Mrs. John Bradley (Florence Briggs Th... | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "
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| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "Allen, Mr. William Henry | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "Moran, Mr. James | \n", + "male | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "McCarthy, Mr. Timothy J | \n", + "male | \n", + "54.0 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "S | \n", + "
| 7 | \n", + "8 | \n", + "0 | \n", + "3 | \n", + "Palsson, Master. Gosta Leonard | \n", + "male | \n", + "2.0 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "S | \n", + "
| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | \n", + "female | \n", + "27.0 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "S | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "Nasser, Mrs. Nicholas (Adele Achem) | \n", + "female | \n", + "14.0 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "C | \n", + "
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+ " Parch Ticket Fare Cabin Embarked Title \n",
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+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
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+ },
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+ "execution_count": 26,
+ "metadata": {},
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+ "0 892 3 Kelly, Mr. James male \n",
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+ "3 895 3 Wirz, Mr. Albert male \n",
+ "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
+ "\n",
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+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "Kelly, Mr. James | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "Wilkes, Mrs. James (Ellen Needs) | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "Myles, Mr. Thomas Francis | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "Wirz, Mr. Albert | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "Hirvonen, Mrs. Alexander (Helga E Lindqvist) | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch \\\n",
+ "0 1 0 3 male 22.0 1 0 \n",
+ "1 2 1 1 female 38.0 1 0 \n",
+ "2 3 1 3 female 26.0 0 0 \n",
+ "3 4 1 1 female 35.0 1 0 \n",
+ "4 5 0 3 male 35.0 0 0 \n",
+ "\n",
+ " Ticket Fare Cabin Embarked Title \n",
+ "0 A/5 21171 7.2500 NaN S 0 \n",
+ "1 PC 17599 71.2833 C85 C 2 \n",
+ "2 STON/O2. 3101282 7.9250 NaN S 1 \n",
+ "3 113803 53.1000 C123 S 2 \n",
+ "4 373450 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "female | \n", + "38.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "male | \n", + "35.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Pclass Sex Age SibSp Parch Ticket Fare Cabin \\\n",
+ "0 892 3 male 34.5 0 0 330911 7.8292 NaN \n",
+ "1 893 3 female 47.0 1 0 363272 7.0000 NaN \n",
+ "2 894 2 male 62.0 0 0 240276 9.6875 NaN \n",
+ "3 895 3 male 27.0 0 0 315154 8.6625 NaN \n",
+ "4 896 3 female 22.0 1 1 3101298 12.2875 NaN \n",
+ "\n",
+ " Embarked Title \n",
+ "0 Q 0 \n",
+ "1 S 2 \n",
+ "2 Q 0 \n",
+ "3 S 0 \n",
+ "4 S 2 "
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.3 Sex\n",
+ "\n",
+ "male: 0\n",
+ "female: 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "sex_mapping = {\"male\": 0, \"female\": 1}\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 22.00 1 0 A/5 21171 \n",
+ "1 2 1 1 1 38.00 1 0 PC 17599 \n",
+ "2 3 1 3 1 26.00 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 35.00 1 0 113803 \n",
+ "4 5 0 3 0 35.00 0 0 373450 \n",
+ "5 6 0 3 0 NaN 0 0 330877 \n",
+ "6 7 0 1 0 54.00 0 0 17463 \n",
+ "7 8 0 3 0 2.00 3 1 349909 \n",
+ "8 9 1 3 1 27.00 0 2 347742 \n",
+ "9 10 1 2 1 14.00 1 0 237736 \n",
+ "10 11 1 3 1 4.00 1 1 PP 9549 \n",
+ "11 12 1 1 1 58.00 0 0 113783 \n",
+ "12 13 0 3 0 20.00 0 0 A/5. 2151 \n",
+ "13 14 0 3 0 39.00 1 5 347082 \n",
+ "14 15 0 3 1 14.00 0 0 350406 \n",
+ "15 16 1 2 1 55.00 0 0 248706 \n",
+ "16 17 0 3 0 2.00 4 1 382652 \n",
+ "17 18 1 2 0 NaN 0 0 244373 \n",
+ "18 19 0 3 1 31.00 1 0 345763 \n",
+ "19 20 1 3 1 NaN 0 0 2649 \n",
+ "20 21 0 2 0 35.00 0 0 239865 \n",
+ "21 22 1 2 0 34.00 0 0 248698 \n",
+ "22 23 1 3 1 15.00 0 0 330923 \n",
+ "23 24 1 1 0 28.00 0 0 113788 \n",
+ "24 25 0 3 1 8.00 3 1 349909 \n",
+ "25 26 1 3 1 38.00 1 5 347077 \n",
+ "26 27 0 3 0 NaN 0 0 2631 \n",
+ "27 28 0 1 0 19.00 3 2 19950 \n",
+ "28 29 1 3 1 NaN 0 0 330959 \n",
+ "29 30 0 3 0 NaN 0 0 349216 \n",
+ ".. ... ... ... ... ... ... ... ... \n",
+ "70 71 0 2 0 32.00 0 0 C.A. 33111 \n",
+ "71 72 0 3 1 16.00 5 2 CA 2144 \n",
+ "72 73 0 2 0 21.00 0 0 S.O.C. 14879 \n",
+ "73 74 0 3 0 26.00 1 0 2680 \n",
+ "74 75 1 3 0 32.00 0 0 1601 \n",
+ "75 76 0 3 0 25.00 0 0 348123 \n",
+ "76 77 0 3 0 NaN 0 0 349208 \n",
+ "77 78 0 3 0 NaN 0 0 374746 \n",
+ "78 79 1 2 0 0.83 0 2 248738 \n",
+ "79 80 1 3 1 30.00 0 0 364516 \n",
+ "80 81 0 3 0 22.00 0 0 345767 \n",
+ "81 82 1 3 0 29.00 0 0 345779 \n",
+ "82 83 1 3 1 NaN 0 0 330932 \n",
+ "83 84 0 1 0 28.00 0 0 113059 \n",
+ "84 85 1 2 1 17.00 0 0 SO/C 14885 \n",
+ "85 86 1 3 1 33.00 3 0 3101278 \n",
+ "86 87 0 3 0 16.00 1 3 W./C. 6608 \n",
+ "87 88 0 3 0 NaN 0 0 SOTON/OQ 392086 \n",
+ "88 89 1 1 1 23.00 3 2 19950 \n",
+ "89 90 0 3 0 24.00 0 0 343275 \n",
+ "90 91 0 3 0 29.00 0 0 343276 \n",
+ "91 92 0 3 0 20.00 0 0 347466 \n",
+ "92 93 0 1 0 46.00 1 0 W.E.P. 5734 \n",
+ "93 94 0 3 0 26.00 1 2 C.A. 2315 \n",
+ "94 95 0 3 0 59.00 0 0 364500 \n",
+ "95 96 0 3 0 NaN 0 0 374910 \n",
+ "96 97 0 1 0 71.00 0 0 PC 17754 \n",
+ "97 98 1 1 0 23.00 0 1 PC 17759 \n",
+ "98 99 1 2 1 34.00 0 1 231919 \n",
+ "99 100 0 2 0 34.00 1 0 244367 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 \n",
+ "5 8.4583 NaN Q 0 \n",
+ "6 51.8625 E46 S 0 \n",
+ "7 21.0750 NaN S 3 \n",
+ "8 11.1333 NaN S 2 \n",
+ "9 30.0708 NaN C 2 \n",
+ "10 16.7000 G6 S 1 \n",
+ "11 26.5500 C103 S 1 \n",
+ "12 8.0500 NaN S 0 \n",
+ "13 31.2750 NaN S 0 \n",
+ "14 7.8542 NaN S 1 \n",
+ "15 16.0000 NaN S 2 \n",
+ "16 29.1250 NaN Q 3 \n",
+ "17 13.0000 NaN S 0 \n",
+ "18 18.0000 NaN S 2 \n",
+ "19 7.2250 NaN C 2 \n",
+ "20 26.0000 NaN S 0 \n",
+ "21 13.0000 D56 S 0 \n",
+ "22 8.0292 NaN Q 1 \n",
+ "23 35.5000 A6 S 0 \n",
+ "24 21.0750 NaN S 1 \n",
+ "25 31.3875 NaN S 2 \n",
+ "26 7.2250 NaN C 0 \n",
+ "27 263.0000 C23 C25 C27 S 0 \n",
+ "28 7.8792 NaN Q 1 \n",
+ "29 7.8958 NaN S 0 \n",
+ ".. ... ... ... ... \n",
+ "70 10.5000 NaN S 0 \n",
+ "71 46.9000 NaN S 1 \n",
+ "72 73.5000 NaN S 0 \n",
+ "73 14.4542 NaN C 0 \n",
+ "74 56.4958 NaN S 0 \n",
+ "75 7.6500 F G73 S 0 \n",
+ "76 7.8958 NaN S 0 \n",
+ "77 8.0500 NaN S 0 \n",
+ "78 29.0000 NaN S 3 \n",
+ "79 12.4750 NaN S 1 \n",
+ "80 9.0000 NaN S 0 \n",
+ "81 9.5000 NaN S 0 \n",
+ "82 7.7875 NaN Q 1 \n",
+ "83 47.1000 NaN S 0 \n",
+ "84 10.5000 NaN S 1 \n",
+ "85 15.8500 NaN S 2 \n",
+ "86 34.3750 NaN S 0 \n",
+ "87 8.0500 NaN S 0 \n",
+ "88 263.0000 C23 C25 C27 S 1 \n",
+ "89 8.0500 NaN S 0 \n",
+ "90 8.0500 NaN S 0 \n",
+ "91 7.8542 NaN S 0 \n",
+ "92 61.1750 E31 S 0 \n",
+ "93 20.5750 NaN S 0 \n",
+ "94 7.2500 NaN S 0 \n",
+ "95 8.0500 NaN S 0 \n",
+ "96 34.6542 A5 C 0 \n",
+ "97 63.3583 D10 D12 C 0 \n",
+ "98 23.0000 NaN S 2 \n",
+ "99 26.0000 NaN S 0 \n",
+ "\n",
+ "[100 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
+ "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
+ "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 30.0\n",
+ "1 35.0\n",
+ "2 21.0\n",
+ "3 35.0\n",
+ "4 30.0\n",
+ "5 30.0\n",
+ "6 30.0\n",
+ "7 9.0\n",
+ "8 35.0\n",
+ "9 35.0\n",
+ "10 21.0\n",
+ "11 21.0\n",
+ "12 30.0\n",
+ "13 30.0\n",
+ "14 21.0\n",
+ "15 35.0\n",
+ "16 9.0\n",
+ "17 30.0\n",
+ "18 35.0\n",
+ "19 35.0\n",
+ "20 30.0\n",
+ "21 30.0\n",
+ "22 21.0\n",
+ "23 30.0\n",
+ "24 21.0\n",
+ "25 35.0\n",
+ "26 30.0\n",
+ "27 30.0\n",
+ "28 21.0\n",
+ "29 30.0\n",
+ " ... \n",
+ "861 30.0\n",
+ "862 35.0\n",
+ "863 21.0\n",
+ "864 30.0\n",
+ "865 35.0\n",
+ "866 21.0\n",
+ "867 30.0\n",
+ "868 30.0\n",
+ "869 9.0\n",
+ "870 30.0\n",
+ "871 35.0\n",
+ "872 30.0\n",
+ "873 30.0\n",
+ "874 35.0\n",
+ "875 21.0\n",
+ "876 30.0\n",
+ "877 30.0\n",
+ "878 30.0\n",
+ "879 35.0\n",
+ "880 35.0\n",
+ "881 30.0\n",
+ "882 21.0\n",
+ "883 30.0\n",
+ "884 30.0\n",
+ "885 35.0\n",
+ "886 9.0\n",
+ "887 21.0\n",
+ "888 21.0\n",
+ "889 30.0\n",
+ "890 30.0\n",
+ "Name: Age, Length: 891, dtype: float64"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head(30)\n",
+ "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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GN2nSJB544AGeffZZ/u3f/o0ZM2bwzW9+87zr+v1+fvrTn1JcXMy3vvUtamtr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\nlGVZ+JaPb/v0eTOmfEyYBOOBMapOCI8VqnGF4XA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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "22.00 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "38.00 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "26.00 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "35.00 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "35.00 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "Q | \n", + "0 | \n", + "
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100 rows × 12 columns
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN S 0 \n",
+ "1 71.2833 C85 C 2 \n",
+ "2 7.9250 NaN S 1 \n",
+ "3 53.1000 C123 S 2 \n",
+ "4 8.0500 NaN S 0 "
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
+ "for dataset in train_test_data:\n",
+ " dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.6 Fare"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "C | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "S | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "5 6 0 3 0 2.0 0 0 330877 \n",
+ "6 7 0 1 0 3.0 0 0 17463 \n",
+ "7 8 0 3 0 0.0 3 1 349909 \n",
+ "8 9 1 3 1 2.0 0 2 347742 \n",
+ "9 10 1 2 1 0.0 1 0 237736 \n",
+ "10 11 1 3 1 0.0 1 1 PP 9549 \n",
+ "11 12 1 1 1 3.0 0 0 113783 \n",
+ "12 13 0 3 0 1.0 0 0 A/5. 2151 \n",
+ "13 14 0 3 0 3.0 1 5 347082 \n",
+ "14 15 0 3 1 0.0 0 0 350406 \n",
+ "15 16 1 2 1 3.0 0 0 248706 \n",
+ "16 17 0 3 0 0.0 4 1 382652 \n",
+ "17 18 1 2 0 2.0 0 0 244373 \n",
+ "18 19 0 3 1 2.0 1 0 345763 \n",
+ "19 20 1 3 1 2.0 0 0 2649 \n",
+ "20 21 0 2 0 2.0 0 0 239865 \n",
+ "21 22 1 2 0 2.0 0 0 248698 \n",
+ "22 23 1 3 1 0.0 0 0 330923 \n",
+ "23 24 1 1 0 2.0 0 0 113788 \n",
+ "24 25 0 3 1 0.0 3 1 349909 \n",
+ "25 26 1 3 1 3.0 1 5 347077 \n",
+ "26 27 0 3 0 2.0 0 0 2631 \n",
+ "27 28 0 1 0 1.0 3 2 19950 \n",
+ "28 29 1 3 1 1.0 0 0 330959 \n",
+ "29 30 0 3 0 2.0 0 0 349216 \n",
+ "30 31 0 1 0 3.0 0 0 PC 17601 \n",
+ "31 32 1 1 1 2.0 1 0 PC 17569 \n",
+ "32 33 1 3 1 1.0 0 0 335677 \n",
+ "33 34 0 2 0 4.0 0 0 C.A. 24579 \n",
+ "34 35 0 1 0 2.0 1 0 PC 17604 \n",
+ "35 36 0 1 0 3.0 1 0 113789 \n",
+ "36 37 1 3 0 2.0 0 0 2677 \n",
+ "37 38 0 3 0 1.0 0 0 A./5. 2152 \n",
+ "38 39 0 3 1 1.0 2 0 345764 \n",
+ "39 40 1 3 1 0.0 1 0 2651 \n",
+ "40 41 0 3 1 3.0 1 0 7546 \n",
+ "41 42 0 2 1 2.0 1 0 11668 \n",
+ "42 43 0 3 0 2.0 0 0 349253 \n",
+ "43 44 1 2 1 0.0 1 2 SC/Paris 2123 \n",
+ "44 45 1 3 1 1.0 0 0 330958 \n",
+ "45 46 0 3 0 2.0 0 0 S.C./A.4. 23567 \n",
+ "46 47 0 3 0 2.0 1 0 370371 \n",
+ "47 48 1 3 1 1.0 0 0 14311 \n",
+ "48 49 0 3 0 2.0 2 0 2662 \n",
+ "49 50 0 3 1 1.0 1 0 349237 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 7.2500 NaN 0 0 \n",
+ "1 71.2833 C85 1 2 \n",
+ "2 7.9250 NaN 0 1 \n",
+ "3 53.1000 C123 0 2 \n",
+ "4 8.0500 NaN 0 0 \n",
+ "5 8.4583 NaN 2 0 \n",
+ "6 51.8625 E46 0 0 \n",
+ "7 21.0750 NaN 0 3 \n",
+ "8 11.1333 NaN 0 2 \n",
+ "9 30.0708 NaN 1 2 \n",
+ "10 16.7000 G6 0 1 \n",
+ "11 26.5500 C103 0 1 \n",
+ "12 8.0500 NaN 0 0 \n",
+ "13 31.2750 NaN 0 0 \n",
+ "14 7.8542 NaN 0 1 \n",
+ "15 16.0000 NaN 0 2 \n",
+ "16 29.1250 NaN 2 3 \n",
+ "17 13.0000 NaN 0 0 \n",
+ "18 18.0000 NaN 0 2 \n",
+ "19 7.2250 NaN 1 2 \n",
+ "20 26.0000 NaN 0 0 \n",
+ "21 13.0000 D56 0 0 \n",
+ "22 8.0292 NaN 2 1 \n",
+ "23 35.5000 A6 0 0 \n",
+ "24 21.0750 NaN 0 1 \n",
+ "25 31.3875 NaN 0 2 \n",
+ "26 7.2250 NaN 1 0 \n",
+ "27 263.0000 C23 C25 C27 0 0 \n",
+ "28 7.8792 NaN 2 1 \n",
+ "29 7.8958 NaN 0 0 \n",
+ "30 27.7208 NaN 1 3 \n",
+ "31 146.5208 B78 1 2 \n",
+ "32 7.7500 NaN 2 1 \n",
+ "33 10.5000 NaN 0 0 \n",
+ "34 82.1708 NaN 1 0 \n",
+ "35 52.0000 NaN 0 0 \n",
+ "36 7.2292 NaN 1 0 \n",
+ "37 8.0500 NaN 0 0 \n",
+ "38 18.0000 NaN 0 1 \n",
+ "39 11.2417 NaN 1 1 \n",
+ "40 9.4750 NaN 0 2 \n",
+ "41 21.0000 NaN 0 2 \n",
+ "42 7.8958 NaN 1 0 \n",
+ "43 41.5792 NaN 1 1 \n",
+ "44 7.8792 NaN 2 1 \n",
+ "45 8.0500 NaN 0 0 \n",
+ "46 15.5000 NaN 2 0 \n",
+ "47 7.7500 NaN 2 1 \n",
+ "48 21.6792 NaN 1 0 \n",
+ "49 17.8000 NaN 0 2 "
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# fill missing Fare with median fare for each Pclass\n",
+ "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
+ "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
+ "train.head(50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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H2yiesTUBJk8McuCQS23mQwqIIiIiIiIyIvUaDjdu3Egmk2HdunXccccdrFmz\nplCWzWZZvXo1jz32GGvXrmXdunU0NTUB8Oijj/Kd73yHdDrdp4rExjWzK/EKVYFRnB+7cIBvpzhm\nXlSGbcFz2xLMr/1kISDqHkQRERERERkpeg2H27ZtY86cOQDMmDGDHTt2FMp2795NbW0tFRUVBINB\nZs+ezZYtWwCora3lgQce6HNF2qq2YWIxu/IDmEOwbMVQKouYTKsLk0i57NjZydVTr2Jy+SRebd6l\ngCgiIiIiIiNCr+scJhIJYrFY4bFlWTiOg23bJBIJ4vF4oSwajZJI5NYHnD9/Pnv27OlzRVyzk9k1\nVzC+amx/6l8yLr8syBtvH+SF7W189ANj+MsZn2Xdjg282ryL//X6v3PHlV8iaJ189lMprpqaeO8H\niQwytTspFrU9KRa1PSkWtb2+6TUcxmIxkslk4bHnedi2fcKyZDLZIyz2R4VZwyR7KolE54DOLwXv\nmxpix65O/vD8Qf7isgrmT/oEWee3vLz/FVY9/SD/zyVLCFnBYldTjlFTE6exsb3Y1ZCzjNqdFIva\nnhSL2p4Uy2C2vTM9ZPY6fnPWrFls2rQJgPr6eqZNm1Yoq6uro6GhgdbWVjKZDFu3bmXmzJkDqsjl\no6/EGGHDSY91QV2YQMDghfo2MlkP27S5espVnFtey6stu/iHrQ/S3NFS7GqKiIiIiIgcp9c0Nm/e\nPILBIIsXL2b16tUsX76cDRs2sG7dOgKBAMuWLWPp0qUsXryYhQsXMnbswIaFjisfGbOTnkowaHJB\nXZiOTo9tO3KfTnQFxEtGT2dvcj/3b/lHXj+8u8g1FRERERER6cnwfd8vdiUANry8ZUQPKe2SyXo8\n+dsjmKbBl2+aQCjYnb//1PQq/7NnMwDXn/8Z5kz4IIZhFKuqkqdhLlIMandSLGp7Uixqe1IsGlba\ndyN7HGcJCgZMLjw/TGfa45mtrT3KLh09nc+ddzUhK8i613/Fz3f9EsdzilRTERERERGRbgqHQ+CC\nujDxqMm2He0caOy5zuOE2HgWv+9z1ERGs3nfH/nRy4/QltGnaCIiIiIiUlwKh0PAsgzePyOK78N/\nb2rB83qO3C0Pxrl+2meYVlnHW0fe4f4t/8iOptcokRG+IiIiIiJyFlI4HCLjagKcOzHIgaYML71y\nfM9gwAyw4NxP8KHxV3Ak3cZDf/oZP3r5YRra3itCbUVERERE5GyncDiEZl1SRjBgsGlLK22J4+8t\nNAyD949dN1uYAAAYTklEQVSbyU0XfJ5zy2t5o/Utvr/1AX66419pTDUXocYiIiIiInK2UjgcQuGQ\nycyLyshkfZ565vjhpV1GR6q5tu5TLDzvGsaW1fDSoT9x7x//nvWv/5r2TGKYay0iIiIiImcjhcMh\nNnVykLE1Nrvf7eCpZ1pOeV/hxPg5LJp2HZ8695PEAzH+Z89mvvv8/fx81y/5c9OrZNzMMNZcRERE\nRETOJnaxK3CmMwyDOVfE+P2z7WzfmSAYMJj7waqTrm9oGAbTquqoqziXHc2v8eKBl3h27ws8u/cF\nAqbNtKrzuHjUhVw8+gKqw1XD/G5ERERERORMpXA4DIIBk49/KM7vn21ny5/bCQVNPnx55SnPsUyL\ny2ou5pLR09mfPMjbR97lnbZ3eaV5J68072Td63BOdBznVU6hpmw0NZFRjImMZlSkGtvUj1VERERE\nRPpHKWKYhEO5gLjxmTae3XaEjrTHx/6ikoB96pG9pmEyITaeCbHxfHjCX9CWaS8ExT3te9mXPNDj\neAOD6nAVY8pGMypcRTwYJx6M5b4CuW15MEbEjpy091JERERERM4+CofDqCxiMvfKOP/f8+1s29HO\n23s6uGbuaMbXhPp8jfJgnMtqLuKymotwPIfDna20po/Qmm7Lb3P7r7W8fsrrmIZJ1C6jLFBGNBAh\nGiijzC4rbEN2kKAZIGgFCVpBQmaQgBUgZAUxMMjdOenj+z6FP37+ufy+ny/ves7AwDYDBK0Agfw2\naOb2bdNWWBURERERKSKFw2EWi1os+HgF219JseutNP/7/z3AB2dWcMWl5YRD/ZsfyDbt3JDSstHH\nlWXcDO2ZBCmngw6ng1S2g5TT0eNxp5umLdPGoVQjPiefKGc4mJhUhisYFa6iOv81KlzFqEgV1eFq\nqsOVmIbmTxIRERERGSoKh0VgWwazL40yYXyQF15K8txLR9j65zZmXRTn8kvKiZVZp/0aQSvIqEg1\no/pwrO/7ZLwMnU6aTjdNp5PG8bJkPQfHc8ges+8DuT4+o3trHPXI6C41MLoOBh8c38HxXJz89Rw/\nt59xMySySd5ofeuEdQxZQWrjE3Nf5ROZHJ/E6Ei1ehtFRERERAaJwmERjasJ8H/NreCNdzrZ+WYn\nL9S3seXPbUyZGGHS+BCTxoUZOzqIZfUMQL7v43rgOD7BgIFpnl5AMgyDkBUiZIWoOK0rnT7Hc0lk\nE7Rl2mnLJGhPt3Mk00ZjRxNvtL7VIzxG7DCT45OYXD6JuspzmVI+mbJApIi1FxEREREZuRQOiywQ\nMJh+foT3TQ3z1rtpdu3u5M2GDt5s6ADAMMAyDUwzt/V8n0y26/6+nHDIJBwyqYjZTDonxORzwpwz\nJnRcqBxKvu+Tzpx+WLVNi8pQBZWh42Nqxs3Q2NHEwVQTB1ONHEo1svPwG+w8/AY05Hoqx0fHMrXy\nXOoqzmVqxbmMCp982RAREREREemmcFgiLMvg/Clhzp8SJtXhcag5S2Ozw+EjLp7n43ng+7levnjM\nwLZy52SzPumsTzrj0rDPoWFfJ89yBNsymFobYfp5ZdTVRnqdFbW/mg5n2PV2ikPNWQ4fyXK4zSGb\nzSXWUNAgErYoj1mMGRVk7KggY0YHqakKnFZwDFpBJsTOYULsnMJznU6aA6mD7E8cZF/yAAeSh9iX\nPMCze18AchP41MYnMCk+gUnxidTGJ1AZqlBgFBERERE5huH7fnFnIsnb8PIWEonOYldjREtnPA41\nORxsynLgUJa2hAdAMGAwbUoZ08+Lcu6E8IADWtPhLDt3J9n5Voqmw9nC85YF8ahFWcTEcXJhNZPx\n6Ojs2bQCtsE5Y0NMHBti0vgQE8eHsQe5d9P1XRpTzexLHmB/Piwmsskex8QCUWrjE5kYP4fRkWrq\nxk3EToepClVimad/v6dIX9TUxGlsbC92NeQspLYnxaK2J8UymG2vpiY+KNcpVQqHZyjf92ltc2nY\nk6FhT4ZkRy4olkVMLpga5bzJEcbXBImETx6GPM+n6XCW199JsXN3dyA0TThnTIBJE4KMHR0gEjZO\n2BOXdXxa2xxaj7i0tLo0tTgcaXcL5QHboPacMFMnRairDVNZHhjk70JOKtvBoY5GDqWacl8djbRn\nEscdZ2BQFa4szJgaC0SJ2BEigTBldoSIHSZiRyizIwTMAJZpYhlW7svMbw0T0zDVMym90i9JUixq\ne1IsantSLAqHfadweBbwfZ+mFod39mR4d2+GdKb7R14Ztxk7OkgoZGKbuaGqHWmPxpYszYezOG7u\n2KMD4cRxQQKBgYWfdMajqcXhYKPDvkMZ2tq9Qll1hc3USRGm1uYm5BnsobBH63A6ae5o4UimjYzR\nyaG2lvwkOO3H9TQORHdgzAVI27QL+91B0jouYJqGiWWYGPmtaZiY5Ldmbt/Kh0/LyB1vGkZ+axX2\nLcPKH2MWyizDxMTAPOp1zPyXdcz55lH1Dpg2ATNQ2Kp3dXDolyQpFrU9KRa1PSkWhcO+Uzg8y3ie\nz8Emh0NNWZpbHVoOu2SyxzcB04SKuEVlucW4MYHTCoSnkki67D+UZd/BLAcbszj5jkXLgknjw0yZ\nGKZ2fJia6iC2PTS9cZWVZbS2pgqPu2ZM7XTSpN0MGTe3Tee3nW4a13PxfA/P93B9D893j9r3jik7\n9nHPc4u9xmR/mZgELBvbDBDsCo5WoEeADFpBwnaIiBUmbIcI22HCVs9t5JjnzrbQqV+SpFjU9qRY\n1PakWBQO+67XCWk8z2PFihXs2rWLYDDIypUrmTx5cqH86aef5sEHH8S2bRYuXMgNN9zQ6zlSPKZp\nMH5MgPFjckM4fd+no9PHcX08N7dEhm0bxKPmaS+R0RexqMX5UyzOnxLGdX0aWxz2H8yy/1CWd/Z0\n8s6e3AcGpgGjqgKMHR2kstymPGZTHrOIl9lEwrnZWgervp5r4HaU0ZkI0p50aU+6JJIO7ancfjrj\nkXV8HMfHdX1s2yAYMAgGcvUoj9rEohbxmEU8alMetfL3ZFonrKPv+4UQ6ePh+X5+P7/1fTzfz5fl\nH/co8/A49jgf3/dOcFz++WPKu6/r9Tje8V1czyXrOWRdh3TWIePm9rOOQ9rL4tGJb3j4hguGd4Lv\naN8ETJuwlQ+NdoiwFe4OkYXHoXyo7NqPFAJmyAqO2N5Nz/fy64jm1xV1u9YXzRaeK5S7PZ8vrEHq\nZgsfNviFbe6jh9y2u310PT56axgmBmZ+ddLcvm2a2JZFwOzZs2326HW2ClvL7O6lPrr3vPs5s9Bz\nbhhGYW3UY/lHtUvX8wpt/LgPXTy3xwcwhf2uY73jP7xxfZd01iXtZElnc+uudrV5A5OgHSAcyH1F\nAgFsy8Y2LIJWkGD+g5Cu/e5toPChSCD/2DSGbuSDiIgMDt/387/r5Nfh9vPrcHsuru92/z951P+Z\nNTUXFbvaQ6rXcLhx40YymQzr1q2jvr6eNWvW8NBDDwGQzWZZvXo1TzzxBJFIhBtvvJG5c+fy0ksv\nnfQcKS2GYVAWKY374yzLYFxNgHE1AWYCHZ0eBw5laTqcm7W15UiWxpbsSc8PBox8ULSIhExCIZOA\nbeS/TKyu39Xybzfr+LkZVo3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+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "71.2833 | \n", + "C85 | \n", + "1 | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "0 | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 5 | \n", + "6 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "330877 | \n", + "8.4583 | \n", + "NaN | \n", + "2 | \n", + "0 | \n", + "
| 6 | \n", + "7 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "0 | \n", + "0 | \n", + "17463 | \n", + "51.8625 | \n", + "E46 | \n", + "0 | \n", + "0 | \n", + "
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| 8 | \n", + "9 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0 | \n", + "2 | \n", + "347742 | \n", + "11.1333 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 9 | \n", + "10 | \n", + "1 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "0 | \n", + "237736 | \n", + "30.0708 | \n", + "NaN | \n", + "1 | \n", + "2 | \n", + "
| 10 | \n", + "11 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "1 | \n", + "PP 9549 | \n", + "16.7000 | \n", + "G6 | \n", + "0 | \n", + "1 | \n", + "
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| 18 | \n", + "19 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "345763 | \n", + "18.0000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 19 | \n", + "20 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2649 | \n", + "7.2250 | \n", + "NaN | \n", + "1 | \n", + "2 | \n", + "
| 20 | \n", + "21 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "239865 | \n", + "26.0000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 21 | \n", + "22 | \n", + "1 | \n", + "2 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "248698 | \n", + "13.0000 | \n", + "D56 | \n", + "0 | \n", + "0 | \n", + "
| 22 | \n", + "23 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "0 | \n", + "0 | \n", + "330923 | \n", + "8.0292 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 23 | \n", + "24 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "113788 | \n", + "35.5000 | \n", + "A6 | \n", + "0 | \n", + "0 | \n", + "
| 24 | \n", + "25 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "3 | \n", + "1 | \n", + "349909 | \n", + "21.0750 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 25 | \n", + "26 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "5 | \n", + "347077 | \n", + "31.3875 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 26 | \n", + "27 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2631 | \n", + "7.2250 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 27 | \n", + "28 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1.0 | \n", + "3 | \n", + "2 | \n", + "19950 | \n", + "263.0000 | \n", + "C23 C25 C27 | \n", + "0 | \n", + "0 | \n", + "
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| 29 | \n", + "30 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "349216 | \n", + "7.8958 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 30 | \n", + "31 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "0 | \n", + "0 | \n", + "PC 17601 | \n", + "27.7208 | \n", + "NaN | \n", + "1 | \n", + "3 | \n", + "
| 31 | \n", + "32 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "PC 17569 | \n", + "146.5208 | \n", + "B78 | \n", + "1 | \n", + "2 | \n", + "
| 32 | \n", + "33 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "335677 | \n", + "7.7500 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 33 | \n", + "34 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "4.0 | \n", + "0 | \n", + "0 | \n", + "C.A. 24579 | \n", + "10.5000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 34 | \n", + "35 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "PC 17604 | \n", + "82.1708 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 35 | \n", + "36 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "113789 | \n", + "52.0000 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 36 | \n", + "37 | \n", + "1 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "2677 | \n", + "7.2292 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 37 | \n", + "38 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "A./5. 2152 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 38 | \n", + "39 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "2 | \n", + "0 | \n", + "345764 | \n", + "18.0000 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 39 | \n", + "40 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "0 | \n", + "2651 | \n", + "11.2417 | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "
| 40 | \n", + "41 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "7546 | \n", + "9.4750 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 41 | \n", + "42 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "11668 | \n", + "21.0000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
| 42 | \n", + "43 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "349253 | \n", + "7.8958 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 43 | \n", + "44 | \n", + "1 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "1 | \n", + "2 | \n", + "SC/Paris 2123 | \n", + "41.5792 | \n", + "NaN | \n", + "1 | \n", + "1 | \n", + "
| 44 | \n", + "45 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "330958 | \n", + "7.8792 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 45 | \n", + "46 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "S.C./A.4. 23567 | \n", + "8.0500 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 46 | \n", + "47 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "370371 | \n", + "15.5000 | \n", + "NaN | \n", + "2 | \n", + "0 | \n", + "
| 47 | \n", + "48 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "14311 | \n", + "7.7500 | \n", + "NaN | \n", + "2 | \n", + "1 | \n", + "
| 48 | \n", + "49 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "2 | \n", + "0 | \n", + "2662 | \n", + "21.6792 | \n", + "NaN | \n", + "1 | \n", + "0 | \n", + "
| 49 | \n", + "50 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "349237 | \n", + "17.8000 | \n", + "NaN | \n", + "0 | \n", + "2 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title \n",
+ "0 0.0 NaN 0 0 \n",
+ "1 2.0 C85 1 2 \n",
+ "2 0.0 NaN 0 1 \n",
+ "3 2.0 C123 0 2 \n",
+ "4 0.0 NaN 0 0 "
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.7 Cabin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "C23 C25 C27 4\n",
+ "G6 4\n",
+ "B96 B98 4\n",
+ "D 3\n",
+ "C22 C26 3\n",
+ "E101 3\n",
+ "F2 3\n",
+ "F33 3\n",
+ "B57 B59 B63 B66 2\n",
+ "C68 2\n",
+ "B58 B60 2\n",
+ "E121 2\n",
+ "D20 2\n",
+ "E8 2\n",
+ "E44 2\n",
+ "B77 2\n",
+ "C65 2\n",
+ "D26 2\n",
+ "E24 2\n",
+ "E25 2\n",
+ "B20 2\n",
+ "C93 2\n",
+ "D33 2\n",
+ "E67 2\n",
+ "D35 2\n",
+ "D36 2\n",
+ "C52 2\n",
+ "F4 2\n",
+ "C125 2\n",
+ "C124 2\n",
+ " ..\n",
+ "F G63 1\n",
+ "A6 1\n",
+ "D45 1\n",
+ "D6 1\n",
+ "D56 1\n",
+ "C101 1\n",
+ "C54 1\n",
+ "D28 1\n",
+ "D37 1\n",
+ "B102 1\n",
+ "D30 1\n",
+ "E17 1\n",
+ "E58 1\n",
+ "F E69 1\n",
+ "D10 D12 1\n",
+ "E50 1\n",
+ "A14 1\n",
+ "C91 1\n",
+ "A16 1\n",
+ "B38 1\n",
+ "B39 1\n",
+ "C95 1\n",
+ "B78 1\n",
+ "B79 1\n",
+ "C99 1\n",
+ "B37 1\n",
+ "A19 1\n",
+ "E12 1\n",
+ "A7 1\n",
+ "D15 1\n",
+ "Name: Cabin, Length: 147, dtype: int64"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.Cabin.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "for dataset in train_test_data:\n",
+ " dataset['Cabin'] = dataset['Cabin'].str[:1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "C85 | \n", + "1 | \n", + "2 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "1 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "C123 | \n", + "0 | \n", + "2 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "NaN | \n", + "0 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title FamilySize \n",
+ "0 0.0 2.0 0 0 0.4 \n",
+ "1 2.0 0.8 1 2 0.4 \n",
+ "2 0.0 2.0 0 1 0.0 \n",
+ "3 2.0 0.8 0 2 0.4 \n",
+ "4 0.0 2.0 0 0 0.0 "
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass Sex Age SibSp Parch Ticket \\\n",
+ "0 1 0 3 0 1.0 1 0 A/5 21171 \n",
+ "1 2 1 1 1 3.0 1 0 PC 17599 \n",
+ "2 3 1 3 1 1.0 0 0 STON/O2. 3101282 \n",
+ "3 4 1 1 1 2.0 1 0 113803 \n",
+ "4 5 0 3 0 2.0 0 0 373450 \n",
+ "\n",
+ " Fare Cabin Embarked Title FamilySize \n",
+ "0 0.0 2.0 0 0 0.4 \n",
+ "1 2.0 0.8 1 2 0.4 \n",
+ "2 0.0 2.0 0 1 0.0 \n",
+ "3 2.0 0.8 0 2 0.4 \n",
+ "4 0.0 2.0 0 0 0.0 "
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
+ "train = train.drop(features_drop, axis=1)\n",
+ "test = test.drop(features_drop, axis=1)\n",
+ "train = train.drop(['PassengerId'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((891, 8), (891,))"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_data = train.drop('Survived', axis=1)\n",
+ "target = train['Survived']\n",
+ "\n",
+ "train_data.shape, target.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "1 | \n", + "0 | \n", + "PC 17599 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "5 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "373450 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Pclass Sex Age Fare Cabin Embarked Title FamilySize\n",
+ "0 3 0 1.0 0.0 2.0 0 0 0.4\n",
+ "1 1 1 3.0 2.0 0.8 1 2 0.4\n",
+ "2 3 1 1.0 0.0 2.0 0 1 0.0\n",
+ "3 1 1 2.0 2.0 0.8 0 2 0.4\n",
+ "4 3 0 2.0 0.0 2.0 0 0 0.0\n",
+ "5 3 0 2.0 0.0 2.0 2 0 0.0\n",
+ "6 1 0 3.0 2.0 1.6 0 0 0.0\n",
+ "7 3 0 0.0 1.0 2.0 0 3 1.6\n",
+ "8 3 1 2.0 0.0 2.0 0 2 0.8\n",
+ "9 2 1 0.0 2.0 1.8 1 2 0.4"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_data.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Modelling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Importing Classifier Modules\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.naive_bayes import GaussianNB\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | Pclass | \n", + "Sex | \n", + "Age | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "Title | \n", + "FamilySize | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "3 | \n", + "0 | \n", + "1.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.4 | \n", + "
| 1 | \n", + "1 | \n", + "1 | \n", + "3.0 | \n", + "2.0 | \n", + "0.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
| 2 | \n", + "3 | \n", + "1 | \n", + "1.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "1 | \n", + "0.0 | \n", + "
| 3 | \n", + "1 | \n", + "1 | \n", + "2.0 | \n", + "2.0 | \n", + "0.8 | \n", + "0 | \n", + "2 | \n", + "0.4 | \n", + "
| 4 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
| 5 | \n", + "3 | \n", + "0 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "2 | \n", + "0 | \n", + "0.0 | \n", + "
| 6 | \n", + "1 | \n", + "0 | \n", + "3.0 | \n", + "2.0 | \n", + "1.6 | \n", + "0 | \n", + "0 | \n", + "0.0 | \n", + "
| 7 | \n", + "3 | \n", + "0 | \n", + "0.0 | \n", + "1.0 | \n", + "2.0 | \n", + "0 | \n", + "3 | \n", + "1.6 | \n", + "
| 8 | \n", + "3 | \n", + "1 | \n", + "2.0 | \n", + "0.0 | \n", + "2.0 | \n", + "0 | \n", + "2 | \n", + "0.8 | \n", + "
| 9 | \n", + "2 | \n", + "1 | \n", + "0.0 | \n", + "2.0 | \n", + "1.8 | \n", + "1 | \n", + "2 | \n", + "0.4 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Survived\n",
+ "0 892 0\n",
+ "1 893 1\n",
+ "2 894 0\n",
+ "3 895 0\n",
+ "4 896 1"
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission = pd.read_csv('submission.csv')\n",
+ "submission.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## References\n",
+ "\n",
+ "This notebook is created by learning from the following notebooks:\n",
+ "\n",
+ "- [Mukesh ChapagainTitanic Solution: A Beginner's Guide](https://www.kaggle.com/chapagain/titanic-solution-a-beginner-s-guide?scriptVersionId=1473689)\n",
+ "- [How to score 0.8134 in Titanic Kaggle Challenge](http://ahmedbesbes.com/how-to-score-08134-in-titanic-kaggle-challenge.html)\n",
+ "- [Titanic: factors to survive](https://olegleyz.github.io/titanic_factors.html)\n",
+ "- [Titanic Survivors Dataset and Data Wrangling](http://www.codeastar.com/data-wrangling/)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/data/transfusion.data.csv b/data/transfusion.data.csv
new file mode 100644
index 0000000..1a35bfe
--- /dev/null
+++ b/data/transfusion.data.csv
@@ -0,0 +1,749 @@
+Recency (months),Frequency (times),Monetary (c.c. blood),Time (months),"whether he/she donated blood in March 2007"
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+14 ,5,1250,64 ,0
+14 ,3,750,41 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,4,1000,45 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,1,250,16 ,0
+16 ,2,500,26 ,0
+21 ,2,500,23 ,0
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+21 ,2,500,23 ,0
+21 ,2,500,23 ,0
+14 ,4,1000,57 ,0
+16 ,5,1250,60 ,0
+23 ,2,500,23 ,0
+14 ,5,1250,74 ,0
+23 ,3,750,28 ,0
+16 ,3,750,40 ,0
+9 ,2,500,52 ,0
+9 ,2,500,52 ,0
+16 ,7,1750,87 ,1
+14 ,4,1000,64 ,0
+14 ,2,500,35 ,0
+16 ,7,1750,93 ,0
+21 ,2,500,25 ,0
+14 ,3,750,52 ,0
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+18 ,8,2000,95 ,0
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+11 ,2,500,52 ,0
+11 ,3,750,76 ,0
+23 ,12,3000,86 ,0
+21 ,3,750,35 ,0
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+23 ,8,2000,64 ,0
+16 ,3,750,50 ,0
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+21 ,5,1250,60 ,0
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+11 ,1,250,11 ,0
+11 ,1,250,11 ,1
+11 ,1,250,11 ,0
+2 ,3,750,75 ,1
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+20 ,14,3500,69 ,1
+4 ,2,500,46 ,0
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+11 ,3,750,37 ,0
+16 ,4,1000,33 ,0
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+16 ,6,1500,50 ,0
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+21 ,6,1500,48 ,0
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+25 ,6,1500,50 ,0
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+21 ,2,500,33 ,0
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+23 ,1,250,23 ,0
+23 ,1,250,23 ,0
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+23 ,1,250,23 ,0
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+16 ,3,750,86 ,0
+23 ,2,500,38 ,0
+21 ,2,500,52 ,0
+23 ,3,750,62 ,0
+39 ,1,250,39 ,0
+72 ,1,250,72 ,0
\ No newline at end of file
diff --git a/data/transfusion.names.txt b/data/transfusion.names.txt
new file mode 100644
index 0000000..f10935f
--- /dev/null
+++ b/data/transfusion.names.txt
@@ -0,0 +1,88 @@
+Title: Blood Transfusion Service Center Data Set
+
+Abstract: Data taken from the Blood Transfusion Service Center in Hsin-Chu City
+in Taiwan -- this is a classification problem.
+
+
+-----------------------------------------------------
+
+Data Set Characteristics: Multivariate
+Number of Instances: 748
+Area: Business
+Attribute Characteristics: Real
+Number of Attributes: 5
+Date Donated: 2008-10-03
+Associated Tasks: Classification
+Missing Values? N/A
+
+-----------------------------------------------------
+
+Source:
+
+Original Owner and Donor
+Prof. I-Cheng Yeh
+Department of Information Management
+Chung-Hua University,
+Hsin Chu, Taiwan 30067, R.O.C.
+e-mail:icyeh 'at' chu.edu.tw
+TEL:886-3-5186511
+
+Date Donated: October 3, 2008
+
+-----------------------------------------------------
+
+Data Set Information:
+
+To demonstrate the RFMTC marketing model (a modified version of RFM), this study
+adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City
+in Taiwan. The center passes their blood transfusion service bus to one
+university in Hsin-Chu City to gather blood donated about every three months. To
+build a FRMTC model, we selected 748 donors at random from the donor database.
+These 748 donor data, each one included R (Recency - months since last
+donation), F (Frequency - total number of donation), M (Monetary - total blood
+donated in c.c.), T (Time - months since first donation), and a binary variable
+representing whether he/she donated blood in March 2007 (1 stand for donating
+blood; 0 stands for not donating blood).
+
+-----------------------------------------------------
+
+Attribute Information:
+
+Given is the variable name, variable type, the measurement unit and a brief
+description. The "Blood Transfusion Service Center" is a classification problem.
+The order of this listing corresponds to the order of numerals along the rows of
+the database.
+
+R (Recency - months since last donation),
+F (Frequency - total number of donation),
+M (Monetary - total blood donated in c.c.),
+T (Time - months since first donation), and
+a binary variable representing whether he/she donated blood in March 2007 (1
+stand for donating blood; 0 stands for not donating blood).
+
+
+Table 1 shows the descriptive statistics of the data. We selected 500 data at
+random as the training set, and the rest 248 as the testing set.
+
+Table 1. Descriptive statistics of the data
+
+Variable Data Type Measurement Description min max mean std
+Recency quantitative Months Input 0.03 74.4 9.74 8.07
+Frequency quantitative Times Input 1 50 5.51 5.84
+Monetary quantitative c.c. blood Input 250 12500 1378.68 1459.83
+Time quantitative Months Input 2.27 98.3 34.42 24.32
+Whether he/she donated blood in March 2007 binary 1=yes 0=no Output 0 1 1 (24%) 0 (76%)
+
+
+-----------------------------------------------------
+
+Citation Request:
+
+NOTE: Reuse of this database is unlimited with retention of copyright notice for
+Prof. I-Cheng Yeh and the following published paper:
+
+Yeh, I-Cheng, Yang, King-Jang, and Ting, Tao-Ming, "Knowledge discovery on RFM
+model using Bernoulli sequence, "Expert Systems with Applications, 2008
+(doi:10.1016/j.eswa.2008.07.018).
+
+
diff --git a/data/tree.dot b/data/tree.dot
new file mode 100644
index 0000000..6680093
--- /dev/null
+++ b/data/tree.dot
@@ -0,0 +1,44 @@
+digraph Tree {
+node [shape=box, style="filled", color="black"] ;
+0 [label="worst radius <= 16.795\nsamples = 426\nvalue = [159, 267]\nclass = benign", fillcolor="#399de567"] ;
+1 [label="worst concave points <= 0.136\nsamples = 284\nvalue = [25, 259]\nclass = benign", fillcolor="#399de5e6"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="radius error <= 1.048\nsamples = 252\nvalue = [4, 248]\nclass = benign", fillcolor="#399de5fb"] ;
+1 -> 2 ;
+3 [label="smoothness error <= 0.003\nsamples = 251\nvalue = [3, 248]\nclass = benign", fillcolor="#399de5fc"] ;
+2 -> 3 ;
+4 [label="samples = 4\nvalue = [1, 3]\nclass = benign", fillcolor="#399de5aa"] ;
+3 -> 4 ;
+5 [label="samples = 247\nvalue = [2, 245]\nclass = benign", fillcolor="#399de5fd"] ;
+3 -> 5 ;
+6 [label="samples = 1\nvalue = [1, 0]\nclass = malignant", fillcolor="#e58139ff"] ;
+2 -> 6 ;
+7 [label="worst texture <= 25.62\nsamples = 32\nvalue = [21, 11]\nclass = malignant", fillcolor="#e5813979"] ;
+1 -> 7 ;
+8 [label="worst smoothness <= 0.179\nsamples = 12\nvalue = [3, 9]\nclass = benign", fillcolor="#399de5aa"] ;
+7 -> 8 ;
+9 [label="samples = 10\nvalue = [1, 9]\nclass = benign", fillcolor="#399de5e3"] ;
+8 -> 9 ;
+10 [label="samples = 2\nvalue = [2, 0]\nclass = malignant", fillcolor="#e58139ff"] ;
+8 -> 10 ;
+11 [label="worst symmetry <= 0.268\nsamples = 20\nvalue = [18, 2]\nclass = malignant", fillcolor="#e58139e3"] ;
+7 -> 11 ;
+12 [label="samples = 3\nvalue = [1, 2]\nclass = benign", fillcolor="#399de57f"] ;
+11 -> 12 ;
+13 [label="samples = 17\nvalue = [17, 0]\nclass = malignant", fillcolor="#e58139ff"] ;
+11 -> 13 ;
+14 [label="texture error <= 0.473\nsamples = 142\nvalue = [134, 8]\nclass = malignant", fillcolor="#e58139f0"] ;
+0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+15 [label="samples = 5\nvalue = [0, 5]\nclass = benign", fillcolor="#399de5ff"] ;
+14 -> 15 ;
+16 [label="worst concavity <= 0.191\nsamples = 137\nvalue = [134, 3]\nclass = malignant", fillcolor="#e58139f9"] ;
+14 -> 16 ;
+17 [label="worst texture <= 30.975\nsamples = 5\nvalue = [2, 3]\nclass = benign", fillcolor="#399de555"] ;
+16 -> 17 ;
+18 [label="samples = 3\nvalue = [0, 3]\nclass = benign", fillcolor="#399de5ff"] ;
+17 -> 18 ;
+19 [label="samples = 2\nvalue = [2, 0]\nclass = malignant", fillcolor="#e58139ff"] ;
+17 -> 19 ;
+20 [label="samples = 132\nvalue = [132, 0]\nclass = malignant", fillcolor="#e58139ff"] ;
+16 -> 20 ;
+}
\ No newline at end of file
diff --git a/log.md b/log.md
index cd30141..7254813 100644
--- a/log.md
+++ b/log.md
@@ -1,85 +1,74 @@
# Master Python for Data Science Handbook - Log
-## Week 1: Introduction: IPython: Beyond Normal Python
-**(delete me or comment me out)**
+## Week 1: Introduction to Ipython
-**Today's Progress**: Finished going through Numpy and Pandas
+**Today's Progress**: Finished going through Introduction to Ipython
-**Thoughts**: I really struggled through..., but I feel like I am slowly getting better at it
+**Thoughts**: Feeling great to learn Ipython. No blockers so far.
-**Link to work**: [introduction(Ipython)](www.example.com)
+**Link to work**: [Introduction(Ipython)](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Introduction-Ipython.ipynb)
## Week 2: Chapter 2: Introduction to Numpy
-**This week's Progress**:
+**This week's Progress**: Finished going through introduction to Numpy.
-**Thoughts**:
+**Thoughts**: Numpy arrays are very interesting. Not only do they provide an efficient way of manipulating Datasets but also make it easier and faster.
+ No blockers so far and having learnt the basis of dataset manipulation I'm more than eager to move to Pandas.
-**Link to work**:
+**Link to work**: [Numpy Arrays](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Numpy.ipynb)
## Week 3: Chapter 3: Data Manipulation with Pandas
-**This week's Progress**:
+**This week's Progress**: Finished up on Data Manipulation with Pandas.
-**Thoughts**:
+**Thoughts**: Wow! It feels great to finally understand Pandas. Its a pretty wide subject but equally productive.
+ Very ready to dig into the challenge and gauge my understanding.
-**Link to work**:
+**Link to work**: [Pandas in General](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Pandas%20in%20General.ipynb)
## Week 4: Practice(Challenge 1)
-**This week's Progress**:
+**This week's Progress**: Completed the Data Manipulation Challenge 1.
-**Thoughts**:
+**Thoughts**: My first Data Science challenge. Very glad to have acquired some quality skills and very ready to dig into visualizations.
-**Link to work**:
+**Link to work**:[Challenge 1](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/kiva_analytics.ipynb)
## Week 5: Chapter 4: Data Visualization
-**This week's Progress**:
+**This week's Progress**: Finished up on data Visualizations and did a self challenge.
-**Thoughts**:
+**Thoughts**: One of the most important chapters in Data Science. Glad to learn it.
-**Link to work**:
+**Link to work**:Learning: [Visualisations](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Visualisations.ipynb)
-## Week 6: Chapter 4: Data Visualization
+**Self Challenge**: [Kiva Data Analytics](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/full_kiva_loans_analysis.ipynb)
-**This week's Progress**:
+## Week 6: Practice(Challenge 2)
-**Thoughts**:
+**This week's Progress**: Finished on practice challenge 2: Blood Donation Prediction.
-**Link to work**:
+**Thoughts**: Glad to be able to tackle a typical machine Learning project.
-## Week 7: Practice(Challenge 2)
+**Link to work**: [Blood Donations Prediction](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Prediction-Blood%20Donations.ipynb)
-**This week's Progress**:
+## Week 7: Chapter 5: Machine Learning
-**Thoughts**:
+**This week's Progress**: Finished going through Machine Learning basics with scikit-learn.
-**Link to work**:
+**Thoughts**: Its so great to finally understand the art of Machine Learning. I love the basics I've learnt and will continue learning to be able to build models which will solve problems facing the society.
-## Week 8: Chapter 5: Machine Learning
+**Link to work(1)**: [Introduction to ML](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/intro_to_ML.ipynb)
-**This week's Progress**:
+**Link to work(2)**: [ML 102](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/ML.ipynb)
-**Thoughts**:
+## Week 8&9: Practice(Challenge 3&4)
-**Link to work**:
+**This week's Progress**: Finished the Santander ML challenge.
-## Week 9: Chapter 5: Machine Learning
+**Thoughts**: Quite a large and frustrating dataset, but I managed. Kudos to me!
-**This week's Progress**:
-
-**Thoughts**:
-
-**Link to work**:
-
-## Week 10&11: Practice(Challenge 3&4)
-
-**This week's Progress**:
-
-**Thoughts**:
-
-**Link to work**:
+**Link to work**: [Santander Challenge](https://github.com/Kasre96/Python4ds_cohort-1/blob/Isaac/notebooks/Santander%20Customer%20Transaction%20Prediction.ipynb)
## Capstone project
diff --git a/notebooks/.ipynb_checkpoints/Blood Donations Prediction-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Blood Donations Prediction-checkpoint.ipynb
new file mode 100644
index 0000000..67970a2
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Blood Donations Prediction-checkpoint.ipynb
@@ -0,0 +1,911 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)| \n", + " | PassengerId | \n", + "Survived | \n", + "
|---|---|---|
| 0 | \n", + "892 | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "1 | \n", + "
| 2 | \n", + "894 | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "1 | \n", + "
\n", + " 1.1. [Case Study ](#study)
\n", + " 1.2. [Objectives](#objs)\n", + "2. [Loading Libraries](#libs)\n", + "3. [Loading the Dataset](#datasets)\n", + "4. [Exploratory Data Analysis](#eda)
\n", + " 4.1. [Numerical EDA](#num-eda)
\n", + " 4.2. [Statistical EDA](#stats-eda)
\n", + " 4.2. [Visual EDA](#visual-eda)
\n", + "5. [Data Training and Predictions](#training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies.\n", + "
\n", + "## Case Study: \n", + "A mobile blood donation vehicle in Taiwan. The Blood Transfusion Service Center drives to different universities and collects blood as part of a blood drive.\n", + "
\n", + "## Objectives \n", + "1. To predict whether or not a donor will give blood the next time the vehicle comes to campus. (March 2007)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Loading Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import warnings\n", + "\n", + "# warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Set default seaborn styles\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Loading the Dataset\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Recency (months) Frequency (times) Monetary (c.c. blood) Time (months) \\\n",
+ "0 2 50 12500 98 \n",
+ "1 0 13 3250 28 \n",
+ "2 1 16 4000 35 \n",
+ "3 2 20 5000 45 \n",
+ "4 1 24 6000 77 \n",
+ "\n",
+ " whether he/she donated blood in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "donations_dataset = pd.read_csv(\"../data/transfusion.data.csv\")\n",
+ "donations_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Exploratory Data analysis(EDA)\n",
+ "\n",
+ "## Numerical EDA\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | Recency (months) | \n", + "Frequency (times) | \n", + "Monetary (c.c. blood) | \n", + "Time (months) | \n", + "whether he/she donated blood in March 2007 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n", + " **Recency(Months)** - Number of months since last donations
\n", + " **Frequency(Times)** - Total Number of Donations donations
\n", + " **Monetary(c.c. blood)** - Total volume of blood donated in c.c.
\n", + " **Time(Months)** - Number of months since first donations
\n", + " **Ehether he/she donated blood in 2007**\n", + "3. The dataset has no null entries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical EDA\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Recency (months) Frequency (times) Monetary (c.c. blood) \\\n",
+ "count 748.000000 748.000000 748.000000 \n",
+ "mean 9.506684 5.514706 1378.676471 \n",
+ "std 8.095396 5.839307 1459.826781 \n",
+ "min 0.000000 1.000000 250.000000 \n",
+ "25% 2.750000 2.000000 500.000000 \n",
+ "50% 7.000000 4.000000 1000.000000 \n",
+ "75% 14.000000 7.000000 1750.000000 \n",
+ "max 74.000000 50.000000 12500.000000 \n",
+ "\n",
+ " Time (months) whether he/she donated blood in March 2007 \n",
+ "count 748.000000 748.000000 \n",
+ "mean 34.282086 0.237968 \n",
+ "std 24.376714 0.426124 \n",
+ "min 2.000000 0.000000 \n",
+ "25% 16.000000 0.000000 \n",
+ "50% 28.000000 0.000000 \n",
+ "75% 50.000000 0.000000 \n",
+ "max 98.000000 1.000000 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Basic data Description\n",
+ "donations_dataset.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Basic dataset statistics\n",
+ "1. The average number of months taken by donors since last donation is **9.5 months**.\n",
+ "2. The average number of months taken by donors since first donation is **34.3 months**.\n",
+ "3. The average number of total donations is **6 donations**.\n",
+ "4. An avergae of **1378.68 c.c.** was donated\n",
+ "5. The maximum total blood volume donated by a single donor was **12500 c.c.** while the minimum was **250 c.c.**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visual EDA\n",
+ "\n",
+ "\n",
+ "### Comparison between total number of donors who donated blood in march 2007 and those who didn't. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Recency (months) | \n", + "Frequency (times) | \n", + "Monetary (c.c. blood) | \n", + "Time (months) | \n", + "whether he/she donated blood in March 2007 | \n", + "
|---|---|---|---|---|---|
| count | \n", + "748.000000 | \n", + "748.000000 | \n", + "748.000000 | \n", + "748.000000 | \n", + "748.000000 | \n", + "
| mean | \n", + "9.506684 | \n", + "5.514706 | \n", + "1378.676471 | \n", + "34.282086 | \n", + "0.237968 | \n", + "
| std | \n", + "8.095396 | \n", + "5.839307 | \n", + "1459.826781 | \n", + "24.376714 | \n", + "0.426124 | \n", + "
| min | \n", + "0.000000 | \n", + "1.000000 | \n", + "250.000000 | \n", + "2.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "2.750000 | \n", + "2.000000 | \n", + "500.000000 | \n", + "16.000000 | \n", + "0.000000 | \n", + "
| 50% | \n", + "7.000000 | \n", + "4.000000 | \n", + "1000.000000 | \n", + "28.000000 | \n", + "0.000000 | \n", + "
| 75% | \n", + "14.000000 | \n", + "7.000000 | \n", + "1750.000000 | \n", + "50.000000 | \n", + "0.000000 | \n", + "
| max | \n", + "74.000000 | \n", + "50.000000 | \n", + "12500.000000 | \n", + "98.000000 | \n", + "1.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Recency (months) Frequency (times) Monetary (c.c. blood) Time (months) \\\n",
+ "0 2 50 12500 98 \n",
+ "1 0 13 3250 28 \n",
+ "2 1 16 4000 35 \n",
+ "3 2 20 5000 45 \n",
+ "4 1 24 6000 77 \n",
+ "\n",
+ " whether he/she donated blood in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# make a copy of the dataset\n",
+ "donations = donations_dataset.copy()\n",
+ "donations.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Recency (months) | \n", + "Frequency (times) | \n", + "Monetary (c.c. blood) | \n", + "Time (months) | \n", + "whether he/she donated blood in March 2007 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Recency (months) Frequency (times) Monetary (c.c. blood) Time (months) \\\n",
+ "0 2 50 12500 98 \n",
+ "1 0 13 3250 28 \n",
+ "2 1 16 4000 35 \n",
+ "3 2 20 5000 45 \n",
+ "4 1 24 6000 77 \n",
+ "\n",
+ " donated \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# rename the last column to a more usable string\n",
+ "donations.rename(columns={'whether he/she donated blood in March 2007':'donated'}, inplace=True)\n",
+ "donations.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | Recency (months) | \n", + "Frequency (times) | \n", + "Monetary (c.c. blood) | \n", + "Time (months) | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Extract the donated column data\n",
+ "donated = donations.donated.value_counts()\n",
+ "donated.rename(index={0:'No', 1:'Yes'}, inplace=True)\n",
+ "# print(donated)\n",
+ "\n",
+ "# Instatiate a figure and draw a bar plot\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "\n",
+ "# plot the bar\n",
+ "sns.barplot(x=donated.index, y=donated.values)\n",
+ "\n",
+ "# display the counts on each bar\n",
+ "for i, v in enumerate(donated.values):\n",
+ " plt.text(i, 50, v, color='k', fontsize=19)\n",
+ "\n",
+ "# label the axes\n",
+ "plt.title('Donation status for March 2007', fontsize=25)\n",
+ "plt.xlabel(\"Donated\", fontsize=20)\n",
+ "plt.ylabel(\"Count\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**76.2%** of the donors didn't donate in March 2007, **23.8%** did."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Relationship between frequency(total number of donations) and Total volume of blood donated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency frequency blood_volume time donated\n",
+ "0 2 50 12500 98 1\n",
+ "1 0 13 3250 28 1\n",
+ "2 1 16 4000 35 1\n",
+ "3 2 20 5000 45 1\n",
+ "4 1 24 6000 77 0"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Rename the columns\n",
+ "donations_renamed = donations.rename(columns={'Recency (months)':'recency', 'Monetary (c.c. blood)':'blood_volume', 'Frequency (times)':'frequency', 'Time (months)':'time'})\n",
+ "donations_renamed.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | recency | \n", + "frequency | \n", + "blood_volume | \n", + "time | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# extract the columns\n",
+ "frequency = donations_renamed.frequency.values\n",
+ "volume = donations_renamed.blood_volume.values\n",
+ "\n",
+ "plt.figure(figsize=(10, 5))\n",
+ "\n",
+ "plt.plot(frequency, volume, color='green')\n",
+ "\n",
+ "plt.title('Times donated vs Total donated', fontsize=25)\n",
+ "plt.xlabel(\"Number of times donated\", fontsize=20)\n",
+ "plt.ylabel(\"Total volume donated\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The total volume of blood donated is directly proportional to the number of donations for a donor"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Data Training and Predictions\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/EDA-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/EDA-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/EDA-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/GeoJSON_and_choropleth-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/GeoJSON_and_choropleth-checkpoint.ipynb
new file mode 100644
index 0000000..7bdc1f6
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/GeoJSON_and_choropleth-checkpoint.ipynb
@@ -0,0 +1,951 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.6.0+159.g84a27d0.dirty\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import folium\n",
+ "\n",
+ "print(folium.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# GeoJSON and choropleth\n",
+ "\n",
+ "**A few examples of how to do that with `folium`.**\n",
+ "\n",
+ "\n",
+ "## Using `GeoJson`\n",
+ "\n",
+ "\n",
+ "Let us load a GeoJSON file representing the US states."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "us_states = os.path.join('data', 'us-states.json')\n",
+ "\n",
+ "geo_json_data = json.load(open(us_states))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is a classical GeoJSON `FeatureCollection` (see https://en.wikipedia.org/wiki/GeoJSON) of the form :\n",
+ "\n",
+ " {\n",
+ " \"type\": \"FeatureCollection\",\n",
+ " \"features\": [\n",
+ " {\n",
+ " \"properties\": {\"name\": \"Alabama\"},\n",
+ " \"id\": \"AL\",\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [[[-87.359296, 35.00118], ...]]\n",
+ " }\n",
+ " },\n",
+ " {\n",
+ " \"properties\": {\"name\": \"Alaska\"},\n",
+ " \"id\": \"AK\",\n",
+ " \"type\": \"Feature\",\n",
+ " \"geometry\": {\n",
+ " \"type\": \"MultiPolygon\",\n",
+ " \"coordinates\": [[[[-131.602021, 55.117982], ... ]]]\n",
+ " }\n",
+ " },\n",
+ " ...\n",
+ " ]\n",
+ " }\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A first way of drawing it on a map, is simply to use `folium.GeoJson` :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(geo_json_data).add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_0.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that you can avoid loading the file on yourself ; in simply providing a file path."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(us_states).add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_1.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can pass a geopandas object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import geopandas\n",
+ "\n",
+ "gdf = geopandas.read_file(us_states)\n",
+ "\n",
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(\n",
+ " gdf,\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_3.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now this is cool and simple, but we may be willing to choose the style of the data.\n",
+ "\n",
+ "You can provide a function of the form `lambda feature: {}` that sets the style of each feature.\n",
+ "\n",
+ "For possible options, see:\n",
+ "\n",
+ "* For `Point` and `MultiPoint`, see http://leafletjs.com/reference.html#marker\n",
+ "* For other features, see http://leafletjs.com/reference.html#path-options and http://leafletjs.com/reference.html#polyline-options\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(\n",
+ " geo_json_data,\n",
+ " style_function=lambda feature: {\n",
+ " 'fillColor': '#ffff00',\n",
+ " 'color': 'black',\n",
+ " 'weight': 2,\n",
+ " 'dashArray': '5, 5'\n",
+ " }\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_3.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What's cool in providing a function, is that you can specify a style depending on the feature. For example, if you want to visualize in green all states whose name contains the letter 'E', just do:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(\n",
+ " geo_json_data,\n",
+ " style_function=lambda feature: {\n",
+ " 'fillColor': 'green' if 'e' in feature['properties']['name'].lower() else '#ffff00',\n",
+ " 'color': 'black',\n",
+ " 'weight': 2,\n",
+ " 'dashArray': '5, 5'\n",
+ " }\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_4.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Wow, this looks almost like a choropleth. To do one, we just need to compute a color for each state.\n",
+ "\n",
+ "Let's imagine we want to draw a choropleth of unemployment in the US.\n",
+ "\n",
+ "First, we may load the data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from branca.colormap import linear\n",
+ "\n",
+ "colormap = linear.YlGn_09.scale(\n",
+ " unemployment.Unemployment.min(),\n",
+ " unemployment.Unemployment.max())\n",
+ "\n",
+ "print(colormap(5.0))\n",
+ "\n",
+ "colormap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need also to convert the table into a dictionnary, in order to map a feature to it's unemployment value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "7.0999999999999996"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "unemployment_dict = unemployment.set_index('State')['Unemployment']\n",
+ "\n",
+ "unemployment_dict['AL']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can do the choropleth."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(\n",
+ " geo_json_data,\n",
+ " name='unemployment',\n",
+ " style_function=lambda feature: {\n",
+ " 'fillColor': colormap(unemployment_dict[feature['id']]),\n",
+ " 'color': 'black',\n",
+ " 'weight': 1,\n",
+ " 'dashArray': '5, 5',\n",
+ " 'fillOpacity': 0.9,\n",
+ " }\n",
+ ").add_to(m)\n",
+ "\n",
+ "folium.LayerControl().add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_5.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Of course, if you can create and/or use a dictionnary providing directly the good color. Thus, the finishing seems faster:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.GeoJson(\n",
+ " geo_json_data,\n",
+ " style_function=lambda feature: {\n",
+ " 'fillColor': color_dict[feature['id']],\n",
+ " 'color': 'black',\n",
+ " 'weight': 1,\n",
+ " 'dashArray': '5, 5',\n",
+ " 'fillOpacity': 0.9,\n",
+ " }\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_6.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that adding a color legend may be a good idea."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "colormap.caption = 'Unemployment color scale'\n",
+ "colormap.add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using `Choropleth` class\n",
+ "\n",
+ "Now if you want to get faster, you can use the `Choropleth` class. Have a look at it's docstring, it has several styling options.\n",
+ "\n",
+ "You can use it in providing a file name (`geo_path`) :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=geopandas.read_file(us_states),\n",
+ " fill_color='red',\n",
+ " fill_opacity=0.3,\n",
+ " line_weight=2,\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or in providing a GeoJSON string (`geo_str`) :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.Choropleth(geo_data=us_states).add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_8.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then, in playing with keyword arguments, you can get a choropleth in a few lines:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=us_states,\n",
+ " data=unemployment,\n",
+ " columns=['State', 'Unemployment'],\n",
+ " key_on='feature.id',\n",
+ " fill_color='YlGn',\n",
+ ").add_to(m)\n",
+ "\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_9.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can force the color scale to a given number of bins (or directly list the bins you would like), by providing the `bins` argument."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=us_states,\n",
+ " data=unemployment,\n",
+ " columns=['State', 'Unemployment'],\n",
+ " key_on='feature.id',\n",
+ " fill_color='YlGn',\n",
+ " bins=[3, 4, 9, 11]\n",
+ ").add_to(m)\n",
+ "\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_10.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also enable the highlight function, to enable highlight functionality when you hover over each area."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "state_data = pd.read_csv(US_Unemployment_Oct2012)\n",
+ "\n",
+ "m = folium.Map(location=[48, -102], zoom_start=3)\n",
+ "folium.Choropleth(\n",
+ " geo_data=us_states,\n",
+ " data=state_data,\n",
+ " columns=['State', 'Unemployment'],\n",
+ " key_on='feature.id',\n",
+ " fill_color='YlGn',\n",
+ " fill_opacity=0.7,\n",
+ " line_opacity=0.2,\n",
+ " legend_name='Unemployment Rate (%)',\n",
+ " highlight=True\n",
+ ").add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_11.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can customize the way missing and `nan` values are displayed on your map using the two parameters `nan_fill_color` and `nan_fill_opacity`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "messed_up_data = unemployment.drop(0)\n",
+ "messed_up_data.loc[4, 'Unemployment'] = float('nan')\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=us_states,\n",
+ " data=messed_up_data,\n",
+ " columns=['State', 'Unemployment'],\n",
+ " nan_fill_color='purple',\n",
+ " nan_fill_opacity=0.4,\n",
+ " key_on='feature.id',\n",
+ " fill_color='YlGn'\n",
+ ").add_to(m)\n",
+ "\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_10.html'))\n",
+ "\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Internally Choropleth uses the `GeoJson` or `TopoJson` class, depending on your settings, and the `StepColormap` class. Both objects are attributes of your `Choropleth` object called `geojson` and `color_scale`. You can make changes to them, but for regular things you won't have to. For example setting a name for in the layer controls or disabling showing the layer on opening the map is possible in `Choropleth` itself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = folium.Map([43, -100], zoom_start=4)\n",
+ "\n",
+ "choropleth = folium.Choropleth(\n",
+ " geo_data=us_states,\n",
+ " data=state_data,\n",
+ " columns=['State', 'Unemployment'],\n",
+ " key_on='feature.id',\n",
+ " fill_color='YlGn',\n",
+ " name='Unenployment',\n",
+ " show=False,\n",
+ ").add_to(m)\n",
+ "\n",
+ "# The underlying GeoJson and StepColormap objects are reachable\n",
+ "print(type(choropleth.geojson))\n",
+ "print(type(choropleth.color_scale))\n",
+ "\n",
+ "folium.LayerControl(collapsed=False).add_to(m)\n",
+ "\n",
+ "m.save(os.path.join('results', 'GeoJSON_and_choropleth_11.html'))\n",
+ "\n",
+ "m"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.1"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/.ipynb_checkpoints/IndabaX-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/IndabaX-checkpoint.ipynb
new file mode 100644
index 0000000..62d37ee
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/IndabaX-checkpoint.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Policy Making in Malaria with AI (beta)\n",
+ "\n",
+ "In most areas of public policy and decision making, it’s difficult to make evidence based decisions because of the difficulty involved in exploring options for truly complex decisions. Policy making in malaria is a good example of these complex decisions. In this notebook, we explore policy making with malaria by considering only two interventions space i.e. spraying and using bed nets with an open-malaria model for Rachuonyo South in Western Kenya. A reward function based on cost-effectiveness per Daly averted is used. \n",
+ "\n",
+ "This notebook is structured as follows:\n",
+ "\n",
+ " 1. **Running the experiments with random actions** : In this section, we run several experiments with random actions and visualize the outcome reward by creating some MatPlotLib visualisations\n",
+ " 2. **Implementing Machine Learning and AI on models** : We implement a Genetic Algorithm Model after which we look at reward visualization with MatPlotLib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting git+https://github.com/slremy/estool\n",
+ " Cloning https://github.com/slremy/estool to c:\\users\\kasre\\appdata\\local\\temp\\pip-req-build-lvaz0_aa\n",
+ "Collecting git+https://github.com/slremy/netsapi\n",
+ " Cloning https://github.com/slremy/netsapi to c:\\users\\kasre\\appdata\\local\\temp\\pip-req-build-_65_q17b\n",
+ "Installing collected packages: estool, netsapi\n",
+ " Running setup.py install for estool: started\n",
+ " Running setup.py install for estool: finished with status 'done'\n",
+ " Running setup.py install for netsapi: started\n",
+ " Running setup.py install for netsapi: finished with status 'done'\n",
+ "Successfully installed estool-1.0 netsapi-1.0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "You are using pip version 10.0.1, however version 19.0.3 is available.\n",
+ "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install git+https://github.com/slremy/estool git+https://github.com/slremy/netsapi --user --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ },
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'netsapi'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mnetsapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchallenge\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnetsapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisualisation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mes\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSimpleGA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRemyGA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'netsapi'"
+ ]
+ }
+ ],
+ "source": [
+ "# !pip install git+https://github.com/slremy/estool git+https://github.com/slremy/netsapi --user --upgrade\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2 \n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "from netsapi.challenge import *\n",
+ "from netsapi.visualisation import *\n",
+ "from es import SimpleGA, RemyGA\n",
+ "\n",
+ "from sys import exit, exc_info, argv\n",
+ "\n",
+ "userId = None"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. Running the experiments with random actions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Running our first experiment!**\n",
+ "\n",
+ "Let's start with initializing our experiment to generate our experiment id. Then we run a simulation describing a current intervention campaign 55% ITN and 70% IRS coverage and obtain our reward."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "actions = np.array([[0.55, 0.70]])\n",
+ "reward = evaluate(actions)\n",
+ "solution = np.append(actions.T, reward)\n",
+ "print(solution)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Running more experiments with different types of actions**\n",
+ "\n",
+ "Let's run experiments with 4 sets of actions [ITN, IRS] and visualise the rewards as a simple scatter of points as a response surface (MatPlotLib may be required but you are free to visualise the data how you see fit)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = []\n",
+ "actions = np.array([[0.55, 0.70]])\n",
+ "reward = evaluate(actions)\n",
+ "solution = np.append(actions.T, reward)\n",
+ "print(solution)\n",
+ "output.append(solution)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actions = np.array([[0.68, 0.70]])\n",
+ "reward = evaluate(actions)\n",
+ "solution = np.append(actions.T, reward)\n",
+ "print(solution)\n",
+ "output.append(solution)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actions = np.array([[0.78, 0.70]])\n",
+ "reward = evaluate(actions)\n",
+ "solution = np.append(actions.T, reward)\n",
+ "print(solution)\n",
+ "output.append(solution)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actions = np.array([[0.88, 0.70]])\n",
+ "reward = evaluate(actions)\n",
+ "solution = np.append(actions.T, reward)\n",
+ "print(solution)\n",
+ "output.append(solution)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = np.array(output)\n",
+ "print(output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "actions = np.empty(shape=(10,2)) #10 actions [ITN, IRS]\n",
+ "actions[:,0] = np.random.rand(10) \n",
+ "actions[:,1] = .70 #don't change this yet!\n",
+ "rewards = np.array(evaluate(actions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = np.hstack((actions,rewards.reshape(-1,1)))\n",
+ "print(output)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "**Visualising rewards**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "ResponseSurface(output)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Implementing Machine Learning and AI on models\n",
+ "\n",
+ "**Genetic Algorithm**\n",
+ "\n",
+ "Implemented in ESTool (Ha, David. \"Neuroevolution for deep reinforcement learning problems.\" Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, 2018.)\n",
+ "\n",
+ "\n",
+ "We use a parameter size of 2, a population size of 20 and consider 10 generations. For each generation we candidate policies to evaluate based on evolutionary pressure, calculate the reward and create a reward vector. We save all the results in a 3 dimensional vector space and aggregate for all generations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "num_paramters = 2\n",
+ "\n",
+ "def mutate(chromosome):\n",
+ " mutation_rate = .1\n",
+ " for j in range(num_paramters):\n",
+ " r = np.random.rand(1);\n",
+ " if(r > mutation_rate):\n",
+ " chromosome[j] = np.remainder(chromosome[j]+np.random.randn(1),0.99);\n",
+ " return chromosome\n",
+ "\n",
+ "def make_random_individuals(x,y):\n",
+ " value=np.random.rand(x,y);\n",
+ " return value\n",
+ "\n",
+ "\n",
+ "def boundary(individual):\n",
+ " processed = np.clip(individual,0,1)\n",
+ " return processed\n",
+ "\n",
+ "\n",
+ "solver = RemyGA(num_paramters, # number of model parameters\n",
+ " random_individuals_fcn=make_random_individuals,\n",
+ " mutate_fcn = mutate,\n",
+ " sigma_init=1, # initial standard deviation\n",
+ " popsize=20, # population size\n",
+ " elite_ratio=0.3, # percentage of the elites\n",
+ " forget_best=False, # forget the historical best elites\n",
+ " weight_decay=0.00, # weight decay coefficient\n",
+ " )\n",
+ "\n",
+ "num_generations = 10\n",
+ "history0= np.empty(shape=(num_generations, solver.popsize, solver.num_params+1))\n",
+ "for i in range(num_generations):\n",
+ " try:\n",
+ " # ask for a set of candidate solutions to be evaluated\n",
+ " solutions = solver.ask(boundary)\n",
+ " # calculate the reward for each given solution using our own method\n",
+ " rewards = evaluate(solutions)\n",
+ " solver.tell(rewards)\n",
+ "\n",
+ " # get best parameter, reward from ES\n",
+ " reward_vector = solver.result()\n",
+ " print(reward_vector[0],reward_vector[1],i, num_generations)\n",
+ " history0[i,:,:] = np.hstack((solutions,np.array(rewards).reshape(-1,1)))\n",
+ " except (KeyboardInterrupt, SystemExit):\n",
+ " print(exc_info())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Visualization of reward vectors**\n",
+ "\n",
+ "Let's investigate the shape of our result vector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "history0.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's reshape our result vector so as to visualize the results for all of the 10 generations in one plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ResponseSurface(history0.reshape(200,3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And finally we are going to visialize the 10 generations in a box plot.\n",
+ "\n",
+ "Remember, as you \"evolve\" your solutions, you're looking for the best. \n",
+ "\n",
+ "Do you see any evidence of that in this plot?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "plt.boxplot([i[:,-1] for i in history0]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to try and break it! "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You're now free to explore! Given the code snippets of how to post single actions.\n",
+ "Several methods, approaches or algorithms may stand to benefit the uses of high bandwidth computation for machine learning."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "popsize=10\n",
+ "num_real_paramters = 2\n",
+ "width = 10\n",
+ "num_paramters = num_real_paramters * width\n",
+ "num_generations = 10\n",
+ "\n",
+ "#converts from binary representation to the actual action\n",
+ "def RealAction(action):\n",
+ " realaction=()\n",
+ " for i in range(int(num_paramters/width)):\n",
+ " a1 = np.array(action[i*width:(i+1)*width])\n",
+ " realaction = realaction + (a1.dot(2**np.arange(a1.size)[::-1])/pow(2,width),)\n",
+ " realaction = np.round(realaction,3);\n",
+ " return realaction\n",
+ "\n",
+ "def mutate(chromosome):\n",
+ " mutation_rate = .2\n",
+ " for j in range(chromosome.shape[0]):\n",
+ " r = np.random.rand(1);\n",
+ " if(r > mutation_rate):\n",
+ " chromosome[j] = int(np.invert(bool(chromosome[j])));\n",
+ " return chromosome\n",
+ "\n",
+ "def make_random_individuals(x,y):\n",
+ " value=np.random.choice([0, 1], size=(x,y));\n",
+ " return value\n",
+ "\n",
+ "def boundary(individual):\n",
+ " processed = individual%(1+np.finfo(float).eps)\n",
+ " return processed\n",
+ "\n",
+ "solver = RemyGA(num_paramters, # number of model parameters\n",
+ " random_individuals_fcn=make_random_individuals,\n",
+ " mutate_fcn = mutate,\n",
+ " sigma_init=1, # initial standard deviation\n",
+ " popsize=popsize, # population size\n",
+ " elite_ratio=0.3, # percentage of the elites\n",
+ " forget_best=False, # forget the historical best elites\n",
+ " weight_decay=0.00, # weight decay coefficient\n",
+ " )\n",
+ "\n",
+ "history1= np.empty(shape=(num_generations, popsize, num_real_paramters+1))\n",
+ "for i in range(num_generations):\n",
+ " try:\n",
+ " # ask for a set of random candidate solutions to be evaluated\n",
+ " solutions = solver.ask(boundary)\n",
+ " # calculate the reward for each given solution using our own method\n",
+ " newsolutions = []\n",
+ " for soln in solutions:\n",
+ " newsolutions.append(RealAction(soln))\n",
+ " newsolutions = np.array(newsolutions)\n",
+ " rewards = evaluate(newsolutions)\n",
+ " solver.tell(rewards)\n",
+ " \n",
+ " # get best parameter, reward from ES\n",
+ " reward_vector = solver.result()\n",
+ " print(RealAction(reward_vector[0]), reward_vector[1], i, num_generations)\n",
+ " history1[i,:,:] = np.hstack((newsolutions,np.array(rewards).reshape(-1,1)))\n",
+ " except (KeyboardInterrupt, SystemExit):\n",
+ " print(exc_info())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "newdata=history1.reshape(num_generations*popsize,num_real_paramters+1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ResponseSurface(newdata)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Introduction-Ipython-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Introduction-Ipython-checkpoint.ipynb
new file mode 100644
index 0000000..e505471
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Introduction-Ipython-checkpoint.ipynb
@@ -0,0 +1,137 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction to Ipython"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Practice with ? and ?? help commands"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Help on the len() function\n",
+ "len?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Test function\n",
+ "def add(a, b):\n",
+ " \"\"\" Return the sum of a and b \"\"\"\n",
+ " return a + b\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Help on add method\n",
+ "add?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access the source code of the method\n",
+ "add??"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Error Handling and Debugging\n",
+ "This involves using Exception mode and python debugger to trace errors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A test function aimed at raising an exception\n",
+ "def calculate(x, y):\n",
+ " \"\"\" Return the quotient of x and y \"\"\"\n",
+ " return eval(x / y)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Turn Exception mode on to Plain and debugger then call the function\n",
+ "%xmode Plain\n",
+ "%pdb on\n",
+ "calculate(1, 0)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Timing and Profiling\n",
+ "`%timeit` and `%%timeit` to show execution time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%timeit sum(range(100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/ML-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/ML-checkpoint.ipynb
new file mode 100644
index 0000000..bd5a252
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/ML-checkpoint.ipynb
@@ -0,0 +1,598 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Deep Machine Learning with Scikit-Learn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Basic libraries\n",
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import os\n",
+ "import copy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Defaults\n",
+ "sns.set(color_codes=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Breast cancer Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.datasets import load_breast_cancer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cancer = load_breast_cancer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cancer.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " State Unemployment\n",
+ "0 AL 7.1\n",
+ "1 AK 6.8\n",
+ "2 AZ 8.1\n",
+ "3 AR 7.2\n",
+ "4 CA 10.1"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "US_Unemployment_Oct2012 = os.path.join('data', 'US_Unemployment_Oct2012.csv')\n",
+ "unemployment = pd.read_csv(US_Unemployment_Oct2012)\n",
+ "\n",
+ "unemployment.head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to create a function that maps one value to a RGB color (of the form `#RRGGBB`).\n",
+ "For this, we'll use colormap tools from `folium.colormap`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "#d8f0a3\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ "| \n", + " | State | \n", + "Unemployment | \n", + "
|---|---|---|
| 0 | \n", + "AL | \n", + "7.1 | \n", + "
| 1 | \n", + "AK | \n", + "6.8 | \n", + "
| 2 | \n", + "AZ | \n", + "8.1 | \n", + "
| 3 | \n", + "AR | \n", + "7.2 | \n", + "
| 4 | \n", + "CA | \n", + "10.1 | \n", + "
"
+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
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+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
+ "0 17.99 10.38 122.80 1001.0 0.11840 \n",
+ "1 20.57 17.77 132.90 1326.0 0.08474 \n",
+ "2 19.69 21.25 130.00 1203.0 0.10960 \n",
+ "3 11.42 20.38 77.58 386.1 0.14250 \n",
+ "4 20.29 14.34 135.10 1297.0 0.10030 \n",
+ "\n",
+ " mean compactness mean concavity mean concave points mean symmetry \\\n",
+ "0 0.27760 0.3001 0.14710 0.2419 \n",
+ "1 0.07864 0.0869 0.07017 0.1812 \n",
+ "2 0.15990 0.1974 0.12790 0.2069 \n",
+ "3 0.28390 0.2414 0.10520 0.2597 \n",
+ "4 0.13280 0.1980 0.10430 0.1809 \n",
+ "\n",
+ " mean fractal dimension ... worst radius worst texture worst perimeter \\\n",
+ "0 0.07871 ... 25.38 17.33 184.60 \n",
+ "1 0.05667 ... 24.99 23.41 158.80 \n",
+ "2 0.05999 ... 23.57 25.53 152.50 \n",
+ "3 0.09744 ... 14.91 26.50 98.87 \n",
+ "4 0.05883 ... 22.54 16.67 152.20 \n",
+ "\n",
+ " worst area worst smoothness worst compactness worst concavity \\\n",
+ "0 2019.0 0.1622 0.6656 0.7119 \n",
+ "1 1956.0 0.1238 0.1866 0.2416 \n",
+ "2 1709.0 0.1444 0.4245 0.4504 \n",
+ "3 567.7 0.2098 0.8663 0.6869 \n",
+ "4 1575.0 0.1374 0.2050 0.4000 \n",
+ "\n",
+ " worst concave points worst symmetry worst fractal dimension \n",
+ "0 0.2654 0.4601 0.11890 \n",
+ "1 0.1860 0.2750 0.08902 \n",
+ "2 0.2430 0.3613 0.08758 \n",
+ "3 0.2575 0.6638 0.17300 \n",
+ "4 0.1625 0.2364 0.07678 \n",
+ "\n",
+ "[5 rows x 30 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cancer_df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
+ "cancer_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# K-Fold Cross Validation\n",
+ "Model Evaluation\n",
+ "## K-Fold for parameter tuning\n",
+ "### The objective here is to find the optimum hyperparam(s) that/which maximizes the mean score\n",
+ "#### KNN for example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | mean radius | \n", + "mean texture | \n", + "mean perimeter | \n", + "mean area | \n", + "mean smoothness | \n", + "mean compactness | \n", + "mean concavity | \n", + "mean concave points | \n", + "mean symmetry | \n", + "mean fractal dimension | \n", + "... | \n", + "worst radius | \n", + "worst texture | \n", + "worst perimeter | \n", + "worst area | \n", + "worst smoothness | \n", + "worst compactness | \n", + "worst concavity | \n", + "worst concave points | \n", + "worst symmetry | \n", + "worst fractal dimension | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "17.99 | \n", + "10.38 | \n", + "122.80 | \n", + "1001.0 | \n", + "0.11840 | \n", + "0.27760 | \n", + "0.3001 | \n", + "0.14710 | \n", + "0.2419 | \n", + "0.07871 | \n", + "... | \n", + "25.38 | \n", + "17.33 | \n", + "184.60 | \n", + "2019.0 | \n", + "0.1622 | \n", + "0.6656 | \n", + "0.7119 | \n", + "0.2654 | \n", + "0.4601 | \n", + "0.11890 | \n", + "
| 1 | \n", + "20.57 | \n", + "17.77 | \n", + "132.90 | \n", + "1326.0 | \n", + "0.08474 | \n", + "0.07864 | \n", + "0.0869 | \n", + "0.07017 | \n", + "0.1812 | \n", + "0.05667 | \n", + "... | \n", + "24.99 | \n", + "23.41 | \n", + "158.80 | \n", + "1956.0 | \n", + "0.1238 | \n", + "0.1866 | \n", + "0.2416 | \n", + "0.1860 | \n", + "0.2750 | \n", + "0.08902 | \n", + "
| 2 | \n", + "19.69 | \n", + "21.25 | \n", + "130.00 | \n", + "1203.0 | \n", + "0.10960 | \n", + "0.15990 | \n", + "0.1974 | \n", + "0.12790 | \n", + "0.2069 | \n", + "0.05999 | \n", + "... | \n", + "23.57 | \n", + "25.53 | \n", + "152.50 | \n", + "1709.0 | \n", + "0.1444 | \n", + "0.4245 | \n", + "0.4504 | \n", + "0.2430 | \n", + "0.3613 | \n", + "0.08758 | \n", + "
| 3 | \n", + "11.42 | \n", + "20.38 | \n", + "77.58 | \n", + "386.1 | \n", + "0.14250 | \n", + "0.28390 | \n", + "0.2414 | \n", + "0.10520 | \n", + "0.2597 | \n", + "0.09744 | \n", + "... | \n", + "14.91 | \n", + "26.50 | \n", + "98.87 | \n", + "567.7 | \n", + "0.2098 | \n", + "0.8663 | \n", + "0.6869 | \n", + "0.2575 | \n", + "0.6638 | \n", + "0.17300 | \n", + "
| 4 | \n", + "20.29 | \n", + "14.34 | \n", + "135.10 | \n", + "1297.0 | \n", + "0.10030 | \n", + "0.13280 | \n", + "0.1980 | \n", + "0.10430 | \n", + "0.1809 | \n", + "0.05883 | \n", + "... | \n", + "22.54 | \n", + "16.67 | \n", + "152.20 | \n", + "1575.0 | \n", + "0.1374 | \n", + "0.2050 | \n", + "0.4000 | \n", + "0.1625 | \n", + "0.2364 | \n", + "0.07678 | \n", + "
5 rows × 30 columns
\n", + ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# A better way of model evaluation\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "# Using the breast cancer\n",
+ "X = cancer.data\n",
+ "y = cancer.target\n",
+ "\n",
+ "# Set up a range of n_neighbors from 1 to 30\n",
+ "neighbors = range(1, 31)\n",
+ "\n",
+ "# Initialize mean scores\n",
+ "scores = []\n",
+ "\n",
+ "# loop through the k ranges\n",
+ "for k in neighbors:\n",
+ " knn = KNeighborsClassifier(n_neighbors=k)\n",
+ " \n",
+ " # Obtain cross val scores (scoring mode=accuracy since its classification(MSE for regression))\n",
+ " score = cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()\n",
+ " \n",
+ " # append the score to scores\n",
+ " scores.append(score)\n",
+ " \n",
+ "# Plot scores vs neighbors\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "plt.plot(neighbors, scores)\n",
+ "\n",
+ "plt.title(\"Scores for different knn n_neighbors\")\n",
+ "plt.xlabel(\"neighbors\")\n",
+ "plt.ylabel(\"score\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The maximum score is at k between 10 and 18"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# K-Fold for model selection\n",
+ "## Used to determine which model returns the highest score for a particular problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9317507129893698"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# KNN\n",
+ "knn = KNeighborsClassifier(n_neighbors=19)\n",
+ "score = cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()\n",
+ "score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9508998790078644"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Logistic regression\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "logr = LogisticRegression()\n",
+ "score = cross_val_score(logr, X, y, cv=10, scoring='accuracy').mean()\n",
+ "score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Logistic regression performs better\n",
+ "### K-Fold can also be used for feature selection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Decision Trees \n",
+ "For classification and regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "# import graphviz for visualizing the tree\n",
+ "import graphviz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0\n",
+ "0.9370629370629371\n"
+ ]
+ }
+ ],
+ "source": [
+ "cancer = load_breast_cancer()\n",
+ "X = cancer.data\n",
+ "y = cancer.target\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)\n",
+ "\n",
+ "tree = DecisionTreeClassifier(random_state=0)\n",
+ "\n",
+ "tree.fit(X_train, y_train)\n",
+ "\n",
+ "tree.predict(X_test)\n",
+ "\n",
+ "training_score = tree.score(X_train, y_train)\n",
+ "test_score = tree.score(X_test, y_test)\n",
+ "\n",
+ "print(training_score)\n",
+ "print(test_score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### here, the training score is very high(100%) and the test score relatively low(93%). Thiks is an indication of overfitting. To handle this, we pre-prune the tree, i.e., controlling the max_depth of the tree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9882629107981221\n",
+ "0.951048951048951\n"
+ ]
+ }
+ ],
+ "source": [
+ "tree = DecisionTreeClassifier(random_state=0, max_depth=4)\n",
+ "\n",
+ "tree.fit(X_train, y_train)\n",
+ "\n",
+ "tree.predict(X_test)\n",
+ "\n",
+ "training_score = tree.score(X_train, y_train)\n",
+ "test_score = tree.score(X_test, y_test)\n",
+ "\n",
+ "print(training_score)\n",
+ "print(test_score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This is slightly good, but still not generalized. Random forests or gradient boost forests are used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'with open(\"../data/tree.dot\") as f:\\n dot_graph = f.read()\\ngraphviz.Source(dot_graph)'"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "export_graphviz(tree, out_file=\"../data/tree.dot\", class_names=[\"malignant\", \"benign\"],\n",
+ " feature_names=cancer.feature_names, impurity=False, filled=True)\n",
+ "\n",
+ "'''with open(\"../data/tree.dot\") as f:\n",
+ " dot_graph = f.read()\n",
+ "graphviz.Source(dot_graph)'''"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Nairobi_Water-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Nairobi_Water-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Nairobi_Water-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Numpy-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Numpy-checkpoint.ipynb
new file mode 100644
index 0000000..5f6feef
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Numpy-checkpoint.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# INTRODUCTION TO NUMPY"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Arrays"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Arrays from Python lists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Create an array of integers\n",
+ "np.array([1,2,3,4,5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### NB: Numpy is constrained to contain arrays of same type. If they are not, numpy upcasts them. Use the `dtype` keyword to explicitly typecast the arrays."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.array([1, 2, 3, 4], dtype='float32')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Multidimensional array using a list of lists\n",
+ "np.array([range(i, i + 3) for i in [2, 3, 4]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Arrays from Scratch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A length-10 integer array filled with zeros\n",
+ "np.zeros(10, dtype='int')\n",
+ "\n",
+ "# Create a 3x5 floating-point array filled with ones\n",
+ "np.ones((3, 5), dtype=np.float)\n",
+ "\n",
+ "# Create a 3x5 array filled with 3.14\n",
+ "np.full((3, 5), 3.14)\n",
+ "\n",
+ "# Create an array filled with a linear sequence\n",
+ "# Starting at 0, ending at 20, stepping by 2\n",
+ "# (this is similar to the built-in range() function)\n",
+ "\n",
+ "np.arange(0, 20, 2)\n",
+ "\n",
+ "# Create an array of five values evenly spaced between 0 and 1\n",
+ "np.linspace(0, 1, 5)\n",
+ "\n",
+ "# Create a 3x3 array of uniformly distributed\n",
+ "# random values between 0 and 1\n",
+ "\n",
+ "np.random.random((3,3))\n",
+ "\n",
+ "# Create a 3x3 array of random integers in the interval [0, 10)\n",
+ "np.random.randint(0, 10, (3, 3))\n",
+ "\n",
+ "# Create a 3x3 identity matrix\n",
+ "np.eye(3, dtype='int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Array Attributes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Seed the Random number generator to ensure it produces same set of numbers on each call\n",
+ "np.random.seed(0)\n",
+ "\n",
+ "# create 3 arrays, 1-dim, 2-dim and 3-dim of numbers between 1 and 10\n",
+ "array1 = np.random.randint(10, size=6)\n",
+ "array2 = np.random.randint(10, size=(3, 4))\n",
+ "array3 = np.random.randint(10, size=(3, 4, 5))\n",
+ "\n",
+ "'''\n",
+ " The main attributes:\n",
+ " ndim - Number of dimensuins\n",
+ " shape - size of each dimension\n",
+ " size - total size of the array\n",
+ " dtype - data type of the array\n",
+ "'''\n",
+ "\n",
+ "print(\"Array2 ndim: \", array2.ndim)\n",
+ "print(\"Array2 shape: \", array2.shape)\n",
+ "print(\"Array2 size: \", array2.size)\n",
+ "print(\"Array2 dtype: \", array2.dtype)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array Indexing - Accessing single array elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access the first element in array 1\n",
+ "print(array1)\n",
+ "first_array1_element = array1[0]\n",
+ "print(\"Array 1 first element: \", first_array1_element)\n",
+ "\n",
+ "# Access the first element in second row of array 2\n",
+ "print(array2)\n",
+ "array2_element = array2[0, 0]\n",
+ "print(array2_element)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array Slicing: Accesing sub-arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ " uses slice notation ':'.\n",
+ " format: x[start, stop, step]\n",
+ "\"\"\"\n",
+ "# Initialize a 1-dim array\n",
+ "x = np.arange(10)\n",
+ "print(x)\n",
+ "\n",
+ "# Select first five elements\n",
+ "print(\"First five elements: \", x[:5])\n",
+ "\n",
+ "# Elements after index 5\n",
+ "print(\"Last five: \", x[5:])\n",
+ "\n",
+ "# Middle sub-array\n",
+ "print(x[4:7])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array copies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# When a sub array is created, it is not a copy of the original array, \n",
+ "# its its view, meaning that any change made on the sub array refelects on the main array.\n",
+ "\n",
+ "print(\"Array 2: \\n\", array2)\n",
+ "\n",
+ "# Create a 2x2 sub array\n",
+ "sub_array2 = array2[:2, :2]\n",
+ "print(\"Sub array: \\n\", sub_array2)\n",
+ "\n",
+ "# Change any value in sub array\n",
+ "sub_array2[0, 0] = 9\n",
+ "print(\"New Sub array: \\n\", sub_array2)\n",
+ "print(\"New main array: \\n\", array2)\n",
+ "\n",
+ "# To make an array copy which is not affected by any changes in the sub array, use the copy() fn.\n",
+ "sub_array_copy = array2[:2, :2].copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Ufuncs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Functions that enable vectorization - A faster way than python loops\n",
+ "\"\"\"\n",
+ " \n",
+ "Operator\tEquivalent ufunc\tDescription\n",
+ "+\tnp.add\tAddition (e.g., 1 + 1 = 2)\n",
+ "-\tnp.subtract\tSubtraction (e.g., 3 - 2 = 1)\n",
+ "-\tnp.negative\tUnary negation (e.g., -2)\n",
+ "*\tnp.multiply\tMultiplication (e.g., 2 * 3 = 6)\n",
+ "/\tnp.divide\tDivision (e.g., 3 / 2 = 1.5)\n",
+ "//\tnp.floor_divide\tFloor division (e.g., 3 // 2 = 1)\n",
+ "**\tnp.power\tExponentiation (e.g., 2 ** 3 = 8)\n",
+ "%\tnp.mod\tModulus/remainder (e.g., 9 % 4 = 1)\n",
+ "\"\"\"\n",
+ "\n",
+ "# More Ufuncs found on docs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Aggregations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ " Aggregations are used to compute operations. This include sum, min, max, percentiles, median, standard deviation, \n",
+ " quartiles, etc.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Broadcasting: \n",
+ "### Computations on arrays of different sizes and dimensions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A 3-dim array of integers an\n",
+ "a = np.ones(shape=(3, 3), dtype=np.int)\n",
+ "\n",
+ "# 1-dim array\n",
+ "b = np.array([1, 2, 3])\n",
+ "\n",
+ "print(\"array a: \\n\", a)\n",
+ "print(\"array b: \\n\", b)\n",
+ "\n",
+ "# Adding array b to a scalar(0-dim array)\n",
+ "print(\"b + 5 = \", b + 5)\n",
+ "\n",
+ "# Adding a to b\n",
+ "ab = a + b\n",
+ "print(\"a + b: \\n\", ab)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array comparisons: boolean arrays and masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Same as operations, element-wise comparisons can be done, eg, greater than, less than, ...\n",
+ "# comparisons return an array of booleans(true or false)\n",
+ "# A 1-D array\n",
+ "a = np.array([1, 2, 3, 5, 5])\n",
+ "print(\"Array a: \", a)\n",
+ "\n",
+ "# Normal comparison\n",
+ "print(\"comparison: \", a <= 3)\n",
+ "\n",
+ "# Equivalent Ufunc\n",
+ "print(\"Ufunc comparison: \", np.less_equal(a, 3))\n",
+ "\n",
+ "# same case applies to 2-D and 3-D arrays."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Masking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" \n",
+ "Instead of returning a boolean array(of True's and False's), We can return an array\n",
+ "of the element matching the particular comparison.\n",
+ "\"\"\"\n",
+ "\n",
+ "# A 2-D array\n",
+ "rng = np.random.RandomState(0)\n",
+ "x = rng.randint(10, size=(3, 4))\n",
+ "print(\"x: \\n\", x)\n",
+ "\n",
+ "# Output all values less than 5\n",
+ "print(\"Less than 5: \", x[x < 5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NUMPY SORTING"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# faster than python list sorting\n",
+ "# Uses numpy .sort() and .argsort() uFuncs\n",
+ "\n",
+ "x = np.array([2, 1, 5, 4, 3])\n",
+ "x_sorted = np.sort(x)\n",
+ "print(\"sorted array: \", x_sorted)\n",
+ "\n",
+ "# .argsort() returns an array of sorted indices\n",
+ "i = np.argsort(x)\n",
+ "print(i)\n",
+ "print(x[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More on numpy from the docs and also handbook"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Pandas in General-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Pandas in General-checkpoint.ipynb
new file mode 100644
index 0000000..36197fb
--- /dev/null
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@@ -0,0 +1,1311 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pandas\n",
+ "Pandas are used together with numpy. They form an extension of the numpy ndarray object.\n",
+ "It consists of Series and Dataframe as its key objects"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pandas Series object\n",
+ "A 1-D array of indexed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import the packages\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "data:\n",
+ " 0 0.10\n",
+ "1 6.35\n",
+ "2 7.00\n",
+ "3 13.70\n",
+ "4 8.00\n",
+ "dtype: float64\n",
+ "values: [ 0.1 6.35 7. 13.7 8. ]\n",
+ "indices: RangeIndex(start=0, stop=5, step=1)\n",
+ "data at index 3: 13.7\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a Series from a list or array\n",
+ "data = pd.Series([0.1, 6.35, 7.0, 13.7, 8.0])\n",
+ "# display the data\n",
+ "print(\"data:\\n\", data)\n",
+ "\n",
+ "# display the values only\n",
+ "print(\"values: \", data.values)\n",
+ "\n",
+ "# display the indices(as a range)\n",
+ "print(\"indices: \", data.index)\n",
+ "\n",
+ "# access a value\n",
+ "print(\"data at index 3: \", data[3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 1\n",
+ "b 2\n",
+ "c 3\n",
+ "d 4\n",
+ "e 5\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Series with custom indices\n",
+ "custom_data = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])\n",
+ "custom_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "kasee 22\n",
+ "Ken 27\n",
+ "Kashee 19\n",
+ "Miro 21\n",
+ "Phoebe 20\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# A series object can also be created from a dictionary. The indices will be the dict keys.\n",
+ "ages = {\n",
+ " 'kasee': 22,\n",
+ " 'Ken': 27,\n",
+ " 'Kashee': 19,\n",
+ " 'Miro': 21,\n",
+ " 'Phoebe': 20\n",
+ "}\n",
+ "\n",
+ "age_data = pd.Series(ages)\n",
+ "age_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The DataFrame Object\n",
+ "Can be created from an array or a dict\n",
+ "format: pd.DataFrame(data, columns='', index='')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 1.1. [Case Study ](#study)
\n", + " 1.2. [Objectives](#objs)\n", + "2. [Loading Libraries](#libs)\n", + "3. [Loading in the Datasets](#datasets)\n", + "4. [Exploratory Data Analysis(EDA)](#eda)
\n", + " 4.1. [Numerical EDA](#num-eda)
\n", + " 4.2. [Statistical EDA](#stats-eda)
\n", + " 4.2. [Visual EDA](#visual-eda)
\n", + "5. [Data Training and Predictions](#training)
\n", + " 5.1. [Training and Test Sets](#sets)
\n", + " 5.2. [Modelling and Evaluation](#modelling)
\n", + " 5.3. [Model Score Comparison](#comparison)
\n", + " 5.4. [Fine Tuning Highest Scoring Model](#fine-tuning)
\n", + " 5.5. [Predictions](#predictions)
\n", + " 5.6. [Submission Formatting](#submission)
\n", + " 5.6. [Final Submission](#final)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies.\n", + "
\n", + "## Case Study: \n", + "A mobile blood donation vehicle in Taiwan. The Blood Transfusion Service Center drives to different universities and collects blood as part of a blood drive.\n", + "
\n", + "## Objectives \n", + "1. To predict whether or not a donor will give blood the next time the vehicle comes to campus. (March 2007)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Loading Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import warnings\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier \n", + "from sklearn.naive_bayes import GaussianNB \n", + "from sklearn.model_selection import cross_val_score \n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set defaults\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Loading in the Datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Training set\n", + "training_dataset = pd.read_csv('../data/donation-training.csv')\n", + "test_dataset = pd.read_csv('../data/donation-test.csv')\n", + "submission_format = pd.read_csv('../data/BloodDonationSubmissionFormat.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Exploratory Data Analysis(EDA)\n", + "\n", + "\n", + "### A peek at the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n",
+ "RangeIndex: 576 entries, 0 to 575\n",
+ "Data columns (total 5 columns):\n",
+ "recency 576 non-null int64\n",
+ "total_donations 576 non-null int64\n",
+ "total_vol_donated 576 non-null int64\n",
+ "donation_time 576 non-null int64\n",
+ "donated 576 non-null int64\n",
+ "dtypes: int64(5)\n",
+ "memory usage: 22.5 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "# dataset info\n",
+ "training_set.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Dataset labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['recency', 'total_donations', 'total_vol_donated', 'donation_time', 'donated']"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.columns.to_list()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Label types"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "recency int64\n",
+ "total_donations int64\n",
+ "total_vol_donated int64\n",
+ "donation_time int64\n",
+ "donated int64\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.dtypes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Dimensionality"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(576, 5)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "recency 0\n",
+ "total_donations 0\n",
+ "total_vol_donated 0\n",
+ "donation_time 0\n",
+ "donated 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "null_values = training_set.isnull().sum()\n",
+ "null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Numerical Analysis Overview\n",
+ "1. The dataset has a total of **576** samples.\n",
+ "2. The dataset has a total of **5** labels, namely:
\n", + " **recency**: Months since last donation
\n", + " **total_donations**: Number of times donor donated
\n", + " **total_vol_donated**: Total volume of blood donated in c.c.
\n", + " **donation_time**: Months since first donation
\n", + " **donated**: Whether donor donated in March 2007
\n", + "3. All the labels features are numerical\n", + "4. The dataset has no null values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2. Statistical EDA\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " population area\n",
+ "Atlanta 7459843 7589347\n",
+ "Connecticut 124765 759874\n",
+ "Kansas 124765 985904384\n",
+ "Seattle 124765 475398475\n",
+ "Viginia 124765 509438"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# States data\n",
+ "population = {\n",
+ " 'Seattle': 124765,\n",
+ " 'Atlanta': 7459843,\n",
+ " 'Kansas': 124765,\n",
+ " 'Connecticut': 124765,\n",
+ " 'Viginia': 124765,\n",
+ "}\n",
+ "\n",
+ "# State areas\n",
+ "area = {\n",
+ " 'Seattle': 475398475,\n",
+ " 'Atlanta': 7589347,\n",
+ " 'Kansas': 985904384,\n",
+ " 'Connecticut': 759874,\n",
+ " 'Viginia': 509438\n",
+ "}\n",
+ "\n",
+ "# create a dataframe\n",
+ "states = pd.DataFrame({\n",
+ " 'population': population,\n",
+ " 'area': area\n",
+ "})\n",
+ "\n",
+ "states"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Atlanta 7459843\n",
+ "Connecticut 124765\n",
+ "Kansas 124765\n",
+ "Seattle 124765\n",
+ "Viginia 124765\n",
+ "Name: population, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Accessing individual data\n",
+ "popn = states['population'] # or states.population\n",
+ "popn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Indexing and Selection\n",
+ "Normal slicing and indexing brings confusion, hence `iloc` and `loc` attribs are used for implicit(normal python list-style) and explicit indexing, respectively"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1 a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.Series(['a', 'b', 'c'], index=[1, 2, 3])\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Trying to access the data locally\n",
+ "print(data[1]) # explicit index when indexing\n",
+ "print(data[1:3]) # implicit index when slicing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "1 a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n",
+ "b\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To solve this, loc and iloc are used\n",
+ "# Explicit indexing\n",
+ "print(data.loc[1])\n",
+ "print(data.loc[1:3])\n",
+ "\n",
+ "# implicit indexing\n",
+ "print(data.iloc[1])\n",
+ "print(data.iloc[1:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# An example with csv file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | population | \n", + "area | \n", + "
|---|---|---|
| Atlanta | \n", + "7459843 | \n", + "7589347 | \n", + "
| Connecticut | \n", + "124765 | \n", + "759874 | \n", + "
| Kansas | \n", + "124765 | \n", + "985904384 | \n", + "
| Seattle | \n", + "124765 | \n", + "475398475 | \n", + "
| Viginia | \n", + "124765 | \n", + "509438 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "0 1969 1 1.0 F 4046\n",
+ "1 1969 1 1.0 M 4440\n",
+ "2 1969 1 2.0 F 4454\n",
+ "3 1969 1 2.0 M 4548\n",
+ "4 1969 1 3.0 F 4548"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the csv data\n",
+ "births = pd.read_csv('C:/Users/user/PycharmProjects/Python4ds_cohort-1/data/births.csv')\n",
+ "births.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pivot table\n",
+ "We create a pivot table and display male and female births as a function of decades"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births decade\n",
+ "0 1969 1 1.0 F 4046 1960\n",
+ "1 1969 1 1.0 M 4440 1960\n",
+ "2 1969 1 2.0 F 4454 1960\n",
+ "3 1969 1 2.0 M 4548 1960\n",
+ "4 1969 1 3.0 F 4548 1960\n",
+ "5 1969 1 3.0 M 4994 1960\n",
+ "6 1969 1 4.0 F 4440 1960\n",
+ "7 1969 1 4.0 M 4520 1960\n",
+ "8 1969 1 5.0 F 4192 1960\n",
+ "9 1969 1 5.0 M 4198 1960"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "births['decade'] = 10 * (births['year'] // 10)\n",
+ "births.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "decade | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "1960 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "1960 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "1960 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "1960 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "1960 | \n", + "
| 5 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "4994 | \n", + "1960 | \n", + "
| 6 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4440 | \n", + "1960 | \n", + "
| 7 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "4520 | \n", + "1960 | \n", + "
| 8 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4192 | \n", + "1960 | \n", + "
| 9 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4198 | \n", + "1960 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "gender F M\n",
+ "decade \n",
+ "1960 1753634 1846572\n",
+ "1970 16263075 17121550\n",
+ "1980 18310351 19243452\n",
+ "1990 19479454 20420553\n",
+ "2000 18229309 19106428"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "births.pivot_table(values='births', index='decade', columns='gender', aggfunc='sum')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# PANDAS DEEP EXAMPLE\n",
+ "## Using the births data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| gender | \n", + "F | \n", + "M | \n", + "
|---|---|---|
| decade | \n", + "\n", + " | \n", + " |
| 1960 | \n", + "1753634 | \n", + "1846572 | \n", + "
| 1970 | \n", + "16263075 | \n", + "17121550 | \n", + "
| 1980 | \n", + "18310351 | \n", + "19243452 | \n", + "
| 1990 | \n", + "19479454 | \n", + "20420553 | \n", + "
| 2000 | \n", + "18229309 | \n", + "19106428 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "0 1969 1 1.0 F 4046\n",
+ "1 1969 1 1.0 M 4440\n",
+ "2 1969 1 2.0 F 4454\n",
+ "3 1969 1 2.0 M 4548\n",
+ "4 1969 1 3.0 F 4548\n",
+ "5 1969 1 3.0 M 4994\n",
+ "6 1969 1 4.0 F 4440\n",
+ "7 1969 1 4.0 M 4520\n",
+ "8 1969 1 5.0 F 4192\n",
+ "9 1969 1 5.0 M 4198"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the csv data into a dataframe\n",
+ "births_df = pd.read_csv('C:/Users/user/PycharmProjects/Python4ds_cohort-1/data/births.csv')\n",
+ "births_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "
| 5 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "4994 | \n", + "
| 6 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4440 | \n", + "
| 7 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "4520 | \n", + "
| 8 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4192 | \n", + "
| 9 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4198 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "8386 1980 1 1.0 F 4005\n",
+ "8387 1980 1 1.0 M 4227\n",
+ "8388 1980 1 2.0 F 4371\n",
+ "8389 1980 1 2.0 M 4640\n",
+ "8390 1980 1 3.0 F 4815\n",
+ "8391 1980 1 3.0 M 5087\n",
+ "8392 1980 1 4.0 F 4758\n",
+ "8393 1980 1 4.0 M 5181\n",
+ "8394 1980 1 5.0 F 4265\n",
+ "8395 1980 1 5.0 M 4426\n",
+ "8396 1980 1 6.0 F 4093\n",
+ "8397 1980 1 6.0 M 4120\n",
+ "8398 1980 1 7.0 F 4730\n",
+ "8399 1980 1 7.0 M 5103\n",
+ "8400 1980 1 8.0 F 4810\n",
+ "8401 1980 1 8.0 M 5012\n",
+ "8402 1980 1 9.0 F 4763\n",
+ "8403 1980 1 9.0 M 4801\n",
+ "8404 1980 1 10.0 F 4810\n",
+ "8405 1980 1 10.0 M 4949"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select the births in january 1980 first 10 days\n",
+ "births_1980 = births_df[((births_df['year'] == 1980) & (births_df['month'] == 1) & (births_df['day'] <= 10))]\n",
+ "births_1980"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 8386 | \n", + "1980 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4005 | \n", + "
| 8387 | \n", + "1980 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4227 | \n", + "
| 8388 | \n", + "1980 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4371 | \n", + "
| 8389 | \n", + "1980 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4640 | \n", + "
| 8390 | \n", + "1980 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4815 | \n", + "
| 8391 | \n", + "1980 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "5087 | \n", + "
| 8392 | \n", + "1980 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4758 | \n", + "
| 8393 | \n", + "1980 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "5181 | \n", + "
| 8394 | \n", + "1980 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4265 | \n", + "
| 8395 | \n", + "1980 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4426 | \n", + "
| 8396 | \n", + "1980 | \n", + "1 | \n", + "6.0 | \n", + "F | \n", + "4093 | \n", + "
| 8397 | \n", + "1980 | \n", + "1 | \n", + "6.0 | \n", + "M | \n", + "4120 | \n", + "
| 8398 | \n", + "1980 | \n", + "1 | \n", + "7.0 | \n", + "F | \n", + "4730 | \n", + "
| 8399 | \n", + "1980 | \n", + "1 | \n", + "7.0 | \n", + "M | \n", + "5103 | \n", + "
| 8400 | \n", + "1980 | \n", + "1 | \n", + "8.0 | \n", + "F | \n", + "4810 | \n", + "
| 8401 | \n", + "1980 | \n", + "1 | \n", + "8.0 | \n", + "M | \n", + "5012 | \n", + "
| 8402 | \n", + "1980 | \n", + "1 | \n", + "9.0 | \n", + "F | \n", + "4763 | \n", + "
| 8403 | \n", + "1980 | \n", + "1 | \n", + "9.0 | \n", + "M | \n", + "4801 | \n", + "
| 8404 | \n", + "1980 | \n", + "1 | \n", + "10.0 | \n", + "F | \n", + "4810 | \n", + "
| 8405 | \n", + "1980 | \n", + "1 | \n", + "10.0 | \n", + "M | \n", + "4949 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day births\n",
+ "count 20.0 20.0 20.000000 20.00000\n",
+ "mean 1980.0 1.0 5.500000 4648.30000\n",
+ "std 0.0 0.0 2.946898 362.23213\n",
+ "min 1980.0 1.0 1.000000 4005.00000\n",
+ "25% 1980.0 1.0 3.000000 4344.50000\n",
+ "50% 1980.0 1.0 5.500000 4760.50000\n",
+ "75% 1980.0 1.0 8.000000 4848.50000\n",
+ "max 1980.0 1.0 10.000000 5181.00000"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# evaluate basic computations on that data\n",
+ "births_1980.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "births | \n", + "
|---|---|---|---|---|
| count | \n", + "20.0 | \n", + "20.0 | \n", + "20.000000 | \n", + "20.00000 | \n", + "
| mean | \n", + "1980.0 | \n", + "1.0 | \n", + "5.500000 | \n", + "4648.30000 | \n", + "
| std | \n", + "0.0 | \n", + "0.0 | \n", + "2.946898 | \n", + "362.23213 | \n", + "
| min | \n", + "1980.0 | \n", + "1.0 | \n", + "1.000000 | \n", + "4005.00000 | \n", + "
| 25% | \n", + "1980.0 | \n", + "1.0 | \n", + "3.000000 | \n", + "4344.50000 | \n", + "
| 50% | \n", + "1980.0 | \n", + "1.0 | \n", + "5.500000 | \n", + "4760.50000 | \n", + "
| 75% | \n", + "1980.0 | \n", + "1.0 | \n", + "8.000000 | \n", + "4848.50000 | \n", + "
| max | \n", + "1980.0 | \n", + "1.0 | \n", + "10.000000 | \n", + "5181.00000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sum mean\n",
+ " births births\n",
+ "gender \n",
+ "F 74035823 9521.067773\n",
+ "M 77738555 10003.674559"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a pivot table with birts totals for all genders\n",
+ "gender_table = pd.pivot_table(births, index=['gender'], values='births', aggfunc=[np.sum,np.mean])\n",
+ "gender_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Prediction-Blood Donations-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Prediction-Blood Donations-checkpoint.ipynb
new file mode 100644
index 0000000..3e2569d
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Prediction-Blood Donations-checkpoint.ipynb
@@ -0,0 +1,2195 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)| \n", + " | sum | \n", + "mean | \n", + "
|---|---|---|
| \n", + " | births | \n", + "births | \n", + "
| gender | \n", + "\n", + " | \n", + " |
| F | \n", + "74035823 | \n", + "9521.067773 | \n", + "
| M | \n", + "77738555 | \n", + "10003.674559 | \n", + "
\n", + " 1.1. [Case Study ](#study)
\n", + " 1.2. [Objectives](#objs)\n", + "2. [Loading Libraries](#libs)\n", + "3. [Loading in the Datasets](#datasets)\n", + "4. [Exploratory Data Analysis(EDA)](#eda)
\n", + " 4.1. [Numerical EDA](#num-eda)
\n", + " 4.2. [Statistical EDA](#stats-eda)
\n", + " 4.2. [Visual EDA](#visual-eda)
\n", + "5. [Data Training and Predictions](#training)
\n", + " 5.1. [Training and Test Sets](#sets)
\n", + " 5.2. [Modelling and Evaluation](#modelling)
\n", + " 5.3. [Model Score Comparison](#comparison)
\n", + " 5.4. [Fine Tuning Highest Scoring Model](#fine-tuning)
\n", + " 5.5. [Predictions](#predictions)
\n", + " 5.6. [Submission Formatting](#submission)
\n", + " 5.6. [Final Submission](#final)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies.\n", + "
\n", + "## Case Study: \n", + "A mobile blood donation vehicle in Taiwan. The Blood Transfusion Service Center drives to different universities and collects blood as part of a blood drive.\n", + "
\n", + "## Objectives \n", + "1. To predict whether or not a donor will give blood the next time the vehicle comes to campus. (March 2007)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Loading Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import warnings\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier \n", + "from sklearn.naive_bayes import GaussianNB \n", + "from sklearn.model_selection import cross_val_score \n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set defaults\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Loading in the Datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Training set\n", + "training_dataset = pd.read_csv('../data/donation-training.csv')\n", + "test_dataset = pd.read_csv('../data/donation-test.csv')\n", + "submission_format = pd.read_csv('../data/BloodDonationSubmissionFormat.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Exploratory Data Analysis(EDA)\n", + "\n", + "\n", + "### A peek at the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Months since Last Donation Number of Donations \\\n",
+ "0 619 2 50 \n",
+ "1 664 0 13 \n",
+ "2 441 1 16 \n",
+ "3 160 2 20 \n",
+ "4 358 1 24 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \\\n",
+ "0 12500 98 \n",
+ "1 3250 28 \n",
+ "2 4000 35 \n",
+ "3 5000 45 \n",
+ "4 6000 77 \n",
+ "\n",
+ " Made Donation in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "619 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "664 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "441 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "160 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "358 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Months since Last Donation Number of Donations \\\n",
+ "0 659 2 12 \n",
+ "1 276 21 7 \n",
+ "2 263 4 1 \n",
+ "3 303 11 11 \n",
+ "4 83 4 12 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \n",
+ "0 3000 52 \n",
+ "1 1750 38 \n",
+ "2 250 4 \n",
+ "3 2750 38 \n",
+ "4 3000 34 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "659 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "276 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "263 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "303 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "83 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Made Donation in March 2007\n",
+ "0 659 0.5\n",
+ "1 276 0.5\n",
+ "2 263 0.5\n",
+ "3 303 0.5\n",
+ "4 83 0.5"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission_format.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### For analysis, we drop the unnamed(key) column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|
| 0 | \n", + "659 | \n", + "0.5 | \n", + "
| 1 | \n", + "276 | \n", + "0.5 | \n", + "
| 2 | \n", + "263 | \n", + "0.5 | \n", + "
| 3 | \n", + "303 | \n", + "0.5 | \n", + "
| 4 | \n", + "83 | \n", + "0.5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Months since Last Donation Number of Donations \\\n",
+ "0 2 50 \n",
+ "1 0 13 \n",
+ "2 1 16 \n",
+ "3 2 20 \n",
+ "4 1 24 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \\\n",
+ "0 12500 98 \n",
+ "1 3250 28 \n",
+ "2 4000 35 \n",
+ "3 5000 45 \n",
+ "4 6000 77 \n",
+ "\n",
+ " Made Donation in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# making a copy of the dataset\n",
+ "training_set = training_dataset.copy()\n",
+ "training_set.drop(columns=['Unnamed: 0'], axis=1, inplace=True)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Months since Last Donation Number of Donations \\\n",
+ "0 2 12 \n",
+ "1 21 7 \n",
+ "2 4 1 \n",
+ "3 11 11 \n",
+ "4 4 12 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \n",
+ "0 3000 52 \n",
+ "1 1750 38 \n",
+ "2 250 4 \n",
+ "3 2750 38 \n",
+ "4 3000 34 "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# making a copy of the dataset\n",
+ "tests_set = test_dataset.copy()\n",
+ "tests_set.drop(columns=['Unnamed: 0'], axis=1, inplace=True)\n",
+ "tests_set.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Renaming the columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time donated\n",
+ "0 2 50 12500 98 1\n",
+ "1 0 13 3250 28 1\n",
+ "2 1 16 4000 35 1\n",
+ "3 2 20 5000 45 1\n",
+ "4 1 24 6000 77 0"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# renaming the Training set\n",
+ "training_set = training_set.rename(columns={'Months since Last Donation':'recency', 'Number of Donations':'total_donations', 'Total Volume Donated (c.c.)':'total_vol_donated', 'Months since First Donation':'donation_time', 'Made Donation in March 2007':'donated'})\n",
+ "training_set.head()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time\n",
+ "0 2 12 3000 52\n",
+ "1 21 7 1750 38\n",
+ "2 4 1 250 4\n",
+ "3 11 11 2750 38\n",
+ "4 4 12 3000 34"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# renaming the test set\n",
+ "test_set = tests_set.rename(columns={'Months since Last Donation':'recency', 'Number of Donations':'total_donations', 'Total Volume Donated (c.c.)':'total_vol_donated', 'Months since First Donation':'donation_time'})\n",
+ "test_set.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.1. Numerical EDA\n",
+ "\n",
+ "\n",
+ "#### Dataset info"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n", + " **recency**: Months since last donation
\n", + " **total_donations**: Number of times donor donated
\n", + " **total_vol_donated**: Total volume of blood donated in c.c.
\n", + " **donation_time**: Months since first donation
\n", + " **donated**: Whether donor donated in March 2007
\n", + "3. All the labels features are numerical\n", + "4. The dataset has no null values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2. Statistical EDA\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time \\\n",
+ "count 576.000000 576.000000 576.000000 576.000000 \n",
+ "mean 9.439236 5.427083 1356.770833 34.050347 \n",
+ "std 8.175454 5.740010 1435.002556 24.227672 \n",
+ "min 0.000000 1.000000 250.000000 2.000000 \n",
+ "25% 2.000000 2.000000 500.000000 16.000000 \n",
+ "50% 7.000000 4.000000 1000.000000 28.000000 \n",
+ "75% 14.000000 7.000000 1750.000000 49.250000 \n",
+ "max 74.000000 50.000000 12500.000000 98.000000 \n",
+ "\n",
+ " donated \n",
+ "count 576.000000 \n",
+ "mean 0.239583 \n",
+ "std 0.427200 \n",
+ "min 0.000000 \n",
+ "25% 0.000000 \n",
+ "50% 0.000000 \n",
+ "75% 0.000000 \n",
+ "max 1.000000 "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Statistical Overview\n",
+ "1. The average number of months taken by donors since last donation is **9 months**.\n",
+ "2. The average number of months taken by donors since first donation is **34 months**.\n",
+ "3. The average number of total donations is **5 donations**.\n",
+ "4. An average of **1356.78 c.c.** of blood was donated\n",
+ "5. The maximum total blood volume donated by a single donor was **12500 c.c.** while the minimum was **250 c.c.**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.3. Visual EDA\n",
+ "\n",
+ "\n",
+ "### Class/Target distribution: A check on total donors who donated and didn't donate in march 2007"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| count | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "
| mean | \n", + "9.439236 | \n", + "5.427083 | \n", + "1356.770833 | \n", + "34.050347 | \n", + "0.239583 | \n", + "
| std | \n", + "8.175454 | \n", + "5.740010 | \n", + "1435.002556 | \n", + "24.227672 | \n", + "0.427200 | \n", + "
| min | \n", + "0.000000 | \n", + "1.000000 | \n", + "250.000000 | \n", + "2.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "2.000000 | \n", + "2.000000 | \n", + "500.000000 | \n", + "16.000000 | \n", + "0.000000 | \n", + "
| 50% | \n", + "7.000000 | \n", + "4.000000 | \n", + "1000.000000 | \n", + "28.000000 | \n", + "0.000000 | \n", + "
| 75% | \n", + "14.000000 | \n", + "7.000000 | \n", + "1750.000000 | \n", + "49.250000 | \n", + "0.000000 | \n", + "
| max | \n", + "74.000000 | \n", + "50.000000 | \n", + "12500.000000 | \n", + "98.000000 | \n", + "1.000000 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Extract the donated column data\n",
+ "donated = training_set.donated.value_counts()\n",
+ "donated.rename(index={0:'No', 1:'Yes'}, inplace=True)\n",
+ "# print(donated)\n",
+ "\n",
+ "# Instatiate a figure and draw a bar plot\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "\n",
+ "# plot the bar\n",
+ "sns.barplot(x=donated.index, y=donated.values)\n",
+ "\n",
+ "# display the counts on each bar\n",
+ "for i, v in enumerate(donated.values):\n",
+ " plt.text(i, 50, v, color='k', fontsize=19)\n",
+ "\n",
+ "# label the axes\n",
+ "plt.title('Donation status for March 2007', fontsize=25)\n",
+ "plt.xlabel(\"Donated\", fontsize=20)\n",
+ "plt.ylabel(\"Count\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this plot, it is clear that **76%** of the donors didn't donate in march 2007!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multi-Variable comparisons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(training_set.drop(columns=['donated']))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Uni-variate comparisons: Checking for outliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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cvc8d0NcC6ZFcVpuImCyMXpOiRe+BPbncxqCFBA1JLERk2tgNRUREOjFZEBGRTkwWRESkE5MFERHpxAFuIuqRuupOP97l1z5MFkTUI3XVnX68y6992A1FREQ6sWVh5PqMz0dYbk4XlW0NgIuzERGThdFTnnPr2kl53BKdiMBuKCIi0gNbFkTUI3VVFyu7V9uHyYKIeqSu6mJl92r79IhuqEOHDmHOnDmYNWsWdu3aJXU4RET0M5K3LCoqKrB582ZkZWXBysoKCxYsgIuLC0aNGiV1aEajq+4bH9BX8uph9A4dOoTt27ejpaUFoaGhWLx4sdQhGZWuqNus1+0j+adWUFCAadOmYdCgQQAAb29v5OTkIDw8XOLIjIMh+w5zn+LuxR9CHcO63bNIniwqKyshl8u1j21tbVFcXKz384cMse6KsIyel5cXSktLHzo+affD5zo5OSE3l7NaOxt/CHW+gAA/XL588ZF/e1TdHjlyNDIzD3VxVL2D5MlCo9FAJpNpHwshWj3W5fbtRmg0oitCM2p79hx86Fhbg4CGzL/oLczMZB36MdLRH0L0sMd98Rs6h4gMJ3mysLOzw7fffqt9XFVVBVtbWwkjIuocHf0hxFazYeRyG6lDMGmSJ4sXX3wR6enpqKmpQb9+/XDs2DG89957UodF1GEd/SHEVrP+2LLQX3tbzJIni2HDhiEqKgohISFQqVQIDAzEhAkT9H6+mZn+v9SIn5chOvpZdfSHEP+/Mgw/L/2093OSCSH404Woixw6dAh/+tOftD+E3nzzTalDImoXJgsiItKpR8zgJiKino3JgoiIdGKyICIinZgsiIhIJyYLIiLSicmCiIh0YrIgIiKdmCyIiEgnJgsiMkhDQwPCwsLaPCcmJgY3btxo85zg4GAUFhbq/brp6elIT0/X+3x9nDhxAjt37gQAfPHFF/jiiy86tXxTwmRBRAapq6tDSUlJm+cUFhbCGBaHOHfuHBobGwEACxcuxMKFCyWOqOeSfCFB0q2wsBApKSnQaDQYMWIE+vfvj4sXL0KtVuPNN9+Er68vlEol4uPjUVRUBEtLS/z+97/HnDlzUFxcjKSkJCgUCgwePBjx8fFwcHBAcHAwfvnLX6KoqAg1NTVYt24dPDw8cOPGDcTExKCmpgZ9+/ZFQkICcnJyIIRAVFQUAGDNmjVwd3fHnDlzJP5kSAoJCQmorKxEWFgYvLy8sHPnTshkMowbNw5xcXHYtWsXKisrsWzZMuzatQunTp3Czp07oVAo0NzcjMTERPzqV7/S67U++eQTZGRkYPDgwRg4cKB2kdETJ04gLS0NGo0GDg4O2LhxI4YOHQovLy/MmzcP+fn5uHv3Lt5//32MHz8ep0+fxubNm6FQKFBfX4+YmBg888wz2L373o5Jw4cPx82bNwEAERERBpe/c+dO7N+/H2ZmZpgwYQI2btzYNR++lAT1eKdOnRKTJ08W9fX1IiUlRfzlL38RQgjR0NAg5s6dK65duyY+/vhjsWrVKqFWq0VlZaWYM2eOUCqVws/PT9y4cUMIIUReXp4IDQ0VQgixZMkSkZCQIIQQ4quvvhLz588XQgjx5ptvis8//1wIIcTXX38tVq5cKa5duyY8PT2FRqMRd+7cER4eHkKhUHTzp0A9RXl5ufD09BTnz58XM2fOFDU1NUIIId59912RnJwshBDC09NTlJeXC7VaLUJCQsTt27eFEELs3btXLF++XAhxrw6eOnXqsa9TXFwsZs+eLRobG0VTU5Pw9fUVW7ZsEdXV1cLNzU2Ul5cLIYT4+OOPRUREhPZ1d+7cKYQQ4q9//asIDw8XQggREREhLl26JIQQoqCgQPj6+gohhNiyZYvYsmVLq38bWn5LS4twcXERzc3NQq1WizVr1ogff/yxg59yz8OWhZF49tlnYWNjg4KCAigUCmRmZgIA7ty5g4sXL+LMmTMICgqCmZkZ5HI5vvzyS1y4cAHl5eVYsWKFtpz7TW4AeOmllwAAo0ePRm1tLQDgzJkz+OCDDwAAHh4e8PDwAACMGDECZ86cwc2bN+Hh4YE+ffp0y/umnuvMmTPw9PTE4MGDAQCvvfYaYmJiWp1jZmaGrVu3Ijc3F1euXMHp06dhZqZf7/fp06fh4eGBAQMGAABmz54NjUaD4uJiTJgwAfb29trX/fOf/6x93oP1+tixYwCAlJQUnDhxAjk5OTh79iyampoe+7qGlm9ubg5nZ2cEBgbi17/+NX73u99h2LBher1HY8JkYST69u0L4N7uaykpKRg3bhwAoLq6Gk888QQyMzNb7cJ29epVaDQa2Nvb4+DBe1usqtVqVFdXa8+5/4X/4PMsLP5bJYQQuHz5MkaNGoWAgABkZ2fj5s2biIiI6Lo3SkZDo9G0eiyEQEtLS6tjTU1NCAwMxLx58/DCCy/AyckJu3bt0qt8mUzWatzDwsICzc3NOl/3UfV60aJFcHFxgYuLC1xdXbF69ep2v69Hlb9t2zb8+9//Rl5eHt544w2kpqZi6tSper1PY8EBbiMzbdo07R0blZWVmDdvHm7duoUXXngBhw8fhhACt2/fxpIlSzBixAjU1dVpd2vLzMxs8yIBgClTpuDLL78EABQUFCAuLg7AvV91J0+eRHV1NSZOnNiF75B6OgsLC7S0tGDq1KnIzc3VtkozMjLg4uICADA3N4darUZZWRlkMhneeustuLi44Pjx41Cr1Xq9jqurK06cOIGGhgYolUocP34cADBx4kScPXsW169fBwDs2bNH+7qPUltbi7KyMqxatQru7u746quvtDGYm5s/lOAMLb+mpgZz5syBo6MjVq1ahenTp6O0tFSv92hM2LIwMuHh4Xj33Xfh6+sLtVqN6Oho/OIXv8CiRYuQkJCAefPmAQDi4uJgY2ODDz/8EJs2bYJSqYS1tTXef//9Nstfv3491q1bh7///e/o168fEhISANxr2UyaNAmOjo5d/h6pZxsyZAiGDx+OTZs2Yfny5QgODoZKpcK4ceMQHx8PAJgxYwaWLVuGjz/+GGPHjoWPjw9kMhnc3NxQVFSk1+uMHTsWoaGhCAwMxMCBAzF8+HAAwNChQ7Fx40aEh4dDpVJpY3mcQYMGITAwEHPnzoWFhQWmTZsGhUKBO3fu4IUXXsA777yDoUOHas83tPwnn3wSr732GgIDA9GvXz88++yzCAgI0Os9GhNufkQ6CSFw584dhIeHIzU1FUOGDJE6JMk0NjZiwYIF+N///V/Y29ujoKAASUlJUCqV8PHx0d4xVlJSgrVr16KpqQlTpkxBfHx8qy4+ImPDbijSSSaTYcCAAdi5c2evThRnz57FwoULUVZWBgBQKBSIjY3Ftm3bcPjwYZw7dw7ffPMNACA6Ohrr16/H0aNHIYRARkaGhJETdZzR/9T56acmaDRsHOljyBBr3L7dqPtEAnBvY/vBgwdoH2dkZGDDhg14++23Ady7a+bpp5+Gg4MDAMDPzw85OTkYNWoUFAoFJk2aBADw9/fHli1bsGjRIoNen3Vbf6zb+vt5vdaX0ScLjUbwgtIhK2sv0tJSceFCKRwdnRAZuRr+/q9KHZbR+Xm/dWVlJeRyufaxra0tKioqHjoul8tRUVFh8OuxbhuGn1XXMvpkQW3LytqLxMT3kJb2EXx9ZyE7+xgiI8MBgAmjgzQaTavbJ4UQkMlkjz1uqCFDrDslzt5CLreROgSTxmRh4tLSUpGW9hHc3NxhaWkJNzd3pKV9hNjYaCaLDrKzs0NVVZX2cVVVFWxtbR86Xl1dDVtbW4PLv327kb+W9SSX26CqqkHqMIyCmZmsXT9EmCxM3IULpXBxcW11zMXFFRcumN594N1t4sSJuHLlCq5evQp7e3tkZ2cjICAAI0aMQJ8+fVBUVITJkyfj4MGDcHd3lzpckxAQ4IfLly/qff7IkaORmXmoCyPqPZgsTJyjoxMKC0/Cze2/X1aFhSfh6OgkYVSmoU+fPkhOTkZERASUSiU8PDwwe/ZsAEBqairWrVuHxsZGjBs3DiEhIRJHaxoe98W/NDkXn67x6uZoehcmCxMXGbkakZHh2jGL/Pw8REaGIzY2TurQjFZubq72366urvi///u/h84ZM2YM9u3b151hEXUpJgsTd39cIjY2GoGB8+Do6ITY2DiOVxCRQTgpj4iIdGLLwsTx1lki6gxsWZi4x906m5aWKnVoRGREmCxMHG+dJaLOwG4oE+fo6ITU1GQcOZKtXe7Dx8eXt84SkUHYsjBx06e/hC1bNmPhwmA0NDRg4cJgbNmyGdOnvyR1aERkRNiyMHH//Oc/MGvWbGzaFI8NG2JhZdUHs2bNxj//+Q+pQyMiI8KWhYkrLT2Pc+f+g927M9Hc3IzduzNx7tx/UFp6XurQiMiIMFmYOEtLK7zxxvJWd0O98cZyWFpaSR0aERkRJgsTp1I1Y8eOPyE/Pw8qlQr5+XnYseNPUKmapQ6NiIwIk4WJc3Iag4CAIMTGRqNv376IjY1GQEAQnJzGSB0aERkRvZJFY2MjfH19cf36dQBAQUEB/Pz8MGvWLGzevFl7XklJCfz9/eHt7Y21a9eipaUFAHDz5k0sXrwYs2fPxooVK9DU1AQAqK+vx7Jly+Dj44PFixe32gOAOkdk5GpkZu5FYmIKFAoFEhNTkJm5F5GRq6UOjYiMiM5k0Rmb1MfHx2PRokXIycnB+PHjsW3bNgBAWloapkyZgiNHjuDVV199aNtK6jh//1cRGxvXqmXBhQSJyFA6k8X9Terv7/T14Cb1FhYW2k3qb9y48dAm9Tk5OVCpVDhz5gy8vb1bHQeAr7/+Gn5+fgAAX19f5OXd61enzuXv/yry8gqhVquRl1fIREFEBtM5z6Kjm9T/9NNPsLa2hoWFRavjPy/LwsIC1tbWqKmpwbBhw/R+A9yn2DDcp5iI2sPgSXmGblL/qM3qH7d5vRACZmaGjblzn2LdsrL2Ii0tVbvcR2TkarYu9NDevYqJTJHBd0MZukn9k08+iYaGBqjV6lbnA/daJdXV1QCAlpYWNDU1YdCgQR16Q9Ta/SXKHxzgTkx8D1lZe6UOjYiMiMHJ4sFN6tVqNbKzs+Hu7t5qk3oA2k3qLS0tMWXKFBw+fBgAcODAAe3m9R4eHjhw4AAA4PDhw5gyZQosLS07670RuEQ5EXUOg7uh2rNJ/YYNG7BmzRps374dTz31FD744AMAwKpVq7BmzRrMnTsXNjY2SE3lF1hn4xLlRNQZZEIIo+7w55hF29zdXZCYmAI3N3fI5TaoqmpAfn4eYmOjkZdXKHV4PZrUYxas2/pbmpyLT9d4SR2GUWhvveYMbhMXGbkakZHhrZb7iIwM56Q8IjIIlyg3cffveoqNjUZg4Dw4OjpxUh4RGYzJohfw938V/v6varuhiIgMxW4oIiLSicmiF8jK2gt3dxeYm5vD3d2FcyyIyGDshjJx9yflpaV9BF/fWcjOPobIyHAA4LgFEemNLQsTx0l5RNQZmCxMHCflEVFnYLIwcY6OTigsPNnqWGHhSTg6OkkUEREZIyYLE8dJeUTUGTjAbeI4KY+IOgOTRS/ASXlE1FHshiIiIp2YLIiISCcmCyIi0onJgoiIdGKyICIinXg3FFEnCA4ORk1NDSws7l1SGzduxLVr17B9+3a0tLQgNDQUixcvljhKovZjsiDqICEEysrKcOLECW2yqKioQFRUFLKysmBlZYUFCxbAxcUFo0aNkjhaovZhsiDqoB9++AEAsHTpUtTW1iIoKAgDBgzAtGnTMGjQIACAt7c3cnJyEB4eLmWoRO3GMQuiDqqvr4erqyu2bt2Kzz77DLt378bNmzchl8u159ja2qKiokLCKIk6hi2LXiAmZjX+9re/oLlZCSurPggODkVSEpco7yzOzs5wdnbWPg4MDERSUhJWrFihPSaEgEwmM6jcIUOsOy3G3kAut5E6BJPGZGHiYmJW47PPPsX69RuxevUqpKZ+iI0b1wMAE0Yn+fbbb6FSqeDqem8peCEERowYgaqqKu05VVVVsLW1Najc27cbodGITo3VmESk5aFJ0aL3+X5/OKjXeQP6WiA90r29YRk9MzNZu36IMFmYuL/97S9Yv34jVqwIR//+/bFixb0+802b4pmy0TdeAAAOvUlEQVQsOklDQwO2bNmC3bt3Q6VSYf/+/UhJSUF0dDRqamrQr18/HDt2DO+9957UoRqVJkULPl3jpde5hqx7tjQ5tyNh9VpMFiauuVmJy5cvwd5eru2GWrBgMZqblVKHZjI8PT1x9uxZvPLKK9BoNFi0aBEmT56MqKgohISEQKVSITAwEBMmTJA6VKJ2Y7IwcTKZGT7//DNs2JCg7YaKj18HmYz3NnSmyMhIREZGtjrm5+cHPz8/iSIi6lz8xjBxMhmg0WiwdWsabGxssHVrGjQaDQwcayWiXq5DLQtDZq0WFBQgKSkJSqUSPj4+iIqKAgCUlJRg7dq1aGpqwpQpUxAfH68tjzpOo9HA2toaNTU/QaPRoKbmJ1hbW6OxsVHq0IjIiLS7ZXF/1urBgwe1/7Ozs8PmzZvx97//HQcOHMCePXtw6dIlKBQKxMbGYtu2bTh8+DDOnTuHb775BgAQHR2N9evX4+jRoxBCICMjo9PeHAEymQz29g4wM7v3f7WZmRns7R0Mvo2TiHq3dieLB2etzps3D59//jkKCgq0s1b79++vnbVaXFyMp59+Gg4ODrCwsICfnx9ycnJw48YNKBQKTJo0CQDg7++PnJycznlnBOBeUj9/vgSenl6oqqqCp6cXzp8vgRC995ZMIjJcu/t77s9ajYuLg0qlQkhICHx8fB6atVpcXIzKyspHzmb9+XG5XM5Zrp1MJpPhiSeewNGjR7Sf9aBBg1BXVydxZERkTNqdLAyZtXpvQFWm93FDcJZr24QQqK2txYoVK5CUlISYmBhs374dAGe8EpH+2p0sDJm1amdnp9fx6upqznLtAg4Ov8COHZ9i+/btsLLqAweHX6C8/Jrek5h6q/bOdCUyRe0es2hoaMAf//hHKJVKNDY2ametnjx5EjU1Nbh79y6OHTsGd3d3TJw4EVeuXMHVq1ehVquRnZ0Nd3d3jBgxAn369EFRUREA4ODBg3B3773T8LtKefk17SS85mYlysuvSRwRERmbdrcsDJ21mpycjIiICCiVSnh4eGD27NkAgNTUVKxbtw6NjY0YN24cQkJCOued0c/IAIgH/ktEpD+ZMPLbYtgN1TZb24GP/VtlZX03RmJ8pO6G6u11e2lybpetDaVvuaaovfWaM7iJiEgnJgsiItKJyaKXMDe3aPVfIiJDMFn0Emp1S6v/EhEZgsmilxg6VI6SkhIMHSrXfTIR0c+wT8LEyWQyyGQyVFdXYezYsQDuLSZo5DfBUS/QZ3w+wnI7f624PuOtAfTeu6Hai8nCRAUE+OHy5YuwtLREc3Mz+vfvDxsbGzQ0NODOnTuwsrLCpEljAAAjR45GZuYhiSMmak15zq3rtlX17UhkvROThYl68Ms/KOgVfPPNCdy5cwcymQwzZnghI+OAhNERkbFhsugF7ieG3j4ZiYjajwPcRESkE5MFERHpxGRBREQ6MVkQEZFOTBZERKQTkwUREenEZEFERDoxWRARkU6clEdEPdbS5NxOL3NAX37ttQc/NSLqkQxZbYCrE3Q9JgsjF5GWhyaF/ntUGPJLbUBfC6RHurcnLCIyMUwWRq5J0dIlK3MCXdMFQETGiQPcRESkE5MFERHpxGRBREQ6cczCyHXV1pP3yub2k0R0D5OFkeuqrScBbj9JRP/FbigiItKJyYKIiHTqEd1Qhw4dwvbt29HS0oLQ0FAsXrxY6pCMSlfNh+CyCB3Huk2mQvJvg4qKCmzevBlZWVmwsrLCggUL4OLiglGjRkkdmlHgkgg9F+s2mRLJk0VBQQGmTZuGQYMGAQC8vb2Rk5OD8PBwiSMzbgEBfrh8+eJDxyftfvjckSNHIzPzUDdE1buwbne+x9VrgHW7q0meLCorKyGXy7WPbW1tUVxcLGFEpuFRF4ihd0NRx7Bud77HffGzbnc9yZOFRqOBTCbTPhZCtHqsy5Ah1l0RlsmSy22kDqHXYN3uXqzbXUvyZGFnZ4dvv/1W+7iqqgq2trZ6P/+nn5qg0YiuCM3kDBlijdu3G6UOw2iYmckwePCAdj+fdbv7sG7rr731WvJk8eKLLyI9PR01NTXo168fjh07hvfee0/v53fkYu6N+Gu1+7Budy/W7a4lebIYNmwYoqKiEBISApVKhcDAQEyYMEHqsIg6jHWbTIlMCMF2LhERtYkzuImISCcmCyIi0onJgoiIdGKyICIinZgsiIhIJyYLIiLSicmCiIh0YrIgIiKdmCw6qKGhAWFhYW2eExMTgxs3brR5TnBwMAoLCzstrvT0dKSnp+t9fmFhIYKDgzvt9QGgvLwcsbGxBj3H0LipZ1mzZg2ysrI6rbyMjAxkZ2cDAD788EN89dVXnVZ2d5RvSpgsOqiurg4lJSVtnlNYWIjeOFH+5s2bKC8vlzoMMmL/+te/0NzcDABYtWoVfv3rXxtV+aZE8rWhjF1CQgIqKysRFhYGLy8v7Ny5EzKZDOPGjUNcXBx27dqFyspKLFu2DLt27cKpU6ewc+dOKBQKNDc3IzExEb/61a90vk5SUhKGDRuGpUuXAgAiIiIwb948ODs7Y+3atbh58yYsLCwQFRUFd3d3vWLPz89HUlIS+vTpg2effVZ7/MqVK1i/fj1qa2vRv39/rF27FhMmTMCaNWtgbW2N77//HhUVFQgLC0NAQAAqKioQGxuLhoYGVFZWYv78+Vi1ahUSEhJw/fp1xMfHY8OGDfjzn/+MI0eOQK1Ww83NDdHR0ZDJZPjkk0+QkZGBwYMHY+DAgVw/yYgIIZCcnIyvv/4atra2UKvVmDp1KjIzMx+6FgYMGAA3Nzd4e3ujqKgI5ubmSEtLg4ODA44cOfLQdaFQKJCbm4tTp05BLpfjyy+/xNSpU+Hv729w+Y9SUFDwyPKnTp2KsLAwPPfcc7h06RKef/55ODs7Y//+/airq8PWrVsxcuRIFBcXIykpCQqFAoMHD0Z8fPxjX8skCOqQ8vJy4enpKc6fPy9mzpwpampqhBBCvPvuuyI5OVkIIYSnp6coLy8XarVahISEiNu3bwshhNi7d69Yvny5EEKIJUuWiFOnTj32db7//nsxf/58IYQQDQ0NYvr06UKpVIqVK1eKTz/9VAghxLVr18T06dNFVVWV2LJli9iyZctjy1MqlWL69Oni0qVLQgghYmNjxZIlS4QQQgQEBIijR48KIYT47rvvxIwZM4RSqRTvvPOOCAsLExqNRpw/f15MnTpVCCHEJ598IrKysoQQQtTX1wtnZ2dx+/ZtcerUKW2Z33zzjYiIiBAtLS1CrVaL//mf/xEHDhwQxcXFYvbs2aKxsVE0NTUJX1/fNuOmnuXIkSNiyZIlorm5Wdy+fVtMnz5d/PWvf33steDo6CiOHz8uhBAiKSlJJCUltXldvPPOOyIzM7PVv9u61h5VflseVX55eblwcnIS33//vVCr1WLmzJkiNTVVCCFEenq62LRpk1AqlcLPz0/cuHFDCCFEXl6eCA0N7fgH2oOxZdFJzpw5A09PTwwePBgA8NprryEmJqbVOWZmZti6dStyc3Nx5coVnD59GmZm+vUEPv/882hubsbVq1fx3XffwcvLC1ZWVjh16hQSEhIAAA4ODpg4cSLOnj2rs7zS0lLY2tpi5MiRAID58+fjww8/RFNTE65du4ZZs2YBACZNmoQnnngCP/zwAwBg+vTpkMlkcHR0RG1tLQDg9ddfx6lTp7Bjxw5cvHgRKpUKd+/ebfV6J0+eRHFxMfz9/QEACoUCw4cPR3V1NTw8PDBgwL3luGfPng2NRqPXZ0LSO336NGbNmgVLS0s8+eSTcHd3h0wma/NaeOmllwAAo0ePxrfffmvwdaHrWvt5+e0xdOhQPP/88wDu7Uvi6uoKABg+fDiuX7+OsrIylJeXY8WKFdrnNDaa9n4aTBad5OdfcEIItLS0tDrW1NSEwMBAzJs3Dy+88AKcnJywa9cuvV9j3rx5OHz4ML777jssW7ZM+zo/f121Wq2zLJlM1uq55ubmjyzv52X26dNH+/z7kpOTUV5eDl9fX8ycORMFBQUPlaNWqxEaGorf/e53AID6+nqYm5tjz549rc61sLDQ9iFTz/fzemRhYaHzWniwDgkhDL4uDC2/PaysrFo9vn99PBiDvb09Dh48COBe/a6urm7XaxkLDnB3kIWFBVpaWjB16lTk5uZqf21nZGTAxcUFwL2KplarUVZWBplMhrfeegsuLi44fvy4Xl/s9/n5+eHw4cO4evUqJk+eDACYNm0a9u3bB+De3Uf/+te/MGnSJJ1lOTk5obq6GufPnwcAfPnllwAAa2tr2Nvb49ixYwCAf//736iursbo0aMfW9Y///lPvP766/Dx8cGVK1dQUVEBjUYDc3Nz7UU8bdo0HDx4EE1NTWhpaUFYWBiOHj0KV1dXnDhxAg0NDVAqlTh+/LjenwdJz9XVFUeOHEFzczPq6urwj3/8AwAeey08SlvXxf1r50FtXWuGelT5+njuuedQV1enbblkZmZi9erV7YrBWLBl0UFDhgzB8OHDsWnTJixfvhzBwcFQqVQYN24c4uPjAQAzZszAsmXL8PHHH2Ps2LHw8fGBTCaDm5sbioqK9H6tp556CoMHD4azs7P2l/3atWuxfv167e2KCQkJem3daWlpiQ8++ADR0dGwsLDQNrkBICUlBe+++y7S09NhaWmJ9PT0h35pPWj58uV4++230bdvX9jZ2WH8+PG4fv06xo4di4aGBkRHRyMlJQXnz59HUFAQ1Go1XnrpJcyfPx8ymQyhoaEIDAzEwIEDMXz4cL0/D5LezJkz8Z///Ae+vr4YOnQoRo4cCWtr68deC48yZsyYx14XL774Ij744APY2Ni0Ot+Q8tvyqPL1YWVlhQ8//BCbNm2CUqmEtbU13n///XbFYCy4+REREenElkUPcu3aNURERDzybwkJCfjlL39pcJnBwcGor69/6PiCBQuwcOFCg8sjMjZ/+MMfcOnSpYeOe3l5YdWqVRJEZJzYsiAiIp04wE1ERDoxWRARkU5MFkREpBOTBRER6cRkQUREOv1/WwojZGu8xpgAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "training_set.drop(columns=['donated']).plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Checking for correlation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "correlation = training_set.corr()\n",
+ "plt.figure(figsize=(10, 10)) \n",
+ "sns.heatmap(correlation, vmax=1, square=True, annot=True, cmap='cubehelix')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Total donations and total volume donated are highly correlated and directly proportional. To confirm, we plot a line graph of total volume donated against total number of times donated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# extract the columns\n",
+ "donations = training_set.total_donations.values\n",
+ "volume = training_set.total_vol_donated.values\n",
+ "\n",
+ "plt.figure(figsize=(10, 5))\n",
+ "\n",
+ "plt.plot(donations, volume, color='green')\n",
+ "\n",
+ "plt.title('Total donated vs Times donated', fontsize=25)\n",
+ "plt.xlabel(\"Number of times donated\", fontsize=20)\n",
+ "plt.ylabel(\"Total volume donated\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Data Training and Prediction\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.1. Training and Test Sets\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Dataset features\n",
+ "training_values = training_set.values\n",
+ "test_values = test_set.values\n",
+ "# print(training_values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Extract training and testing features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train = training_values[:, 0:4]\n",
+ "y_train = training_values[:, 4]\n",
+ "X_test = test_values[:, 0:4]\n",
+ "\n",
+ "scores = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.2. Modelling and Evaluation\n",
+ "\n",
+ "\n",
+ "### I. Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7638888888888888"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "logit_model = LogisticRegression(solver='lbfgs')\n",
+ "\n",
+ "# Fit/Train model into data\n",
+ "logit_model.fit(X_train, y_train)\n",
+ "\n",
+ "# Training Score\n",
+ "logit_score = logit_model.score(X_train, y_train)\n",
+ "# print(logit_score)\n",
+ "scores['LR'] = logit_score\n",
+ "\n",
+ "logit_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### II. Decision Trees"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9375"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "tree_model = DecisionTreeClassifier() # max_depth = 3\n",
+ "# Fit a decision tree\n",
+ "tree_model = tree_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "tree_model_score = tree_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['DT'] = tree_model_score\n",
+ "tree_model_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### III. Random Forest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\users\\kasre\\pycharmprojects\\python4ds_cohort-1\\env\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
+ " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.921875"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "rf = RandomForestClassifier() # n_estimators = 100\n",
+ "# Fit\n",
+ "rf_model = rf.fit(X_train, y_train) \n",
+ "# training score\n",
+ "rf_score = rf_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['RF'] = rf_score\n",
+ "rf_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IV. Support Vector Machine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9114583333333334"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "svm_model = SVC(probability=True, gamma='auto') \n",
+ "# Fit\n",
+ "svm_model = svm_model.fit(X_train, y_train) \n",
+ "# Training Score\n",
+ "svm_score = svm_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['SVM'] = svm_score\n",
+ "svm_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### V. KNN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.828125"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# instantiate (k = 5)\n",
+ "knn_model = KNeighborsClassifier(n_neighbors=5) \n",
+ "# fit the model\n",
+ "knn_model.fit(X_train, y_train) \n",
+ "# Training Score\n",
+ "knn_score = knn_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['KNN'] = knn_score\n",
+ "knn_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### VI. Naive Bayes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7465277777777778"
+ ]
+ },
+ "execution_count": 95,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "bayes_model = GaussianNB() \n",
+ "# Fit the model\n",
+ "bayes_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "NB_score = bayes_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['NB'] = NB_score\n",
+ "NB_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.3. Model Score Comparison\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " score\n",
+ "DT 0.937500\n",
+ "KNN 0.828125\n",
+ "LR 0.763889\n",
+ "NB 0.746528\n",
+ "RF 0.921875\n",
+ "SVM 0.911458"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scores_df = pd.DataFrame({\n",
+ " 'score': scores\n",
+ "})\n",
+ "\n",
+ "scores_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Decision Tree Classifier** has the highest score of **0.9375**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.4. Fine Tuning Highest Scoring Model\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | score | \n", + "
|---|---|
| DT | \n", + "0.937500 | \n", + "
| KNN | \n", + "0.828125 | \n", + "
| LR | \n", + "0.763889 | \n", + "
| NB | \n", + "0.746528 | \n", + "
| RF | \n", + "0.921875 | \n", + "
| SVM | \n", + "0.911458 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "depths = np.arange(1, 33)\n",
+ "DT_scores = []\n",
+ "for depth in depths:\n",
+ " DT = DecisionTreeClassifier(max_depth=depth)\n",
+ " DT.fit(X_train, y_train)\n",
+ " score = DT.score(X_train, y_train)\n",
+ " DT_scores.append(score)\n",
+ " \n",
+ "plt.figure(figsize=(12, 8))\n",
+ "plt.plot(depths, DT_scores, color='red')\n",
+ "plt.title(\"Score vs Max depth\", fontsize=20)\n",
+ "plt.xlabel(\"Max depth\", fontsize=19)\n",
+ "plt.ylabel(\"Score\", fontsize=19)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For better results, max depth rangges between 10 and 13"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9305555555555556"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "tree_model = DecisionTreeClassifier(max_depth=11) # max_depth = 13\n",
+ "# Fit a decision tree\n",
+ "tree_model = tree_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "tree_model_score = tree_model.score(X_train, y_train)\n",
+ "\n",
+ "tree_model_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.5. Predictions\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 6.1. [Numerical EDA](#num-eda)
\n", + " 6.2. [Statistical EDA](stat-eda)
\n", + " 6.3. [Visual EDA](#visual-eda)
\n", + "7. [Data Preprocessing](#preprocessing)\n", + "8. [Feature Engineering](#f-eng)
\n", + " 8.1. [Scaling the Data](#scaling)
\n", + "9. [Model Selection and Training](#selection)
\n", + " 9.1. [Choosing a performance Metric](#metric)
\n", + " 9.2. [Model Validation](#validation)
\n", + "10. [Model Evaluation and Optimization](#evaluation)\n", + "11. [Prediction](#pred)\n", + "12. [Submission](#submission)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Santander's mission is to help people and businesses prosper. They are always looking for ways to help their customers understand their financial health and identify which products and services might help them achieve their monetary goals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Definition\n", + "\n", + "Santander is continually challenging their Machine Learning Algorithms to make sure they can accurately identify new ways to solve their most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Objective\n", + "\n", + "To identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Libraries\n", + "Here we load the key libraries that we going to use\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Import key libraries\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "import plotly\n", + "import warnings\n", + "\n", + "# import ML libraries\n", + "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Library Defaults\n", + "Here we set the default visualization settings for seaborn and warning handling" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seaborn visualization default\n", + "sns.set(color_codes=True)\n", + "\n", + "# Ignore warnings in production\n", + "# warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. The Data\n", + "Loading in the data\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n",
+ "RangeIndex: 10000 entries, 0 to 9999\n",
+ "Columns: 202 entries, ID_code to var_199\n",
+ "dtypes: float64(200), int64(1), object(1)\n",
+ "memory usage: 15.4+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "santander_training.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### dataset dimensionality"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10000, 202)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_training.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observations\n",
+ "From the above observations we can see that:\n",
+ "1. The data has a total of 202 features/labels and 50 000 samples.\n",
+ "2. Of all the features/labels, 200 are of type float, 1 of type integer and one of object type\n",
+ "\n",
+ "### Null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ "Length: 202, dtype: int64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "null_values = santander_training.isnull().sum()\n",
+ "null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The training set has no null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ID_code 0\n",
+ "var_0 0\n",
+ "var_1 0\n",
+ "var_2 0\n",
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+ "var_14 0\n",
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+ "var_19 0\n",
+ "var_20 0\n",
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+ "var_22 0\n",
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+ "var_24 0\n",
+ "var_25 0\n",
+ "var_26 0\n",
+ "var_27 0\n",
+ "var_28 0\n",
+ " ..\n",
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+ "var_182 0\n",
+ "var_183 0\n",
+ "var_184 0\n",
+ "var_185 0\n",
+ "var_186 0\n",
+ "var_187 0\n",
+ "var_188 0\n",
+ "var_189 0\n",
+ "var_190 0\n",
+ "var_191 0\n",
+ "var_192 0\n",
+ "var_193 0\n",
+ "var_194 0\n",
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+ "Length: 201, dtype: int64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_null_values = santander_test.isnull().sum()\n",
+ "test_null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The test set also has no null values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6.2. Statistical EDA\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 1. There's no much difference between label means for the training and test sets, same case for standard deviation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3. Visual EDA\n", + "\n", + "### Distribution of the target(whether a customer made a transaction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " 0 1\n",
+ "0 0.000000 1.000000\n",
+ "1 1.000000 0.000000\n",
+ "2 0.904762 0.095238\n",
+ "3 1.000000 0.000000\n",
+ "4 1.000000 0.000000\n",
+ "5 0.000000 1.000000\n",
+ "6 0.666667 0.333333\n",
+ "7 1.000000 0.000000\n",
+ "8 1.000000 0.000000\n",
+ "9 1.000000 0.000000"
+ ]
+ },
+ "execution_count": 126,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Probabilities\n",
+ "donation_prob = tree_model.predict_proba(X_test)\n",
+ "prob_df = pd.DataFrame(donation_prob)\n",
+ "prob_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.6. Submission Formatting\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 0 | \n", + "1 | \n", + "
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| 6 | \n", + "0.666667 | \n", + "0.333333 | \n", + "
| 7 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Unnamed: 0\n",
+ "0 659\n",
+ "1 276\n",
+ "2 263\n",
+ "3 303\n",
+ "4 83"
+ ]
+ },
+ "execution_count": 122,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "donated_prob = prob_df[1]\n",
+ "# print(donated_prob)\n",
+ "submission_df = test_dataset.copy()\n",
+ "# print(submission_df.head())\n",
+ "submission_df.drop(columns=['Months since Last Donation', 'Number of Donations', 'Total Volume Donated (c.c.)', 'Months since First Donation'], inplace=True)\n",
+ "submission_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.7. Final Submission\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Unnamed: 0 Made donation in March 2007\n",
+ "0 659 1.000000\n",
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+ "3 303 0.000000\n",
+ "4 83 0.000000\n",
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+ "13 401 0.095238\n",
+ "14 696 1.000000\n",
+ "15 192 0.222222\n",
+ "16 176 0.000000\n",
+ "17 571 0.000000\n",
+ "18 139 0.000000\n",
+ "19 423 0.000000"
+ ]
+ },
+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission_df['Made donation in March 2007'] = donated_prob\n",
+ "submission_df.head(20)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Santander Customer Transaction Prediction-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Santander Customer Transaction Prediction-checkpoint.ipynb
new file mode 100644
index 0000000..050300b
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Santander Customer Transaction Prediction-checkpoint.ipynb
@@ -0,0 +1,2836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)\n",
+ "2. [Problem Definition](#problem)\n",
+ "3. [Objective](#obj)\n",
+ "4. [Libraries](#libs)\n",
+ "5. [Data](#data)\n",
+ "6. [Exploration Data Analysis(EDA)](#eda)| \n", + " | Unnamed: 0 | \n", + "Made donation in March 2007 | \n", + "
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| 0 | \n", + "659 | \n", + "1.000000 | \n", + "
| 1 | \n", + "276 | \n", + "0.000000 | \n", + "
| 2 | \n", + "263 | \n", + "0.095238 | \n", + "
| 3 | \n", + "303 | \n", + "0.000000 | \n", + "
| 4 | \n", + "83 | \n", + "0.000000 | \n", + "
| 5 | \n", + "500 | \n", + "1.000000 | \n", + "
| 6 | \n", + "530 | \n", + "0.333333 | \n", + "
| 7 | \n", + "244 | \n", + "0.000000 | \n", + "
| 8 | \n", + "249 | \n", + "0.000000 | \n", + "
| 9 | \n", + "728 | \n", + "0.000000 | \n", + "
| 10 | \n", + "129 | \n", + "1.000000 | \n", + "
| 11 | \n", + "534 | \n", + "0.000000 | \n", + "
| 12 | \n", + "317 | \n", + "0.000000 | \n", + "
| 13 | \n", + "401 | \n", + "0.095238 | \n", + "
| 14 | \n", + "696 | \n", + "1.000000 | \n", + "
| 15 | \n", + "192 | \n", + "0.222222 | \n", + "
| 16 | \n", + "176 | \n", + "0.000000 | \n", + "
| 17 | \n", + "571 | \n", + "0.000000 | \n", + "
| 18 | \n", + "139 | \n", + "0.000000 | \n", + "
| 19 | \n", + "423 | \n", + "0.000000 | \n", + "
\n", + " 6.1. [Numerical EDA](#num-eda)
\n", + " 6.2. [Statistical EDA](stat-eda)
\n", + " 6.3. [Visual EDA](#visual-eda)
\n", + "7. [Data Preprocessing](#preprocessing)\n", + "8. [Feature Engineering](#f-eng)
\n", + " 8.1. [Scaling the Data](#scaling)
\n", + "9. [Model Selection and Training](#selection)
\n", + " 9.1. [Choosing a performance Metric](#metric)
\n", + " 9.2. [Model Validation](#validation)
\n", + "10. [Model Evaluation and Optimization](#evaluation)\n", + "11. [Prediction](#pred)\n", + "12. [Submission](#submission)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Santander's mission is to help people and businesses prosper. They are always looking for ways to help their customers understand their financial health and identify which products and services might help them achieve their monetary goals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Definition\n", + "\n", + "Santander is continually challenging their Machine Learning Algorithms to make sure they can accurately identify new ways to solve their most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Objective\n", + "\n", + "To identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Libraries\n", + "Here we load the key libraries that we going to use\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Import key libraries\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "import plotly\n", + "import warnings\n", + "\n", + "# import ML libraries\n", + "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Library Defaults\n", + "Here we set the default visualization settings for seaborn and warning handling" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seaborn visualization default\n", + "sns.set(color_codes=True)\n", + "\n", + "# Ignore warnings in production\n", + "# warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. The Data\n", + "Loading in the data\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ " var_7 ... var_190 var_191 var_192 var_193 var_194 var_195 \\\n",
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+ "2 14.6155 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 \n",
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+ "4 19.2514 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 \n",
+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
+ "0 7.8784 8.5635 12.7803 -1.0914 \n",
+ "1 8.1267 8.7889 18.3560 1.9518 \n",
+ "2 -6.5213 8.2675 14.7222 0.3965 \n",
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+ "\n",
+ "[5 rows x 202 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# %%time\n",
+ "# Read the training and test datasets\n",
+ "path = '../data/santander/'\n",
+ "santander_training = pd.read_csv(path + 'train.csv', nrows=10000)\n",
+ "santander_training.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
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+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_test = pd.read_csv(path + 'test.csv', nrows=10000)\n",
+ "santander_test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 6. Exploratory Data Analysis(EDA)\n",
+ "Training set information\n",
+ "\n",
+ "## 6.1. Numerical EDA\n",
+ "\n",
+ "### Dataset information"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 1 | \n", + "test_1 | \n", + "8.5304 | \n", + "1.2543 | \n", + "11.3047 | \n", + "5.1858 | \n", + "9.1974 | \n", + "-4.0117 | \n", + "6.0196 | \n", + "18.6316 | \n", + "-4.4131 | \n", + "... | \n", + "10.6165 | \n", + "8.8349 | \n", + "0.9403 | \n", + "10.1282 | \n", + "15.5765 | \n", + "0.4773 | \n", + "-1.4852 | \n", + "9.8714 | \n", + "19.1293 | \n", + "-20.9760 | \n", + "
| 2 | \n", + "test_2 | \n", + "5.4827 | \n", + "-10.3581 | \n", + "10.1407 | \n", + "7.0479 | \n", + "10.2628 | \n", + "9.8052 | \n", + "4.8950 | \n", + "20.2537 | \n", + "1.5233 | \n", + "... | \n", + "-0.7484 | \n", + "10.9935 | \n", + "1.9803 | \n", + "2.1800 | \n", + "12.9813 | \n", + "2.1281 | \n", + "-7.1086 | \n", + "7.0618 | \n", + "19.8956 | \n", + "-23.1794 | \n", + "
| 3 | \n", + "test_3 | \n", + "8.5374 | \n", + "-1.3222 | \n", + "12.0220 | \n", + "6.5749 | \n", + "8.8458 | \n", + "3.1744 | \n", + "4.9397 | \n", + "20.5660 | \n", + "3.3755 | \n", + "... | \n", + "9.5702 | \n", + "9.0766 | \n", + "1.6580 | \n", + "3.5813 | \n", + "15.1874 | \n", + "3.1656 | \n", + "3.9567 | \n", + "9.2295 | \n", + "13.0168 | \n", + "-4.2108 | \n", + "
| 4 | \n", + "test_4 | \n", + "11.7058 | \n", + "-0.1327 | \n", + "14.1295 | \n", + "7.7506 | \n", + "9.1035 | \n", + "-8.5848 | \n", + "6.8595 | \n", + "10.6048 | \n", + "2.9890 | \n", + "... | \n", + "4.2259 | \n", + "9.1723 | \n", + "1.2835 | \n", + "3.3778 | \n", + "19.5542 | \n", + "-0.2860 | \n", + "-5.1612 | \n", + "7.2882 | \n", + "13.9260 | \n", + "-9.1846 | \n", + "
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+ "\n",
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+ "\n",
+ " var_199 \n",
+ "count 10000.000000 \n",
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+ "[8 rows x 201 columns]"
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+ "execution_count": 9,
+ "metadata": {},
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+ "source": [
+ "santander_training.describe()"
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| std | \n", + "0.299213 | \n", + "3.041680 | \n", + "4.077271 | \n", + "2.638402 | \n", + "2.045605 | \n", + "1.624462 | \n", + "7.850295 | \n", + "0.870482 | \n", + "3.434960 | \n", + "3.314391 | \n", + "... | \n", + "4.585233 | \n", + "3.039712 | \n", + "1.476938 | \n", + "4.005774 | \n", + "3.171094 | \n", + "1.419062 | \n", + "5.430852 | \n", + "0.918565 | \n", + "3.008292 | \n", + "10.466326 | \n", + "
| min | \n", + "0.000000 | \n", + "1.335000 | \n", + "-13.422700 | \n", + "3.309000 | \n", + "1.154100 | \n", + "5.943000 | \n", + "-29.013300 | \n", + "2.673300 | \n", + "7.642200 | \n", + "-9.991100 | \n", + "... | \n", + "-11.593900 | \n", + "-1.215800 | \n", + "-3.515900 | \n", + "-9.442700 | \n", + "10.387000 | \n", + "-4.488300 | \n", + "-12.325500 | \n", + "6.256900 | \n", + "7.233700 | \n", + "-36.302500 | \n", + "
| 25% | \n", + "0.000000 | \n", + "8.452675 | \n", + "-4.779075 | \n", + "8.723700 | \n", + "5.253625 | \n", + "9.878450 | \n", + "-11.202600 | \n", + "4.766850 | \n", + "14.007650 | \n", + "-2.266200 | \n", + "... | \n", + "-0.106575 | \n", + "5.151700 | \n", + "0.919650 | \n", + "0.569625 | \n", + "15.615875 | \n", + "-1.201925 | \n", + "-1.978400 | \n", + "8.259475 | \n", + "13.855300 | \n", + "-11.460400 | \n", + "
| 50% | \n", + "0.000000 | \n", + "10.515750 | \n", + "-1.600700 | \n", + "10.543200 | \n", + "6.820850 | \n", + "11.108350 | \n", + "-4.852750 | \n", + "5.387700 | \n", + "16.582050 | \n", + "0.316250 | \n", + "... | \n", + "3.290200 | \n", + "7.356400 | \n", + "1.933300 | \n", + "3.390000 | \n", + "17.950000 | \n", + "-0.203500 | \n", + "2.449500 | \n", + "8.895450 | \n", + "15.915950 | \n", + "-3.000700 | \n", + "
| 75% | \n", + "0.000000 | \n", + "12.759675 | \n", + "1.360375 | \n", + "12.517650 | \n", + "8.323000 | \n", + "12.276350 | \n", + "1.004025 | \n", + "5.998100 | \n", + "19.190900 | \n", + "2.914325 | \n", + "... | \n", + "6.420875 | \n", + "9.532025 | \n", + "2.966875 | \n", + "6.176625 | \n", + "20.483700 | \n", + "0.784025 | \n", + "6.483725 | \n", + "9.597925 | \n", + "18.088900 | \n", + "4.654200 | \n", + "
| max | \n", + "1.000000 | \n", + "19.289300 | \n", + "8.584900 | \n", + "18.347700 | \n", + "12.977300 | \n", + "15.366400 | \n", + "17.251600 | \n", + "7.875500 | \n", + "26.965600 | \n", + "9.103800 | \n", + "... | \n", + "16.614400 | \n", + "16.398500 | \n", + "7.139000 | \n", + "16.782600 | \n", + "27.485800 | \n", + "3.555400 | \n", + "18.321500 | \n", + "11.843900 | \n", + "25.442200 | \n", + "25.831600 | \n", + "
8 rows × 201 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 10.678751 -1.637075 10.674745 6.784620 11.061973 \n",
+ "std 3.060380 4.089832 2.619009 2.066282 1.618390 \n",
+ "min 1.578200 -13.378500 2.516400 0.954400 6.012800 \n",
+ "25% 8.401725 -4.710650 8.705775 5.210275 9.858950 \n",
+ "50% 10.547400 -1.649700 10.541350 6.812450 11.080850 \n",
+ "75% 12.761825 1.303725 12.449400 8.362650 12.254600 \n",
+ "max 20.064900 9.023900 18.277200 12.837200 15.432900 \n",
+ "\n",
+ " var_5 var_6 var_7 var_8 var_9 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean -5.059958 5.417156 16.514916 0.299064 7.574839 \n",
+ "std 7.907097 0.869217 3.422408 3.350265 1.228507 \n",
+ "min -27.098300 2.314800 6.652900 -8.919400 4.313800 \n",
+ "25% -11.260725 4.774725 13.886150 -2.283400 6.637775 \n",
+ "50% -4.922950 5.377700 16.391400 0.357550 7.643850 \n",
+ "75% 1.017025 6.008600 19.096700 2.973550 8.597625 \n",
+ "max 15.999800 8.021300 27.856800 8.663600 11.003600 \n",
+ "\n",
+ " ... var_190 var_191 var_192 var_193 \\\n",
+ "count ... 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean ... 3.207419 7.476404 1.960699 3.324941 \n",
+ "std ... 4.572294 3.032355 1.480675 4.000938 \n",
+ "min ... -12.900200 -1.295600 -3.340900 -10.697200 \n",
+ "25% ... -0.104850 5.169025 0.941925 0.528975 \n",
+ "50% ... 3.134700 7.394300 1.936700 3.482300 \n",
+ "75% ... 6.385200 9.521550 2.990200 6.172125 \n",
+ "max ... 17.094600 16.355900 7.213700 16.241500 \n",
+ "\n",
+ " var_194 var_195 var_196 var_197 var_198 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 18.027858 -0.124677 2.375043 8.911049 15.877401 \n",
+ "std 3.162509 1.427808 5.453573 0.918185 3.019350 \n",
+ "min 9.472600 -4.516700 -13.251900 6.218200 7.328700 \n",
+ "25% 15.661700 -1.153850 -1.858725 8.262350 13.813875 \n",
+ "50% 18.022800 -0.169750 2.522900 8.888700 15.966600 \n",
+ "75% 20.452025 0.845475 6.631450 9.579525 18.083525 \n",
+ "max 26.962100 4.255700 15.680400 11.559100 24.845300 \n",
+ "\n",
+ " var_199 \n",
+ "count 10000.000000 \n",
+ "mean -3.243134 \n",
+ "std 10.442345 \n",
+ "min -39.457800 \n",
+ "25% -11.066725 \n",
+ "50% -2.733050 \n",
+ "75% 4.983725 \n",
+ "max 24.027500 \n",
+ "\n",
+ "[8 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_test.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observations\n",
+ "From the above statistical overview, we can say that:| \n", + " | var_0 | \n", + "var_1 | \n", + "var_2 | \n", + "var_3 | \n", + "var_4 | \n", + "var_5 | \n", + "var_6 | \n", + "var_7 | \n", + "var_8 | \n", + "var_9 | \n", + "... | \n", + "var_190 | \n", + "var_191 | \n", + "var_192 | \n", + "var_193 | \n", + "var_194 | \n", + "var_195 | \n", + "var_196 | \n", + "var_197 | \n", + "var_198 | \n", + "var_199 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "... | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "
| mean | \n", + "10.678751 | \n", + "-1.637075 | \n", + "10.674745 | \n", + "6.784620 | \n", + "11.061973 | \n", + "-5.059958 | \n", + "5.417156 | \n", + "16.514916 | \n", + "0.299064 | \n", + "7.574839 | \n", + "... | \n", + "3.207419 | \n", + "7.476404 | \n", + "1.960699 | \n", + "3.324941 | \n", + "18.027858 | \n", + "-0.124677 | \n", + "2.375043 | \n", + "8.911049 | \n", + "15.877401 | \n", + "-3.243134 | \n", + "
| std | \n", + "3.060380 | \n", + "4.089832 | \n", + "2.619009 | \n", + "2.066282 | \n", + "1.618390 | \n", + "7.907097 | \n", + "0.869217 | \n", + "3.422408 | \n", + "3.350265 | \n", + "1.228507 | \n", + "... | \n", + "4.572294 | \n", + "3.032355 | \n", + "1.480675 | \n", + "4.000938 | \n", + "3.162509 | \n", + "1.427808 | \n", + "5.453573 | \n", + "0.918185 | \n", + "3.019350 | \n", + "10.442345 | \n", + "
| min | \n", + "1.578200 | \n", + "-13.378500 | \n", + "2.516400 | \n", + "0.954400 | \n", + "6.012800 | \n", + "-27.098300 | \n", + "2.314800 | \n", + "6.652900 | \n", + "-8.919400 | \n", + "4.313800 | \n", + "... | \n", + "-12.900200 | \n", + "-1.295600 | \n", + "-3.340900 | \n", + "-10.697200 | \n", + "9.472600 | \n", + "-4.516700 | \n", + "-13.251900 | \n", + "6.218200 | \n", + "7.328700 | \n", + "-39.457800 | \n", + "
| 25% | \n", + "8.401725 | \n", + "-4.710650 | \n", + "8.705775 | \n", + "5.210275 | \n", + "9.858950 | \n", + "-11.260725 | \n", + "4.774725 | \n", + "13.886150 | \n", + "-2.283400 | \n", + "6.637775 | \n", + "... | \n", + "-0.104850 | \n", + "5.169025 | \n", + "0.941925 | \n", + "0.528975 | \n", + "15.661700 | \n", + "-1.153850 | \n", + "-1.858725 | \n", + "8.262350 | \n", + "13.813875 | \n", + "-11.066725 | \n", + "
| 50% | \n", + "10.547400 | \n", + "-1.649700 | \n", + "10.541350 | \n", + "6.812450 | \n", + "11.080850 | \n", + "-4.922950 | \n", + "5.377700 | \n", + "16.391400 | \n", + "0.357550 | \n", + "7.643850 | \n", + "... | \n", + "3.134700 | \n", + "7.394300 | \n", + "1.936700 | \n", + "3.482300 | \n", + "18.022800 | \n", + "-0.169750 | \n", + "2.522900 | \n", + "8.888700 | \n", + "15.966600 | \n", + "-2.733050 | \n", + "
| 75% | \n", + "12.761825 | \n", + "1.303725 | \n", + "12.449400 | \n", + "8.362650 | \n", + "12.254600 | \n", + "1.017025 | \n", + "6.008600 | \n", + "19.096700 | \n", + "2.973550 | \n", + "8.597625 | \n", + "... | \n", + "6.385200 | \n", + "9.521550 | \n", + "2.990200 | \n", + "6.172125 | \n", + "20.452025 | \n", + "0.845475 | \n", + "6.631450 | \n", + "9.579525 | \n", + "18.083525 | \n", + "4.983725 | \n", + "
| max | \n", + "20.064900 | \n", + "9.023900 | \n", + "18.277200 | \n", + "12.837200 | \n", + "15.432900 | \n", + "15.999800 | \n", + "8.021300 | \n", + "27.856800 | \n", + "8.663600 | \n", + "11.003600 | \n", + "... | \n", + "17.094600 | \n", + "16.355900 | \n", + "7.213700 | \n", + "16.241500 | \n", + "26.962100 | \n", + "4.255700 | \n", + "15.680400 | \n", + "11.559100 | \n", + "24.845300 | \n", + "24.027500 | \n", + "
8 rows × 200 columns
\n", + "\n", + " 1. There's no much difference between label means for the training and test sets, same case for standard deviation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3. Visual EDA\n", + "\n", + "### Distribution of the target(whether a customer made a transaction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "# Extract the target column\n",
+ "target = santander_training.target\n",
+ "\n",
+ "# Plot a countplot\n",
+ "sns.countplot(data=santander_training, x=target)\n",
+ "\n",
+ "# Display the count on each respective bar\n",
+ "# for i, v in enumerate(target.values):\n",
+ "# plt.text(i, 2000, v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"How many customers did/didn't make a transaction?\", fontsize=20)\n",
+ "plt.xlabel(\"Made Transaction?\", fontsize=19)\n",
+ "plt.ylabel(\"Total Count\", fontsize=19)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observation\n",
+ "From the above plot, we see that:\n",
+ "1. The number of customers who didn't make a transaction are much more than those who did. This will help us in evaluating the accuracy of our predictions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Correlation\n",
+ "Here we look at the measure of relation between each variable and the other"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "corr = santander_training.corr()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "sns.heatmap(corr, annot=True, vmax=1, square=True, cmap='cubehelix')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 7. Data Preprocessing\n",
+ "\n",
+ "Here we extract the features and the target class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make a copy of the datasets\n",
+ "training = santander_training.copy()\n",
+ "test = santander_test.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "\u001b[1;32mc:\\users\\kasre\\pycharmprojects\\python4ds_cohort-1\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2658\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2659\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2660\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 1"
+ ]
+ }
+ ],
+ "source": [
+ "prediction_df[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/Titanic Data Exploration-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Titanic Data Exploration-checkpoint.ipynb
new file mode 100644
index 0000000..e0abbc1
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/Titanic Data Exploration-checkpoint.ipynb
@@ -0,0 +1,92 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
\n", + "# Introduction\n", + "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly\n", + "import seaborn as sns\n", + "import warnings\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defaults" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings.filterwarnings(\"ignore\")\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = '../data/titanic/input'\n", + "titanic_train = pd.r" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb new file mode 100644 index 0000000..e1815dd --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %maptplotlib magic command to enable interactive plots in notebooks\n", + "# inline attr to enable static pplots within the notebook\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# set default plot style\n", + "plt.style.use('seaborn-whitegrid')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### all plots start by creating a figure and an axes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " target var_0 var_1 var_2 var_3 var_4 var_5 var_6 var_7 \\\n",
+ "0 0 8.9255 -6.7863 11.9081 5.0930 11.4607 -9.2834 5.1187 18.6266 \n",
+ "1 0 11.5006 -4.1473 13.8588 5.3890 12.3622 7.0433 5.6208 16.5338 \n",
+ "2 0 8.6093 -2.7457 12.0805 7.8928 10.5825 -9.0837 6.9427 14.6155 \n",
+ "3 0 11.0604 -2.1518 8.9522 7.1957 12.5846 -1.8361 5.8428 14.9250 \n",
+ "4 0 9.8369 -1.4834 12.8746 6.6375 12.2772 2.4486 5.9405 19.2514 \n",
+ "\n",
+ " var_8 ... var_190 var_191 var_192 var_193 var_194 var_195 var_196 \\\n",
+ "0 -4.9200 ... 4.4354 3.9642 3.1364 1.6910 18.5227 -2.3978 7.8784 \n",
+ "1 3.1468 ... 7.6421 7.7214 2.5837 10.9516 15.4305 2.0339 8.1267 \n",
+ "2 -4.9193 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 -6.5213 \n",
+ "3 -5.8609 ... 4.4666 4.7433 0.7178 1.4214 23.0347 -1.2706 -2.9275 \n",
+ "4 6.2654 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 3.9267 \n",
+ "\n",
+ " var_197 var_198 var_199 \n",
+ "0 8.5635 12.7803 -1.0914 \n",
+ "1 8.7889 18.3560 1.9518 \n",
+ "2 8.2675 14.7222 0.3965 \n",
+ "3 10.2922 17.9697 -8.9996 \n",
+ "4 9.5031 17.9974 -8.8104 \n",
+ "\n",
+ "[5 rows x 201 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Drop unwanted columns in training set\n",
+ "training_set = training.drop(columns=['ID_code'], axis=1)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 3 | \n", + "0 | \n", + "11.0604 | \n", + "-2.1518 | \n", + "8.9522 | \n", + "7.1957 | \n", + "12.5846 | \n", + "-1.8361 | \n", + "5.8428 | \n", + "14.9250 | \n", + "-5.8609 | \n", + "... | \n", + "4.4666 | \n", + "4.7433 | \n", + "0.7178 | \n", + "1.4214 | \n", + "23.0347 | \n", + "-1.2706 | \n", + "-2.9275 | \n", + "10.2922 | \n", + "17.9697 | \n", + "-8.9996 | \n", + "
| 4 | \n", + "0 | \n", + "9.8369 | \n", + "-1.4834 | \n", + "12.8746 | \n", + "6.6375 | \n", + "12.2772 | \n", + "2.4486 | \n", + "5.9405 | \n", + "19.2514 | \n", + "6.2654 | \n", + "... | \n", + "-1.4905 | \n", + "9.5214 | \n", + "-0.1508 | \n", + "9.1942 | \n", + "13.2876 | \n", + "-1.5121 | \n", + "3.9267 | \n", + "9.5031 | \n", + "17.9974 | \n", + "-8.8104 | \n", + "
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+ " \n",
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+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 var_5 var_6 var_7 \\\n",
+ "0 11.0656 7.7798 12.9536 9.4292 11.4327 -2.3805 5.8493 18.2675 \n",
+ "1 8.5304 1.2543 11.3047 5.1858 9.1974 -4.0117 6.0196 18.6316 \n",
+ "2 5.4827 -10.3581 10.1407 7.0479 10.2628 9.8052 4.8950 20.2537 \n",
+ "3 8.5374 -1.3222 12.0220 6.5749 8.8458 3.1744 4.9397 20.5660 \n",
+ "4 11.7058 -0.1327 14.1295 7.7506 9.1035 -8.5848 6.8595 10.6048 \n",
+ "\n",
+ " var_8 var_9 ... var_190 var_191 var_192 var_193 var_194 var_195 \\\n",
+ "0 2.1337 8.8100 ... -2.1556 11.8495 -1.4300 2.4508 13.7112 2.4669 \n",
+ "1 -4.4131 5.9739 ... 10.6165 8.8349 0.9403 10.1282 15.5765 0.4773 \n",
+ "2 1.5233 8.3442 ... -0.7484 10.9935 1.9803 2.1800 12.9813 2.1281 \n",
+ "3 3.3755 7.4578 ... 9.5702 9.0766 1.6580 3.5813 15.1874 3.1656 \n",
+ "4 2.9890 7.1437 ... 4.2259 9.1723 1.2835 3.3778 19.5542 -0.2860 \n",
+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
+ "0 4.3654 10.7200 15.4722 -8.7197 \n",
+ "1 -1.4852 9.8714 19.1293 -20.9760 \n",
+ "2 -7.1086 7.0618 19.8956 -23.1794 \n",
+ "3 3.9567 9.2295 13.0168 -4.2108 \n",
+ "4 -5.1612 7.2882 13.9260 -9.1846 \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Drop unwanted columns in test set\n",
+ "test_set = test.drop(columns=['ID_code'], axis=1)\n",
+ "test_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 3 | \n", + "8.5374 | \n", + "-1.3222 | \n", + "12.0220 | \n", + "6.5749 | \n", + "8.8458 | \n", + "3.1744 | \n", + "4.9397 | \n", + "20.5660 | \n", + "3.3755 | \n", + "7.4578 | \n", + "... | \n", + "9.5702 | \n", + "9.0766 | \n", + "1.6580 | \n", + "3.5813 | \n", + "15.1874 | \n", + "3.1656 | \n", + "3.9567 | \n", + "9.2295 | \n", + "13.0168 | \n", + "-4.2108 | \n", + "
| 4 | \n", + "11.7058 | \n", + "-0.1327 | \n", + "14.1295 | \n", + "7.7506 | \n", + "9.1035 | \n", + "-8.5848 | \n", + "6.8595 | \n", + "10.6048 | \n", + "2.9890 | \n", + "7.1437 | \n", + "... | \n", + "4.2259 | \n", + "9.1723 | \n", + "1.2835 | \n", + "3.3778 | \n", + "19.5542 | \n", + "-0.2860 | \n", + "-5.1612 | \n", + "7.2882 | \n", + "13.9260 | \n", + "-9.1846 | \n", + "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 var_5 var_6 var_7 var_8 \\\n",
+ "0 8.9255 -6.7863 11.9081 5.0930 11.4607 -9.2834 5.1187 18.6266 -4.9200 \n",
+ "1 11.5006 -4.1473 13.8588 5.3890 12.3622 7.0433 5.6208 16.5338 3.1468 \n",
+ "2 8.6093 -2.7457 12.0805 7.8928 10.5825 -9.0837 6.9427 14.6155 -4.9193 \n",
+ "3 11.0604 -2.1518 8.9522 7.1957 12.5846 -1.8361 5.8428 14.9250 -5.8609 \n",
+ "4 9.8369 -1.4834 12.8746 6.6375 12.2772 2.4486 5.9405 19.2514 6.2654 \n",
+ "\n",
+ " var_9 ... var_190 var_191 var_192 var_193 var_194 var_195 var_196 \\\n",
+ "0 5.7470 ... 4.4354 3.9642 3.1364 1.6910 18.5227 -2.3978 7.8784 \n",
+ "1 8.0851 ... 7.6421 7.7214 2.5837 10.9516 15.4305 2.0339 8.1267 \n",
+ "2 5.9525 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 -6.5213 \n",
+ "3 8.2450 ... 4.4666 4.7433 0.7178 1.4214 23.0347 -1.2706 -2.9275 \n",
+ "4 7.6784 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 3.9267 \n",
+ "\n",
+ " var_197 var_198 var_199 \n",
+ "0 8.5635 12.7803 -1.0914 \n",
+ "1 8.7889 18.3560 1.9518 \n",
+ "2 8.2675 14.7222 0.3965 \n",
+ "3 10.2922 17.9697 -8.9996 \n",
+ "4 9.5031 17.9974 -8.8104 \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract the target class and drop it. Extract x.\n",
+ "y = training_set['target'].values\n",
+ "training_set.drop(columns=['target'], axis=1, inplace=True)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = training_set.values\n",
+ "# print(X)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 8. Feature Engineering\n",
+ "\n",
+ "Feature engineering is important for better model performance.\n",
+ "Looking at our data, the features are not uniform scalewise. This calls for feature scaling to make sure they are uniform\n",
+ "\n",
+ "## 8.1. Scaling the Data\n",
+ "\n",
+ "We'll use scikit learn's StandardScaler class.\n",
+ "Before scaling, we need to split the training set into Folds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Instantiate the scaler\n",
+ "scaler = StandardScaler()\n",
+ "\n",
+ "# Transform training data\n",
+ "X = scaler.fit_transform(X)\n",
+ "\n",
+ "# Transform test_set\n",
+ "X_test = test_set.values\n",
+ "X_test = scaler.transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 9. Model Selection & Training\n",
+ "\n",
+ "Here we'll train several models with the training data and choose the best performing model.\n",
+ "\n",
+ "## 9.1. Choosing a performace metric\n",
+ "\n",
+ "For choosing the best model, we'll use K-fold cross validation of 10 folds and compute the mean score of each fold.\n",
+ "\n",
+ "## 9.2. Model Evaluation\n",
+ "\n",
+ "We'll compare the score of 4 models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a score collection for comparison\n",
+ "scores = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Gradient Boosting Classifier\n",
+ "We implement a Gradient Boosting classifier with a max_depth of 1 and 0 random state"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9008002205002205\n"
+ ]
+ }
+ ],
+ "source": [
+ "GBC = GradientBoostingClassifier(max_depth=1, random_state=0)\n",
+ "\n",
+ "GBC_score = cross_val_score(GBC, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(GBC_score)\n",
+ "\n",
+ "scores['GBC'] = GBC_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Random Forest Classifier\n",
+ "We'll implement another ensemble, Random Forest Classifier, with default parameters(10 estimators)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9007002207002207\n"
+ ]
+ }
+ ],
+ "source": [
+ "RFC = RandomForestClassifier(n_estimators=10)\n",
+ "\n",
+ "RFC_score = cross_val_score(RFC, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(RFC_score)\n",
+ "\n",
+ "scores['RFC'] = RFC_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### MLP Classifier\n",
+ "Here we'll implement a neural network, MLP Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.8533966827966829\n"
+ ]
+ }
+ ],
+ "source": [
+ "MLP = MLPClassifier(hidden_layer_sizes=(10, 10), random_state=0, max_iter=1000)\n",
+ "\n",
+ "MLP_score = cross_val_score(MLP, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(MLP_score)\n",
+ "\n",
+ "scores['MLP'] = MLP_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Kernelized SVM\n",
+ "Here we'll implement a kernelized Support vector machine with a Gaussian model kernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9040005228005228\n"
+ ]
+ }
+ ],
+ "source": [
+ "svc = SVC(kernel='rbf', random_state=0, gamma='auto')\n",
+ "\n",
+ "svc_score = cross_val_score(svc, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(svc_score)\n",
+ "\n",
+ "scores['svc'] = svc_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Lets look at the score for each model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'GBC': 0.9008002205002205,\n",
+ " 'RFC': 0.9007002207002207,\n",
+ " 'MLP': 0.8533966827966829,\n",
+ " 'svc': 0.9040005228005228}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scores"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Its evident that support vector classifier performed well. We'll fine tune its parameters for optimization\n",
+ "\n",
+ "# 10. Model Evaluation and Optimization\n",
+ "\n",
+ "We'll use GridSearchCV to fine tune the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize the tuning params\n",
+ "Cs = [0.001, 0.01, 0.1, 1, 10]\n",
+ "gammas = [0.001, 0.01, 0.1, 1, 10]\n",
+ "\n",
+ "param_grid = {'C': Cs, 'gamma': gammas}\n",
+ "\n",
+ "gridscv = GridSearchCV(\n",
+ " estimator=SVC(kernel='rbf', random_state=0),\n",
+ " param_grid=param_grid,\n",
+ " scoring='accuracy',\n",
+ " n_jobs=1,\n",
+ " cv=10\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# gridscv.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# best_params = gridscv.best_params_\n",
+ "# best_score = gridscv.best_score_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 11. Prediction\n",
+ "\n",
+ "Here we fit the model with the fine tuned params and make predictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+ " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
+ " max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
+ " tol=0.001, verbose=False)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = SVC(kernel='rbf', random_state=0, gamma='auto')\n",
+ "\n",
+ "model.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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| 1 | \n", + "11.5006 | \n", + "-4.1473 | \n", + "13.8588 | \n", + "5.3890 | \n", + "12.3622 | \n", + "7.0433 | \n", + "5.6208 | \n", + "16.5338 | \n", + "3.1468 | \n", + "8.0851 | \n", + "... | \n", + "7.6421 | \n", + "7.7214 | \n", + "2.5837 | \n", + "10.9516 | \n", + "15.4305 | \n", + "2.0339 | \n", + "8.1267 | \n", + "8.7889 | \n", + "18.3560 | \n", + "1.9518 | \n", + "
| 2 | \n", + "8.6093 | \n", + "-2.7457 | \n", + "12.0805 | \n", + "7.8928 | \n", + "10.5825 | \n", + "-9.0837 | \n", + "6.9427 | \n", + "14.6155 | \n", + "-4.9193 | \n", + "5.9525 | \n", + "... | \n", + "2.9057 | \n", + "9.7905 | \n", + "1.6704 | \n", + "1.6858 | \n", + "21.6042 | \n", + "3.1417 | \n", + "-6.5213 | \n", + "8.2675 | \n", + "14.7222 | \n", + "0.3965 | \n", + "
| 3 | \n", + "11.0604 | \n", + "-2.1518 | \n", + "8.9522 | \n", + "7.1957 | \n", + "12.5846 | \n", + "-1.8361 | \n", + "5.8428 | \n", + "14.9250 | \n", + "-5.8609 | \n", + "8.2450 | \n", + "... | \n", + "4.4666 | \n", + "4.7433 | \n", + "0.7178 | \n", + "1.4214 | \n", + "23.0347 | \n", + "-1.2706 | \n", + "-2.9275 | \n", + "10.2922 | \n", + "17.9697 | \n", + "-8.9996 | \n", + "
| 4 | \n", + "9.8369 | \n", + "-1.4834 | \n", + "12.8746 | \n", + "6.6375 | \n", + "12.2772 | \n", + "2.4486 | \n", + "5.9405 | \n", + "19.2514 | \n", + "6.2654 | \n", + "7.6784 | \n", + "... | \n", + "-1.4905 | \n", + "9.5214 | \n", + "-0.1508 | \n", + "9.1942 | \n", + "13.2876 | \n", + "-1.5121 | \n", + "3.9267 | \n", + "9.5031 | \n", + "17.9974 | \n", + "-8.8104 | \n", + "
5 rows × 200 columns
\n", + "\n",
+ "\n",
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+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predictions = model.predict(X_test)\n",
+ "\n",
+ "prediction_df = pd.DataFrame(predictions)\n",
+ "\n",
+ "prediction_df.head(50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "1",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\users\\kasre\\pycharmprojects\\python4ds_cohort-1\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2655\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2656\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2657\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 1",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | 0 | \n", + "
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\n", + "# Introduction\n", + "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly\n", + "import seaborn as sns\n", + "import warnings\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defaults" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings.filterwarnings(\"ignore\")\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = '../data/titanic/input'\n", + "titanic_train = pd.r" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb new file mode 100644 index 0000000..e1815dd --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Visualisations-checkpoint.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %maptplotlib magic command to enable interactive plots in notebooks\n", + "# inline attr to enable static pplots within the notebook\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# set default plot style\n", + "plt.style.use('seaborn-whitegrid')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### all plots start by creating a figure and an axes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Figure\n",
+ "fig = plt.figure()\n",
+ "\n",
+ "# axes\n",
+ "ax = plt.axes()\n",
+ "\n",
+ "# this creates a figure in its simplest for"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A simple sinusoid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# The data\n",
+ "x = np.linspace(0, 10, 1000)\n",
+ "plt.plot(x, np.sin(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Multiple lines\n",
+ "plt.plot(x, np.sin(x))\n",
+ "plt.plot(x, np.cos(x))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Labelled axes with custom colors and legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot fn with x data, ydata, line color(and style) and label\n",
+ "# Sinusoid\n",
+ "plt.plot(x, np.sin(x), color='green', label='sin x', linestyle='solid')\n",
+ "\n",
+ "# Cosine\n",
+ "plt.plot(x, np.cos(x), color='blue', linestyle='dotted', label='cos x')\n",
+ "\n",
+ "# Set title, x-axis label and y-axis label\n",
+ "plt.title('Sine and cosine waves')\n",
+ "plt.xlabel('x')\n",
+ "# plt.axis('equal')\n",
+ "\n",
+ "# Set y-limits\n",
+ "plt.ylim(-2, 2)\n",
+ "\n",
+ "# Include a legend at the lower-center\n",
+ "plt.legend(loc='lower center', ncol=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Histogram"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 10., 28., 83., 179., 227., 219., 154., 70., 22., 8.]),\n",
+ " array([-2.9255609 , -2.33289716, -1.74023341, -1.14756966, -0.55490591,\n",
+ " 0.03775784, 0.63042159, 1.22308534, 1.81574908, 2.40841283,\n",
+ " 3.00107658]),\n",
+ " )"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Data\n",
+ "data = np.random.randn(1000)\n",
+ "\n",
+ "plt.hist(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/full_kiva_loans_analysis-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/full_kiva_loans_analysis-checkpoint.ipynb
new file mode 100644
index 0000000..305f73a
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/full_kiva_loans_analysis-checkpoint.ipynb
@@ -0,0 +1,2372 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)\n",
+ "2. [Retreiving the Data](#retreiving-data)
\n", + " 2.1. [Loading the Libraries](#load-libraries)
\n", + " 2.2. [Loading the datasets](#load-datasets)\n", + "3. [Taking a glimpse of the data](#data-glimpse)
\n", + " 3.1. [General overview of tables](#gen-overview)
\n", + " 3.2. [Statistical overview of the data](#stat-overview)\n", + "4. [Data cleaning and preparation](#data-prep)
\n", + " 4.1. [Handling Missing Data](#missing-data)\n", + "5. [Data Exploration and Visualization](#data-exploration)
\n", + " 5.1. [Loan counts per sector](#per-sector)
\n", + " 5.2. [Loan counts per world region](#per-region)
\n", + " 5.3. [World Region MPIs](#region-mpis)
\n", + " 5.4. [Repayment Intervals](#repayment)
\n", + " 5.5. [Gender Comparisons](#genders)
\n", + " 5.6. [Loan amounts per gender](#loans-per-gender)
\n", + " 5.7. [Total loan amounts per country](#loans-per-country)
\n", + " 5.8. [Total Loans distribution across the world](#loan-dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world.The kiva datasets contain data on money funded to borrowers from different countries.
\n", + "My main objective is to pair the Kiva's datasets and do some analysis to help them understand their borrowers and improve their efficiency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Retreiving the Data\n", + "\n", + "\n", + "### 2.1. Loading the Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import the desired libraries\n", + "\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import os\n", + "\n", + "# Set default seaborn settings\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Loading in the datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading the main dataset\n", + "kiva_loans = pd.read_csv('../data/kiva/kiva_loans.csv')\n", + "\n", + "# Kiva mpi region locations\n", + "mpi_region_locations = pd.read_csv('../data/kiva/kiva_mpi_region_locations.csv')\n", + "\n", + "# Loan theme ids\n", + "loan_theme_ids = pd.read_csv('../data/kiva/loan_theme_ids.csv')\n", + "\n", + "# Loan themes by region\n", + "loan_themes_region = pd.read_csv('../data/kiva/loan_themes_by_region.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Taking a glimpse of the data\n", + "\n", + "\n", + "### 3.1. General overview of tables\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "We'll therefore deal with only the available data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Data Exploration and Visualization\n", + "\n", + "\n", + "### 5.1. Loan counts per sector\n", + "\n", + "\n", + "#### Top sectors in which more loans were given" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + " 2.1. [Loading the Libraries](#load-libraries)
\n", + " 2.2. [Loading the datasets](#load-datasets)\n", + "3. [Taking a glimpse of the data](#data-glimpse)
\n", + " 3.1. [General overview of tables](#gen-overview)
\n", + " 3.2. [Statistical overview of the data](#stat-overview)\n", + "4. [Data cleaning and preparation](#data-prep)
\n", + " 4.1. [Handling Missing Data](#missing-data)\n", + "5. [Data Exploration and Visualization](#data-exploration)
\n", + " 5.1. [Loan counts per sector](#per-sector)
\n", + " 5.2. [Loan counts per world region](#per-region)
\n", + " 5.3. [World Region MPIs](#region-mpis)
\n", + " 5.4. [Repayment Intervals](#repayment)
\n", + " 5.5. [Gender Comparisons](#genders)
\n", + " 5.6. [Loan amounts per gender](#loans-per-gender)
\n", + " 5.7. [Total loan amounts per country](#loans-per-country)
\n", + " 5.8. [Total Loans distribution across the world](#loan-dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world.The kiva datasets contain data on money funded to borrowers from different countries.
\n", + "My main objective is to pair the Kiva's datasets and do some analysis to help them understand their borrowers and improve their efficiency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Retreiving the Data\n", + "\n", + "\n", + "### 2.1. Loading the Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import the desired libraries\n", + "\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import os\n", + "\n", + "# Set default seaborn settings\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Loading in the datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading the main dataset\n", + "kiva_loans = pd.read_csv('../data/kiva/kiva_loans.csv')\n", + "\n", + "# Kiva mpi region locations\n", + "mpi_region_locations = pd.read_csv('../data/kiva/kiva_mpi_region_locations.csv')\n", + "\n", + "# Loan theme ids\n", + "loan_theme_ids = pd.read_csv('../data/kiva/loan_theme_ids.csv')\n", + "\n", + "# Loan themes by region\n", + "loan_themes_region = pd.read_csv('../data/kiva/loan_themes_by_region.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Taking a glimpse of the data\n", + "\n", + "\n", + "### 3.1. General overview of tables\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# kiva loans overview\n",
+ "kiva_loans.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " LocationName ISO country region world_region MPI \\\n",
+ "0 Badakhshan, Afghanistan AFG Afghanistan Badakhshan South Asia 0.387 \n",
+ "1 Badghis, Afghanistan AFG Afghanistan Badghis South Asia 0.466 \n",
+ "2 Baghlan, Afghanistan AFG Afghanistan Baghlan South Asia 0.300 \n",
+ "3 Balkh, Afghanistan AFG Afghanistan Balkh South Asia 0.301 \n",
+ "4 Bamyan, Afghanistan AFG Afghanistan Bamyan South Asia 0.325 \n",
+ "\n",
+ " geo lat lon \n",
+ "0 (36.7347725, 70.81199529999999) 36.734772 70.811995 \n",
+ "1 (35.1671339, 63.7695384) 35.167134 63.769538 \n",
+ "2 (35.8042947, 69.2877535) 35.804295 69.287754 \n",
+ "3 (36.7550603, 66.8975372) 36.755060 66.897537 \n",
+ "4 (34.8100067, 67.8212104) 34.810007 67.821210 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# mpi regions overview\n",
+ "mpi_region_locations.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | LocationName | \n", + "ISO | \n", + "country | \n", + "region | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Badakhshan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badakhshan | \n", + "South Asia | \n", + "0.387 | \n", + "(36.7347725, 70.81199529999999) | \n", + "36.734772 | \n", + "70.811995 | \n", + "
| 1 | \n", + "Badghis, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badghis | \n", + "South Asia | \n", + "0.466 | \n", + "(35.1671339, 63.7695384) | \n", + "35.167134 | \n", + "63.769538 | \n", + "
| 2 | \n", + "Baghlan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Baghlan | \n", + "South Asia | \n", + "0.300 | \n", + "(35.8042947, 69.2877535) | \n", + "35.804295 | \n", + "69.287754 | \n", + "
| 3 | \n", + "Balkh, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Balkh | \n", + "South Asia | \n", + "0.301 | \n", + "(36.7550603, 66.8975372) | \n", + "36.755060 | \n", + "66.897537 | \n", + "
| 4 | \n", + "Bamyan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Bamyan | \n", + "South Asia | \n", + "0.325 | \n", + "(34.8100067, 67.8212104) | \n", + "34.810007 | \n", + "67.821210 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id Loan Theme ID Loan Theme Type Partner ID\n",
+ "0 638631 a1050000000skGl General 151.0\n",
+ "1 640322 a1050000000skGl General 151.0\n",
+ "2 641006 a1050000002X1ij Higher Education 160.0\n",
+ "3 641019 a1050000002X1ij Higher Education 160.0\n",
+ "4 641594 a1050000002VbsW Subsistence Agriculture 336.0"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Loan theme by ids overview\n",
+ "loan_theme_ids.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "Loan Theme ID | \n", + "Loan Theme Type | \n", + "Partner ID | \n", + "
|---|---|---|---|---|
| 0 | \n", + "638631 | \n", + "a1050000000skGl | \n", + "General | \n", + "151.0 | \n", + "
| 1 | \n", + "640322 | \n", + "a1050000000skGl | \n", + "General | \n", + "151.0 | \n", + "
| 2 | \n", + "641006 | \n", + "a1050000002X1ij | \n", + "Higher Education | \n", + "160.0 | \n", + "
| 3 | \n", + "641019 | \n", + "a1050000002X1ij | \n", + "Higher Education | \n", + "160.0 | \n", + "
| 4 | \n", + "641594 | \n", + "a1050000002VbsW | \n", + "Subsistence Agriculture | \n", + "336.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Partner ID Field Partner Name sector \\\n",
+ "0 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "1 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "2 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "3 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "4 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "\n",
+ " Loan Theme ID Loan Theme Type country forkiva \\\n",
+ "0 a1050000000slfi Higher Education Cambodia No \n",
+ "1 a10500000068jPe Vulnerable Populations Cambodia No \n",
+ "2 a1050000000slfi Higher Education Cambodia No \n",
+ "3 a10500000068jPe Vulnerable Populations Cambodia No \n",
+ "4 a1050000002X1Uu Sanitation Cambodia No \n",
+ "\n",
+ " region geocode_old ISO ... amount \\\n",
+ "0 Banteay Meanchey (13.75, 103.0) KHM ... 450 \n",
+ "1 Battambang Province NaN KHM ... 20275 \n",
+ "2 Battambang Province NaN KHM ... 9150 \n",
+ "3 Kampong Cham Province (12.0, 105.5) KHM ... 604950 \n",
+ "4 Kampong Cham Province (12.0, 105.5) KHM ... 275 \n",
+ "\n",
+ " LocationName geocode \\\n",
+ "0 Banteay Meanchey, Cambodia [(13.6672596, 102.8975098)] \n",
+ "1 Battambang Province, Cambodia [(13.0286971, 102.989615)] \n",
+ "2 Battambang Province, Cambodia [(13.0286971, 102.989615)] \n",
+ "3 Kampong Cham Province, Cambodia [(12.0982918, 105.3131185)] \n",
+ "4 Kampong Cham Province, Cambodia [(12.0982918, 105.3131185)] \n",
+ "\n",
+ " names geo lat \\\n",
+ "0 Banteay Meanchey Province; Cambodia (13.6672596, 102.8975098) 13.667260 \n",
+ "1 Battambang Province; Cambodia (13.0286971, 102.989615) 13.028697 \n",
+ "2 Battambang Province; Cambodia (13.0286971, 102.989615) 13.028697 \n",
+ "3 Kampong Cham Province; Cambodia (12.0982918, 105.3131185) 12.098292 \n",
+ "4 Kampong Cham Province; Cambodia (12.0982918, 105.3131185) 12.098292 \n",
+ "\n",
+ " lon mpi_region mpi_geo \\\n",
+ "0 102.897510 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "1 102.989615 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "2 102.989615 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "3 105.313119 Kampong Cham, Cambodia (11.9924294, 105.4645408) \n",
+ "4 105.313119 Kampong Cham, Cambodia (11.9924294, 105.4645408) \n",
+ "\n",
+ " rural_pct \n",
+ "0 90.0 \n",
+ "1 90.0 \n",
+ "2 90.0 \n",
+ "3 90.0 \n",
+ "4 90.0 \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Loan themes by region\n",
+ "loan_themes_region.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2. Statistical overview\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Simple overview of all numerical values for the main dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Partner ID | \n", + "Field Partner Name | \n", + "sector | \n", + "Loan Theme ID | \n", + "Loan Theme Type | \n", + "country | \n", + "forkiva | \n", + "region | \n", + "geocode_old | \n", + "ISO | \n", + "... | \n", + "amount | \n", + "LocationName | \n", + "geocode | \n", + "names | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "mpi_region | \n", + "mpi_geo | \n", + "rural_pct | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000000slfi | \n", + "Higher Education | \n", + "Cambodia | \n", + "No | \n", + "Banteay Meanchey | \n", + "(13.75, 103.0) | \n", + "KHM | \n", + "... | \n", + "450 | \n", + "Banteay Meanchey, Cambodia | \n", + "[(13.6672596, 102.8975098)] | \n", + "Banteay Meanchey Province; Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "13.667260 | \n", + "102.897510 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 1 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a10500000068jPe | \n", + "Vulnerable Populations | \n", + "Cambodia | \n", + "No | \n", + "Battambang Province | \n", + "NaN | \n", + "KHM | \n", + "... | \n", + "20275 | \n", + "Battambang Province, Cambodia | \n", + "[(13.0286971, 102.989615)] | \n", + "Battambang Province; Cambodia | \n", + "(13.0286971, 102.989615) | \n", + "13.028697 | \n", + "102.989615 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 2 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000000slfi | \n", + "Higher Education | \n", + "Cambodia | \n", + "No | \n", + "Battambang Province | \n", + "NaN | \n", + "KHM | \n", + "... | \n", + "9150 | \n", + "Battambang Province, Cambodia | \n", + "[(13.0286971, 102.989615)] | \n", + "Battambang Province; Cambodia | \n", + "(13.0286971, 102.989615) | \n", + "13.028697 | \n", + "102.989615 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 3 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a10500000068jPe | \n", + "Vulnerable Populations | \n", + "Cambodia | \n", + "No | \n", + "Kampong Cham Province | \n", + "(12.0, 105.5) | \n", + "KHM | \n", + "... | \n", + "604950 | \n", + "Kampong Cham Province, Cambodia | \n", + "[(12.0982918, 105.3131185)] | \n", + "Kampong Cham Province; Cambodia | \n", + "(12.0982918, 105.3131185) | \n", + "12.098292 | \n", + "105.313119 | \n", + "Kampong Cham, Cambodia | \n", + "(11.9924294, 105.4645408) | \n", + "90.0 | \n", + "
| 4 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000002X1Uu | \n", + "Sanitation | \n", + "Cambodia | \n", + "No | \n", + "Kampong Cham Province | \n", + "(12.0, 105.5) | \n", + "KHM | \n", + "... | \n", + "275 | \n", + "Kampong Cham Province, Cambodia | \n", + "[(12.0982918, 105.3131185)] | \n", + "Kampong Cham Province; Cambodia | \n", + "(12.0982918, 105.3131185) | \n", + "12.098292 | \n", + "105.313119 | \n", + "Kampong Cham, Cambodia | \n", + "(11.9924294, 105.4645408) | \n", + "90.0 | \n", + "
5 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount partner_id \\\n",
+ "count 6.712050e+05 671205.000000 671205.000000 657698.000000 \n",
+ "mean 9.932486e+05 785.995061 842.397107 178.199616 \n",
+ "std 1.966113e+05 1130.398941 1198.660073 94.247581 \n",
+ "min 6.530470e+05 0.000000 25.000000 9.000000 \n",
+ "25% 8.230720e+05 250.000000 275.000000 126.000000 \n",
+ "50% 9.927800e+05 450.000000 500.000000 145.000000 \n",
+ "75% 1.163653e+06 900.000000 1000.000000 204.000000 \n",
+ "max 1.340339e+06 100000.000000 100000.000000 536.000000 \n",
+ "\n",
+ " term_in_months lender_count \n",
+ "count 671205.000000 671205.000000 \n",
+ "mean 13.739022 20.590922 \n",
+ "std 8.598919 28.459551 \n",
+ "min 1.000000 0.000000 \n",
+ "25% 8.000000 7.000000 \n",
+ "50% 13.000000 13.000000 \n",
+ "75% 14.000000 24.000000 \n",
+ "max 158.000000 2986.000000 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kiva_loans.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Simple overview of categorical(object) values(non-numerical)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "partner_id | \n", + "term_in_months | \n", + "lender_count | \n", + "
|---|---|---|---|---|---|---|
| count | \n", + "6.712050e+05 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "657698.000000 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "
| mean | \n", + "9.932486e+05 | \n", + "785.995061 | \n", + "842.397107 | \n", + "178.199616 | \n", + "13.739022 | \n", + "20.590922 | \n", + "
| std | \n", + "1.966113e+05 | \n", + "1130.398941 | \n", + "1198.660073 | \n", + "94.247581 | \n", + "8.598919 | \n", + "28.459551 | \n", + "
| min | \n", + "6.530470e+05 | \n", + "0.000000 | \n", + "25.000000 | \n", + "9.000000 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "8.230720e+05 | \n", + "250.000000 | \n", + "275.000000 | \n", + "126.000000 | \n", + "8.000000 | \n", + "7.000000 | \n", + "
| 50% | \n", + "9.927800e+05 | \n", + "450.000000 | \n", + "500.000000 | \n", + "145.000000 | \n", + "13.000000 | \n", + "13.000000 | \n", + "
| 75% | \n", + "1.163653e+06 | \n", + "900.000000 | \n", + "1000.000000 | \n", + "204.000000 | \n", + "14.000000 | \n", + "24.000000 | \n", + "
| max | \n", + "1.340339e+06 | \n", + "100000.000000 | \n", + "100000.000000 | \n", + "536.000000 | \n", + "158.000000 | \n", + "2986.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " activity sector \\\n",
+ "count 671205 671205 \n",
+ "unique 163 15 \n",
+ "top Farming Agriculture \n",
+ "freq 72955 180302 \n",
+ "\n",
+ " use country_code \\\n",
+ "count 666973 671197 \n",
+ "unique 424912 86 \n",
+ "top to buy a water filter to provide safe drinking... PH \n",
+ "freq 5217 160441 \n",
+ "\n",
+ " country region currency posted_time \\\n",
+ "count 671205 614405 671205 671205 \n",
+ "unique 87 12695 67 667399 \n",
+ "top Philippines Kaduna PHP 2017-05-15 00:00:00+00:00 \n",
+ "freq 160441 10000 160440 25 \n",
+ "\n",
+ " disbursed_time funded_time tags \\\n",
+ "count 668809 622874 499789 \n",
+ "unique 5719 498007 86719 \n",
+ "top 2017-02-01 08:00:00+00:00 2016-09-21 13:03:24+00:00 user_favorite \n",
+ "freq 2800 33 27088 \n",
+ "\n",
+ " borrower_genders repayment_interval date \n",
+ "count 666984 671205 671205 \n",
+ "unique 11298 4 1298 \n",
+ "top female monthly 2017-03-20 \n",
+ "freq 426502 342717 1308 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kiva_loans.describe(include=[\"O\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Data cleaning and preparation\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1. Handling Missing Data\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Taking a look at the total counts of all missing data in our main datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# General method to check for missing values and their percentages of the total values\n",
+ "def check_missing_data(dataset):\n",
+ " \"\"\"\n",
+ " Method to check for total missing values in a dataset\n",
+ " \n",
+ " @params: dataset: the dataset(DataFrame) to be checked\n",
+ " \n",
+ " returns: A DataFrame with sums of missing values and their percentage to the total missing values\n",
+ " \n",
+ " \"\"\"\n",
+ " # Extract total null values\n",
+ " null_values = dataset.isnull().sum()\n",
+ "\n",
+ " # sort them\n",
+ " total = null_values.sort_values(ascending=False)\n",
+ "\n",
+ " # percentage of missing values sum to total null values\n",
+ " percentage = null_values / (dataset.isnull().count().sort_values(ascending=False))\n",
+ "\n",
+ " # merge the total and percentage into one dataframe\n",
+ " missing_data = pd.concat([total, percentage], axis='columns', keys=['Total', 'Percentage'], sort=False)\n",
+ "\n",
+ " return missing_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in kiva_loans data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "671205 | \n", + "671205 | \n", + "666973 | \n", + "671197 | \n", + "671205 | \n", + "614405 | \n", + "671205 | \n", + "671205 | \n", + "668809 | \n", + "622874 | \n", + "499789 | \n", + "666984 | \n", + "671205 | \n", + "671205 | \n", + "
| unique | \n", + "163 | \n", + "15 | \n", + "424912 | \n", + "86 | \n", + "87 | \n", + "12695 | \n", + "67 | \n", + "667399 | \n", + "5719 | \n", + "498007 | \n", + "86719 | \n", + "11298 | \n", + "4 | \n", + "1298 | \n", + "
| top | \n", + "Farming | \n", + "Agriculture | \n", + "to buy a water filter to provide safe drinking... | \n", + "PH | \n", + "Philippines | \n", + "Kaduna | \n", + "PHP | \n", + "2017-05-15 00:00:00+00:00 | \n", + "2017-02-01 08:00:00+00:00 | \n", + "2016-09-21 13:03:24+00:00 | \n", + "user_favorite | \n", + "female | \n", + "monthly | \n", + "2017-03-20 | \n", + "
| freq | \n", + "72955 | \n", + "180302 | \n", + "5217 | \n", + "160441 | \n", + "160441 | \n", + "10000 | \n", + "160440 | \n", + "25 | \n", + "2800 | \n", + "33 | \n", + "27088 | \n", + "426502 | \n", + "342717 | \n", + "1308 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "tags 171416 0.255385\n",
+ "region 56800 0.084624\n",
+ "funded_time 48331 0.072006\n",
+ "partner_id 13507 0.020124\n",
+ "use 4232 0.006305\n",
+ "borrower_genders 4221 0.006289\n",
+ "disbursed_time 2396 0.003570\n",
+ "country_code 8 0.000012\n",
+ "date 0 0.000000\n",
+ "funded_amount 0 0.000000\n",
+ "loan_amount 0 0.000000\n",
+ "activity 0 0.000000\n",
+ "sector 0 0.000000\n",
+ "currency 0 0.000000\n",
+ "country 0 0.000000\n",
+ "repayment_interval 0 0.000000\n",
+ "posted_time 0 0.000000\n",
+ "term_in_months 0 0.000000\n",
+ "lender_count 0 0.000000\n",
+ "id 0 0.000000"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(kiva_loans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in mpi regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| tags | \n", + "171416 | \n", + "0.255385 | \n", + "
| region | \n", + "56800 | \n", + "0.084624 | \n", + "
| funded_time | \n", + "48331 | \n", + "0.072006 | \n", + "
| partner_id | \n", + "13507 | \n", + "0.020124 | \n", + "
| use | \n", + "4232 | \n", + "0.006305 | \n", + "
| borrower_genders | \n", + "4221 | \n", + "0.006289 | \n", + "
| disbursed_time | \n", + "2396 | \n", + "0.003570 | \n", + "
| country_code | \n", + "8 | \n", + "0.000012 | \n", + "
| date | \n", + "0 | \n", + "0.000000 | \n", + "
| funded_amount | \n", + "0 | \n", + "0.000000 | \n", + "
| loan_amount | \n", + "0 | \n", + "0.000000 | \n", + "
| activity | \n", + "0 | \n", + "0.000000 | \n", + "
| sector | \n", + "0 | \n", + "0.000000 | \n", + "
| currency | \n", + "0 | \n", + "0.000000 | \n", + "
| country | \n", + "0 | \n", + "0.000000 | \n", + "
| repayment_interval | \n", + "0 | \n", + "0.000000 | \n", + "
| posted_time | \n", + "0 | \n", + "0.000000 | \n", + "
| term_in_months | \n", + "0 | \n", + "0.000000 | \n", + "
| lender_count | \n", + "0 | \n", + "0.000000 | \n", + "
| id | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "lon 1880 0.678211\n",
+ "lat 1880 0.678211\n",
+ "MPI 1788 0.645022\n",
+ "region 1788 0.645022\n",
+ "LocationName 1788 0.645022\n",
+ "world_region 1764 0.636364\n",
+ "country 1764 0.636364\n",
+ "ISO 1764 0.636364\n",
+ "geo 0 0.000000"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(mpi_region_locations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in id loan themes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| lon | \n", + "1880 | \n", + "0.678211 | \n", + "
| lat | \n", + "1880 | \n", + "0.678211 | \n", + "
| MPI | \n", + "1788 | \n", + "0.645022 | \n", + "
| region | \n", + "1788 | \n", + "0.645022 | \n", + "
| LocationName | \n", + "1788 | \n", + "0.645022 | \n", + "
| world_region | \n", + "1764 | \n", + "0.636364 | \n", + "
| country | \n", + "1764 | \n", + "0.636364 | \n", + "
| ISO | \n", + "1764 | \n", + "0.636364 | \n", + "
| geo | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "Partner ID 14813 0.019013\n",
+ "Loan Theme Type 14813 0.019013\n",
+ "Loan Theme ID 14813 0.019013\n",
+ "id 0 0.000000"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(loan_theme_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in regions loan theme"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| Partner ID | \n", + "14813 | \n", + "0.019013 | \n", + "
| Loan Theme Type | \n", + "14813 | \n", + "0.019013 | \n", + "
| Loan Theme ID | \n", + "14813 | \n", + "0.019013 | \n", + "
| id | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "geocode_old 14536 0.923742\n",
+ "mpi_geo 6065 0.385422\n",
+ "names 2075 0.131863\n",
+ "lon 2074 0.131800\n",
+ "lat 2074 0.131800\n",
+ "geocode 2074 0.131800\n",
+ "rural_pct 1392 0.088460\n",
+ "mpi_region 14 0.000890\n",
+ "ISO 14 0.000890\n",
+ "country 0 0.000000\n",
+ "Field Partner Name 0 0.000000\n",
+ "sector 0 0.000000\n",
+ "Loan Theme ID 0 0.000000\n",
+ "Loan Theme Type 0 0.000000\n",
+ "number 0 0.000000\n",
+ "forkiva 0 0.000000\n",
+ "region 0 0.000000\n",
+ "amount 0 0.000000\n",
+ "LocationName 0 0.000000\n",
+ "geo 0 0.000000\n",
+ "Partner ID 0 0.000000"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(loan_themes_region)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the percentages, it's clear that there are relatively bigger chunks of missing data hence filling them in would lead to false/misguided conclusions. | \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| geocode_old | \n", + "14536 | \n", + "0.923742 | \n", + "
| mpi_geo | \n", + "6065 | \n", + "0.385422 | \n", + "
| names | \n", + "2075 | \n", + "0.131863 | \n", + "
| lon | \n", + "2074 | \n", + "0.131800 | \n", + "
| lat | \n", + "2074 | \n", + "0.131800 | \n", + "
| geocode | \n", + "2074 | \n", + "0.131800 | \n", + "
| rural_pct | \n", + "1392 | \n", + "0.088460 | \n", + "
| mpi_region | \n", + "14 | \n", + "0.000890 | \n", + "
| ISO | \n", + "14 | \n", + "0.000890 | \n", + "
| country | \n", + "0 | \n", + "0.000000 | \n", + "
| Field Partner Name | \n", + "0 | \n", + "0.000000 | \n", + "
| sector | \n", + "0 | \n", + "0.000000 | \n", + "
| Loan Theme ID | \n", + "0 | \n", + "0.000000 | \n", + "
| Loan Theme Type | \n", + "0 | \n", + "0.000000 | \n", + "
| number | \n", + "0 | \n", + "0.000000 | \n", + "
| forkiva | \n", + "0 | \n", + "0.000000 | \n", + "
| region | \n", + "0 | \n", + "0.000000 | \n", + "
| amount | \n", + "0 | \n", + "0.000000 | \n", + "
| LocationName | \n", + "0 | \n", + "0.000000 | \n", + "
| geo | \n", + "0 | \n", + "0.000000 | \n", + "
| Partner ID | \n", + "0 | \n", + "0.000000 | \n", + "
\n", + "We'll therefore deal with only the available data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Data Exploration and Visualization\n", + "\n", + "\n", + "### 5.1. Loan counts per sector\n", + "\n", + "\n", + "#### Top sectors in which more loans were given" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sectors = kiva_loans['sector'].value_counts()\n",
+ "# print(sectors)\n",
+ "\n",
+ "# Inistantiate the figure object\n",
+ "fig = plt.figure(figsize=(15,8))\n",
+ "\n",
+ "# Plot a barplot of The sectors and their loan counts\n",
+ "bar_plot = sns.barplot(x=sectors.index, y=sectors.values)\n",
+ "\n",
+ "# Set labels and title\n",
+ "plt.title(\"Loan Counts per Sector\", fontsize=20)\n",
+ "plt.ylabel(\"Sector\", fontsize=15)\n",
+ "plt.xlabel(\"Count\", fontsize=15)\n",
+ "\n",
+ "labels = bar_plot.get_xticklabels()\n",
+ "bar_plot.set_xticklabels(labels, rotation=45)\n",
+ "\n",
+ "# show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above chart, **Agriculture** was the sector with the highest loan counts, followed by **Food**, then **Retail**.
\n", + "**Wholesale** had the least loan counts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Loan counts per world region\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + "**Wholesale** had the least loan counts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Loan counts per world region\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "world_regions = mpi_region_locations['world_region'].value_counts()\n",
+ "# print(world_regions)\n",
+ "fig = plt.figure(figsize=(16, 9))\n",
+ "\n",
+ "sns.barplot(x=world_regions.values, y=world_regions.index)\n",
+ "\n",
+ "plt.title(\"Loan Counts Per world Region\", fontsize=30)\n",
+ "\n",
+ "for i, v in enumerate(world_regions.values):\n",
+ " plt.text(10,i,v,color='k',fontsize=19)\n",
+ "\n",
+ "plt.xlabel(\"Count\", fontsize=25)\n",
+ "plt.ylabel(\"World Region\", fontsize=25)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Sub-Saharan Africa** has the highest number of loans followed by **Latin America and Carribean** region, while **Europe and Central Asia** has the least number of loans.\n",
+ "
\n", + "
\n", + "To account for this trend, we take a look at each region's mean poverty index.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "
\n", + "To account for this trend, we take a look at each region's mean poverty index.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Sub-Saharan Africa 0.337128\n",
+ "South Asia 0.219630\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Arab States 0.115287\n",
+ "Latin America and Caribbean 0.063665\n",
+ "Europe and Central Asia 0.025273"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "poverty_index_table = pd.pivot_table(mpi_region_locations, aggfunc=[np.mean], index=['world_region'], values=['MPI'])['mean'].sort_values(by=['MPI'], ascending=False)\n",
+ "poverty_index_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Arab States | \n", + "0.115287 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "poverty_index = poverty_index_table['MPI'].round(2)\n",
+ "# print(poverty_index['MPI'])\n",
+ "fig = plt.figure(figsize=(15, 8))\n",
+ "\n",
+ "sns.barplot(x=poverty_index.values, y=poverty_index.index)\n",
+ "\n",
+ "for i, v in enumerate(poverty_index.values):\n",
+ " plt.text(0.01, i, v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"Region Poverty Indexes\", fontsize=30)\n",
+ "plt.ylabel(\"World Region\", fontsize=25)\n",
+ "plt.xlabel(\"MPI\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above charts, its clear that **Sub-Saharan Africa** has the highest Multidimensional Poverty Index while **Europe and Central ASia** has the lowest. This explains why the two regions have the highest and lowest loan counts respectively.\n",
+ "
\n", + "
\n", + "A [study](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa) conducted by The World Bank shows that the number of extremely poor people continues to rise in Sub-Saharan africa while it falls in other regions ([World bank Data Blog](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa)).\n", + "
\n", + "
\n", + "From this study, it is expected that the number of loan counts from **Sub-Saharan Africa** will always be high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.4. Repayment Intervals\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + "
\n", + "A [study](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa) conducted by The World Bank shows that the number of extremely poor people continues to rise in Sub-Saharan africa while it falls in other regions ([World bank Data Blog](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa)).\n", + "
\n", + "
\n", + "From this study, it is expected that the number of loan counts from **Sub-Saharan Africa** will always be high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.4. Repayment Intervals\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "intervals = kiva_loans['repayment_interval'].value_counts()\n",
+ "# print(intervals)\n",
+ "plt.figure(figsize=(15,8))\n",
+ "\n",
+ "plt.pie(intervals.values, labels=intervals.index, shadow=True, autopct='%1.1f%%', startangle=90)\n",
+ "\n",
+ "plt.axis('equal')\n",
+ "plt.title('Repayment Intervals', fontsize=30)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a percentage of **51.1%**, majority of the loans are repaid **monthly**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.5. Gender Comparisons\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "genders = []\n",
+ "\n",
+ "for gender in kiva_loans['borrower_genders'].values:\n",
+ " if str(gender) != \"nan\":\n",
+ " genders.extend([lst.strip() for lst in gender.split(\",\")])\n",
+ " \n",
+ "# print(genders)\n",
+ "# Add a new genders column with the simplified data\n",
+ "kiva_loans['genders'] = pd.Series(np.array(genders))\n",
+ "\n",
+ "# Dropping redundant borrower_genders column\n",
+ "kiva_loans.drop(columns=['borrower_genders'], inplace=True)\n",
+ "# print(kiva_loans.head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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0ITgjm81HtVyZT2pZVoMx5nic5akG4MypPTWx09o0nLV683Buy3lsGsWK4qzVWtLs+qqMMRexaYRqqjHmfpwRvyqcZdIuY9M0jm9oNuplWdZyY8xUnLVxB+Hs2vYPnBIxGGc3q8Nw1jYtaov7oRXJ8zOvM8bcBngTG0R0OMuyNhhjfobz4soDPGWMOSnx+Tqc++IynCXowDkY64Z2ymIbYy7GOcgwG+fnfCfwJs4BleOBq9l8FzO72XWkyu25Hmcr7SLgemPMvjjlew3OCijX4LwzU0cLo66WZcWMMefi3BcB4B/GmENw1gdejnMg50/YtM3zerYy0isim1PBFdnST7fzfC/j7D+/0a9wRu9+g3Pgyc0tXMbGect0s13MLMtqNMacg1PosoGDEh/NfQM8RGJ7WJx1dF3ZktOyrMWJucMP4Czg3wu4KvHRksXAhYnF71u6vueNMWfhlIRc4PLER3Nv4+wWV9vC136GUxj64+w89USzr0/HKVAfbuWm7aq3gRqcOcqnJz4ixpi8xEYVHc6yrGcSG5LcjzM/+ezER3NfAidux1rEu5LlI2PMj3GKXDbOY6H5rn734BzAuAebj4hvvA7Xb49lWXXGmINxDrocg/MYOLHZ2d7AOQCtxQNDLcv62BhzHE4574FTZo9r4awrgFM6YttnkUyhKQoibcSyrLhlWb/FWcD9PpwyWo9Tdr4E7gLGWJZ1ZUs7lVmW9W7isg/ilMFw4mMVTmn6Gc4T6X8S1wubtgt1hWVZayzLOgnnbdrbcApkOc40hAacEejnceZbDm+t3CZd39M4I2J/xZmLvAFnh66FONMzjgOObmVDAxKbB4zEGbGbi3M/VeFsDnEZzouGditviQyrcZZ9ejfxvZtwfoauHtxmWdYTOJsE3Iiz+cB6nBH1Cpw55JcA+1qW1ZbLabWW5RmcUc57cEbUG3F+Lu8AJ1mWdRnOqCZsvtlC8nW4fnsS2wQfAFyEs5pIBc6I7SzgFziFN9bqFTjX8Q7O7bgG54VXOc7t2JC4zquBEZZltTo3X0S25LFtLZ0nIiKpJTFNKB94NfEiSkRku2mKgoiIdAhjzOE489QXAndaltXi+rGJqS8bt7+e00HxRCSDqOCKiEhHWQ+cnDidhVN2N5PYrjp56bkXOiCXiGQYTVEQEZEOY4z5Ati4VvRLOLv2LcM5sHAEzjbUG5fFusuyrC1KsIjItmgEV0REOtLpwGScpd9OSXy05D84K5KIiOwwjeCKiEiHSuwodz7wA5zl3LrjrD6wCmdN6EfcWjdYRDKDCq6IiIiIZBStgysiIiIiGUUFV0REREQyigquiIiIiGQUFVwRERERySgquCIiIiKSUVRwRURERCSjqOCKiIiISEZRwRURERGRjKKCKyIiIiIZRQVXRERERDKK3+0AIiIZKBsoAHw4f2d9QBCIA1EglvRvNdDgTkwRkcykgisisn2ygWHAHkAvoGdDU7R/NBrvZ2P38Xg8Pf0+b9eA31sAeMORWDhu23Y8jh23bTs3y5vts2PYkaYGvF4PHo/H4/F5PYFgANuO2dFIjR2Prse2y/F41nh8gZXeYGgVUA6sBb4BvgXCbt0BIiLpwmPbttsZRERSSXdgT2B4OBIb3RiO7ef3eYZlBXw9Kqsb61esrbUrqhr962saQ1W1Td6q2jDVdRs/mqiuC9MYjm1xpbdeMcHuuehNT9X0F7f4micYwpddgC8nH29OQfLpuD+vW6O/oGc00KO/x5fXNceONK2zY1HLEwzN8PqD84GvgQXAhna+X0RE0oZGcEWkM8sG9o/G4hPrG6PHZgW8e3o8nqyyivqGJaur/YtXVeUsX1PjWbG2lrKKOmJxO3/nv1Xrgwl2uJFouJFo1drmX/ICOZs+8xPo1qcw0LN/YbDHgIODfQbXBXsPigW69smx7XiDHQ3P92blvOHx+qYBnwNNO59XRCR9qeCKSGfSBZgQjsQOawrHjsnO8g9bsa62YZa1NnteaXmgdEUVldWN4MyXbVs2nl2+jniUSMVKIhUrqafEA+Rt/JIvv3sgq98e47MHjRqbvfvejYFuhaF4uOFrbyA02eMPTAU+wZnvKyKS8VRwRSSTBYFDG5qip8Ri8aOygr4Bi1ZWNc601ubNL63wWsvW0xSOBdwO2RZiNZXUWyXUWyVBIOgJhgj1N3uFdhsxMqdozCXBXoNy4pGmpR5/YLI3kPUsMB3nIDcRkYyjObgikmkKgONq68NnBwO+I1auq418OHtl3tyF5d7SFRuIxtz5m3fr5ePpuXgKLc3B7RA+P1l9i8gZMiaWN+rgel9etzjx+IverOwngGlAxJ1gIiJtTyO4IpIJ8oCTauvDFwYDvglfL60MT5u5Mr/kqzI21DRlux0uJcSiNK2waFph+dZ/8Ey+v1tfcocfcG7+6IO/7+9a6CMWec0byn0CeBtodDuuiMiuUMEVkXTlA46pbYhcHvR7D7WWrg+/8/my/E/nr6a+MZrldrhUF12/mqrpL3qrpr9Y4MvvQe7wA07PH33IscFeA4PxaGSKL5R7BzCVrR0dJyKSolRwRSTd9IpG4xdFYvFfr1tfH3r5g0X50+etoqY+olK7k2I1FVR//rqn+vPXC3y5Xcjdc/xJXfY/8XBfdv4GTyB0m8frnYSWIRORNKI5uCKSDjzA/nUNkd/4/d4Tps9dZb/y4aLsb5enT+dyfQ7uTggNHEGX4uPrsov29RGPPu/NyrkdmOF2rp1hjFkCTLQsa4m7SUSkI2gEV0RSWS5wVl1D5JqmcKzvi9MWZr/7+TJvTb2Oh+oIjcu/onH5V7m+3C7k7334mV2KTzgVn3+ZL5T7D+AZtMWwiKQoFVwRSUXdmyKxq4HLv1xUYb80bWHe7G/WoTec3BGrq2LDJy/4Nkx/KSe7aMzwrvufdGdW/2F3ejzef3j8gTuA2o7IYYyZCFyLs13x7sArie99Cs4o/3HAacA5OC+OwsBZlmVZSdfhA/4JTMSZxz3JsqzbOyK/iHQcr9sBRESSdAlHYjc2hWPLPpy18peX3fJe7vX3T8+bZancpgQ7TsPCmax+4ob8lQ9fnV9fOvMP8UjTSjsWvQanUHaE/YFLgLHA5cA6y7LGAnOBM3HK7kTLskYBryXOk+ynAJZl7QsUAycbYw7qoOwi0kE0gisiqSAvEo39Kh63r/l0/mr/429+HSqrqHc7k2xFpHwFa567JSfQcyDdD/3Rddm77/V7j9f3V4/Pfw/Qnj+8+ZZlLQcwxpQD7yb+fynQDTgbONMYMww4Bpjd7PJHAGOMMYclPs8DRgMftmNmEelgKrgi4qacSDR+eSwev3bGgjWBxyZ/nb1yXYe82y1tJFK+nDX/+3tusPcguk/80Q2hQSOv9fgDf/Z4fffRPnN0w80+jyadHoizQ9tdwGSgDNin2fl9wNWWZb0AYIzpSQdNsRCRjqOCKyJuCMRi8Z9HYvEb5nyzLuu/kxdkLyurcTuT7ILw2qWUPXtzbrDPYLof+uO/hgbu+SePP3CNx+t7CIh3UIxxwELLsm43xmQDfwGWNzvPe8BPjTGvAlnARzhTHqZ2UEYR6QAquCLS0Q6sb4z8t3TFhj4PvDw/Z/GqarfzSBsKr1lC2dN/zQ32Lcrtddyl/xfo1ucKb1bOuWw5VaA9vAXsbYz5Cuegs2nAqGbnuRcYCszCeQ58xLKsqR2QTUQ6kNbBFZGO0rO+MXJHNBo/9Z7n52Z/PHeV23k6VDqug7vrPOTvfZjd48jzG/F4/usNZl8DVLmdSkQyn0ZwRaS9eWNx+4JIJHb7OyVLQ4+/afkbmqLbvpRkAJuaOe966r75LLvH4eedl7vn+DM8geBlHo/3KbQFsIi0IxVcEWlPo+saIv8tq6g1dz47J3vRSg3edUbxhlrWvXZ3qHrmW6FeJ15xvz+v2y+9oZzzgQVuZxORzKR1cEWkPeQ2NkXvqGuIlDzy2pd7//pfH6jcCk2rvmXF/b/KrZz21Nh4uGFGPNx4CxB0O5eIZB6N4IpIW9uvvjHy8hcLynrd/9L8YFVt81WdpFOz41R/8Ya3bsEn2b1OvPyy0ABzsjcr51TgK7ejiUjm0AiuiLQVX1M4el1dQ/jjfz87u/8/H5+pciutitVtoOzpv+ZUvPPoHvFw4+d2NPJLnJUPRER2mUZwRaQtDKypa3xl2ZqaEbc9MSu4bkN7rO8vmahm9jvehqXzc/r84Lc3Bbr2/r43K+c0YK3buUQkvWkEV0R21Qn1jZEFL0wt3esP93yicis7LLq+jJUP/Ta3esaUA+Lhxq+BQ93OJCLpTQVXRHZWoKau8d+VVfXP//nBT3Ofe2+hN66Fn2Rn2XEq3388uOa5W7rFGmpfj0eabsLZVldEZIep4IrIzuhbVdMw21q6/uIrbpsW/Gpxpdt5JEM0LJ7Divt+mR0uW/TLeFP9B0A3tzOJSPpRwRWRHRKJRveqrWv86tWPFg//y8Mlgeo6HUgmbStWt4FVj/0pt2betP3i4YbZQJHbmUQkvajgish2q1xfdWJTOFZyz/Pzuj7zzrde7fQt7caOUzHlwazK9x4bEI80zQQOcjuSiKQPFVwR2S5layt+6/UHX/jzQ59lfThnldtxpJOonjHFu+a5WwriTQ1T7HjsXLfziEh6UMEVkW3xrSorfzRG4Obf/vsj/9dL1rudRzqZhkWzWTnp99mx+ur/xCNN/0DPXSKyDfojISJbk1u2bv2H5TWxs39z50f+sop6t/NIJxUpX86KB67MiVSsvCzeVP8ykO12JhFJXSq4ItKixsamfuXraxbMW7Sh+PoHPvPXNUTcjiSdXLy+mlWT/pBbv2jO4fGmhhKgu9uZRCQ1qeCKyBZWrl4zqKEpOn/y9OX973x2ri8a09FkkhrsWIS1L9yaXTPn3WHxpobpQA+3M4lI6lHBFZHNfFoya49QKGfui9MWdX323W/1N0JSUsXbjwSrZ789OFFye7qdR0RSi568ROQ7b749bXhR0ZCZL3+4JO/FaYs8bucR2ZrKdx4NVs96a5BKrog0p4IrIgC8MeV9s9++Y0pe+2R57ovTFulvg6SFynf/G6ye8eZu8aaGT4FebucRkdSgJzER4Y0p75txY/f9/LVPlue+MLVUfxckrVS+/3iwasbkgfFww2dAb7fziIj79EQm0sl9V26nq9xK+lr//hPBqpLXByRKbh+384iIu/RkJtKJvfL6O0PHjd3389enL8994X2VW0lv66c9Faj67LX+8aaGT4AubucREffoCU2kk7rh5jsK99ln7+lTSlbmPq9yKxli/QdPB2rnf9A/3lT/BhBwO4+IuENPaiKd0OEn/LjLD045/mNrRV3XZ97RUmCSWcqnPJjVuPLbMfGmhkcArQYi0gnpiU2kkyksKs6+5spLp3iC+bvd/dxcn9t5RNqcHWfNc7fkRGsqTolHwn90O46IdDwVXJFOpLCo2HfbzX98ZNSo0fvdNGmGXzuUSaayI42sfuKGXDvc8Hvbjp/pdh4R6VgquCKdRGFRsed3V15yw/HHHvHDGx4q8dc2RNyOJNKuYrXrWf3kn7PtSNNDwAS384hIx1HBFekkfnbBWef85Nwzf/f3x2b6VpfXuR1HpEOE1y5lzfO35sQjTW8Ae7idR0Q6hgquSCdw+W+uP/Dyi39y70OvLvB9uajC7TgiHaph0Wwq3n4kLx5umAp0dTuPiLQ/FVyRDFd8yCm7XXHphS++P6ss6/0ZK3REuXRKNbPe9tbO/7BnvKn+CbSygkjGU8EVyWCFRcVdrr3miucaYsGuT0yx9HiXTq3irYezorUbDrFj0UvcziIi7UtPeCIZqrCoOPjTn5z5r4MmHLDPbU/O9ttaMEE6OTsWYc2zf8u1Y9FbgdFu5xGR9qOCK5KBCouKPcVj977oyit+dvatT8z2b6htcjuSSEqIVK6i/M0HsuNNDa8CuW7nEZH2oYIrkoFCoax9b7r+mhsmf7rcP6+03O04Iimldt5UT33pzN7xpvr73M4iIu1DBVckwxQWFfe+9reX3xXI7qJteEVase71e7LjTfWn2nb8LLeziEjb05OfSAYpLCoOHHXYQX8644cnjb31ydmBeFwTb0VaYocbKfvf33PsaOQBoMjtPCLGFRAEAAAgAElEQVTStlRwRTJIr57dT7nphmvOfejVBb41lfVuxxFJaeGyxVS+/0Qo3lT/EuBzO4+ItB0VXJEMUVhUPPS63/3y+hUV4dB7XyzXOp8i26H68zd8kcrVu9vx2KVuZxGRtqOCK5IBCouKc8fvv+/vjz3qUHPvi18G3M4jkj5s1r5yZ64di/4d6Od2GhFpGyq4ImmusKjY4/V6z7zx+qtPeOqtb3yV1Y1uRxJJK5HyFVR//npQqyqIZA4VXJH0Zy658OzzcvK6dX39kyWamiCyE9Z/+L9APNx4GHCs21lEZNep4IqkscKi4mBhn14XX3HphcV3PzdPqyZIq75eV8/VUxYBsLCigV++XspVby7ins9WEW+2zV1NU5Tr3l3CbyYv4s/vLWVDQxSAJ+as5ddvlPL0vLUAxOI2f526jFgG/N7Z0TDrXrs7Jx5ufATIcTuPiOwaFVyR9Hb4TTdcfVzJV2t91rL1bmeRFPW/+eu4Y/pKwjGniN756UouHteXW48ZQm7Qx9TFVZud/+l56xjZO4fbjh3CScN7MGnWGgBmra7l9uOKmLGyFoA3vqnk6KHd8Hkz442DhkWzaVg0Oz8eafqL21lEZNeo4IqkqcKi4l6HHfy9nx34veIhj76xwO92HkldffOD/HHibt99Xl4XZURvZ5ByRO8cvlxbt9n5l1U1MbZf/hZf93s9RGJxvB4PdeEYX62tZ1z//A66FR2j/M0HcrDjPwdGuZ1FRHaeCq5IGiosKvYEA4Gzb7zhmkMeeX2Br6Y+4nYkSWEHDuqCP2mUtTA/wNwyp7R+tryGxmh8s/MXdQvx2YoaAD5dXkNT1Bn5PXF4D26atpxTR/Tg2fnr+MHInjw0o4x7PlvF+sQ0hnQXq9tA5buPZcWb6v8LZMbQtEgnpIIrkp72uuLS88+I2ME8rXkrO+rK8QN4dv46/vTuErqGfBRkbf4GwOmje7GmNszv31rMuvoIvXKdlecm7FbADYcNYnDXEPWROFWNUbqE/BxR1I2Xv65w46a0i+pZb3tj9TVD0QFnImlLBVckzRQWFWfn5mRfeOF5Z+7z4CtfBez0P75HOljJyhp+Pb4/fzl8MNVNMfbpm7fZ1+evqefwoq787ajdKcwLfDedYaOn5q3lrNG9aIzZ+Dzg8UBjZPNR4LRmx6l499G8eFP9v9DzpEha0gNXJP0c+8vLLhi7eHWt11qqA8tkx/XLD/Knd5dy5eRScoI+igc482j/8PZiIrE4AwqCPDprDVdOLmXakirOHN3ru8suWFdP79wg3XMC7Ns3l89W1HDPZ6s4emg3t25Ou6i3PiNaU9EX+L7bWURkx3lsDf+IpI3CouIeubk5t33x4Wun3/jIjOxvl29wO5Jsp1svH0/PxVOomv6i21FkO2UPGUOf7/9mmTcrZwgQczuPiGw/jeCKpJdjrvrFT4ctXFHtV7kVaV8Ni2YTqVzdHTjb7SwismNUcEXSRGFRca/8vNwjzz7j1H2emPJNwO08Ip1BxduT8uLhhn8CesyJpBEVXJH0cezVv75k+IIl632lK6u2fW4R2WWNy7+iqWxxrh2PX+h2FhHZfiq4ImmgsKi4d5eC/CNO/8GJYzR6K9KxKt99NM+Ohm8CQm5nEZHto4Irkh6Ov+bKS4bNX1TpXbK62u0sIp1K06qFNK60smzbPs/tLCKyfVRwRVJcYVFxYSiUdcgPTjl+zFNvafRWxA0bPnkx1w43/A7tbiaSFlRwRVLf8Recc/qA5WtqPUvLatzOItIpNS6ZR6yxridwsNtZRGTbVHBFUlhhUXFf4MCzzjh15OufLA26nUekM6ua/lJurLH2GrdziMi2qeCKpLaDx++/b16vnj16fjp/tdtZRDq1mnlTPR5f4FBgoNtZRGTrVHBFUlRhUXE2cOjll/xkjzenL/VGY9p1UMRNdriRmrnve+KRpsvcziIiW6eCK5K69unWtSB3/3FjRk35bKkeqyIpoLrk9Szg52jJMJGUpidNkRRUWFTsAY694pLz+84vLbfLNzS6HUlEgEjlKppWlwKc5nYWEWmdCq5Iatrd4/EMOPnEY/d9/eOlWhpMJIVUffpyfqyx7vdu5xCR1qngiqSmiccffWg3nz+YN/vbdW5nEZEk9QtnQjw2CBjtdhYRaZkKrkiKKSwqLgDG/+TcM4omT1/qt3VsmUhqsePUzJ0aiEfC57gdRURapoIrknrGhUJZgTGjR5gPZ6/SrkkiKah2/gcB7Nj5aGczkZSkgiuSQgqLir3AsWefdlLuirU18XUbGtyOJCItCK9ZTLyxLhs4wO0sIrIlFVyR1DII6H7i8UcP/2D2au1cJpKigr0HEakqz41GIjrYTCQF+d0OICKb2TcUyrL3GmWG3fvGVL31KZJCgr0HkTNiAvmjJ+LNymF5eYNdGPcc7HcGi+Ju5xORTVRwRVJEYnrCwWf98MS8FWtq4pXVWvtWxG3B3oPIHTHBzhs90ePJymHx6hr70deXet77YjmA9+HrjvKFsvxjgRKXo4pIEhVckdSxO5B/7NGHDZ0+f42mJ4i4ZMtSW8uk15eSKLWbvbMybeaK0IkHDTktK+BTwRVJISq4IqljH4/HE9tr1J57PvWfTzU9QaQD7UipTfbxnFX+48YPPpuA72pAi/qJpAgVXJEUkNiad/yxR00MNjTFvCvW1rodSSTj7WypTbZwxQaiMbsrMBT4pj3zisj2U8EVSQ39ga4nH3/k4Onz1+hxKdJOtii1ZTteapub8+06+6Ax/SeigiuSMvREKpIaRgL2nnsOH/rku8u1fJ9IG2qPUptsprU2d8ywXsfn5wTv3/W0ItIWVHBFUsOE7OxQzW79+/T+eskct7OIpL32LrXJ5peWE/B5D05cr+bhiqQAFVwRlxUWFRcAA487amKsrKI2WtcY9bmdSSQddWSpTVZWUU84Gg+GstgD+La9vo+IbD8VXBH37QbED55QPGh+aYUekyI7oKVS++jrS3m3nUttc/MWrrMn7N3/EFRwRVKCnkxF3LcHYI8YMWLoG59XaPRWZBtSpdQmm2mty917aK/j83KCD7rx/UVkcyq4Iu7b2+fzVg8Z1L/fl8/oIGyRlqRiqU02r7Qcn+bhiqQMFVwRFxUWFYeAQRMPOqCxtiFsr69pcjuSSMoI9h5E7p4T7Ly9UrPUJltdXkcsFs8GhgClbucR6exUcEXctRtgHz5xwm7zF1Wk1BO2iBvSqdQ2N3dheXz8Xv0OQQVXxHUquCLu2h3w7DV61B6fWOsDbocRcUM6l9pkXy2uzN3H9C7OzvI/7HYWkc5OBVfEXXsBNQP6F/b99p3ZbmcR6TDfldrRh3i8oVwWpWmpTbZsTTWRaHxsdpbbSUREBVfEJYVFxX5gaCiUta5Ht4K8letq3Y4k0q6al9rFZbU8+saytC61yZavqSEY8O7hdg4RUcEVcVNfwLvfPqML1lfVRyPReNDtQCJtLdNLbbLyDY14PZ5soDtQ6XYekc5MBVfEPb0Azz57jei5fG2NlhWSjNGZSm1zqyvqGgYVFowAPnI7i0hnpoIr4p5CgGF77N5z2Zo6PRYlrQV67UbeiAM7ZalNtmhlVWBQYcFIVHBFXKUnVRH3DAYaBg4c2PeLxXXawUzSTkul9r+Tl/PO58ugE5XaZItWVuV8b1TfMaEsPb2KuEmPQBH3DATqC/v07r3iM21fL+lBpXbrlpXVEI7Gx4a0koKIq1RwRVyQWEGht8fjWdG7V7euK9ZqBQVJXSq122/ZmmqCAe9Qt3OIdHYquCLu6A5QtPtuObaNp7ou7HYekc0Eeu1G7ogJdv7oiSq1O6B8QyM+rycHKACq3c4j0lmp4Iq4oydgj91ndI9V62pi6LEoKaClUvuYSu0Oq22INHXL9/VGBVfENXpSFXFHT8DXt2/vnMrqRrezSCeWKLXkj56ISm3bqK4Nx7rlh3oDC93OItJZqeCKuGMQ0NSjW7ee1XURr9thpHNpodTaj01e7lGpbRvraxo9g/oW9HY7h0hnpoIr4o5CoLFbty45VbURPQ6l3W0qtYfgDeWp1LajiqpGP6CCK+IiPbGKuKMLEOnSpUvespqwyoW0i+aldklZrf3Y5BUqte2ssroxZNt2b49Hd7GIW1RwRdyRD9Tl5efnV5dpBQVpOyq17ltf0+RtDMd2y9ZmDyKu0aNPpIMVFhV7gDxgQ35ebl51ndbAlV2jUptaqmqbiETjA7K12YOIa1RwRTpeFk7psHNzc3K0Bq7sjECvgeSOOFClNgVtqG0C7EK3c4h0Ziq4Ih0vB7ABcnOyQyq4sr1aKrWPv7nC83aJSm0qqappwuv19nI7h0hnpoIr0vGSCm4oq0YFV7ZCpTb91DVG8Hk9uW7nEOnMVHBFOl7OdydCQX99Y8TNLJKCVGrTWzRm4/Xo+VXETXoAinS8bMDj8/k8AHHb5TSSEgK9BpK75wTy95qoUpvmYrE4Ho/H53YOkc5MBVek4+UAnkDA74nF425nERe1Vmrf/WIZ8bhKbbqKxmwVXBGXqeCKdDw/4PH7fJ543LbR6Fyn4fGo1HYG0Vgcr1fPryJu0gNQpON5AE8gEPDatgpuZxG3oetBp9Nl/A9UajOcbYMe1iLuUsEV6XheEqsoSOdx86NfsN/wPrw/c7lKbYZzdujVa1cRN6nginQ8D+BpCofjXq82q+8sNtSGefeL5W7HkA6Q6Ld6ESviIq/bAUQ6IS9gNzWF416vVwVXJMN4vB5svUsj4ioVXJGOFwc8sVjM9no8aAxXJLMkHtIquCIuUsEV6XjfrQ0Wi8VtnwZxRTJKdpafaCze6HYOkc5MBVek48VJjO40RaKxUFBT4UUySX5OkFjMrnY7h0hnpoIr0vG+G8GtrWto6pKX5WYWEWlj+blBYnF7g9s5RDozFVyRjhcjMYJbV9dQ3yUv6HIcEWlL+TkBwC53O4dIZ6aCK9LxGkgU3JraurqCXI3gimSSvJwgXo9nnds5RDozFVyRjldHouDW1tbWagRXJLPk5wQJBHyr3M4h0pmp4Ip0vPqNJ6qrq6sLclVwRTJJQW4wlhXwrXU7h0hnpoIr0vHqSCyVuX7DhtouuYGYy3lEpA11zctqAirdziHSmanginS8ehKPvYqK9fVd84IquCIZpGt+VhSocDuHSGemgivSwcpKSyJAGPCtLa+o65IX1I5HIhmka36WjQquiKtUcEXcUQ0EVq9eW9dV6+CKZJTC7jkBYKHbOUQ6MxVcEXdUAYFZc79aX9gzP+B2GBFpG9lZfkJZ/gCwwu0sIp2ZCq6IOzYAwRUrVzeGI9FIz64ht/OISBvo1yuXxqboSpJ2LBSRjqeCK+KOdUAWQNnaisqBvfNdjiMibWFArzziNl+7nUOks1PBFXHHciAAsHp1WdmAPiq4Ipmgf++8eE7IP9PtHCKdnQquiDvWATGAJUuXlu3WJzfqch4RaQO79+1S5/d5F7idQ6SzU8EVccc6Eps9LPh64bpBffK0Fq5IBhhYmG8D37idQ6SzU8EVcUcVEAV8n30xe92APgU+twOJyK7r3S07G7DcziHS2angirigrLTExllGKOfrbxbV+nxeCnKDbscSkV3QvSBELG434LyAFREXqeCKuGcZkGvbNmvWVW4Y0DvP7TwisguG9O9COBLX6K1IClDBFXHPYhJLha1cuXpVUf+uLscRkV0xakiPaHaWf4rbOUREBVfETd+tpDBr9tzSUUO6hl3OIyK7YIzpVRfwe6e5nUNEVHBF3FROYiWFyW9NXTqyqKcejyJpyu/zslufghzgM7eziIgKroibKoBGIDBzzpdVsWgs0r+X5uGKpKM9BnahMRxdCtS4nUVEVHBFXFNWWhIHvgS6Any7aMmSkUN6uBtKRHbKiN172H6f9123c4iIQwVXxF1zgGyAOXPmLRxd1C3ich4R2QljhvWqzc7yv+d2DhFxqOCKuGspYANMeffDpaOLerocR0R2xvBB3QPAx27nEBGHCq6Iu1YDEcD/yaczKgJ+b7x3t2y3M4nIDhjQOw9saoCVbmcREYcKroiLykpLYsACoItt2yxctGz5yCEaxRVJJ6OG9CBm2x+6nUNENlHBFXHfXCAXYO68+d/svUd3zcMVSSMT9u5Xk5cdeM7tHCKyiQquiPsWk5iH+8LLby4cO6KPx+txOZGIbJesgI8Ru/fIAt50O4uIbKKCK+K+lTg7mvlKZsxZX1tbX2sGdXc7k4hsh72G9qQpHJsHrHc7i4hsooIr4rKy0pII8DWJ9XBnzJoz93uj+sTcTSUi22PCXv0acrMDT7qdQ0Q2p4Irkho+AfIAXnr1zQXj9+qrgiuS4rweOGBUX7xez8tuZxGRzangiqSGrxP/eia/Na0s4CM6uG+Bq4FEZOtGDumJ7UwxKnU7i4hsTgVXJAWUlZZsABYBBbZt8/mMOXMOGtMv7nYuEWndIfv2bwwFfZPcziEiW1LBFUkdHwFdAJ55/pW5E/ftr2kKIinK64ED9+5v+33eZ9zOIiJbUsEVSR3zAQ/A5LemlWHHmoYO7OpyJBFpSWJ6wipgodtZRGRLKrgiKaKstGQdsJTErmaflcycdfCYfhrFFUlBx00YXJ8d9P3H7Rwi0jIVXJHUMpXENIVHHnt29mFjB9oBvx6mIqmkIDdI8YhCr8/nfcTtLCLSMj1ziqSWuTjTFDyffDajctXqNasP3Lu/25lEJMnh4wbGI7H4q0Cl21lEpGUquCIppKy0pBKwgG4Az7346scnHjgo7G4qEdnI44GTDy5qyA0Fbnc7i4i0TgVXJPW8BRQA3Pfwk9/0KAhG9xigg81EUsFee/QiFPSvAT51O4uItE4FVyT1zANqgFAkErXfnfrRpydMGBR1O5SIwEkHD6nLDvn/CdhuZxGR1qngiqSYstKSCDAF6A3wr7semvG90X3Jzwm4G0ykk+teEGLM0F5er8fzhNtZRGTrVHBFUtP0xL/e0sXL6ud99e03R4wbqBEjERcdWbxbLBqLP4vzDouIpDAVXJEUVFZaUgF8QWIU99HHn/nk+AmDox6Pu7lEOiuf18OJBw1pygkF7nA7i4hsmwquSOp6F8gCePHVt1aGmxqq9xvex+VIIp3TxP0G2H6f90tglttZRGTbVHBFUte3wBog37Zt/vfCq1PPPnKolgwT6WA+r4dzjxtRn5sduMrtLCKyfVRwRVJUWWlJHHgD6A5w250PfpkX8tSP3VOjuCId6dD9BthZAd+XwAduZxGR7aOCK5LavgCagKxYLGZPevyZKeceazSKK9JBfF4P5xw3okGjtyLpRQVXJIWVlZbUA68ChQB3/mfS135PtOZ7o/u6G0ykk0iM3s4DPnQ7i4hsPxVckdQ3FWgEQrZt89CkJ6acc8ywsDfDVlSw4zFWz3ySZR/fzfJP7iFcu5ZwXTnLPr6H5Z/cw5p5L2Db8RYvW7N6PqtnPvnd51XLSlj20V2smffid/+3euaTxCKN7X47JHNo9FYkfangiqS4stKSOuBFoA/AfQ8/9W0s0rjhwDH93Q3WxurWfo1tx9ltwmV0H3oE5V+/ybqvXqWnOZqB438Otk1t2VdbXG7t/Jcp/3oyyRtLVa+YwcAJPyfaWEUsXE/tmgVkd98dXyDUgbdI0t2hYwfaWQHfXOAjt7OIyI5RwRVJDx8CdUC2bdvc9+B/p/z46GERbwYN4wZye4Idx7bjxKNNeLw+GjesJLvHEAByew+nvvzbLS6X3X0QfUafutn/eXxB7HgUOx4Dj4fq5Z/TZbfiDrkdkhl8Xg/nHrunRm9F0pQKrkgaKCstaQBeILHxw6Qnnl9UV1tdPnHfAe4Ga0NefxaR+kqWTL2VNXOfo+vgCYCNJ7G7hdefRTy65RSD/H5jgM2Lfo+hh7F65pPk9x1FzcpZFAwcR2XpNNbMe4Fw7doOuDWS7o4+YJAdDPjmAB+7nUVEdpwKrkj6+BioBnJs2+bu+x5588fHDItkBXxu52oT6xd9SE4vw+6HXs2gg39N2exnnBHYhHi0Ca8/e7uuK7v77vQfdz55ffemoXIxwdweRBur6WmOpuKbd9rrJkiG6JIX5LzjRzTmZgcudjuLiOwcFVyRNFFWWtIEPAf0Anjqf68uW7Z0+aLTjxga2/ol04MvkP3dHFlfIAfbjhPq0p/68lLAmaOb02PwDl1n5cL36VZ0KPFY5LuR4HhMq6zJ1v3khBFNHo/nIWCe21lEZOeo4Iqkl8+A9UAewFV/+Mtrx35vUHxgn3x3U7WBbkMOorFqJcs/uYcVn95Hz+HH0HvUKVR88xbLProL246R13cvAFZ8+gB2PLrV64vUVxKPNhDq0o+sgr5EGjawsuRhug4e3xE3R9LUnoO7M36v/vXZWf5r3c4iIjvPY9v2ts8lIimjsKi4GLgcWATw979cUzzugIMO/90904PuJhNJb16vh7uvmtgwoE/BBcDTbucRkZ2nEVyR9PMFzlunfQCuu/G2z/NDVB+23wC9WhXZBceNH2x375I9D3jG7SwismtUcEXSTFlpSRx4HMgCApFI1L7xb7e/cMGJI2L5OQGX04mkp275WZxz7J5NOaHA+SQvqiwiaUkFVyQNlZWWrMbZ/KEfwKuT3109Y9bc2ecfv+fWJ6aKSIsuOmlkk9fruQdY4HYWEdl1Krgi6estoBzoAnDlNX9+p3hE7+ieg7u7m0okzYwu6sm4kX1rQ0H/9W5nEZG2oYIrkqYSy4Y9AnQHPGVry5seeOSJV684ba+I36eHtsj2yAn5uerH+zZmZ/nPA2rdziMibUPPgiLpbQHwCdAX4P/+/dBXlRVrl5577PCMWBtXpL1dcuqoSHbQ/xTwuttZRKTtqOCKpLGy0hIbeBbnoJiQbdtcfPnVLxy6X7/w3kN7uZxOJLWN36svxSP7lmeHAle4nUVE2pYKrkiaKystWQ88SeKAsyXLVjb847a7n7vyrDERraog0rLuBSGuOG3vptzs4KlAndt5RKRtqeCKZIYPgZlAf4BHHn9u0RczZs385Rl7R9yNJZJ6vB64+px9wwG/7+84uwOKSIZRwRXJAIm1cScBTSS28f35r659u2+3QNVJB+2uNT1Fkpx+xND4wN75c7OC/hvdziIi7UMFVyRDlJWWbADuA3oBvtq6+tjPf/WHJ848cmhk2G7dXE4nkhpGDenBqYfsUV+QFzoJ0MGYIhlKBVckg5SVlswDpgADAL6YOXfDXfdOevF35+4bycvWfFzp3LrmZ3H1OfuFc7KDpwGr3c4jIu1HBVck8zwHLAV6A9xxzyNfz5335ZyrfrRPxOtxN5iIW4J+L9dfMC4S8HtuBd50O4+ItC8VXJEMU1ZaEgb+A/iAHIALL71qcn4wsuaik0bqLVnpdDwe+PVZY6LdCrLeyssJ/dHtPCLS/lRwRTJQWWnJGuB+oBDwNTY2xc86/7Inxu3Zo/aEA3XQmXQuZx05LD58UNfFPbrm/RBnzWgRyXAquCKZaybwBjAQ8Kxctabxwkt/M+nMI/ZoKh5Z6HI0kY5xyD797ePGD6rp2S3/QKDR7Twi0jFUcEUyVGKXs+eAGSQOOpsxa/6Ga/548+O/OmPvSNGALq7mE2lvwwd345JTR4WDfu9BwFq384hIx1HBFclgZaUlUeBBYBnOdAVeeu2tlffcP+nFP11QHO3ZNeRqPpH20rtbNteeNzYSCTednp0dmud2HhHpWCq4IhmurLSkAfg30AB0B7j9rocXvPXO+1NvuGj/SHaW39V8Im0tJ+Tn+ouKo+Gmxuu7devyitt5RKTjeWxb8+1FOoPCouKBwHVANVDr8Xh49rH/nJzbre/IGx/+PBCN6W+BpL/sLD9//mlxtGuO95m+fXqcgw4qE+mUNIIr0kmUlZYsB+4AegJZtm3z4wt+8WrArl9+zTn7Rfw+LZIr6S0U9HHDheOiOYHY63379DgXlVuRTksFV6QTKSst+RJ4BOgP+JrC4fjJp1/wZK6vYfnvzx0bVcmVdJUV9HH9heOiPrth2qABhd8H4m5nEhH3qOCKdD4fAK8CgwBvfX1D7OQzLnwy5K1b+vvzxkb8Pv1ZkPSSFfBx/QXjop5Y3afffLPwKFRuRTo9zcEV6YQKi4q9wFnA0cASIJ6Tk+178ekHzwx7cgfdPGlGIBpTR5DUlxXwcd0FY6NBGkrWr688ePz++2m3PhFRwRXprBIl90zgGBIlNxTK8r70zENnRr35g29+9ItAJKqSK6kr6Pdy3QXjolmexi/Wr684UOVWRDbSe5EinVRZaUkceBqYDAwmsaXvKWdc+LQ/Vr3k2vPHRgJ+/YmQ1JQV8HHt+WNjIW/jLJVbEWlOz14inVii5D4DvI4zJ9cpuWde9LQnUrXougvGRUJBn7shRZrpmpfFzZceEA15G7/4dmHpBJVbEWlOBVekk0uU3P8Br5FUck8946Jn6zesXvDPKyZEtOOZpIoBvfO49YoJsbKVSybf/u/7x5947OERtzOJSOrRHFwRAaCwqNgDnAaciDMnN+bxeLj9H3+acNQRhx5y48OfBxau2OBqRuncRhf15Opz9om9+dZ7D//q6r9cknhxJiKyBRVcEflOouR+HzgZWAGEAX5x6fnDr7j0gu/f+eycwKfzy9yMKJ3UofsNsC88cc/YY0/+7y9/veXum1RuRWRrVHBFZDOJknsI8BNgHVALcPwxh/W99eY/nvPKh0uynnu/VNObpMOcddSw+JFj+4XvffC/F153zRVPup1HRFKfCq6ItKiwqHgk8AucUdwKgFEjhuVPuv/2875eXtfl7ufm+qMx/f2Q9uP3efjFaXtHB/UJ1Nxz36STbr3pDx+5nUlE0oMKroi0qrCouD/wa6AAWAXQs0f34FOT7jrDm1Uw8OZHZwSq68KuZpTM1LNriGt+vG+0obZyxT9vv+fwJx++Y5HbmUQkfajgishWFRYVdwEuA4YCywA7EHQC+2YAAA1SSURBVPB77r/rH0cVj913v1senxn4anGluyElo+w3vDe/OmPv2NvvffDezf+864xZH7+23u1MIpJeVHBFZJsKi4qzgHOBg3BKbhTg0ot+NPTXV/zsBy9/sCjw3HsLvXH9OZFd4PV6+NHRJnb4fv2it91x790PP/a/a8tKSxrdziUi6UcFV0S2S2Jr3+NxlhJbA9QBjB5pCu698+9n1ob9PW97clZgfU2TmzElTfXuls1vf7RPJFy/Yf21N/zjms9nzn2srLREGziIyE5RwRWRHVJYVLw3cAngAcoAsoJB77//78aJhxx4wPfuem6e/9P5q13NKOnl4H36c/EpI2PPv/R6yY1/v/PiRfM/mOd2JhFJbyq4IrLDCouKewI/AwywnMSUhR+ecuyAG679zRmzvq0MPfDyl/6GpqibMSXF5YT8XHrqqOiwgXlNN/z1tkmvvPHO9WWlJRVu5xKR9KeCKyI7pbCo2A+chLMpRCVQBdC7V4/gvXf+7YSioqLhdz47NzB3YbmbMSVFHTimHz89aWT08y9mLbn2z7dct2Ll6ufLSku07a6ItAkVXBHZJYVFxcOBi4F8YCVgA1z2s3OGXXbx+SfPX7Q+8OArX2lurgBQ2COHn39/dKRnga/xplvumPzSq2/9qay0xHI7l4hkFhVcEdllhUXFecDZwIE483LrAbp17RK49W9/nHjwhP2Ln377G99rHy/xxLXUQqfk93n4/sSi+KkTi+LPPP/qvJtuueuh+vqG/5aVltS4nU1EMo8KrkgaMcY8DEwErrUs66k2vu5JwFTLsibtzOUTW/yOA84HgjgbQ8QBDp5Q3POv1199clZ2QZ+7n58XWLBE6+Z2JiOH9ODyH46OlK8tK7/mupvfmjNvwb+BmWWlJXoCEpF2oYIrkkaMMXEgZFlWm28ftqsFd6PExhCn4hTxapz5uXg8nv9v795jo6ryOIB/586rj5lO6Wum0JY603oB5VGQASoPFXBRWNT1hQsRRde3rG7UZNddo4kmxkSTzW5ifCzsaiKurBG0QSxbFrcCMvJody3MbR1g+pyZttPHdIbSzmP/OBfs7hpSFGln+H6Sk7nNbe+cTNPJt2d+9/zw9BMPXLnh7jUrD7o7dZurjunYBS21ZWUacO+qqdE5cl7s1d+/sX/Tu1vfisfjH/s8roGxnhsRpTYGXKIkIcvyxwB+CqAewGsAngAgATgE4FFFUQZlWfYB2AZgHkSpwCYAGwEUAbhHUZTPZVleAuAlABkAsgE8qSjK9pEBV5blu7/r+uczX5vDWQZgPYBiAB0ATgOArSDP+OrLzy2dO2dWxZZdjdrPvvRqhqPx7//C0LiTkabDTYvt8dWL7PE9tfs9zz7/yjZ/oOtNn8fFdrtEdFFIYz0BIhodRVFWq4drAfwCQKWiKLMABAA8pZ6zAvhUUZQKAGkAblEUZRGA5yECKwA8DuB+RVFmA7gfwIsjn0eW5SvOcf1R83lc3wB4AcA7AHIATAKg8QW6Tq/dsHHHY7969u2rp5q8b/9m6fANlaUJnZZvR8nOaNDi1msd8bd+vTRaZD518ra1D/7t/keeedQf6Potwy0RXUy6sZ4AEZ23awGUA/hSlmVA1LseHnH+U/XRC+CLEccT1ON1AFbJsnw7gPkATOd5/VHzeVxRALttDmcdRAe0SgA9AHqra2r91TW1f7l51fWTNj5y3/I7l1438f1dTbq/f9Wsicb4yVIy0eskrJg/OXHHsvJYU9Px1nUbHq9zHaqvAfC+z+PiPnFEdNEx4BIlHy2ADxRF2QgAsiybMOJv+X/qc7+r00ItgH8A2AOgBsB753P978PncQVtDuebAD6HKFsoBdANILStqrptW1X1n2+9aUXRYw9vWH7nsvLCLbsa9TVftSDGHRfGNa2kwbK5JYm7ri+PtrS2+R549Om6f+511QH4AEADbyIjorHCgEuUfPYAeEqW5RcBdAJ4HYAHogzhnGRZzgFwOYBFEDWxL0ME2gty/XNRw47b5nD+DsAsAHdgRND9cPvO1g+379x8+y03Fj/20L3L1ywrt723q0m/51ArojHW6I4n6UYdrruqKHHLEnu0u6ur88lnnjtcXVP7NYC/AviXz+PiL4yIxhQDLlGSURSlXpblFwDshqijr4MIqqP52aAsy38C0ABgWL1GhizLmRfi+qOhli0cVMsW5gC4DSOC7taPdrRs/WjHpjW3rip56IF7lt+zcopt14EWaeeXXskfjFyoadD3UGw1Y2Xl5Og1s4twrNHT/OxzLylVO3c3QATbIz6PKzbWcyQiAriLAhGNMbXl71UQQTcPQBeAs9tILaqcm/vgfevmzXdWzGr09iSq9nkNh475weqFi0OSNHBOs2H1wslDJTZzYveevUde+8NbzU2ek+0QpQgH2WKXiMYbBlwiGhdsDqce3wbdXIzYQxcALBaz7pcP33vljSuWV5rNpuwd+7zaalez1MsWwD8Ki8mA650l8ZVXl8b6+vqD72/9qP7NzVt8g4OngxBb0e33eVzcyJiIxiUGXCIaV9SgOwPACgBlEDfKBSBKKgAAN/7kGtt96++aXzFj2hVHGjsTNQdb9UeUTtbq/kBGgxZzp1lxTcXEoemOPOlwfYP7j69vbtzzxYF+AMcA7ARwVC0zISIatxhwiWhcUlv/TgKwEGLrMgOAXgB9Z76n0FZgfPyh9dMXL6ycU2jLz3c1dMRr6zr0RxoZdkfLqNeiQs7H4lmFw3Om2KSTLe3tn+3a3bDpnQ+6O7uCEYidNmp9Hlf7WM+ViGi0GHCJaNyzOZzpACoA3ADRlW0IYlX37E1N06aUm9ev/dnUhZULZtuseXmH3f74gQa//qA7gPAploiOlJmuh3OaFZXTrUMzywu0Le0Bf23t3vo3Nm0JNLe2JwD4AVQBOOzzuHhnHxElHQZcIkoa6qruZQCWQDSN0AIYhNiB4WzYlcvtpp/fsbq8csH8GWX2omJPS0/0oLvLcPREUNPU0nvJre6mGbSYWpqDK+258ellOcOlEy26Jk9zc+0X+/+96d2tXW3tPgmiBMQFsU9yE7f6IqJkxoBLRElJXdWdAhF0KyDC7imIG9PO1ohOyLbo16252b5g3lVl5WV2e35udvbxtp7hhuM9+oYTQcl9MojwYGqVlGak6USgdeTGZ5blDJfYLLrWjs4ut7uxqXafq3nbJ9WR3r5+LYA4gCMA9gJw+zyuwbGdORHRhcGAS0RJz+ZwZkCEXSdE2NVDrOh2Q6zwnlWQn2tYteK6ogXz5pTK8uVlJZOsBf7gQPRrT1B7zNura/GH0NY5gNNDybGlqyldj2KrGcVWMybbTLHp9pzoxAKzztvqD7jdSlPtPteJ7VW7gv2hATPEPwHDEK2XXQAUliAQUSpiwCWilGJzOA0A7ABmApgPwKKeikLcoBYe+f1paUZpxbIlhYsXOkumyLLdas3Pz8/JMofCp2NtgVC82T+ga+2MaNs6B9AaCKG7bxBj8baZbTKiyGpCidWMYqspWmozxYqtWVqjQavxBXp6Onx+f3Nzc/v+A4dbP/m0pi8cOWWCaNShgehIVwegHsA33N6LiFIdAy4RpSy1ZjcHQAlEi+IZAArV0wmIvXZDEB/Vn6XX6zQzp0+zzJ51RZ5cdlleSUmJzWYtsBbk50wwGvT6YN+paChyOt4fHtL0h4c1/ZFhXX94WApFhhAKD6E/MoSByDCGo3HEEwkkEgkkEkA8kYBW0kCnlSCpj3qthKxMAyxmIyyZBlhMhni2yRDNNhkS2SYjLGajZM5I0w1Fo1F/INjT3uHzeb3ejqPubzpdh+o7vz7aGEokEiYAWfi27XIHRJh1A/D6PK4+EBFdQhhwieiSYnM4TRCB1wEReO0QYVejjkEAEXX8341WhbYC45TL7Vk2a356fl5uRm5Odnq2JSvdYrFkms1ms8lkMmVmZGRkZqalS5JW0migkTQaaDQajSRpNLF4IhGLxeLxeCIei8Xj0Vg0Fg5HIv2hgVB/X19fb2/vQFd3T7izqzvS3hEIt7Z1hE94WyM9vX0xABnqSFPnfEYbRKBVIAJt6Ed7AYmIkgADLhFd0tSShnyINsH5ACarwwaxIpqA+Kg/BlG/OnJEMWL3hh9AgqgbNqiPZ47PPK9GnUc7AC+A4xBlB50Autl4gYjovzHgEhF9B5vDKUHU7+apoxDABIhSAAsAszr0ECu953oz1ZzjvBYiKPdD1Aj3QDS0CKpfd0ME2R6fx5Ucd74REY0xBlwioh9AbS2cASBdfTSqpzQjBiBCcBxiRTYO0awirI4hn8fFN2MioguEAZeIiIiIUoo01hMgIiIiIrqQGHCJiIiIKKUw4BIRERFRSmHAJSIiIqKUwoBLRERERCmFAZeIiIiIUgoDLhERERGlFAZcIiIiIkopDLhERERElFIYcImIiIgopTDgEhEREVFKYcAlIiIiopTCgEtEREREKYUBl4iIiIhSCgMuEREREaUUBlwiIiIiSikMuERERESUUhhwiYiIiCilMOASERERUUphwCUiIiKilMKAS0REREQp5T+Pm92jfXg7tAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "kiva_genders = kiva_loans['genders'].value_counts()\n",
+ "# print(kiva_genders)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "plt.pie(kiva_genders.values, labels=kiva_genders.index, shadow=True, autopct='%1.1f%%', startangle=90)\n",
+ "\n",
+ "plt.axis('equal')\n",
+ "plt.title('Loan Count Per gender', fontsize=30)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above chart, it is clear that majority of the loans were given to **Females**, with a percentage of around **80%**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.6. Total Loan amounts per gender\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gender_loans = pd.pivot_table(kiva_loans, values=['funded_amount'], aggfunc=[np.sum], index=['genders'])\n",
+ "gender_loan_sums = gender_loans['sum']['funded_amount']\n",
+ "#print(gender_loan_sums)\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "\n",
+ "sns.barplot(x=gender_loan_sums.index, y=gender_loan_sums.values)\n",
+ "\n",
+ "for i, v in enumerate(gender_loan_sums.values):\n",
+ " plt.text(i, (0.1e8), v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"Total loans per gender\", fontsize=30)\n",
+ "plt.xlabel(\"Gender\", fontsize=25)\n",
+ "plt.ylabel(\"Total Loan amount(USD)\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.7. Total Loan amounts per Country\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " funded_amount country world_region MPI \\\n",
+ "0 600.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "1 1000.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "2 250.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "4 800.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "5 800.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "\n",
+ " geo lat lon c_code \n",
+ "0 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "1 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "2 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "4 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "5 (28.3998551, 83.6895693) 28.399855 83.689569 NI "
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# merge the two datasets\n",
+ "loan_regions = pd.merge(kiva_loans, mpi_region_locations).dropna()\n",
+ "\n",
+ "# print(loan_regions.head())\n",
+ "labels = [loan_regions['funded_amount'], loan_regions['country'], loan_regions['world_region'], loan_regions['MPI'], loan_regions['geo'], loan_regions['lat'], loan_regions['lon'], loan_regions['country_code']]\n",
+ "keys = ['funded_amount', 'country', 'world_region', 'MPI', 'geo', 'lat', 'lon', 'c_code']\n",
+ "countries = pd.concat(labels, ignore_index=True, axis=1)\n",
+ "countries.columns = keys\n",
+ "countries.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | funded_amount | \n", + "country | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "c_code | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "600.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 1 | \n", + "1000.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 2 | \n", + "250.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 4 | \n", + "800.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 5 | \n", + "800.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "country_loans = pd.pivot_table(countries, index=['country'], aggfunc=[np.sum], values=['funded_amount'])\n",
+ "country_loan_sums = country_loans['sum'].sort_values(ascending=False, by=['funded_amount']).head(5)\n",
+ "# print(country_loan_sums.head())\n",
+ "funded_amts = country_loan_sums['funded_amount']\n",
+ "\n",
+ "plt.figure(figsize=(15, 8))\n",
+ "\n",
+ "sns.barplot(x=funded_amts.values, y=funded_amts.index)\n",
+ "\n",
+ "plt.title(\"Total loan amounts per Country(Top 5)\", fontsize=30)\n",
+ "plt.xlabel(\"Total Loan amount(USD)\", fontsize=25)\n",
+ "plt.ylabel(\"Country\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Nicaragua** had the largest vamount of loans borrowed, followed by **Peru** then **Guatemala**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.8. Total Loans Distribution across the world\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "amts = country_loans['sum']['funded_amount'].dropna()\n",
+ "# states = pd.Series(countries['country'].unique()).sort_values()\n",
+ "country_totals = pd.DataFrame({\n",
+ " 'countries': amts.index,\n",
+ " 'loan_totals': amts.values\n",
+ "})\n",
+ "\n",
+ "# Chloropleth map\n",
+ "m = folium.Map()\n",
+ "\n",
+ "world_geo = '../data/geodata/world-countries.json'\n",
+ "\n",
+ "bins = list(pd.Series(amts.values).quantile([0, 0.25, 0.5, 0.75, 1]))\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=world_geo,\n",
+ " data=country_totals,\n",
+ " columns=['countries', 'loan_totals'],\n",
+ " key_on='feature.properties.name',\n",
+ " fill_color='YlOrRd',\n",
+ " highlight=True,\n",
+ " fill_opacity=0.7,\n",
+ " line_opacity=0.2,\n",
+ " bins=bins,\n",
+ " reset=True,\n",
+ " legend_name='Loan Totals',\n",
+ ").add_to(m)\n",
+ "\n",
+ "display(m)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/intro_to_ML-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/intro_to_ML-checkpoint.ipynb
new file mode 100644
index 0000000..131f1fb
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/intro_to_ML-checkpoint.ipynb
@@ -0,0 +1,524 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Learning how to do data modelling with Scikit-Learn Algorithms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import folium\n",
+ "\n",
+ "sns.set(color_codes=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ " iris = sns.load_dataset('iris')\n",
+ "iris = iris.dropna(axis=0)\n",
+ "iris.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | sepal_length | \n", + "sepal_width | \n", + "petal_length | \n", + "petal_width | \n", + "species | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 1 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 2 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "setosa | \n", + "
| 3 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "setosa | \n", + "
| 4 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(iris, hue=\"species\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Supervised Learning\n",
+ "## Classification\n",
+ "### K-Neighbors Algorithm\n",
+ "This algorithm uses labelled data to predict where other certain new labelled data belongs to."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split, cross_val_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction values: \n",
+ " ['virginica' 'versicolor' 'virginica' 'versicolor' 'virginica' 'virginica'\n",
+ " 'versicolor' 'versicolor' 'setosa' 'virginica' 'setosa' 'setosa'\n",
+ " 'virginica' 'virginica' 'setosa' 'virginica' 'versicolor' 'setosa'\n",
+ " 'setosa' 'setosa' 'versicolor' 'setosa' 'versicolor' 'virginica'\n",
+ " 'virginica' 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'setosa'\n",
+ " 'virginica' 'virginica' 'versicolor' 'setosa' 'virginica' 'setosa'\n",
+ " 'setosa' 'setosa' 'setosa' 'versicolor' 'versicolor' 'setosa'\n",
+ " 'versicolor' 'virginica' 'versicolor']\n",
+ "Score: 0.9777777777777777\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Step 1: Import a class of model\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "\n",
+ "# Step 2: Initiate model with heper params\n",
+ "model = KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ "# step 3.0: Modify data into desired feature and target matrices\n",
+ "X = iris.drop('species', axis=1).values\n",
+ "y = iris['species'].values\n",
+ "\n",
+ "# Step 3.1: Divide the dataset into training and test sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+ "\n",
+ "# step 4: Fit/train the model with the training sets\n",
+ "model.fit(X_train, y_train)\n",
+ "\n",
+ "# Step 5: # Predict outputs of training set\n",
+ "prediction = model.predict(X_test)\n",
+ "print(\"Prediction values: \\n\", prediction)\n",
+ "\n",
+ "# Compute the accuracy score\n",
+ "score = model.score(X_test, y_test)\n",
+ "print(\"Score: \", score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparing score values for different values of K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "k_values = np.arange(1,11)\n",
+ "scores = []\n",
+ "# print(k_values)\n",
+ "for value in k_values:\n",
+ " model = KNeighborsClassifier(n_neighbors=value)\n",
+ " \n",
+ " X = iris.drop('species', axis=1).values\n",
+ " y = iris['species'].values\n",
+ " \n",
+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+ " \n",
+ " model.fit(X_train, y_train)\n",
+ " \n",
+ " prediction = model.predict(X_test)\n",
+ " \n",
+ " score = model.score(X_test, y_test)\n",
+ " \n",
+ " scores.append(score)\n",
+ " \n",
+ "# Plot score vs k\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "plt.plot(k_values, scores)\n",
+ "plt.title('Score Values for different k values')\n",
+ "plt.xlabel('K')\n",
+ "plt.ylabel('Score')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cross validation: A better way of measuring accuracy\n",
+ "This tests on several sets of the data which are auto-generated accordingly\n",
+ "### Cross_Validation_Score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "scores: [0.96666667 1. 1. 0.93333333 1. ]\n",
+ "Mean: 0.9800000000000001\n"
+ ]
+ }
+ ],
+ "source": [
+ "# We already have our model, feature and target ready\n",
+ "scores = cross_val_score(model, X, y, cv=5) # imported from sklearn.model_selection\n",
+ "print(\"scores: \", scores)\n",
+ "print(\"Mean: \", scores.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NAIVE BAYES CLASSIFICATION\n",
+ "### Gaussian NB model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['virginica', 'versicolor', 'versicolor', 'versicolor', 'virginica',\n",
+ " 'virginica', 'versicolor', 'versicolor', 'setosa', 'virginica',\n",
+ " 'setosa', 'setosa', 'virginica', 'virginica', 'setosa',\n",
+ " 'virginica', 'versicolor', 'setosa', 'setosa', 'setosa',\n",
+ " 'versicolor', 'setosa', 'versicolor', 'virginica', 'virginica',\n",
+ " 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'setosa',\n",
+ " 'versicolor', 'virginica', 'versicolor', 'setosa', 'virginica',\n",
+ " 'setosa', 'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
+ " 'setosa', 'versicolor', 'virginica', 'versicolor'], dtype='\n",
+ "Aims at minimizing cost functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "\n",
+ "# prepare data\n",
+ "# X, predictor var, house size in K sq feet \n",
+ "X = np.arange(1, 11)\n",
+ "# print(X)\n",
+ "# y, observed response, price of hose in $\n",
+ "y = np.array([300, 350, 500, 700, 800, 850, 900, 900, 1000, 1200])\n",
+ "# print y\n",
+ "\n",
+ "model = LinearRegression()\n",
+ "\n",
+ "X = X[:, np.newaxis]\n",
+ "\n",
+ "model.fit(X, y)\n",
+ "\n",
+ "predictions = model.predict(X)\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.scatter(X, y, color='red')\n",
+ "plt.plot(X, predictions, color='blue')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.945818181818182"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.score(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([92.72727273])"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.coef_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "239.9999999999999"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.intercept_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hence, our regression formular is **y=240 + 92.7x**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/kiva_analytics-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/kiva_analytics-checkpoint.ipynb
new file mode 100644
index 0000000..f5df506
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/kiva_analytics-checkpoint.ipynb
@@ -0,0 +1,2388 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# KIVA ANALYTICS\n",
+ "The kiva datasets contain data on money funded to borrowers from different countries.\n",
+ "A lot of information can be deduced from the datasets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importing the packages\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Taking a look at the main dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + "The main columns with missing data are disbursed_time column, funded_time column and borrower_genders column.
\n", + "Dropping these columns would lead to loss of other valuable data and filling them in with other values would lead to misguided(false) conclusions, hence we are going to work with only the available data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
while the maximum amount was **100000.00 USD**.\n", + "6. The minimum loan amount disbursed by the field agent to a borrower was **25.00 USD** while
while the maximum amount was **100,000.00 USD**. \n", + "7. The minimum duration for which a loan was disbursed was **1 month** while the maximum duration was **158 months**.\n", + "8. There were circumstance(s) when no lender contributed to a certain loan (0 lender count) while the maximum number of lenders a particular loan received was **2986 lenders**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loans borrowed and disbursed per country\n", + "To achieve this, we create a pivot table with the countries as the indices and a total summation of the funded and loaned amounts as the values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "Separating the funded and loan amount columns into Series objects for easier manipulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Selecting the individual summation columns\n", + "funded_amts = loans_per_country_summation['funded_amount']\n", + "loan_amts = loans_per_country_summation['loan_amount']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we select the countries at minimum and maximum values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Country at minimum funded amount: \n", + " country\n", + "Virgin Islands 0.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at maximum funded amount: \n", + " country\n", + "Philippines 54476375.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at minimum loan amount: \n", + " country\n", + "Guam 4300.0\n", + "Name: loan_amount, dtype: float64\n", + "Country at maximum loan amount: \n", + " country\n", + "Philippines 55342225.0\n", + "Name: loan_amount, dtype: float64\n" + ] + } + ], + "source": [ + "# Countries at minimum and maximum funded amount\n", + "min_funded_amt_country = funded_amts[(funded_amts == funded_amts.min())]\n", + "max_funded_amt_country = funded_amts[(funded_amts == funded_amts.max())]\n", + "\n", + "# Countries at minimum and maximum loan amount\n", + "min_loan_amt_country = loan_amts[(loan_amts == loan_amts.min())]\n", + "max_loan_amt_country = loan_amts[(loan_amts == loan_amts.max())]\n", + "\n", + "print(\"Country at minimum funded amount: \\n\", min_funded_amt_country)\n", + "print(\"Country at maximum funded amount: \\n\", max_funded_amt_country)\n", + "print(\"Country at minimum loan amount: \\n\", min_loan_amt_country)\n", + "print(\"Country at maximum loan amount: \\n\", max_loan_amt_country)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the above per country analysis, we can see that:\n", + "1. The country that received the largest amount of loan for all sectors was **Philippines**, receiving
a total of **55,342,225.00 USD**.\n", + "2. The country that received the smallest amount of loan for all sectors was **Guam**, receiving
a total of **4,300.00 USD**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loans borrowed per sector across all countries\n", + "To achieve this, we create a pivot table with the sectors as the indices and the summation of
\n", + "loan amounts as the column." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\"\u001b[0m, line \u001b[0;32m4\u001b[0m, in \u001b[0;35m\u001b[0m\n calculate(1, 0)\n",
+ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m4\u001b[1;36m, in \u001b[1;35mcalculate\u001b[1;36m\u001b[0m\n\u001b[1;33m return eval(x / y)\u001b[0m\n",
+ "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m division by zero\n"
+ ],
+ "output_type": "error"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> \u001b[1;32m\u001b[0m(4)\u001b[0;36mcalculate\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32m 1 \u001b[1;33m\u001b[1;31m# A test function aimed at raising an exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[1;32m 2 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mcalculate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[1;32m 3 \u001b[1;33m \u001b[1;34m\"\"\" Return the quotient of x and y \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\u001b[1;32m----> 4 \u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[0m\n",
+ "ipdb> quit\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Turn Exception mode on to Plain and debugger then call the function\n",
+ "%xmode Plain\n",
+ "%pdb on\n",
+ "calculate(1, 0)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Timing and Profiling\n",
+ "`%timeit` and `%%timeit` to show execution time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4.13 µs ± 452 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit sum(range(100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/ML.ipynb b/notebooks/ML.ipynb
new file mode 100644
index 0000000..d3e2ee9
--- /dev/null
+++ b/notebooks/ML.ipynb
@@ -0,0 +1,872 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Deep Machine Learning with Scikit-Learn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Basic libraries\n",
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import os\n",
+ "import copy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Defaults\n",
+ "sns.set(color_codes=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Breast cancer Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.datasets import load_breast_cancer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cancer = load_breast_cancer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cancer.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the kiva_loans.csv file\n",
+ "kiva_main_dataset = pd.read_csv('F:/isaka/python4DS/kiva_loans.csv')\n",
+ "\n",
+ "# Display the first 5 entries\n",
+ "kiva_main_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Handling Missing Data\n",
+ "The very first thing before starting on any analytics is to take care of all missing data in the relevant columns(key interest points) by either dropping them or filling them in.| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n", + "The main columns with missing data are disbursed_time column, funded_time column and borrower_genders column.
\n", + "Dropping these columns would lead to loss of other valuable data and filling them in with other values would lead to misguided(false) conclusions, hence we are going to work with only the available data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return all columns and rows without a missing value\n",
+ "kiva_available_data = kiva_main_dataset[kiva_main_dataset.notnull()]\n",
+ "\n",
+ "kiva_available_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic derivations\n",
+ "Now we have a clean dataset without any missing values. The first thing is to look at the basic derivations/computations of the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount partner_id \\\n",
+ "count 6.712050e+05 671205.000000 671205.000000 657698.000000 \n",
+ "mean 9.932486e+05 785.995061 842.397107 178.199616 \n",
+ "std 1.966113e+05 1130.398941 1198.660073 94.247581 \n",
+ "min 6.530470e+05 0.000000 25.000000 9.000000 \n",
+ "25% 8.230720e+05 250.000000 275.000000 126.000000 \n",
+ "50% 9.927800e+05 450.000000 500.000000 145.000000 \n",
+ "75% 1.163653e+06 900.000000 1000.000000 204.000000 \n",
+ "max 1.340339e+06 100000.000000 100000.000000 536.000000 \n",
+ "\n",
+ " term_in_months lender_count \n",
+ "count 671205.000000 671205.000000 \n",
+ "mean 13.739022 20.590922 \n",
+ "std 8.598919 28.459551 \n",
+ "min 1.000000 0.000000 \n",
+ "25% 8.000000 7.000000 \n",
+ "50% 13.000000 13.000000 \n",
+ "75% 14.000000 24.000000 \n",
+ "max 158.000000 2986.000000 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return the basic derivations of the dataset\n",
+ "kiva_available_data.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### From the above table, we can see that:\n",
+ "1. A total of **671,205** loans were given out during that specific period.\n",
+ "2. An average of **842.40 USD** was disbursed to the borrowers by the field agent.\n",
+ "3. An average of **786.00 USD** was disbursed to the field agent by Kiva.\n",
+ "4. The loans were disburded for an average duration of **14 months**.\n",
+ "5. The minimum loan amount disbursed by Kiva to the field agent was **0.00 USD** while | \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "partner_id | \n", + "term_in_months | \n", + "lender_count | \n", + "
|---|---|---|---|---|---|---|
| count | \n", + "6.712050e+05 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "657698.000000 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "
| mean | \n", + "9.932486e+05 | \n", + "785.995061 | \n", + "842.397107 | \n", + "178.199616 | \n", + "13.739022 | \n", + "20.590922 | \n", + "
| std | \n", + "1.966113e+05 | \n", + "1130.398941 | \n", + "1198.660073 | \n", + "94.247581 | \n", + "8.598919 | \n", + "28.459551 | \n", + "
| min | \n", + "6.530470e+05 | \n", + "0.000000 | \n", + "25.000000 | \n", + "9.000000 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "8.230720e+05 | \n", + "250.000000 | \n", + "275.000000 | \n", + "126.000000 | \n", + "8.000000 | \n", + "7.000000 | \n", + "
| 50% | \n", + "9.927800e+05 | \n", + "450.000000 | \n", + "500.000000 | \n", + "145.000000 | \n", + "13.000000 | \n", + "13.000000 | \n", + "
| 75% | \n", + "1.163653e+06 | \n", + "900.000000 | \n", + "1000.000000 | \n", + "204.000000 | \n", + "14.000000 | \n", + "24.000000 | \n", + "
| max | \n", + "1.340339e+06 | \n", + "100000.000000 | \n", + "100000.000000 | \n", + "536.000000 | \n", + "158.000000 | \n", + "2986.000000 | \n", + "
while the maximum amount was **100000.00 USD**.\n", + "6. The minimum loan amount disbursed by the field agent to a borrower was **25.00 USD** while
while the maximum amount was **100,000.00 USD**. \n", + "7. The minimum duration for which a loan was disbursed was **1 month** while the maximum duration was **158 months**.\n", + "8. There were circumstance(s) when no lender contributed to a certain loan (0 lender count) while the maximum number of lenders a particular loan received was **2986 lenders**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loans borrowed and disbursed per country\n", + "To achieve this, we create a pivot table with the countries as the indices and a total summation of the funded and loaned amounts as the values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sum \n",
+ " funded_amount loan_amount\n",
+ "country \n",
+ "Afghanistan 14000.0 14000.0\n",
+ "Albania 2490000.0 2666500.0\n",
+ "Armenia 11186675.0 12915400.0\n",
+ "Azerbaijan 2699575.0 2888700.0\n",
+ "Belize 114025.0 114025.0\n",
+ "Benin 516825.0 518950.0\n",
+ "Bhutan 15625.0 20000.0\n",
+ "Bolivia 18276200.0 19843250.0\n",
+ "Brazil 661025.0 662200.0\n",
+ "Burkina Faso 2909975.0 2972700.0"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a per_country pivot table\n",
+ "loans_per_country = pd.pivot_table(kiva_available_data, index=['country'], values=['funded_amount', 'loan_amount'], aggfunc=[np.sum])\n",
+ "\n",
+ "loans_per_country.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | sum | \n", + "|
|---|---|---|
| \n", + " | funded_amount | \n", + "loan_amount | \n", + "
| country | \n", + "\n", + " | \n", + " |
| Afghanistan | \n", + "14000.0 | \n", + "14000.0 | \n", + "
| Albania | \n", + "2490000.0 | \n", + "2666500.0 | \n", + "
| Armenia | \n", + "11186675.0 | \n", + "12915400.0 | \n", + "
| Azerbaijan | \n", + "2699575.0 | \n", + "2888700.0 | \n", + "
| Belize | \n", + "114025.0 | \n", + "114025.0 | \n", + "
| Benin | \n", + "516825.0 | \n", + "518950.0 | \n", + "
| Bhutan | \n", + "15625.0 | \n", + "20000.0 | \n", + "
| Bolivia | \n", + "18276200.0 | \n", + "19843250.0 | \n", + "
| Brazil | \n", + "661025.0 | \n", + "662200.0 | \n", + "
| Burkina Faso | \n", + "2909975.0 | \n", + "2972700.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " funded_amount loan_amount\n",
+ "country \n",
+ "Afghanistan 14000.0 14000.0\n",
+ "Albania 2490000.0 2666500.0\n",
+ "Armenia 11186675.0 12915400.0\n",
+ "Azerbaijan 2699575.0 2888700.0\n",
+ "Belize 114025.0 114025.0"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Formatting to a more clear dataframe\n",
+ "loans_per_country_summation = loans_per_country['sum']\n",
+ "\n",
+ "loans_per_country_summation.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we have a clear DataFrame with the countries as indices and the summations of loaned and funded amounts as values. | \n", + " | funded_amount | \n", + "loan_amount | \n", + "
|---|---|---|
| country | \n", + "\n", + " | \n", + " |
| Afghanistan | \n", + "14000.0 | \n", + "14000.0 | \n", + "
| Albania | \n", + "2490000.0 | \n", + "2666500.0 | \n", + "
| Armenia | \n", + "11186675.0 | \n", + "12915400.0 | \n", + "
| Azerbaijan | \n", + "2699575.0 | \n", + "2888700.0 | \n", + "
| Belize | \n", + "114025.0 | \n", + "114025.0 | \n", + "
\n", + "Separating the funded and loan amount columns into Series objects for easier manipulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Selecting the individual summation columns\n", + "funded_amts = loans_per_country_summation['funded_amount']\n", + "loan_amts = loans_per_country_summation['loan_amount']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we select the countries at minimum and maximum values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Country at minimum funded amount: \n", + " country\n", + "Virgin Islands 0.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at maximum funded amount: \n", + " country\n", + "Philippines 54476375.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at minimum loan amount: \n", + " country\n", + "Guam 4300.0\n", + "Name: loan_amount, dtype: float64\n", + "Country at maximum loan amount: \n", + " country\n", + "Philippines 55342225.0\n", + "Name: loan_amount, dtype: float64\n" + ] + } + ], + "source": [ + "# Countries at minimum and maximum funded amount\n", + "min_funded_amt_country = funded_amts[(funded_amts == funded_amts.min())]\n", + "max_funded_amt_country = funded_amts[(funded_amts == funded_amts.max())]\n", + "\n", + "# Countries at minimum and maximum loan amount\n", + "min_loan_amt_country = loan_amts[(loan_amts == loan_amts.min())]\n", + "max_loan_amt_country = loan_amts[(loan_amts == loan_amts.max())]\n", + "\n", + "print(\"Country at minimum funded amount: \\n\", min_funded_amt_country)\n", + "print(\"Country at maximum funded amount: \\n\", max_funded_amt_country)\n", + "print(\"Country at minimum loan amount: \\n\", min_loan_amt_country)\n", + "print(\"Country at maximum loan amount: \\n\", max_loan_amt_country)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the above per country analysis, we can see that:\n", + "1. The country that received the largest amount of loan for all sectors was **Philippines**, receiving
a total of **55,342,225.00 USD**.\n", + "2. The country that received the smallest amount of loan for all sectors was **Guam**, receiving
a total of **4,300.00 USD**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loans borrowed per sector across all countries\n", + "To achieve this, we create a pivot table with the sectors as the indices and the summation of
\n", + "loan amounts as the column." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "sector \n",
+ "Agriculture 143067875.0\n",
+ "Arts 12240325.0\n",
+ "Clothing 37300925.0\n",
+ "Construction 6695150.0\n",
+ "Education 30968525.0\n",
+ "Entertainment 1389150.0\n",
+ "Food 121606150.0\n",
+ "Health 9843400.0\n",
+ "Housing 23663950.0\n",
+ "Manufacturing 5447900.0\n",
+ "Personal Use 14955350.0\n",
+ "Retail 98122900.0\n",
+ "Services 48057450.0\n",
+ "Transportation 11066900.0\n",
+ "Wholesale 995200.0"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# return loan amount totals per sector\n",
+ "loans_per_sector = pd.pivot_table(kiva_available_data, index=['sector'], values=['loan_amount'], aggfunc=[np.sum])\n",
+ "loans_per_sector_summation = loans_per_sector['sum']\n",
+ "loans_per_sector_summation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Basic analysis of the sectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| sector | \n", + "\n", + " |
| Agriculture | \n", + "143067875.0 | \n", + "
| Arts | \n", + "12240325.0 | \n", + "
| Clothing | \n", + "37300925.0 | \n", + "
| Construction | \n", + "6695150.0 | \n", + "
| Education | \n", + "30968525.0 | \n", + "
| Entertainment | \n", + "1389150.0 | \n", + "
| Food | \n", + "121606150.0 | \n", + "
| Health | \n", + "9843400.0 | \n", + "
| Housing | \n", + "23663950.0 | \n", + "
| Manufacturing | \n", + "5447900.0 | \n", + "
| Personal Use | \n", + "14955350.0 | \n", + "
| Retail | \n", + "98122900.0 | \n", + "
| Services | \n", + "48057450.0 | \n", + "
| Transportation | \n", + "11066900.0 | \n", + "
| Wholesale | \n", + "995200.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "count 1.500000e+01\n",
+ "mean 3.769474e+07\n",
+ "std 4.587355e+07\n",
+ "min 9.952000e+05\n",
+ "25% 8.269275e+06\n",
+ "50% 1.495535e+07\n",
+ "75% 4.267919e+07\n",
+ "max 1.430679e+08"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return the basic sector analytics\n",
+ "loans_per_sector_summation.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table we see that:\n",
+ "1. The borrowed loans were used in a total of **15** different sectors across all countries.\n",
+ "2. The average borrowed loan amount used per sector in all countries was **37,694,740 USD**.\n",
+ "3. The minimum amount used in a particular sector was **995,200 USD** while the maximum amount was **143,067,900 USD**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Additional per-sector analysis\n",
+ "Here we return the sectors with the minimum and maximum amounts of loan used on them, respectively "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Agriculture 143067875.0\n",
+ "Arts 12240325.0\n",
+ "Clothing 37300925.0\n",
+ "Construction 6695150.0\n",
+ "Education 30968525.0\n",
+ "Entertainment 1389150.0\n",
+ "Food 121606150.0\n",
+ "Health 9843400.0\n",
+ "Housing 23663950.0\n",
+ "Manufacturing 5447900.0\n",
+ "Personal Use 14955350.0\n",
+ "Retail 98122900.0\n",
+ "Services 48057450.0\n",
+ "Transportation 11066900.0\n",
+ "Wholesale 995200.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# return a series object of all loan amount totals per sector.\n",
+ "loan_amts_per_sector = loans_per_sector_summation['loan_amount']\n",
+ "loan_amts_per_sector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Wholesale 995200.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return sector with minimum amount\n",
+ "loan_amts_per_sector[loan_amts_per_sector == loan_amts_per_sector.min()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Agriculture 143067875.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return sector with maximum amount\n",
+ "loan_amts_per_sector[loan_amts_per_sector == loan_amts_per_sector.max()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above, \n",
+ "1. Most of the funds borrowed were used on **Agriculture**, with a total of **143,067,900 USD** used on it.\n",
+ "2. The sector with the smallest amount used on it was **Wholesale**, with a total of **995,200 USD** used on it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Date/Time based Analytics\n",
+ "Since the dates provided in the dataset are all objects, we first convert them to date/time values for easier manipulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract all time columns and convert them to datetime values\n",
+ "kiva_available_data['posted_time'] = pd.to_datetime(kiva_available_data['posted_time'])\n",
+ "kiva_available_data['funded_time'] = pd.to_datetime(kiva_available_data['funded_time'])\n",
+ "\n",
+ "# converting to YYYY-mm-dd formats then back to datetime values\n",
+ "kiva_available_data['posted_time'] = pd.to_datetime(kiva_available_data['posted_time'].dt.strftime(\"%Y-%m-%d\"))\n",
+ "kiva_available_data['funded_time'] = pd.to_datetime(kiva_available_data['funded_time'].dt.strftime(\"%Y-%m-%d\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Next we create a column containing the time difference between when the loan was funded and when it was posted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| count | \n", + "1.500000e+01 | \n", + "
| mean | \n", + "3.769474e+07 | \n", + "
| std | \n", + "4.587355e+07 | \n", + "
| min | \n", + "9.952000e+05 | \n", + "
| 25% | \n", + "8.269275e+06 | \n", + "
| 50% | \n", + "1.495535e+07 | \n", + "
| 75% | \n", + "4.267919e+07 | \n", + "
| max | \n", + "1.430679e+08 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency ... posted_time disbursed_time \\\n",
+ "0 Lahore PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "1 Lahore PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "2 Maynaguri INR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "3 Lahore PKR ... 2014-01-01 2013-12-24 08:00:00+00:00 \n",
+ "4 Abdul Hakeem PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "\n",
+ " funded_time term_in_months lender_count tags \\\n",
+ "0 2014-01-02 12.0 12 NaN \n",
+ "1 2014-01-02 11.0 14 NaN \n",
+ "2 2014-01-01 43.0 6 user_favorite, user_favorite \n",
+ "3 2014-01-01 11.0 8 NaN \n",
+ "4 2014-01-01 14.0 16 NaN \n",
+ "\n",
+ " borrower_genders repayment_interval date funding_period \n",
+ "0 female irregular 2014-01-01 1 days \n",
+ "1 female, female irregular 2014-01-01 1 days \n",
+ "2 female bullet 2014-01-01 0 days \n",
+ "3 female irregular 2014-01-01 0 days \n",
+ "4 female monthly 2014-01-01 0 days \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a funding period column: Difference between time posted and time funded for a loan\n",
+ "kiva_available_data['funding_period'] = (kiva_available_data['funded_time'] - kiva_available_data['posted_time'])\n",
+ "\n",
+ "kiva_available_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "... | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "funding_period | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "1 days | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "1 days | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "0 days | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "0 days | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "0 days | \n", + "
5 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "42344 1080950 125.0 125.0 Services Services \n",
+ "\n",
+ " use country_code country region currency ... \\\n",
+ "42344 add more stock to my shop KE Kenya NaN KES ... \n",
+ "\n",
+ " posted_time disbursed_time funded_time term_in_months \\\n",
+ "42344 2014-04-08 2015-06-09 14:40:00+00:00 2015-06-03 6.0 \n",
+ "\n",
+ " lender_count tags borrower_genders repayment_interval date \\\n",
+ "42344 5 NaN female irregular 2014-04-08 \n",
+ "\n",
+ " funding_period \n",
+ "42344 421 days \n",
+ "\n",
+ "[1 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract the funding period column where there are no null values\n",
+ "funding_period = kiva_available_data[kiva_available_data['funding_period'].notnull()]['funding_period']\n",
+ "\n",
+ "# return the maximum funding period\n",
+ "min_funding_period = funding_period.max()\n",
+ "\n",
+ "#Fetch the data at that period\n",
+ "kiva_available_data.query('funding_period == @min_funding_period')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### From the data above, we can see that:\n",
+ " 1. The maximum amount it took for a loan from the time it was posted on kiva to the time it was funded was **421 days**.\n",
+ " 2. This loan also took 6 days to be disbursed to the borrower from the time it was funded.\n",
+ " 3. The loan, a total of **125 USD**, was loaned to a female in kenya, which she used to add more stock to her shop."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More date/time analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 622874\n",
+ "mean 14 days 12:29:57.844186\n",
+ "std 14 days 10:18:20.006715\n",
+ "min -18 days +00:00:00\n",
+ "25% 5 days 00:00:00\n",
+ "50% 9 days 00:00:00\n",
+ "75% 22 days 00:00:00\n",
+ "max 421 days 00:00:00\n",
+ "Name: funding_period, dtype: object"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "funding_period.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table, we see that:\n",
+ "1. It took an average of **14 days** for a loan to be funded from the time of posting.\n",
+ "2. Some loans were funded the same day they were posted."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Miscellaneous Analytics\n",
+ "### Here we look at other general trends from the dataset\n",
+ "First we create a pivot table to compare the average loan amounts borrowed and their respective repayment intervals."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "... | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "funding_period | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 42344 | \n", + "1080950 | \n", + "125.0 | \n", + "125.0 | \n", + "Services | \n", + "Services | \n", + "add more stock to my shop | \n", + "KE | \n", + "Kenya | \n", + "NaN | \n", + "KES | \n", + "... | \n", + "2014-04-08 | \n", + "2015-06-09 14:40:00+00:00 | \n", + "2015-06-03 | \n", + "6.0 | \n", + "5 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-04-08 | \n", + "421 days | \n", + "
1 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean\n",
+ " loan_amount\n",
+ "repayment_interval \n",
+ "bullet 896.558294\n",
+ "irregular 691.401979\n",
+ "monthly 945.586446\n",
+ "weekly 234.676080"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a per repayment_interval pivot table\n",
+ "repayment_interval = pd.pivot_table(kiva_available_data, index=['repayment_interval'], values=['loan_amount'], aggfunc=[np.mean])\n",
+ "\n",
+ "repayment_interval"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Restack the dataframe for easier manipulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | mean | \n", + "
|---|---|
| \n", + " | loan_amount | \n", + "
| repayment_interval | \n", + "\n", + " |
| bullet | \n", + "896.558294 | \n", + "
| irregular | \n", + "691.401979 | \n", + "
| monthly | \n", + "945.586446 | \n", + "
| weekly | \n", + "234.676080 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "repayment_interval \n",
+ "bullet 896.558294\n",
+ "irregular 691.401979\n",
+ "monthly 945.586446\n",
+ "weekly 234.676080"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "repayment_interval_mean = repayment_interval['mean']\n",
+ "repayment_interval_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table, we see that:\n",
+ "1. Majority of the loans were repaid on a monthly basis, followed by the bullet method, then irregularly.\n",
+ "2. Loan borrowers didn't prefer repaying on weekly basis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MORE DATASETS\n",
+ "Taking a look at the regions dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| repayment_interval | \n", + "\n", + " |
| bullet | \n", + "896.558294 | \n", + "
| irregular | \n", + "691.401979 | \n", + "
| monthly | \n", + "945.586446 | \n", + "
| weekly | \n", + "234.676080 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " LocationName ISO country region world_region MPI \\\n",
+ "0 Badakhshan, Afghanistan AFG Afghanistan Badakhshan South Asia 0.387 \n",
+ "1 Badghis, Afghanistan AFG Afghanistan Badghis South Asia 0.466 \n",
+ "2 Baghlan, Afghanistan AFG Afghanistan Baghlan South Asia 0.300 \n",
+ "3 Balkh, Afghanistan AFG Afghanistan Balkh South Asia 0.301 \n",
+ "4 Bamyan, Afghanistan AFG Afghanistan Bamyan South Asia 0.325 \n",
+ "\n",
+ " geo lat lon \n",
+ "0 (36.7347725, 70.81199529999999) 36.734772 70.811995 \n",
+ "1 (35.1671339, 63.7695384) 35.167134 63.769538 \n",
+ "2 (35.8042947, 69.2877535) 35.804295 69.287754 \n",
+ "3 (36.7550603, 66.8975372) 36.755060 66.897537 \n",
+ "4 (34.8100067, 67.8212104) 34.810007 67.821210 "
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the regions dataset into memory\n",
+ "regions = pd.read_csv('F:/isaka/kiva/kiva_mpi_region_locations.csv')\n",
+ "regions.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Average poverty index per region"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | LocationName | \n", + "ISO | \n", + "country | \n", + "region | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Badakhshan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badakhshan | \n", + "South Asia | \n", + "0.387 | \n", + "(36.7347725, 70.81199529999999) | \n", + "36.734772 | \n", + "70.811995 | \n", + "
| 1 | \n", + "Badghis, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badghis | \n", + "South Asia | \n", + "0.466 | \n", + "(35.1671339, 63.7695384) | \n", + "35.167134 | \n", + "63.769538 | \n", + "
| 2 | \n", + "Baghlan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Baghlan | \n", + "South Asia | \n", + "0.300 | \n", + "(35.8042947, 69.2877535) | \n", + "35.804295 | \n", + "69.287754 | \n", + "
| 3 | \n", + "Balkh, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Balkh | \n", + "South Asia | \n", + "0.301 | \n", + "(36.7550603, 66.8975372) | \n", + "36.755060 | \n", + "66.897537 | \n", + "
| 4 | \n", + "Bamyan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Bamyan | \n", + "South Asia | \n", + "0.325 | \n", + "(34.8100067, 67.8212104) | \n", + "34.810007 | \n", + "67.821210 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean\n",
+ " MPI\n",
+ "world_region \n",
+ "Arab States 0.115287\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Europe and Central Asia 0.025273\n",
+ "Latin America and Caribbean 0.063665\n",
+ "South Asia 0.219630\n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# create an MPI pivot table.\n",
+ "mpi_avg = pd.pivot_table(regions, index=['world_region'], values=['MPI'], aggfunc=[np.mean])\n",
+ "mpi_avg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | mean | \n", + "
|---|---|
| \n", + " | MPI | \n", + "
| world_region | \n", + "\n", + " |
| Arab States | \n", + "0.115287 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Arab States 0.115287\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Europe and Central Asia 0.025273\n",
+ "Latin America and Caribbean 0.063665\n",
+ "South Asia 0.219630\n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Refactoring the dataframe\n",
+ "mpi_avg_mean = mpi_avg['mean']\n",
+ "mpi_avg_mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Arab States | \n", + "0.115287 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Region with maximum poverty index\n",
+ "# Extract the maximum p_index\n",
+ "max_mpi = mpi_avg_mean['MPI'].max()\n",
+ "\n",
+ "# Query the region at that particular index\n",
+ "mpi_avg_mean.query('MPI == @max_mpi')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Europe and Central Asia 0.025273"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Region with minimum poverty index\n",
+ "# Extract the minimum p_index\n",
+ "min_mpi = mpi_avg_mean['MPI'].min()\n",
+ "\n",
+ "# Query the region at that particular index\n",
+ "mpi_avg_mean.query('MPI == @min_mpi')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# From the above data, we can say that\n",
+ "1. **Sub-Saharan Africa** region has the highest poverty index, with a value of **0.337**.\n",
+ "2. **Europe and Centarl Asia** region has the lowest poverty index, with a value of **0.025**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/.ipynb_checkpoints/tests-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/tests-checkpoint.ipynb
new file mode 100644
index 0000000..a995a9b
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/tests-checkpoint.ipynb
@@ -0,0 +1,71 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Testing of Miscellaneous code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([342717, 257158, 70728, 602], dtype=int64)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv('F:/isaka/python4DS/kiva_loans.csv')\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Introduction-Ipython.ipynb b/notebooks/Introduction-Ipython.ipynb
new file mode 100644
index 0000000..73befd1
--- /dev/null
+++ b/notebooks/Introduction-Ipython.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Introduction to Ipython"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Practice with ? and ?? help commands"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Help on the len() function\n",
+ "len?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Test function\n",
+ "def add(a, b):\n",
+ " \"\"\" Return the sum of a and b \"\"\"\n",
+ " return a + b\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Help on add method\n",
+ "add?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access the source code of the method\n",
+ "add??"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Error Handling and Debugging\n",
+ "This involves using Exception mode and python debugger to trace errors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A test function aimed at raising an exception\n",
+ "def calculate(x, y):\n",
+ " \"\"\" Return the quotient of x and y \"\"\"\n",
+ " return eval(x / y)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Exception reporting mode: Plain\n",
+ "Automatic pdb calling has been turned ON\n"
+ ]
+ },
+ {
+ "ename": "ZeroDivisionError",
+ "evalue": "division by zero",
+ "traceback": [
+ "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
+ " File \u001b[0;32m\"| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Europe and Central Asia | \n", + "0.025273 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
+ "0 17.99 10.38 122.80 1001.0 0.11840 \n",
+ "1 20.57 17.77 132.90 1326.0 0.08474 \n",
+ "2 19.69 21.25 130.00 1203.0 0.10960 \n",
+ "3 11.42 20.38 77.58 386.1 0.14250 \n",
+ "4 20.29 14.34 135.10 1297.0 0.10030 \n",
+ "\n",
+ " mean compactness mean concavity mean concave points mean symmetry \\\n",
+ "0 0.27760 0.3001 0.14710 0.2419 \n",
+ "1 0.07864 0.0869 0.07017 0.1812 \n",
+ "2 0.15990 0.1974 0.12790 0.2069 \n",
+ "3 0.28390 0.2414 0.10520 0.2597 \n",
+ "4 0.13280 0.1980 0.10430 0.1809 \n",
+ "\n",
+ " mean fractal dimension ... worst radius worst texture worst perimeter \\\n",
+ "0 0.07871 ... 25.38 17.33 184.60 \n",
+ "1 0.05667 ... 24.99 23.41 158.80 \n",
+ "2 0.05999 ... 23.57 25.53 152.50 \n",
+ "3 0.09744 ... 14.91 26.50 98.87 \n",
+ "4 0.05883 ... 22.54 16.67 152.20 \n",
+ "\n",
+ " worst area worst smoothness worst compactness worst concavity \\\n",
+ "0 2019.0 0.1622 0.6656 0.7119 \n",
+ "1 1956.0 0.1238 0.1866 0.2416 \n",
+ "2 1709.0 0.1444 0.4245 0.4504 \n",
+ "3 567.7 0.2098 0.8663 0.6869 \n",
+ "4 1575.0 0.1374 0.2050 0.4000 \n",
+ "\n",
+ " worst concave points worst symmetry worst fractal dimension \n",
+ "0 0.2654 0.4601 0.11890 \n",
+ "1 0.1860 0.2750 0.08902 \n",
+ "2 0.2430 0.3613 0.08758 \n",
+ "3 0.2575 0.6638 0.17300 \n",
+ "4 0.1625 0.2364 0.07678 \n",
+ "\n",
+ "[5 rows x 30 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cancer_df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
+ "cancer_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# K-Fold Cross Validation\n",
+ "Model Evaluation\n",
+ "## K-Fold for parameter tuning\n",
+ "### The objective here is to find the optimum hyperparam(s) that/which maximizes the mean score\n",
+ "#### KNN for example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | mean radius | \n", + "mean texture | \n", + "mean perimeter | \n", + "mean area | \n", + "mean smoothness | \n", + "mean compactness | \n", + "mean concavity | \n", + "mean concave points | \n", + "mean symmetry | \n", + "mean fractal dimension | \n", + "... | \n", + "worst radius | \n", + "worst texture | \n", + "worst perimeter | \n", + "worst area | \n", + "worst smoothness | \n", + "worst compactness | \n", + "worst concavity | \n", + "worst concave points | \n", + "worst symmetry | \n", + "worst fractal dimension | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "17.99 | \n", + "10.38 | \n", + "122.80 | \n", + "1001.0 | \n", + "0.11840 | \n", + "0.27760 | \n", + "0.3001 | \n", + "0.14710 | \n", + "0.2419 | \n", + "0.07871 | \n", + "... | \n", + "25.38 | \n", + "17.33 | \n", + "184.60 | \n", + "2019.0 | \n", + "0.1622 | \n", + "0.6656 | \n", + "0.7119 | \n", + "0.2654 | \n", + "0.4601 | \n", + "0.11890 | \n", + "
| 1 | \n", + "20.57 | \n", + "17.77 | \n", + "132.90 | \n", + "1326.0 | \n", + "0.08474 | \n", + "0.07864 | \n", + "0.0869 | \n", + "0.07017 | \n", + "0.1812 | \n", + "0.05667 | \n", + "... | \n", + "24.99 | \n", + "23.41 | \n", + "158.80 | \n", + "1956.0 | \n", + "0.1238 | \n", + "0.1866 | \n", + "0.2416 | \n", + "0.1860 | \n", + "0.2750 | \n", + "0.08902 | \n", + "
| 2 | \n", + "19.69 | \n", + "21.25 | \n", + "130.00 | \n", + "1203.0 | \n", + "0.10960 | \n", + "0.15990 | \n", + "0.1974 | \n", + "0.12790 | \n", + "0.2069 | \n", + "0.05999 | \n", + "... | \n", + "23.57 | \n", + "25.53 | \n", + "152.50 | \n", + "1709.0 | \n", + "0.1444 | \n", + "0.4245 | \n", + "0.4504 | \n", + "0.2430 | \n", + "0.3613 | \n", + "0.08758 | \n", + "
| 3 | \n", + "11.42 | \n", + "20.38 | \n", + "77.58 | \n", + "386.1 | \n", + "0.14250 | \n", + "0.28390 | \n", + "0.2414 | \n", + "0.10520 | \n", + "0.2597 | \n", + "0.09744 | \n", + "... | \n", + "14.91 | \n", + "26.50 | \n", + "98.87 | \n", + "567.7 | \n", + "0.2098 | \n", + "0.8663 | \n", + "0.6869 | \n", + "0.2575 | \n", + "0.6638 | \n", + "0.17300 | \n", + "
| 4 | \n", + "20.29 | \n", + "14.34 | \n", + "135.10 | \n", + "1297.0 | \n", + "0.10030 | \n", + "0.13280 | \n", + "0.1980 | \n", + "0.10430 | \n", + "0.1809 | \n", + "0.05883 | \n", + "... | \n", + "22.54 | \n", + "16.67 | \n", + "152.20 | \n", + "1575.0 | \n", + "0.1374 | \n", + "0.2050 | \n", + "0.4000 | \n", + "0.1625 | \n", + "0.2364 | \n", + "0.07678 | \n", + "
5 rows × 30 columns
\n", + ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# A better way of model evaluation\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "# Using the breast cancer\n",
+ "X = cancer.data\n",
+ "y = cancer.target\n",
+ "\n",
+ "# Set up a range of n_neighbors from 1 to 30\n",
+ "neighbors = range(1, 31)\n",
+ "\n",
+ "# Initialize mean scores\n",
+ "scores = []\n",
+ "\n",
+ "# loop through the k ranges\n",
+ "for k in neighbors:\n",
+ " knn = KNeighborsClassifier(n_neighbors=k)\n",
+ " \n",
+ " # Obtain cross val scores (scoring mode=accuracy since its classification(MSE for regression))\n",
+ " score = cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()\n",
+ " \n",
+ " # append the score to scores\n",
+ " scores.append(score)\n",
+ " \n",
+ "# Plot scores vs neighbors\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "plt.plot(neighbors, scores)\n",
+ "\n",
+ "plt.title(\"Scores for different knn n_neighbors\")\n",
+ "plt.xlabel(\"neighbors\")\n",
+ "plt.ylabel(\"score\")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The maximum score is at k between 10 and 18"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# K-Fold for model selection\n",
+ "## Used to determine which model returns the highest score for a particular problem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9317507129893698"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# KNN\n",
+ "knn = KNeighborsClassifier(n_neighbors=19)\n",
+ "score = cross_val_score(knn, X, y, cv=10, scoring='accuracy').mean()\n",
+ "score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9508998790078644"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Logistic regression\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "logr = LogisticRegression()\n",
+ "score = cross_val_score(logr, X, y, cv=10, scoring='accuracy').mean()\n",
+ "score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Logistic regression performs better\n",
+ "### K-Fold can also be used for feature selection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Decision Trees \n",
+ "For classification and regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "# import graphviz for visualizing the tree\n",
+ "import graphviz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0\n",
+ "0.9370629370629371\n"
+ ]
+ }
+ ],
+ "source": [
+ "cancer = load_breast_cancer()\n",
+ "X = cancer.data\n",
+ "y = cancer.target\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)\n",
+ "\n",
+ "tree = DecisionTreeClassifier(random_state=0)\n",
+ "\n",
+ "tree.fit(X_train, y_train)\n",
+ "\n",
+ "tree.predict(X_test)\n",
+ "\n",
+ "training_score = tree.score(X_train, y_train)\n",
+ "test_score = tree.score(X_test, y_test)\n",
+ "\n",
+ "print(training_score)\n",
+ "print(test_score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### here, the training score is very high(100%) and the test score relatively low(93%). Thiks is an indication of overfitting. To handle this, we pre-prune the tree, i.e., controlling the max_depth of the tree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9882629107981221\n",
+ "0.951048951048951\n"
+ ]
+ }
+ ],
+ "source": [
+ "tree = DecisionTreeClassifier(random_state=0, max_depth=4)\n",
+ "\n",
+ "tree.fit(X_train, y_train)\n",
+ "\n",
+ "tree.predict(X_test)\n",
+ "\n",
+ "training_score = tree.score(X_train, y_train)\n",
+ "test_score = tree.score(X_test, y_test)\n",
+ "\n",
+ "print(training_score)\n",
+ "print(test_score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This is slightly good, but still not generalized. Random forests or gradient boost forests are used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ "\r\n",
+ "\r\n",
+ "\r\n",
+ "\r\n",
+ "\r\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "export_graphviz(tree, out_file=\"../data/tree.dot\", class_names=[\"malignant\", \"benign\"],\n",
+ " feature_names=cancer.feature_names, impurity=False, filled=True)\n",
+ "\n",
+ "with open(\"../data/tree.dot\") as f:\n",
+ " dot_graph = f.read()\n",
+ "graphviz.Source(dot_graph)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Numpy.ipynb b/notebooks/Numpy.ipynb
new file mode 100644
index 0000000..f9c6dfa
--- /dev/null
+++ b/notebooks/Numpy.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# INTRODUCTION TO NUMPY"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Arrays"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Arrays from Python lists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Create an array of integers\n",
+ "np.array([1,2,3,4,5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### NB: Numpy is constrained to contain arrays of same type. If they are not, numpy upcasts them. Use the `dtype` keyword to explicitly typecast the arrays."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.array([1, 2, 3, 4], dtype='float32')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Multidimensional array using a list of lists\n",
+ "np.array([range(i, i + 3) for i in [2, 3, 4]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Arrays from Scratch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A length-10 integer array filled with zeros\n",
+ "np.zeros(10, dtype='int')\n",
+ "\n",
+ "# Create a 3x5 floating-point array filled with ones\n",
+ "np.ones((3, 5), dtype=np.float)\n",
+ "\n",
+ "# Create a 3x5 array filled with 3.14\n",
+ "np.full((3, 5), 3.14)\n",
+ "\n",
+ "# Create an array filled with a linear sequence\n",
+ "# Starting at 0, ending at 20, stepping by 2\n",
+ "# (this is similar to the built-in range() function)\n",
+ "\n",
+ "np.arange(0, 20, 2)\n",
+ "\n",
+ "# Create an array of five values evenly spaced between 0 and 1\n",
+ "np.linspace(0, 1, 5)\n",
+ "\n",
+ "# Create a 3x3 array of uniformly distributed\n",
+ "# random values between 0 and 1\n",
+ "\n",
+ "np.random.random((3,3))\n",
+ "\n",
+ "# Create a 3x3 array of random integers in the interval [0, 10)\n",
+ "np.random.randint(0, 10, (3, 3))\n",
+ "\n",
+ "# Create a 3x3 identity matrix\n",
+ "np.eye(3, dtype='int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Array Attributes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Seed the Random number generator to ensure it produces same set of numbers on each call\n",
+ "np.random.seed(0)\n",
+ "\n",
+ "# create 3 arrays, 1-dim, 2-dim and 3-dim of numbers between 1 and 10\n",
+ "array1 = np.random.randint(10, size=6)\n",
+ "array2 = np.random.randint(10, size=(3, 4))\n",
+ "array3 = np.random.randint(10, size=(3, 4, 5))\n",
+ "\n",
+ "'''\n",
+ " The main attributes:\n",
+ " ndim - Number of dimensuins\n",
+ " shape - size of each dimension\n",
+ " size - total size of the array\n",
+ " dtype - data type of the array\n",
+ "'''\n",
+ "\n",
+ "print(\"Array2 ndim: \", array2.ndim)\n",
+ "print(\"Array2 shape: \", array2.shape)\n",
+ "print(\"Array2 size: \", array2.size)\n",
+ "print(\"Array2 dtype: \", array2.dtype)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array Indexing - Accessing single array elements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access the first element in array 1\n",
+ "print(array1)\n",
+ "first_array1_element = array1[0]\n",
+ "print(\"Array 1 first element: \", first_array1_element)\n",
+ "\n",
+ "# Access the first element in second row of array 2\n",
+ "print(array2)\n",
+ "array2_element = array2[0, 0]\n",
+ "print(array2_element)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array Slicing: Accesing sub-arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ " uses slice notation ':'.\n",
+ " format: x[start, stop, step]\n",
+ "\"\"\"\n",
+ "# Initialize a 1-dim array\n",
+ "x = np.arange(10)\n",
+ "print(x)\n",
+ "\n",
+ "# Select first five elements\n",
+ "print(\"First five elements: \", x[:5])\n",
+ "\n",
+ "# Elements after index 5\n",
+ "print(\"Last five: \", x[5:])\n",
+ "\n",
+ "# Middle sub-array\n",
+ "print(x[4:7])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array copies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# When a sub array is created, it is not a copy of the original array, \n",
+ "# its its view, meaning that any change made on the sub array refelects on the main array.\n",
+ "\n",
+ "print(\"Array 2: \\n\", array2)\n",
+ "\n",
+ "# Create a 2x2 sub array\n",
+ "sub_array2 = array2[:2, :2]\n",
+ "print(\"Sub array: \\n\", sub_array2)\n",
+ "\n",
+ "# Change any value in sub array\n",
+ "sub_array2[0, 0] = 9\n",
+ "print(\"New Sub array: \\n\", sub_array2)\n",
+ "print(\"New main array: \\n\", array2)\n",
+ "\n",
+ "# To make an array copy which is not affected by any changes in the sub array, use the copy() fn.\n",
+ "sub_array_copy = array2[:2, :2].copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Ufuncs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Functions that enable vectorization - A faster way than python loops\n",
+ "\"\"\"\n",
+ " \n",
+ "Operator\tEquivalent ufunc\tDescription\n",
+ "+\tnp.add\tAddition (e.g., 1 + 1 = 2)\n",
+ "-\tnp.subtract\tSubtraction (e.g., 3 - 2 = 1)\n",
+ "-\tnp.negative\tUnary negation (e.g., -2)\n",
+ "*\tnp.multiply\tMultiplication (e.g., 2 * 3 = 6)\n",
+ "/\tnp.divide\tDivision (e.g., 3 / 2 = 1.5)\n",
+ "//\tnp.floor_divide\tFloor division (e.g., 3 // 2 = 1)\n",
+ "**\tnp.power\tExponentiation (e.g., 2 ** 3 = 8)\n",
+ "%\tnp.mod\tModulus/remainder (e.g., 9 % 4 = 1)\n",
+ "\"\"\"\n",
+ "\n",
+ "# More Ufuncs found on docs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Aggregations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ " Aggregations are used to compute operations. This include sum, min, max, percentiles, median, standard deviation, \n",
+ " quartiles, etc.\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Broadcasting: \n",
+ "### Computations on arrays of different sizes and dimensions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A 3-dim array of integers an\n",
+ "a = np.ones(shape=(3, 3), dtype=np.int)\n",
+ "\n",
+ "# 1-dim array\n",
+ "b = np.array([1, 2, 3])\n",
+ "\n",
+ "print(\"array a: \\n\", a)\n",
+ "print(\"array b: \\n\", b)\n",
+ "\n",
+ "# Adding array b to a scalar(0-dim array)\n",
+ "print(\"b + 5 = \", b + 5)\n",
+ "\n",
+ "# Adding a to b\n",
+ "ab = a + b\n",
+ "print(\"a + b: \\n\", ab)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Array comparisons: boolean arrays and masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Same as operations, element-wise comparisons can be done, eg, greater than, less than, ...\n",
+ "# comparisons return an array of booleans(true or false)\n",
+ "# A 1-D array\n",
+ "a = np.array([1, 2, 3, 5, 5])\n",
+ "print(\"Array a: \", a)\n",
+ "\n",
+ "# Normal comparison\n",
+ "print(\"comparison: \", a <= 3)\n",
+ "\n",
+ "# Equivalent Ufunc\n",
+ "print(\"Ufunc comparison: \", np.less_equal(a, 3))\n",
+ "\n",
+ "# same case applies to 2-D and 3-D arrays."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Masking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" \n",
+ "Instead of returning a boolean array(of True's and False's), We can return an array\n",
+ "of the element matching the particular comparison.\n",
+ "\"\"\"\n",
+ "\n",
+ "# A 2-D array\n",
+ "rng = np.random.RandomState(0)\n",
+ "x = rng.randint(10, size=(3, 4))\n",
+ "print(\"x: \\n\", x)\n",
+ "\n",
+ "# Output all values less than 5\n",
+ "print(\"Less than 5: \", x[x < 5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NUMPY SORTING"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# faster than python list sorting\n",
+ "# Uses numpy .sort() and .argsort() uFuncs\n",
+ "\n",
+ "x = np.array([2, 1, 5, 4, 3])\n",
+ "x_sorted = np.sort(x)\n",
+ "print(\"sorted array: \", x_sorted)\n",
+ "\n",
+ "# .argsort() returns an array of sorted indices\n",
+ "i = np.argsort(x)\n",
+ "print(i)\n",
+ "print(x[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More on numpy from the docs and also handbook"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Pandas in General.ipynb b/notebooks/Pandas in General.ipynb
new file mode 100644
index 0000000..36197fb
--- /dev/null
+++ b/notebooks/Pandas in General.ipynb
@@ -0,0 +1,1311 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Pandas\n",
+ "Pandas are used together with numpy. They form an extension of the numpy ndarray object.\n",
+ "It consists of Series and Dataframe as its key objects"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pandas Series object\n",
+ "A 1-D array of indexed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import the packages\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "data:\n",
+ " 0 0.10\n",
+ "1 6.35\n",
+ "2 7.00\n",
+ "3 13.70\n",
+ "4 8.00\n",
+ "dtype: float64\n",
+ "values: [ 0.1 6.35 7. 13.7 8. ]\n",
+ "indices: RangeIndex(start=0, stop=5, step=1)\n",
+ "data at index 3: 13.7\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a Series from a list or array\n",
+ "data = pd.Series([0.1, 6.35, 7.0, 13.7, 8.0])\n",
+ "# display the data\n",
+ "print(\"data:\\n\", data)\n",
+ "\n",
+ "# display the values only\n",
+ "print(\"values: \", data.values)\n",
+ "\n",
+ "# display the indices(as a range)\n",
+ "print(\"indices: \", data.index)\n",
+ "\n",
+ "# access a value\n",
+ "print(\"data at index 3: \", data[3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 1\n",
+ "b 2\n",
+ "c 3\n",
+ "d 4\n",
+ "e 5\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Series with custom indices\n",
+ "custom_data = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])\n",
+ "custom_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "kasee 22\n",
+ "Ken 27\n",
+ "Kashee 19\n",
+ "Miro 21\n",
+ "Phoebe 20\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# A series object can also be created from a dictionary. The indices will be the dict keys.\n",
+ "ages = {\n",
+ " 'kasee': 22,\n",
+ " 'Ken': 27,\n",
+ " 'Kashee': 19,\n",
+ " 'Miro': 21,\n",
+ " 'Phoebe': 20\n",
+ "}\n",
+ "\n",
+ "age_data = pd.Series(ages)\n",
+ "age_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The DataFrame Object\n",
+ "Can be created from an array or a dict\n",
+ "format: pd.DataFrame(data, columns='', index='')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 1.1. [Case Study ](#study)
\n", + " 1.2. [Objectives](#objs)\n", + "2. [Loading Libraries](#libs)\n", + "3. [Loading in the Datasets](#datasets)\n", + "4. [Exploratory Data Analysis(EDA)](#eda)
\n", + " 4.1. [Numerical EDA](#num-eda)
\n", + " 4.2. [Statistical EDA](#stats-eda)
\n", + " 4.2. [Visual EDA](#visual-eda)
\n", + "5. [Data Training and Predictions](#training)
\n", + " 5.1. [Training and Test Sets](#sets)
\n", + " 5.2. [Modelling and Evaluation](#modelling)
\n", + " 5.3. [Model Score Comparison](#comparison)
\n", + " 5.4. [Fine Tuning Highest Scoring Model](#fine-tuning)
\n", + " 5.5. [Predictions](#predictions)
\n", + " 5.6. [Submission Formatting](#submission)
\n", + " 5.6. [Final Submission](#final)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies.\n", + "
\n", + "## Case Study: \n", + "A mobile blood donation vehicle in Taiwan. The Blood Transfusion Service Center drives to different universities and collects blood as part of a blood drive.\n", + "
\n", + "## Objectives \n", + "1. To predict whether or not a donor will give blood the next time the vehicle comes to campus. (March 2007)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Loading Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import warnings\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier \n", + "from sklearn.naive_bayes import GaussianNB \n", + "from sklearn.model_selection import cross_val_score \n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set defaults\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Loading in the Datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Training set\n", + "training_dataset = pd.read_csv('../data/donation-training.csv')\n", + "test_dataset = pd.read_csv('../data/donation-test.csv')\n", + "submission_format = pd.read_csv('../data/BloodDonationSubmissionFormat.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Exploratory Data Analysis(EDA)\n", + "\n", + "\n", + "### A peek at the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n",
+ "RangeIndex: 576 entries, 0 to 575\n",
+ "Data columns (total 5 columns):\n",
+ "recency 576 non-null int64\n",
+ "total_donations 576 non-null int64\n",
+ "total_vol_donated 576 non-null int64\n",
+ "donation_time 576 non-null int64\n",
+ "donated 576 non-null int64\n",
+ "dtypes: int64(5)\n",
+ "memory usage: 22.5 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "# dataset info\n",
+ "training_set.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Dataset labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['recency', 'total_donations', 'total_vol_donated', 'donation_time', 'donated']"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.columns.to_list()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Label types"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "recency int64\n",
+ "total_donations int64\n",
+ "total_vol_donated int64\n",
+ "donation_time int64\n",
+ "donated int64\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.dtypes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Dimensionality"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(576, 5)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "recency 0\n",
+ "total_donations 0\n",
+ "total_vol_donated 0\n",
+ "donation_time 0\n",
+ "donated 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "null_values = training_set.isnull().sum()\n",
+ "null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Numerical Analysis Overview\n",
+ "1. The dataset has a total of **576** samples.\n",
+ "2. The dataset has a total of **5** labels, namely:
\n", + " **recency**: Months since last donation
\n", + " **total_donations**: Number of times donor donated
\n", + " **total_vol_donated**: Total volume of blood donated in c.c.
\n", + " **donation_time**: Months since first donation
\n", + " **donated**: Whether donor donated in March 2007
\n", + "3. All the labels features are numerical\n", + "4. The dataset has no null values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2. Statistical EDA\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " population area\n",
+ "Atlanta 7459843 7589347\n",
+ "Connecticut 124765 759874\n",
+ "Kansas 124765 985904384\n",
+ "Seattle 124765 475398475\n",
+ "Viginia 124765 509438"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# States data\n",
+ "population = {\n",
+ " 'Seattle': 124765,\n",
+ " 'Atlanta': 7459843,\n",
+ " 'Kansas': 124765,\n",
+ " 'Connecticut': 124765,\n",
+ " 'Viginia': 124765,\n",
+ "}\n",
+ "\n",
+ "# State areas\n",
+ "area = {\n",
+ " 'Seattle': 475398475,\n",
+ " 'Atlanta': 7589347,\n",
+ " 'Kansas': 985904384,\n",
+ " 'Connecticut': 759874,\n",
+ " 'Viginia': 509438\n",
+ "}\n",
+ "\n",
+ "# create a dataframe\n",
+ "states = pd.DataFrame({\n",
+ " 'population': population,\n",
+ " 'area': area\n",
+ "})\n",
+ "\n",
+ "states"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Atlanta 7459843\n",
+ "Connecticut 124765\n",
+ "Kansas 124765\n",
+ "Seattle 124765\n",
+ "Viginia 124765\n",
+ "Name: population, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Accessing individual data\n",
+ "popn = states['population'] # or states.population\n",
+ "popn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data Indexing and Selection\n",
+ "Normal slicing and indexing brings confusion, hence `iloc` and `loc` attribs are used for implicit(normal python list-style) and explicit indexing, respectively"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1 a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.Series(['a', 'b', 'c'], index=[1, 2, 3])\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Trying to access the data locally\n",
+ "print(data[1]) # explicit index when indexing\n",
+ "print(data[1:3]) # implicit index when slicing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "1 a\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n",
+ "b\n",
+ "2 b\n",
+ "3 c\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To solve this, loc and iloc are used\n",
+ "# Explicit indexing\n",
+ "print(data.loc[1])\n",
+ "print(data.loc[1:3])\n",
+ "\n",
+ "# implicit indexing\n",
+ "print(data.iloc[1])\n",
+ "print(data.iloc[1:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# An example with csv file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | population | \n", + "area | \n", + "
|---|---|---|
| Atlanta | \n", + "7459843 | \n", + "7589347 | \n", + "
| Connecticut | \n", + "124765 | \n", + "759874 | \n", + "
| Kansas | \n", + "124765 | \n", + "985904384 | \n", + "
| Seattle | \n", + "124765 | \n", + "475398475 | \n", + "
| Viginia | \n", + "124765 | \n", + "509438 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "0 1969 1 1.0 F 4046\n",
+ "1 1969 1 1.0 M 4440\n",
+ "2 1969 1 2.0 F 4454\n",
+ "3 1969 1 2.0 M 4548\n",
+ "4 1969 1 3.0 F 4548"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the csv data\n",
+ "births = pd.read_csv('C:/Users/user/PycharmProjects/Python4ds_cohort-1/data/births.csv')\n",
+ "births.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pivot table\n",
+ "We create a pivot table and display male and female births as a function of decades"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births decade\n",
+ "0 1969 1 1.0 F 4046 1960\n",
+ "1 1969 1 1.0 M 4440 1960\n",
+ "2 1969 1 2.0 F 4454 1960\n",
+ "3 1969 1 2.0 M 4548 1960\n",
+ "4 1969 1 3.0 F 4548 1960\n",
+ "5 1969 1 3.0 M 4994 1960\n",
+ "6 1969 1 4.0 F 4440 1960\n",
+ "7 1969 1 4.0 M 4520 1960\n",
+ "8 1969 1 5.0 F 4192 1960\n",
+ "9 1969 1 5.0 M 4198 1960"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "births['decade'] = 10 * (births['year'] // 10)\n",
+ "births.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "decade | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "1960 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "1960 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "1960 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "1960 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "1960 | \n", + "
| 5 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "4994 | \n", + "1960 | \n", + "
| 6 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4440 | \n", + "1960 | \n", + "
| 7 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "4520 | \n", + "1960 | \n", + "
| 8 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4192 | \n", + "1960 | \n", + "
| 9 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4198 | \n", + "1960 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "gender F M\n",
+ "decade \n",
+ "1960 1753634 1846572\n",
+ "1970 16263075 17121550\n",
+ "1980 18310351 19243452\n",
+ "1990 19479454 20420553\n",
+ "2000 18229309 19106428"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "births.pivot_table(values='births', index='decade', columns='gender', aggfunc='sum')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# PANDAS DEEP EXAMPLE\n",
+ "## Using the births data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| gender | \n", + "F | \n", + "M | \n", + "
|---|---|---|
| decade | \n", + "\n", + " | \n", + " |
| 1960 | \n", + "1753634 | \n", + "1846572 | \n", + "
| 1970 | \n", + "16263075 | \n", + "17121550 | \n", + "
| 1980 | \n", + "18310351 | \n", + "19243452 | \n", + "
| 1990 | \n", + "19479454 | \n", + "20420553 | \n", + "
| 2000 | \n", + "18229309 | \n", + "19106428 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "0 1969 1 1.0 F 4046\n",
+ "1 1969 1 1.0 M 4440\n",
+ "2 1969 1 2.0 F 4454\n",
+ "3 1969 1 2.0 M 4548\n",
+ "4 1969 1 3.0 F 4548\n",
+ "5 1969 1 3.0 M 4994\n",
+ "6 1969 1 4.0 F 4440\n",
+ "7 1969 1 4.0 M 4520\n",
+ "8 1969 1 5.0 F 4192\n",
+ "9 1969 1 5.0 M 4198"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the csv data into a dataframe\n",
+ "births_df = pd.read_csv('C:/Users/user/PycharmProjects/Python4ds_cohort-1/data/births.csv')\n",
+ "births_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4046 | \n", + "
| 1 | \n", + "1969 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4440 | \n", + "
| 2 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4454 | \n", + "
| 3 | \n", + "1969 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4548 | \n", + "
| 4 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4548 | \n", + "
| 5 | \n", + "1969 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "4994 | \n", + "
| 6 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4440 | \n", + "
| 7 | \n", + "1969 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "4520 | \n", + "
| 8 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4192 | \n", + "
| 9 | \n", + "1969 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4198 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day gender births\n",
+ "8386 1980 1 1.0 F 4005\n",
+ "8387 1980 1 1.0 M 4227\n",
+ "8388 1980 1 2.0 F 4371\n",
+ "8389 1980 1 2.0 M 4640\n",
+ "8390 1980 1 3.0 F 4815\n",
+ "8391 1980 1 3.0 M 5087\n",
+ "8392 1980 1 4.0 F 4758\n",
+ "8393 1980 1 4.0 M 5181\n",
+ "8394 1980 1 5.0 F 4265\n",
+ "8395 1980 1 5.0 M 4426\n",
+ "8396 1980 1 6.0 F 4093\n",
+ "8397 1980 1 6.0 M 4120\n",
+ "8398 1980 1 7.0 F 4730\n",
+ "8399 1980 1 7.0 M 5103\n",
+ "8400 1980 1 8.0 F 4810\n",
+ "8401 1980 1 8.0 M 5012\n",
+ "8402 1980 1 9.0 F 4763\n",
+ "8403 1980 1 9.0 M 4801\n",
+ "8404 1980 1 10.0 F 4810\n",
+ "8405 1980 1 10.0 M 4949"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select the births in january 1980 first 10 days\n",
+ "births_1980 = births_df[((births_df['year'] == 1980) & (births_df['month'] == 1) & (births_df['day'] <= 10))]\n",
+ "births_1980"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "gender | \n", + "births | \n", + "
|---|---|---|---|---|---|
| 8386 | \n", + "1980 | \n", + "1 | \n", + "1.0 | \n", + "F | \n", + "4005 | \n", + "
| 8387 | \n", + "1980 | \n", + "1 | \n", + "1.0 | \n", + "M | \n", + "4227 | \n", + "
| 8388 | \n", + "1980 | \n", + "1 | \n", + "2.0 | \n", + "F | \n", + "4371 | \n", + "
| 8389 | \n", + "1980 | \n", + "1 | \n", + "2.0 | \n", + "M | \n", + "4640 | \n", + "
| 8390 | \n", + "1980 | \n", + "1 | \n", + "3.0 | \n", + "F | \n", + "4815 | \n", + "
| 8391 | \n", + "1980 | \n", + "1 | \n", + "3.0 | \n", + "M | \n", + "5087 | \n", + "
| 8392 | \n", + "1980 | \n", + "1 | \n", + "4.0 | \n", + "F | \n", + "4758 | \n", + "
| 8393 | \n", + "1980 | \n", + "1 | \n", + "4.0 | \n", + "M | \n", + "5181 | \n", + "
| 8394 | \n", + "1980 | \n", + "1 | \n", + "5.0 | \n", + "F | \n", + "4265 | \n", + "
| 8395 | \n", + "1980 | \n", + "1 | \n", + "5.0 | \n", + "M | \n", + "4426 | \n", + "
| 8396 | \n", + "1980 | \n", + "1 | \n", + "6.0 | \n", + "F | \n", + "4093 | \n", + "
| 8397 | \n", + "1980 | \n", + "1 | \n", + "6.0 | \n", + "M | \n", + "4120 | \n", + "
| 8398 | \n", + "1980 | \n", + "1 | \n", + "7.0 | \n", + "F | \n", + "4730 | \n", + "
| 8399 | \n", + "1980 | \n", + "1 | \n", + "7.0 | \n", + "M | \n", + "5103 | \n", + "
| 8400 | \n", + "1980 | \n", + "1 | \n", + "8.0 | \n", + "F | \n", + "4810 | \n", + "
| 8401 | \n", + "1980 | \n", + "1 | \n", + "8.0 | \n", + "M | \n", + "5012 | \n", + "
| 8402 | \n", + "1980 | \n", + "1 | \n", + "9.0 | \n", + "F | \n", + "4763 | \n", + "
| 8403 | \n", + "1980 | \n", + "1 | \n", + "9.0 | \n", + "M | \n", + "4801 | \n", + "
| 8404 | \n", + "1980 | \n", + "1 | \n", + "10.0 | \n", + "F | \n", + "4810 | \n", + "
| 8405 | \n", + "1980 | \n", + "1 | \n", + "10.0 | \n", + "M | \n", + "4949 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " year month day births\n",
+ "count 20.0 20.0 20.000000 20.00000\n",
+ "mean 1980.0 1.0 5.500000 4648.30000\n",
+ "std 0.0 0.0 2.946898 362.23213\n",
+ "min 1980.0 1.0 1.000000 4005.00000\n",
+ "25% 1980.0 1.0 3.000000 4344.50000\n",
+ "50% 1980.0 1.0 5.500000 4760.50000\n",
+ "75% 1980.0 1.0 8.000000 4848.50000\n",
+ "max 1980.0 1.0 10.000000 5181.00000"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# evaluate basic computations on that data\n",
+ "births_1980.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | year | \n", + "month | \n", + "day | \n", + "births | \n", + "
|---|---|---|---|---|
| count | \n", + "20.0 | \n", + "20.0 | \n", + "20.000000 | \n", + "20.00000 | \n", + "
| mean | \n", + "1980.0 | \n", + "1.0 | \n", + "5.500000 | \n", + "4648.30000 | \n", + "
| std | \n", + "0.0 | \n", + "0.0 | \n", + "2.946898 | \n", + "362.23213 | \n", + "
| min | \n", + "1980.0 | \n", + "1.0 | \n", + "1.000000 | \n", + "4005.00000 | \n", + "
| 25% | \n", + "1980.0 | \n", + "1.0 | \n", + "3.000000 | \n", + "4344.50000 | \n", + "
| 50% | \n", + "1980.0 | \n", + "1.0 | \n", + "5.500000 | \n", + "4760.50000 | \n", + "
| 75% | \n", + "1980.0 | \n", + "1.0 | \n", + "8.000000 | \n", + "4848.50000 | \n", + "
| max | \n", + "1980.0 | \n", + "1.0 | \n", + "10.000000 | \n", + "5181.00000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sum mean\n",
+ " births births\n",
+ "gender \n",
+ "F 74035823 9521.067773\n",
+ "M 77738555 10003.674559"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a pivot table with birts totals for all genders\n",
+ "gender_table = pd.pivot_table(births, index=['gender'], values='births', aggfunc=[np.sum,np.mean])\n",
+ "gender_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Prediction-Blood Donations.ipynb b/notebooks/Prediction-Blood Donations.ipynb
new file mode 100644
index 0000000..0773b56
--- /dev/null
+++ b/notebooks/Prediction-Blood Donations.ipynb
@@ -0,0 +1,2185 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)| \n", + " | sum | \n", + "mean | \n", + "
|---|---|---|
| \n", + " | births | \n", + "births | \n", + "
| gender | \n", + "\n", + " | \n", + " |
| F | \n", + "74035823 | \n", + "9521.067773 | \n", + "
| M | \n", + "77738555 | \n", + "10003.674559 | \n", + "
\n", + " 1.1. [Case Study ](#study)
\n", + " 1.2. [Objectives](#objs)\n", + "2. [Loading Libraries](#libs)\n", + "3. [Loading in the Datasets](#datasets)\n", + "4. [Exploratory Data Analysis(EDA)](#eda)
\n", + " 4.1. [Numerical EDA](#num-eda)
\n", + " 4.2. [Statistical EDA](#stats-eda)
\n", + " 4.2. [Visual EDA](#visual-eda)
\n", + "5. [Data Training and Predictions](#training)
\n", + " 5.1. [Training and Test Sets](#sets)
\n", + " 5.2. [Modelling and Evaluation](#modelling)
\n", + " 5.3. [Model Score Comparison](#comparison)
\n", + " 5.4. [Fine Tuning Highest Scoring Model](#fine-tuning)
\n", + " 5.5. [Predictions](#predictions)
\n", + " 5.6. [Submission Formatting](#submission)
\n", + " 5.6. [Final Submission](#final)
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Blood donation has been around for a long time. The first successful recorded transfusion was between two dogs in 1665, and the first medical use of human blood in a transfusion occurred in 1818. Even today, donated blood remains a critical resource during emergencies.\n", + "
\n", + "## Case Study: \n", + "A mobile blood donation vehicle in Taiwan. The Blood Transfusion Service Center drives to different universities and collects blood as part of a blood drive.\n", + "
\n", + "## Objectives \n", + "1. To predict whether or not a donor will give blood the next time the vehicle comes to campus. (March 2007)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Loading Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import warnings\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier \n", + "from sklearn.naive_bayes import GaussianNB \n", + "from sklearn.model_selection import cross_val_score \n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set defaults\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Loading in the Datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Training set\n", + "training_dataset = pd.read_csv('../data/donation-training.csv')\n", + "test_dataset = pd.read_csv('../data/donation-test.csv')\n", + "submission_format = pd.read_csv('../data/BloodDonationSubmissionFormat.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Exploratory Data Analysis(EDA)\n", + "\n", + "\n", + "### A peek at the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Months since Last Donation Number of Donations \\\n",
+ "0 619 2 50 \n",
+ "1 664 0 13 \n",
+ "2 441 1 16 \n",
+ "3 160 2 20 \n",
+ "4 358 1 24 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \\\n",
+ "0 12500 98 \n",
+ "1 3250 28 \n",
+ "2 4000 35 \n",
+ "3 5000 45 \n",
+ "4 6000 77 \n",
+ "\n",
+ " Made Donation in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "619 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "664 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "441 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "160 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "358 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Months since Last Donation Number of Donations \\\n",
+ "0 659 2 12 \n",
+ "1 276 21 7 \n",
+ "2 263 4 1 \n",
+ "3 303 11 11 \n",
+ "4 83 4 12 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \n",
+ "0 3000 52 \n",
+ "1 1750 38 \n",
+ "2 250 4 \n",
+ "3 2750 38 \n",
+ "4 3000 34 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "659 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "276 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "263 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "303 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "83 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unnamed: 0 Made Donation in March 2007\n",
+ "0 659 0.5\n",
+ "1 276 0.5\n",
+ "2 263 0.5\n",
+ "3 303 0.5\n",
+ "4 83 0.5"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission_format.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### For analysis, we drop the unnamed(key) column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|
| 0 | \n", + "659 | \n", + "0.5 | \n", + "
| 1 | \n", + "276 | \n", + "0.5 | \n", + "
| 2 | \n", + "263 | \n", + "0.5 | \n", + "
| 3 | \n", + "303 | \n", + "0.5 | \n", + "
| 4 | \n", + "83 | \n", + "0.5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Months since Last Donation Number of Donations \\\n",
+ "0 2 50 \n",
+ "1 0 13 \n",
+ "2 1 16 \n",
+ "3 2 20 \n",
+ "4 1 24 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \\\n",
+ "0 12500 98 \n",
+ "1 3250 28 \n",
+ "2 4000 35 \n",
+ "3 5000 45 \n",
+ "4 6000 77 \n",
+ "\n",
+ " Made Donation in March 2007 \n",
+ "0 1 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 0 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# making a copy of the dataset\n",
+ "training_set = training_dataset.copy()\n",
+ "training_set.drop(columns=['Unnamed: 0'], axis=1, inplace=True)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "Made Donation in March 2007 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Months since Last Donation Number of Donations \\\n",
+ "0 2 12 \n",
+ "1 21 7 \n",
+ "2 4 1 \n",
+ "3 11 11 \n",
+ "4 4 12 \n",
+ "\n",
+ " Total Volume Donated (c.c.) Months since First Donation \n",
+ "0 3000 52 \n",
+ "1 1750 38 \n",
+ "2 250 4 \n",
+ "3 2750 38 \n",
+ "4 3000 34 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# making a copy of the dataset\n",
+ "tests_set = test_dataset.copy()\n",
+ "tests_set.drop(columns=['Unnamed: 0'], axis=1, inplace=True)\n",
+ "tests_set.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Renaming the columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Months since Last Donation | \n", + "Number of Donations | \n", + "Total Volume Donated (c.c.) | \n", + "Months since First Donation | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time donated\n",
+ "0 2 50 12500 98 1\n",
+ "1 0 13 3250 28 1\n",
+ "2 1 16 4000 35 1\n",
+ "3 2 20 5000 45 1\n",
+ "4 1 24 6000 77 0"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# renaming the Training set\n",
+ "training_set = training_set.rename(columns={'Months since Last Donation':'recency', 'Number of Donations':'total_donations', 'Total Volume Donated (c.c.)':'total_vol_donated', 'Months since First Donation':'donation_time', 'Made Donation in March 2007':'donated'})\n",
+ "training_set.head()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "50 | \n", + "12500 | \n", + "98 | \n", + "1 | \n", + "
| 1 | \n", + "0 | \n", + "13 | \n", + "3250 | \n", + "28 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "16 | \n", + "4000 | \n", + "35 | \n", + "1 | \n", + "
| 3 | \n", + "2 | \n", + "20 | \n", + "5000 | \n", + "45 | \n", + "1 | \n", + "
| 4 | \n", + "1 | \n", + "24 | \n", + "6000 | \n", + "77 | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time\n",
+ "0 2 12 3000 52\n",
+ "1 21 7 1750 38\n",
+ "2 4 1 250 4\n",
+ "3 11 11 2750 38\n",
+ "4 4 12 3000 34"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# renaming the test set\n",
+ "test_set = tests_set.rename(columns={'Months since Last Donation':'recency', 'Number of Donations':'total_donations', 'Total Volume Donated (c.c.)':'total_vol_donated', 'Months since First Donation':'donation_time'})\n",
+ "test_set.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.1. Numerical EDA\n",
+ "\n",
+ "\n",
+ "#### Dataset info"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2 | \n", + "12 | \n", + "3000 | \n", + "52 | \n", + "
| 1 | \n", + "21 | \n", + "7 | \n", + "1750 | \n", + "38 | \n", + "
| 2 | \n", + "4 | \n", + "1 | \n", + "250 | \n", + "4 | \n", + "
| 3 | \n", + "11 | \n", + "11 | \n", + "2750 | \n", + "38 | \n", + "
| 4 | \n", + "4 | \n", + "12 | \n", + "3000 | \n", + "34 | \n", + "
\n", + " **recency**: Months since last donation
\n", + " **total_donations**: Number of times donor donated
\n", + " **total_vol_donated**: Total volume of blood donated in c.c.
\n", + " **donation_time**: Months since first donation
\n", + " **donated**: Whether donor donated in March 2007
\n", + "3. All the labels features are numerical\n", + "4. The dataset has no null values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.2. Statistical EDA\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " recency total_donations total_vol_donated donation_time \\\n",
+ "count 576.000000 576.000000 576.000000 576.000000 \n",
+ "mean 9.439236 5.427083 1356.770833 34.050347 \n",
+ "std 8.175454 5.740010 1435.002556 24.227672 \n",
+ "min 0.000000 1.000000 250.000000 2.000000 \n",
+ "25% 2.000000 2.000000 500.000000 16.000000 \n",
+ "50% 7.000000 4.000000 1000.000000 28.000000 \n",
+ "75% 14.000000 7.000000 1750.000000 49.250000 \n",
+ "max 74.000000 50.000000 12500.000000 98.000000 \n",
+ "\n",
+ " donated \n",
+ "count 576.000000 \n",
+ "mean 0.239583 \n",
+ "std 0.427200 \n",
+ "min 0.000000 \n",
+ "25% 0.000000 \n",
+ "50% 0.000000 \n",
+ "75% 0.000000 \n",
+ "max 1.000000 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "training_set.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Statistical Overview\n",
+ "1. The average number of months taken by donors since last donation is **9 months**.\n",
+ "2. The average number of months taken by donors since first donation is **34 months**.\n",
+ "3. The average number of total donations is **5 donations**.\n",
+ "4. An average of **1356.78 c.c.** of blood was donated\n",
+ "5. The maximum total blood volume donated by a single donor was **12500 c.c.** while the minimum was **250 c.c.**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.3. Visual EDA\n",
+ "\n",
+ "\n",
+ "### Class/Target distribution: A check on total donors who donated and didn't donate in march 2007"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | recency | \n", + "total_donations | \n", + "total_vol_donated | \n", + "donation_time | \n", + "donated | \n", + "
|---|---|---|---|---|---|
| count | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "576.000000 | \n", + "
| mean | \n", + "9.439236 | \n", + "5.427083 | \n", + "1356.770833 | \n", + "34.050347 | \n", + "0.239583 | \n", + "
| std | \n", + "8.175454 | \n", + "5.740010 | \n", + "1435.002556 | \n", + "24.227672 | \n", + "0.427200 | \n", + "
| min | \n", + "0.000000 | \n", + "1.000000 | \n", + "250.000000 | \n", + "2.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "2.000000 | \n", + "2.000000 | \n", + "500.000000 | \n", + "16.000000 | \n", + "0.000000 | \n", + "
| 50% | \n", + "7.000000 | \n", + "4.000000 | \n", + "1000.000000 | \n", + "28.000000 | \n", + "0.000000 | \n", + "
| 75% | \n", + "14.000000 | \n", + "7.000000 | \n", + "1750.000000 | \n", + "49.250000 | \n", + "0.000000 | \n", + "
| max | \n", + "74.000000 | \n", + "50.000000 | \n", + "12500.000000 | \n", + "98.000000 | \n", + "1.000000 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Extract the donated column data\n",
+ "donated = training_set.donated.value_counts()\n",
+ "donated.rename(index={0:'No', 1:'Yes'}, inplace=True)\n",
+ "# print(donated)\n",
+ "\n",
+ "# Instatiate a figure and draw a bar plot\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "\n",
+ "# plot the bar\n",
+ "sns.barplot(x=donated.index, y=donated.values)\n",
+ "\n",
+ "# display the counts on each bar\n",
+ "for i, v in enumerate(donated.values):\n",
+ " plt.text(i, 50, v, color='k', fontsize=19)\n",
+ "\n",
+ "# label the axes\n",
+ "plt.title('Donation status for March 2007', fontsize=25)\n",
+ "plt.xlabel(\"Donated\", fontsize=20)\n",
+ "plt.ylabel(\"Count\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this plot, it is clear that **76%** of the donors didn't donate in march 2007!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multi-Variable comparisons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(training_set.drop(columns=['donated']))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Uni-variate comparisons: Checking for outliers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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cvc8d0NcC6ZFcVpuImCyMXpOiRe+BPbncxqCFBA1JLERk2tgNRUREOjFZEBGRTkwWRESkE5MFERHpxAFuIuqRuupOP97l1z5MFkTUI3XVnX68y6992A1FREQ6sWVh5PqMz0dYbk4XlW0NgIuzERGThdFTnnPr2kl53BKdiMBuKCIi0gNbFkTUI3VVFyu7V9uHyYKIeqSu6mJl92r79IhuqEOHDmHOnDmYNWsWdu3aJXU4RET0M5K3LCoqKrB582ZkZWXBysoKCxYsgIuLC0aNGiV1aEajq+4bH9BX8uph9A4dOoTt27ejpaUFoaGhWLx4sdQhGZWuqNus1+0j+adWUFCAadOmYdCgQQAAb29v5OTkIDw8XOLIjIMh+w5zn+LuxR9CHcO63bNIniwqKyshl8u1j21tbVFcXKz384cMse6KsIyel5cXSktLHzo+affD5zo5OSE3l7NaOxt/CHW+gAA/XL588ZF/e1TdHjlyNDIzD3VxVL2D5MlCo9FAJpNpHwshWj3W5fbtRmg0oitCM2p79hx86Fhbg4CGzL/oLczMZB36MdLRH0L0sMd98Rs6h4gMJ3mysLOzw7fffqt9XFVVBVtbWwkjIuocHf0hxFazYeRyG6lDMGmSJ4sXX3wR6enpqKmpQb9+/XDs2DG89957UodF1GEd/SHEVrP+2LLQX3tbzJIni2HDhiEqKgohISFQqVQIDAzEhAkT9H6+mZn+v9SIn5chOvpZdfSHEP+/Mgw/L/2093OSCSH404Woixw6dAh/+tOftD+E3nzzTalDImoXJgsiItKpR8zgJiKino3JgoiIdGKyICIinZgsiIhIJyYLIiLSicmCiIh0YrIgIiKdmCyIiEgnJgsiMkhDQwPCwsLaPCcmJgY3btxo85zg4GAUFhbq/brp6elIT0/X+3x9nDhxAjt37gQAfPHFF/jiiy86tXxTwmRBRAapq6tDSUlJm+cUFhbCGBaHOHfuHBobGwEACxcuxMKFCyWOqOeSfCFB0q2wsBApKSnQaDQYMWIE+vfvj4sXL0KtVuPNN9+Er68vlEol4uPjUVRUBEtLS/z+97/HnDlzUFxcjKSkJCgUCgwePBjx8fFwcHBAcHAwfvnLX6KoqAg1NTVYt24dPDw8cOPGDcTExKCmpgZ9+/ZFQkICcnJyIIRAVFQUAGDNmjVwd3fHnDlzJP5kSAoJCQmorKxEWFgYvLy8sHPnTshkMowbNw5xcXHYtWsXKisrsWzZMuzatQunTp3Czp07oVAo0NzcjMTERPzqV7/S67U++eQTZGRkYPDgwRg4cKB2kdETJ04gLS0NGo0GDg4O2LhxI4YOHQovLy/MmzcP+fn5uHv3Lt5//32MHz8ep0+fxubNm6FQKFBfX4+YmBg888wz2L373o5Jw4cPx82bNwEAERERBpe/c+dO7N+/H2ZmZpgwYQI2btzYNR++lAT1eKdOnRKTJ08W9fX1IiUlRfzlL38RQgjR0NAg5s6dK65duyY+/vhjsWrVKqFWq0VlZaWYM2eOUCqVws/PT9y4cUMIIUReXp4IDQ0VQgixZMkSkZCQIIQQ4quvvhLz588XQgjx5ptvis8//1wIIcTXX38tVq5cKa5duyY8PT2FRqMRd+7cER4eHkKhUHTzp0A9RXl5ufD09BTnz58XM2fOFDU1NUIIId59912RnJwshBDC09NTlJeXC7VaLUJCQsTt27eFEELs3btXLF++XAhxrw6eOnXqsa9TXFwsZs+eLRobG0VTU5Pw9fUVW7ZsEdXV1cLNzU2Ul5cLIYT4+OOPRUREhPZ1d+7cKYQQ4q9//asIDw8XQggREREhLl26JIQQoqCgQPj6+gohhNiyZYvYsmVLq38bWn5LS4twcXERzc3NQq1WizVr1ogff/yxg59yz8OWhZF49tlnYWNjg4KCAigUCmRmZgIA7ty5g4sXL+LMmTMICgqCmZkZ5HI5vvzyS1y4cAHl5eVYsWKFtpz7TW4AeOmllwAAo0ePRm1tLQDgzJkz+OCDDwAAHh4e8PDwAACMGDECZ86cwc2bN+Hh4YE+ffp0y/umnuvMmTPw9PTE4MGDAQCvvfYaYmJiWp1jZmaGrVu3Ijc3F1euXMHp06dhZqZf7/fp06fh4eGBAQMGAABmz54NjUaD4uJiTJgwAfb29trX/fOf/6x93oP1+tixYwCAlJQUnDhxAjk5OTh79iyampoe+7qGlm9ubg5nZ2cEBgbi17/+NX73u99h2LBher1HY8JkYST69u0L4N7uaykpKRg3bhwAoLq6Gk888QQyMzNb7cJ29epVaDQa2Nvb4+DBe1usqtVqVFdXa8+5/4X/4PMsLP5bJYQQuHz5MkaNGoWAgABkZ2fj5s2biIiI6Lo3SkZDo9G0eiyEQEtLS6tjTU1NCAwMxLx58/DCCy/AyckJu3bt0qt8mUzWatzDwsICzc3NOl/3UfV60aJFcHFxgYuLC1xdXbF69ep2v69Hlb9t2zb8+9//Rl5eHt544w2kpqZi6tSper1PY8EBbiMzbdo07R0blZWVmDdvHm7duoUXXngBhw8fhhACt2/fxpIlSzBixAjU1dVpd2vLzMxs8yIBgClTpuDLL78EABQUFCAuLg7AvV91J0+eRHV1NSZOnNiF75B6OgsLC7S0tGDq1KnIzc3VtkozMjLg4uICADA3N4darUZZWRlkMhneeustuLi44Pjx41Cr1Xq9jqurK06cOIGGhgYolUocP34cADBx4kScPXsW169fBwDs2bNH+7qPUltbi7KyMqxatQru7u746quvtDGYm5s/lOAMLb+mpgZz5syBo6MjVq1ahenTp6O0tFSv92hM2LIwMuHh4Xj33Xfh6+sLtVqN6Oho/OIXv8CiRYuQkJCAefPmAQDi4uJgY2ODDz/8EJs2bYJSqYS1tTXef//9Nstfv3491q1bh7///e/o168fEhISANxr2UyaNAmOjo5d/h6pZxsyZAiGDx+OTZs2Yfny5QgODoZKpcK4ceMQHx8PAJgxYwaWLVuGjz/+GGPHjoWPjw9kMhnc3NxQVFSk1+uMHTsWoaGhCAwMxMCBAzF8+HAAwNChQ7Fx40aEh4dDpVJpY3mcQYMGITAwEHPnzoWFhQWmTZsGhUKBO3fu4IUXXsA777yDoUOHas83tPwnn3wSr732GgIDA9GvXz88++yzCAgI0Os9GhNufkQ6CSFw584dhIeHIzU1FUOGDJE6JMk0NjZiwYIF+N///V/Y29ujoKAASUlJUCqV8PHx0d4xVlJSgrVr16KpqQlTpkxBfHx8qy4+ImPDbijSSSaTYcCAAdi5c2evThRnz57FwoULUVZWBgBQKBSIjY3Ftm3bcPjwYZw7dw7ffPMNACA6Ohrr16/H0aNHIYRARkaGhJETdZzR/9T56acmaDRsHOljyBBr3L7dqPtEAnBvY/vBgwdoH2dkZGDDhg14++23Ady7a+bpp5+Gg4MDAMDPzw85OTkYNWoUFAoFJk2aBADw9/fHli1bsGjRIoNen3Vbf6zb+vt5vdaX0ScLjUbwgtIhK2sv0tJSceFCKRwdnRAZuRr+/q9KHZbR+Xm/dWVlJeRyufaxra0tKioqHjoul8tRUVFh8OuxbhuGn1XXMvpkQW3LytqLxMT3kJb2EXx9ZyE7+xgiI8MBgAmjgzQaTavbJ4UQkMlkjz1uqCFDrDslzt5CLreROgSTxmRh4tLSUpGW9hHc3NxhaWkJNzd3pKV9hNjYaCaLDrKzs0NVVZX2cVVVFWxtbR86Xl1dDVtbW4PLv327kb+W9SSX26CqqkHqMIyCmZmsXT9EmCxM3IULpXBxcW11zMXFFRcumN594N1t4sSJuHLlCq5evQp7e3tkZ2cjICAAI0aMQJ8+fVBUVITJkyfj4MGDcHd3lzpckxAQ4IfLly/qff7IkaORmXmoCyPqPZgsTJyjoxMKC0/Cze2/X1aFhSfh6OgkYVSmoU+fPkhOTkZERASUSiU8PDwwe/ZsAEBqairWrVuHxsZGjBs3DiEhIRJHaxoe98W/NDkXn67x6uZoehcmCxMXGbkakZHh2jGL/Pw8REaGIzY2TurQjFZubq72366urvi///u/h84ZM2YM9u3b151hEXUpJgsTd39cIjY2GoGB8+Do6ITY2DiOVxCRQTgpj4iIdGLLwsTx1lki6gxsWZi4x906m5aWKnVoRGREmCxMHG+dJaLOwG4oE+fo6ITU1GQcOZKtXe7Dx8eXt84SkUHYsjBx06e/hC1bNmPhwmA0NDRg4cJgbNmyGdOnvyR1aERkRNiyMHH//Oc/MGvWbGzaFI8NG2JhZdUHs2bNxj//+Q+pQyMiI8KWhYkrLT2Pc+f+g927M9Hc3IzduzNx7tx/UFp6XurQiMiIMFmYOEtLK7zxxvJWd0O98cZyWFpaSR0aERkRJgsTp1I1Y8eOPyE/Pw8qlQr5+XnYseNPUKmapQ6NiIwIk4WJc3Iag4CAIMTGRqNv376IjY1GQEAQnJzGSB0aERkRvZJFY2MjfH19cf36dQBAQUEB/Pz8MGvWLGzevFl7XklJCfz9/eHt7Y21a9eipaUFAHDz5k0sXrwYs2fPxooVK9DU1AQAqK+vx7Jly+Dj44PFixe32gOAOkdk5GpkZu5FYmIKFAoFEhNTkJm5F5GRq6UOjYiMiM5k0Rmb1MfHx2PRokXIycnB+PHjsW3bNgBAWloapkyZgiNHjuDVV199aNtK6jh//1cRGxvXqmXBhQSJyFA6k8X9Terv7/T14Cb1FhYW2k3qb9y48dAm9Tk5OVCpVDhz5gy8vb1bHQeAr7/+Gn5+fgAAX19f5OXd61enzuXv/yry8gqhVquRl1fIREFEBtM5z6Kjm9T/9NNPsLa2hoWFRavjPy/LwsIC1tbWqKmpwbBhw/R+A9yn2DDcp5iI2sPgSXmGblL/qM3qH7d5vRACZmaGjblzn2LdsrL2Ii0tVbvcR2TkarYu9NDevYqJTJHBd0MZukn9k08+iYaGBqjV6lbnA/daJdXV1QCAlpYWNDU1YdCgQR16Q9Ta/SXKHxzgTkx8D1lZe6UOjYiMiMHJ4sFN6tVqNbKzs+Hu7t5qk3oA2k3qLS0tMWXKFBw+fBgAcODAAe3m9R4eHjhw4AAA4PDhw5gyZQosLS07670RuEQ5EXUOg7uh2rNJ/YYNG7BmzRps374dTz31FD744AMAwKpVq7BmzRrMnTsXNjY2SE3lF1hn4xLlRNQZZEIIo+7w55hF29zdXZCYmAI3N3fI5TaoqmpAfn4eYmOjkZdXKHV4PZrUYxas2/pbmpyLT9d4SR2GUWhvveYMbhMXGbkakZHhrZb7iIwM56Q8IjIIlyg3cffveoqNjUZg4Dw4OjpxUh4RGYzJohfw938V/v6varuhiIgMxW4oIiLSicmiF8jK2gt3dxeYm5vD3d2FcyyIyGDshjJx9yflpaV9BF/fWcjOPobIyHAA4LgFEemNLQsTx0l5RNQZmCxMHCflEVFnYLIwcY6OTigsPNnqWGHhSTg6OkkUEREZIyYLE8dJeUTUGTjAbeI4KY+IOgOTRS/ASXlE1FHshiIiIp2YLIiISCcmCyIi0onJgoiIdGKyICIinXg3FFEnCA4ORk1NDSws7l1SGzduxLVr17B9+3a0tLQgNDQUixcvljhKovZjsiDqICEEysrKcOLECW2yqKioQFRUFLKysmBlZYUFCxbAxcUFo0aNkjhaovZhsiDqoB9++AEAsHTpUtTW1iIoKAgDBgzAtGnTMGjQIACAt7c3cnJyEB4eLmWoRO3GMQuiDqqvr4erqyu2bt2Kzz77DLt378bNmzchl8u159ja2qKiokLCKIk6hi2LXiAmZjX+9re/oLlZCSurPggODkVSEpco7yzOzs5wdnbWPg4MDERSUhJWrFihPSaEgEwmM6jcIUOsOy3G3kAut5E6BJPGZGHiYmJW47PPPsX69RuxevUqpKZ+iI0b1wMAE0Yn+fbbb6FSqeDqem8peCEERowYgaqqKu05VVVVsLW1Najc27cbodGITo3VmESk5aFJ0aL3+X5/OKjXeQP6WiA90r29YRk9MzNZu36IMFmYuL/97S9Yv34jVqwIR//+/bFixb0+802b4pmy0TdeAAAOvUlEQVQsOklDQwO2bNmC3bt3Q6VSYf/+/UhJSUF0dDRqamrQr18/HDt2DO+9957UoRqVJkULPl3jpde5hqx7tjQ5tyNh9VpMFiauuVmJy5cvwd5eru2GWrBgMZqblVKHZjI8PT1x9uxZvPLKK9BoNFi0aBEmT56MqKgohISEQKVSITAwEBMmTJA6VKJ2Y7IwcTKZGT7//DNs2JCg7YaKj18HmYz3NnSmyMhIREZGtjrm5+cHPz8/iSIi6lz8xjBxMhmg0WiwdWsabGxssHVrGjQaDQwcayWiXq5DLQtDZq0WFBQgKSkJSqUSPj4+iIqKAgCUlJRg7dq1aGpqwpQpUxAfH68tjzpOo9HA2toaNTU/QaPRoKbmJ1hbW6OxsVHq0IjIiLS7ZXF/1urBgwe1/7Ozs8PmzZvx97//HQcOHMCePXtw6dIlKBQKxMbGYtu2bTh8+DDOnTuHb775BgAQHR2N9evX4+jRoxBCICMjo9PeHAEymQz29g4wM7v3f7WZmRns7R0Mvo2TiHq3dieLB2etzps3D59//jkKCgq0s1b79++vnbVaXFyMp59+Gg4ODrCwsICfnx9ycnJw48YNKBQKTJo0CQDg7++PnJycznlnBOBeUj9/vgSenl6oqqqCp6cXzp8vgRC995ZMIjJcu/t77s9ajYuLg0qlQkhICHx8fB6atVpcXIzKyspHzmb9+XG5XM5Zrp1MJpPhiSeewNGjR7Sf9aBBg1BXVydxZERkTNqdLAyZtXpvQFWm93FDcJZr24QQqK2txYoVK5CUlISYmBhs374dAGe8EpH+2p0sDJm1amdnp9fx6upqznLtAg4Ov8COHZ9i+/btsLLqAweHX6C8/Jrek5h6q/bOdCUyRe0es2hoaMAf//hHKJVKNDY2ametnjx5EjU1Nbh79y6OHTsGd3d3TJw4EVeuXMHVq1ehVquRnZ0Nd3d3jBgxAn369EFRUREA4ODBg3B3773T8LtKefk17SS85mYlysuvSRwRERmbdrcsDJ21mpycjIiICCiVSnh4eGD27NkAgNTUVKxbtw6NjY0YN24cQkJCOued0c/IAIgH/ktEpD+ZMPLbYtgN1TZb24GP/VtlZX03RmJ8pO6G6u11e2lybpetDaVvuaaovfWaM7iJiEgnJgsiItKJyaKXMDe3aPVfIiJDMFn0Emp1S6v/EhEZgsmilxg6VI6SkhIMHSrXfTIR0c+wT8LEyWQyyGQyVFdXYezYsQDuLSZo5DfBUS/QZ3w+wnI7f624PuOtAfTeu6Hai8nCRAUE+OHy5YuwtLREc3Mz+vfvDxsbGzQ0NODOnTuwsrLCpEljAAAjR45GZuYhiSMmak15zq3rtlX17UhkvROThYl68Ms/KOgVfPPNCdy5cwcymQwzZnghI+OAhNERkbFhsugF7ieG3j4ZiYjajwPcRESkE5MFERHpxGRBREQ6MVkQEZFOTBZERKQTkwUREenEZEFERDoxWRARkU6clEdEPdbS5NxOL3NAX37ttQc/NSLqkQxZbYCrE3Q9JgsjF5GWhyaF/ntUGPJLbUBfC6RHurcnLCIyMUwWRq5J0dIlK3MCXdMFQETGiQPcRESkE5MFERHpxGRBREQ6cczCyHXV1pP3yub2k0R0D5OFkeuqrScBbj9JRP/FbigiItKJyYKIiHTqEd1Qhw4dwvbt29HS0oLQ0FAsXrxY6pCMSlfNh+CyCB3Huk2mQvJvg4qKCmzevBlZWVmwsrLCggUL4OLiglGjRkkdmlHgkgg9F+s2mRLJk0VBQQGmTZuGQYMGAQC8vb2Rk5OD8PBwiSMzbgEBfrh8+eJDxyftfvjckSNHIzPzUDdE1buwbne+x9VrgHW7q0meLCorKyGXy7WPbW1tUVxcLGFEpuFRF4ihd0NRx7Bud77HffGzbnc9yZOFRqOBTCbTPhZCtHqsy5Ah1l0RlsmSy22kDqHXYN3uXqzbXUvyZGFnZ4dvv/1W+7iqqgq2trZ6P/+nn5qg0YiuCM3kDBlijdu3G6UOw2iYmckwePCAdj+fdbv7sG7rr731WvJk8eKLLyI9PR01NTXo168fjh07hvfee0/v53fkYu6N+Gu1+7Budy/W7a4lebIYNmwYoqKiEBISApVKhcDAQEyYMEHqsIg6jHWbTIlMCMF2LhERtYkzuImISCcmCyIi0onJgoiIdGKyICIinZgsiIhIJyYLIiLSicmCiIh0YrIgIiKdmCw6qKGhAWFhYW2eExMTgxs3brR5TnBwMAoLCzstrvT0dKSnp+t9fmFhIYKDgzvt9QGgvLwcsbGxBj3H0LipZ1mzZg2ysrI6rbyMjAxkZ2cDAD788EN89dVXnVZ2d5RvSpgsOqiurg4lJSVtnlNYWIjeOFH+5s2bKC8vlzoMMmL/+te/0NzcDABYtWoVfv3rXxtV+aZE8rWhjF1CQgIqKysRFhYGLy8v7Ny5EzKZDOPGjUNcXBx27dqFyspKLFu2DLt27cKpU6ewc+dOKBQKNDc3IzExEb/61a90vk5SUhKGDRuGpUuXAgAiIiIwb948ODs7Y+3atbh58yYsLCwQFRUFd3d3vWLPz89HUlIS+vTpg2effVZ7/MqVK1i/fj1qa2vRv39/rF27FhMmTMCaNWtgbW2N77//HhUVFQgLC0NAQAAqKioQGxuLhoYGVFZWYv78+Vi1ahUSEhJw/fp1xMfHY8OGDfjzn/+MI0eOQK1Ww83NDdHR0ZDJZPjkk0+QkZGBwYMHY+DAgVw/yYgIIZCcnIyvv/4atra2UKvVmDp1KjIzMx+6FgYMGAA3Nzd4e3ujqKgI5ubmSEtLg4ODA44cOfLQdaFQKJCbm4tTp05BLpfjyy+/xNSpU+Hv729w+Y9SUFDwyPKnTp2KsLAwPPfcc7h06RKef/55ODs7Y//+/airq8PWrVsxcuRIFBcXIykpCQqFAoMHD0Z8fPxjX8skCOqQ8vJy4enpKc6fPy9mzpwpampqhBBCvPvuuyI5OVkIIYSnp6coLy8XarVahISEiNu3bwshhNi7d69Yvny5EEKIJUuWiFOnTj32db7//nsxf/58IYQQDQ0NYvr06UKpVIqVK1eKTz/9VAghxLVr18T06dNFVVWV2LJli9iyZctjy1MqlWL69Oni0qVLQgghYmNjxZIlS4QQQgQEBIijR48KIYT47rvvxIwZM4RSqRTvvPOOCAsLExqNRpw/f15MnTpVCCHEJ598IrKysoQQQtTX1wtnZ2dx+/ZtcerUKW2Z33zzjYiIiBAtLS1CrVaL//mf/xEHDhwQxcXFYvbs2aKxsVE0NTUJX1/fNuOmnuXIkSNiyZIlorm5Wdy+fVtMnz5d/PWvf33steDo6CiOHz8uhBAiKSlJJCUltXldvPPOOyIzM7PVv9u61h5VflseVX55eblwcnIS33//vVCr1WLmzJkiNTVVCCFEenq62LRpk1AqlcLPz0/cuHFDCCFEXl6eCA0N7fgH2oOxZdFJzpw5A09PTwwePBgA8NprryEmJqbVOWZmZti6dStyc3Nx5coVnD59GmZm+vUEPv/882hubsbVq1fx3XffwcvLC1ZWVjh16hQSEhIAAA4ODpg4cSLOnj2rs7zS0lLY2tpi5MiRAID58+fjww8/RFNTE65du4ZZs2YBACZNmoQnnngCP/zwAwBg+vTpkMlkcHR0RG1tLQDg9ddfx6lTp7Bjxw5cvHgRKpUKd+/ebfV6J0+eRHFxMfz9/QEACoUCw4cPR3V1NTw8PDBgwL3luGfPng2NRqPXZ0LSO336NGbNmgVLS0s8+eSTcHd3h0wma/NaeOmllwAAo0ePxrfffmvwdaHrWvt5+e0xdOhQPP/88wDu7Uvi6uoKABg+fDiuX7+OsrIylJeXY8WKFdrnNDaa9n4aTBad5OdfcEIItLS0tDrW1NSEwMBAzJs3Dy+88AKcnJywa9cuvV9j3rx5OHz4ML777jssW7ZM+zo/f121Wq2zLJlM1uq55ubmjyzv52X26dNH+/z7kpOTUV5eDl9fX8ycORMFBQUPlaNWqxEaGorf/e53AID6+nqYm5tjz549rc61sLDQ9iFTz/fzemRhYaHzWniwDgkhDL4uDC2/PaysrFo9vn99PBiDvb09Dh48COBe/a6urm7XaxkLDnB3kIWFBVpaWjB16lTk5uZqf21nZGTAxcUFwL2KplarUVZWBplMhrfeegsuLi44fvy4Xl/s9/n5+eHw4cO4evUqJk+eDACYNm0a9u3bB+De3Uf/+te/MGnSJJ1lOTk5obq6GufPnwcAfPnllwAAa2tr2Nvb49ixYwCAf//736iursbo0aMfW9Y///lPvP766/Dx8cGVK1dQUVEBjUYDc3Nz7UU8bdo0HDx4EE1NTWhpaUFYWBiOHj0KV1dXnDhxAg0NDVAqlTh+/LjenwdJz9XVFUeOHEFzczPq6urwj3/8AwAeey08SlvXxf1r50FtXWuGelT5+njuuedQV1enbblkZmZi9erV7YrBWLBl0UFDhgzB8OHDsWnTJixfvhzBwcFQqVQYN24c4uPjAQAzZszAsmXL8PHHH2Ps2LHw8fGBTCaDm5sbioqK9H6tp556CoMHD4azs7P2l/3atWuxfv167e2KCQkJem3daWlpiQ8++ADR0dGwsLDQNrkBICUlBe+++y7S09NhaWmJ9PT0h35pPWj58uV4++230bdvX9jZ2WH8+PG4fv06xo4di4aGBkRHRyMlJQXnz59HUFAQ1Go1XnrpJcyfPx8ymQyhoaEIDAzEwIEDMXz4cL0/D5LezJkz8Z///Ae+vr4YOnQoRo4cCWtr68deC48yZsyYx14XL774Ij744APY2Ni0Ot+Q8tvyqPL1YWVlhQ8//BCbNm2CUqmEtbU13n///XbFYCy4+REREenElkUPcu3aNURERDzybwkJCfjlL39pcJnBwcGor69/6PiCBQuwcOFCg8sjMjZ/+MMfcOnSpYeOe3l5YdWqVRJEZJzYsiAiIp04wE1ERDoxWRARkU5MFkREpBOTBRER6cRkQUREOv1/WwojZGu8xpgAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "training_set.drop(columns=['donated']).plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Checking for correlation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "correlation = training_set.corr()\n",
+ "plt.figure(figsize=(10, 10)) \n",
+ "sns.heatmap(correlation, vmax=1, square=True, annot=True, cmap='cubehelix')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Total donations and total volume donated are highly correlated and directly proportional. To confirm, we plot a line graph of total volume donated against total number of times donated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# extract the columns\n",
+ "donations = training_set.total_donations.values\n",
+ "volume = training_set.total_vol_donated.values\n",
+ "\n",
+ "plt.figure(figsize=(10, 5))\n",
+ "\n",
+ "plt.plot(donations, volume, color='green')\n",
+ "\n",
+ "plt.title('Total donated vs Times donated', fontsize=25)\n",
+ "plt.xlabel(\"Number of times donated\", fontsize=20)\n",
+ "plt.ylabel(\"Total volume donated\", fontsize=20)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Data Training and Prediction\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.1. Training and Test Sets\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Dataset features\n",
+ "training_values = training_set.values\n",
+ "test_values = test_set.values\n",
+ "# print(training_values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Extract training and testing features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train = training_values[:, 0:4]\n",
+ "y_train = training_values[:, 4]\n",
+ "X_test = test_values[:, 0:4]\n",
+ "\n",
+ "scores = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.2. Modelling and Evaluation\n",
+ "\n",
+ "\n",
+ "### I. Logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7638888888888888"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "logit_model = LogisticRegression(solver='lbfgs')\n",
+ "\n",
+ "# Fit/Train model into data\n",
+ "logit_model.fit(X_train, y_train)\n",
+ "\n",
+ "# Training Score\n",
+ "logit_score = logit_model.score(X_train, y_train)\n",
+ "# print(logit_score)\n",
+ "scores['LR'] = logit_score\n",
+ "\n",
+ "logit_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### II. Decision Trees"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9375"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "tree_model = DecisionTreeClassifier() # max_depth = 3\n",
+ "# Fit a decision tree\n",
+ "tree_model = tree_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "tree_model_score = tree_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['DT'] = tree_model_score\n",
+ "tree_model_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### III. Random Forest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9253472222222222"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "rf = RandomForestClassifier() # n_estimators = 100\n",
+ "# Fit\n",
+ "rf_model = rf.fit(X_train, y_train) \n",
+ "# training score\n",
+ "rf_score = rf_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['RF'] = rf_score\n",
+ "rf_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### IV. Support Vector Machine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9114583333333334"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "svm_model = SVC(probability=True, gamma='auto') \n",
+ "# Fit\n",
+ "svm_model = svm_model.fit(X_train, y_train) \n",
+ "# Training Score\n",
+ "svm_score = svm_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['SVM'] = svm_score\n",
+ "svm_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### V. KNN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.828125"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# instantiate (k = 5)\n",
+ "knn_model = KNeighborsClassifier(n_neighbors=5) \n",
+ "# fit the model\n",
+ "knn_model.fit(X_train, y_train) \n",
+ "# Training Score\n",
+ "knn_score = knn_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['KNN'] = knn_score\n",
+ "knn_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### VI. Naive Bayes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7465277777777778"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "bayes_model = GaussianNB() \n",
+ "# Fit the model\n",
+ "bayes_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "NB_score = bayes_model.score(X_train, y_train)\n",
+ "\n",
+ "scores['NB'] = NB_score\n",
+ "NB_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.3. Model Score Comparison\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " score\n",
+ "DT 0.937500\n",
+ "KNN 0.828125\n",
+ "LR 0.763889\n",
+ "NB 0.746528\n",
+ "RF 0.925347\n",
+ "SVM 0.911458"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scores_df = pd.DataFrame({\n",
+ " 'score': scores\n",
+ "})\n",
+ "\n",
+ "scores_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Decision Tree Classifier** has the highest score of **0.9375**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.4. Fine Tuning Highest Scoring Model\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | score | \n", + "
|---|---|
| DT | \n", + "0.937500 | \n", + "
| KNN | \n", + "0.828125 | \n", + "
| LR | \n", + "0.763889 | \n", + "
| NB | \n", + "0.746528 | \n", + "
| RF | \n", + "0.925347 | \n", + "
| SVM | \n", + "0.911458 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "depths = np.arange(1, 33)\n",
+ "DT_scores = []\n",
+ "for depth in depths:\n",
+ " DT = DecisionTreeClassifier(max_depth=depth)\n",
+ " DT.fit(X_train, y_train)\n",
+ " score = DT.score(X_train, y_train)\n",
+ " DT_scores.append(score)\n",
+ " \n",
+ "plt.figure(figsize=(12, 8))\n",
+ "plt.plot(depths, DT_scores, color='red')\n",
+ "plt.title(\"Score vs Max depth\", fontsize=20)\n",
+ "plt.xlabel(\"Max depth\", fontsize=19)\n",
+ "plt.ylabel(\"Score\", fontsize=19)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For better results, max depth rangges between 10 and 13"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9305555555555556"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Instantiate\n",
+ "tree_model = DecisionTreeClassifier(max_depth=11) # max_depth = 13\n",
+ "# Fit a decision tree\n",
+ "tree_model = tree_model.fit(X_train, y_train) \n",
+ "# Training score\n",
+ "tree_model_score = tree_model.score(X_train, y_train)\n",
+ "\n",
+ "tree_model_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.5. Predictions\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 6.1. [Numerical EDA](#num-eda)
\n", + " 6.2. [Statistical EDA](stat-eda)
\n", + " 6.3. [Visual EDA](#visual-eda)
\n", + "7. [Data Preprocessing](#preprocessing)\n", + "8. [Feature Engineering](#f-eng)
\n", + " 8.1. [Scaling the Data](#scaling)
\n", + "9. [Model Selection and Training](#selection)
\n", + " 9.1. [Choosing a performance Metric](#metric)
\n", + " 9.2. [Model Validation](#validation)
\n", + "10. [Model Evaluation and Optimization](#evaluation)\n", + "11. [Prediction](#pred)\n", + "12. [Submission](#submission)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Santander's mission is to help people and businesses prosper. They are always looking for ways to help their customers understand their financial health and identify which products and services might help them achieve their monetary goals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Definition\n", + "\n", + "Santander is continually challenging their Machine Learning Algorithms to make sure they can accurately identify new ways to solve their most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Objective\n", + "\n", + "To identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Libraries\n", + "Here we load the key libraries that we going to use\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Import key libraries\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "import plotly\n", + "import warnings\n", + "\n", + "# import ML libraries\n", + "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Library Defaults\n", + "Here we set the default visualization settings for seaborn and warning handling" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seaborn visualization default\n", + "sns.set(color_codes=True)\n", + "\n", + "# Ignore warnings in production\n", + "# warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. The Data\n", + "Loading in the data\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n",
+ "RangeIndex: 10000 entries, 0 to 9999\n",
+ "Columns: 202 entries, ID_code to var_199\n",
+ "dtypes: float64(200), int64(1), object(1)\n",
+ "memory usage: 15.4+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "santander_training.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### dataset dimensionality"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10000, 202)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_training.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observations\n",
+ "From the above observations we can see that:\n",
+ "1. The data has a total of 202 features/labels and 50 000 samples.\n",
+ "2. Of all the features/labels, 200 are of type float, 1 of type integer and one of object type\n",
+ "\n",
+ "### Null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ID_code 0\n",
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+ "Length: 202, dtype: int64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "null_values = santander_training.isnull().sum()\n",
+ "null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The training set has no null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ID_code 0\n",
+ "var_0 0\n",
+ "var_1 0\n",
+ "var_2 0\n",
+ "var_3 0\n",
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+ "var_11 0\n",
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+ "var_14 0\n",
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+ "var_16 0\n",
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+ "var_19 0\n",
+ "var_20 0\n",
+ "var_21 0\n",
+ "var_22 0\n",
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+ "var_24 0\n",
+ "var_25 0\n",
+ "var_26 0\n",
+ "var_27 0\n",
+ "var_28 0\n",
+ " ..\n",
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+ "var_172 0\n",
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+ "var_175 0\n",
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+ "var_177 0\n",
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+ "var_181 0\n",
+ "var_182 0\n",
+ "var_183 0\n",
+ "var_184 0\n",
+ "var_185 0\n",
+ "var_186 0\n",
+ "var_187 0\n",
+ "var_188 0\n",
+ "var_189 0\n",
+ "var_190 0\n",
+ "var_191 0\n",
+ "var_192 0\n",
+ "var_193 0\n",
+ "var_194 0\n",
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+ "var_198 0\n",
+ "var_199 0\n",
+ "Length: 201, dtype: int64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_null_values = santander_test.isnull().sum()\n",
+ "test_null_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The test set also has no null values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6.2. Statistical EDA\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + " 1. There's no much difference between label means for the training and test sets, same case for standard deviation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3. Visual EDA\n", + "\n", + "### Distribution of the target(whether a customer made a transaction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " 0 1\n",
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+ "7 1.000000 0.000000\n",
+ "8 1.000000 0.000000\n",
+ "9 1.000000 0.000000"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Probabilities\n",
+ "donation_prob = tree_model.predict_proba(X_test)\n",
+ "prob_df = pd.DataFrame(donation_prob)\n",
+ "prob_df.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.6. Submission Formatting\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 0 | \n", + "1 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Unnamed: 0\n",
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+ "2 263\n",
+ "3 303\n",
+ "4 83"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "donated_prob = prob_df[1]\n",
+ "# print(donated_prob)\n",
+ "submission_df = test_dataset.copy()\n",
+ "# print(submission_df.head())\n",
+ "submission_df.drop(columns=['Months since Last Donation', 'Number of Donations', 'Total Volume Donated (c.c.)', 'Months since First Donation'], inplace=True)\n",
+ "submission_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5.7. Final Submission\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Unnamed: 0 | \n", + "
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+ "\n",
+ "\n",
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+ "text/plain": [
+ " Unnamed: 0 Made donation in March 2007\n",
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+ "14 696 1.000000\n",
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+ "16 176 0.000000\n",
+ "17 571 0.000000\n",
+ "18 139 0.000000\n",
+ "19 423 0.000000"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "submission_df['Made donation in March 2007'] = donated_prob\n",
+ "submission_df.head(20)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Santander Customer Transaction Prediction.ipynb b/notebooks/Santander Customer Transaction Prediction.ipynb
new file mode 100644
index 0000000..050300b
--- /dev/null
+++ b/notebooks/Santander Customer Transaction Prediction.ipynb
@@ -0,0 +1,2836 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)\n",
+ "2. [Problem Definition](#problem)\n",
+ "3. [Objective](#obj)\n",
+ "4. [Libraries](#libs)\n",
+ "5. [Data](#data)\n",
+ "6. [Exploration Data Analysis(EDA)](#eda)| \n", + " | Unnamed: 0 | \n", + "Made donation in March 2007 | \n", + "
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| 3 | \n", + "303 | \n", + "0.000000 | \n", + "
| 4 | \n", + "83 | \n", + "0.000000 | \n", + "
| 5 | \n", + "500 | \n", + "1.000000 | \n", + "
| 6 | \n", + "530 | \n", + "0.333333 | \n", + "
| 7 | \n", + "244 | \n", + "0.000000 | \n", + "
| 8 | \n", + "249 | \n", + "0.000000 | \n", + "
| 9 | \n", + "728 | \n", + "0.000000 | \n", + "
| 10 | \n", + "129 | \n", + "1.000000 | \n", + "
| 11 | \n", + "534 | \n", + "0.000000 | \n", + "
| 12 | \n", + "317 | \n", + "0.000000 | \n", + "
| 13 | \n", + "401 | \n", + "0.095238 | \n", + "
| 14 | \n", + "696 | \n", + "1.000000 | \n", + "
| 15 | \n", + "192 | \n", + "0.222222 | \n", + "
| 16 | \n", + "176 | \n", + "0.000000 | \n", + "
| 17 | \n", + "571 | \n", + "0.000000 | \n", + "
| 18 | \n", + "139 | \n", + "0.000000 | \n", + "
| 19 | \n", + "423 | \n", + "0.000000 | \n", + "
\n", + " 6.1. [Numerical EDA](#num-eda)
\n", + " 6.2. [Statistical EDA](stat-eda)
\n", + " 6.3. [Visual EDA](#visual-eda)
\n", + "7. [Data Preprocessing](#preprocessing)\n", + "8. [Feature Engineering](#f-eng)
\n", + " 8.1. [Scaling the Data](#scaling)
\n", + "9. [Model Selection and Training](#selection)
\n", + " 9.1. [Choosing a performance Metric](#metric)
\n", + " 9.2. [Model Validation](#validation)
\n", + "10. [Model Evaluation and Optimization](#evaluation)\n", + "11. [Prediction](#pred)\n", + "12. [Submission](#submission)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Santander's mission is to help people and businesses prosper. They are always looking for ways to help their customers understand their financial health and identify which products and services might help them achieve their monetary goals." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Problem Definition\n", + "\n", + "Santander is continually challenging their Machine Learning Algorithms to make sure they can accurately identify new ways to solve their most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Objective\n", + "\n", + "To identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Libraries\n", + "Here we load the key libraries that we going to use\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Import key libraries\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "import plotly\n", + "import warnings\n", + "\n", + "# import ML libraries\n", + "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Library Defaults\n", + "Here we set the default visualization settings for seaborn and warning handling" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seaborn visualization default\n", + "sns.set(color_codes=True)\n", + "\n", + "# Ignore warnings in production\n", + "# warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. The Data\n", + "Loading in the data\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ " var_7 ... var_190 var_191 var_192 var_193 var_194 var_195 \\\n",
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+ "2 14.6155 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 \n",
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+ "4 19.2514 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 \n",
+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
+ "0 7.8784 8.5635 12.7803 -1.0914 \n",
+ "1 8.1267 8.7889 18.3560 1.9518 \n",
+ "2 -6.5213 8.2675 14.7222 0.3965 \n",
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+ "\n",
+ "[5 rows x 202 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# %%time\n",
+ "# Read the training and test datasets\n",
+ "path = '../data/santander/'\n",
+ "santander_training = pd.read_csv(path + 'train.csv', nrows=10000)\n",
+ "santander_training.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
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+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_test = pd.read_csv(path + 'test.csv', nrows=10000)\n",
+ "santander_test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 6. Exploratory Data Analysis(EDA)\n",
+ "Training set information\n",
+ "\n",
+ "## 6.1. Numerical EDA\n",
+ "\n",
+ "### Dataset information"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 1 | \n", + "test_1 | \n", + "8.5304 | \n", + "1.2543 | \n", + "11.3047 | \n", + "5.1858 | \n", + "9.1974 | \n", + "-4.0117 | \n", + "6.0196 | \n", + "18.6316 | \n", + "-4.4131 | \n", + "... | \n", + "10.6165 | \n", + "8.8349 | \n", + "0.9403 | \n", + "10.1282 | \n", + "15.5765 | \n", + "0.4773 | \n", + "-1.4852 | \n", + "9.8714 | \n", + "19.1293 | \n", + "-20.9760 | \n", + "
| 2 | \n", + "test_2 | \n", + "5.4827 | \n", + "-10.3581 | \n", + "10.1407 | \n", + "7.0479 | \n", + "10.2628 | \n", + "9.8052 | \n", + "4.8950 | \n", + "20.2537 | \n", + "1.5233 | \n", + "... | \n", + "-0.7484 | \n", + "10.9935 | \n", + "1.9803 | \n", + "2.1800 | \n", + "12.9813 | \n", + "2.1281 | \n", + "-7.1086 | \n", + "7.0618 | \n", + "19.8956 | \n", + "-23.1794 | \n", + "
| 3 | \n", + "test_3 | \n", + "8.5374 | \n", + "-1.3222 | \n", + "12.0220 | \n", + "6.5749 | \n", + "8.8458 | \n", + "3.1744 | \n", + "4.9397 | \n", + "20.5660 | \n", + "3.3755 | \n", + "... | \n", + "9.5702 | \n", + "9.0766 | \n", + "1.6580 | \n", + "3.5813 | \n", + "15.1874 | \n", + "3.1656 | \n", + "3.9567 | \n", + "9.2295 | \n", + "13.0168 | \n", + "-4.2108 | \n", + "
| 4 | \n", + "test_4 | \n", + "11.7058 | \n", + "-0.1327 | \n", + "14.1295 | \n", + "7.7506 | \n", + "9.1035 | \n", + "-8.5848 | \n", + "6.8595 | \n", + "10.6048 | \n", + "2.9890 | \n", + "... | \n", + "4.2259 | \n", + "9.1723 | \n", + "1.2835 | \n", + "3.3778 | \n", + "19.5542 | \n", + "-0.2860 | \n", + "-5.1612 | \n", + "7.2882 | \n", + "13.9260 | \n", + "-9.1846 | \n", + "
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+ "\n",
+ " var_199 \n",
+ "count 10000.000000 \n",
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+ "[8 rows x 201 columns]"
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+ "execution_count": 9,
+ "metadata": {},
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+ "source": [
+ "santander_training.describe()"
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| std | \n", + "0.299213 | \n", + "3.041680 | \n", + "4.077271 | \n", + "2.638402 | \n", + "2.045605 | \n", + "1.624462 | \n", + "7.850295 | \n", + "0.870482 | \n", + "3.434960 | \n", + "3.314391 | \n", + "... | \n", + "4.585233 | \n", + "3.039712 | \n", + "1.476938 | \n", + "4.005774 | \n", + "3.171094 | \n", + "1.419062 | \n", + "5.430852 | \n", + "0.918565 | \n", + "3.008292 | \n", + "10.466326 | \n", + "
| min | \n", + "0.000000 | \n", + "1.335000 | \n", + "-13.422700 | \n", + "3.309000 | \n", + "1.154100 | \n", + "5.943000 | \n", + "-29.013300 | \n", + "2.673300 | \n", + "7.642200 | \n", + "-9.991100 | \n", + "... | \n", + "-11.593900 | \n", + "-1.215800 | \n", + "-3.515900 | \n", + "-9.442700 | \n", + "10.387000 | \n", + "-4.488300 | \n", + "-12.325500 | \n", + "6.256900 | \n", + "7.233700 | \n", + "-36.302500 | \n", + "
| 25% | \n", + "0.000000 | \n", + "8.452675 | \n", + "-4.779075 | \n", + "8.723700 | \n", + "5.253625 | \n", + "9.878450 | \n", + "-11.202600 | \n", + "4.766850 | \n", + "14.007650 | \n", + "-2.266200 | \n", + "... | \n", + "-0.106575 | \n", + "5.151700 | \n", + "0.919650 | \n", + "0.569625 | \n", + "15.615875 | \n", + "-1.201925 | \n", + "-1.978400 | \n", + "8.259475 | \n", + "13.855300 | \n", + "-11.460400 | \n", + "
| 50% | \n", + "0.000000 | \n", + "10.515750 | \n", + "-1.600700 | \n", + "10.543200 | \n", + "6.820850 | \n", + "11.108350 | \n", + "-4.852750 | \n", + "5.387700 | \n", + "16.582050 | \n", + "0.316250 | \n", + "... | \n", + "3.290200 | \n", + "7.356400 | \n", + "1.933300 | \n", + "3.390000 | \n", + "17.950000 | \n", + "-0.203500 | \n", + "2.449500 | \n", + "8.895450 | \n", + "15.915950 | \n", + "-3.000700 | \n", + "
| 75% | \n", + "0.000000 | \n", + "12.759675 | \n", + "1.360375 | \n", + "12.517650 | \n", + "8.323000 | \n", + "12.276350 | \n", + "1.004025 | \n", + "5.998100 | \n", + "19.190900 | \n", + "2.914325 | \n", + "... | \n", + "6.420875 | \n", + "9.532025 | \n", + "2.966875 | \n", + "6.176625 | \n", + "20.483700 | \n", + "0.784025 | \n", + "6.483725 | \n", + "9.597925 | \n", + "18.088900 | \n", + "4.654200 | \n", + "
| max | \n", + "1.000000 | \n", + "19.289300 | \n", + "8.584900 | \n", + "18.347700 | \n", + "12.977300 | \n", + "15.366400 | \n", + "17.251600 | \n", + "7.875500 | \n", + "26.965600 | \n", + "9.103800 | \n", + "... | \n", + "16.614400 | \n", + "16.398500 | \n", + "7.139000 | \n", + "16.782600 | \n", + "27.485800 | \n", + "3.555400 | \n", + "18.321500 | \n", + "11.843900 | \n", + "25.442200 | \n", + "25.831600 | \n", + "
8 rows × 201 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 10.678751 -1.637075 10.674745 6.784620 11.061973 \n",
+ "std 3.060380 4.089832 2.619009 2.066282 1.618390 \n",
+ "min 1.578200 -13.378500 2.516400 0.954400 6.012800 \n",
+ "25% 8.401725 -4.710650 8.705775 5.210275 9.858950 \n",
+ "50% 10.547400 -1.649700 10.541350 6.812450 11.080850 \n",
+ "75% 12.761825 1.303725 12.449400 8.362650 12.254600 \n",
+ "max 20.064900 9.023900 18.277200 12.837200 15.432900 \n",
+ "\n",
+ " var_5 var_6 var_7 var_8 var_9 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean -5.059958 5.417156 16.514916 0.299064 7.574839 \n",
+ "std 7.907097 0.869217 3.422408 3.350265 1.228507 \n",
+ "min -27.098300 2.314800 6.652900 -8.919400 4.313800 \n",
+ "25% -11.260725 4.774725 13.886150 -2.283400 6.637775 \n",
+ "50% -4.922950 5.377700 16.391400 0.357550 7.643850 \n",
+ "75% 1.017025 6.008600 19.096700 2.973550 8.597625 \n",
+ "max 15.999800 8.021300 27.856800 8.663600 11.003600 \n",
+ "\n",
+ " ... var_190 var_191 var_192 var_193 \\\n",
+ "count ... 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean ... 3.207419 7.476404 1.960699 3.324941 \n",
+ "std ... 4.572294 3.032355 1.480675 4.000938 \n",
+ "min ... -12.900200 -1.295600 -3.340900 -10.697200 \n",
+ "25% ... -0.104850 5.169025 0.941925 0.528975 \n",
+ "50% ... 3.134700 7.394300 1.936700 3.482300 \n",
+ "75% ... 6.385200 9.521550 2.990200 6.172125 \n",
+ "max ... 17.094600 16.355900 7.213700 16.241500 \n",
+ "\n",
+ " var_194 var_195 var_196 var_197 var_198 \\\n",
+ "count 10000.000000 10000.000000 10000.000000 10000.000000 10000.000000 \n",
+ "mean 18.027858 -0.124677 2.375043 8.911049 15.877401 \n",
+ "std 3.162509 1.427808 5.453573 0.918185 3.019350 \n",
+ "min 9.472600 -4.516700 -13.251900 6.218200 7.328700 \n",
+ "25% 15.661700 -1.153850 -1.858725 8.262350 13.813875 \n",
+ "50% 18.022800 -0.169750 2.522900 8.888700 15.966600 \n",
+ "75% 20.452025 0.845475 6.631450 9.579525 18.083525 \n",
+ "max 26.962100 4.255700 15.680400 11.559100 24.845300 \n",
+ "\n",
+ " var_199 \n",
+ "count 10000.000000 \n",
+ "mean -3.243134 \n",
+ "std 10.442345 \n",
+ "min -39.457800 \n",
+ "25% -11.066725 \n",
+ "50% -2.733050 \n",
+ "75% 4.983725 \n",
+ "max 24.027500 \n",
+ "\n",
+ "[8 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "santander_test.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observations\n",
+ "From the above statistical overview, we can say that:| \n", + " | var_0 | \n", + "var_1 | \n", + "var_2 | \n", + "var_3 | \n", + "var_4 | \n", + "var_5 | \n", + "var_6 | \n", + "var_7 | \n", + "var_8 | \n", + "var_9 | \n", + "... | \n", + "var_190 | \n", + "var_191 | \n", + "var_192 | \n", + "var_193 | \n", + "var_194 | \n", + "var_195 | \n", + "var_196 | \n", + "var_197 | \n", + "var_198 | \n", + "var_199 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "... | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "10000.000000 | \n", + "
| mean | \n", + "10.678751 | \n", + "-1.637075 | \n", + "10.674745 | \n", + "6.784620 | \n", + "11.061973 | \n", + "-5.059958 | \n", + "5.417156 | \n", + "16.514916 | \n", + "0.299064 | \n", + "7.574839 | \n", + "... | \n", + "3.207419 | \n", + "7.476404 | \n", + "1.960699 | \n", + "3.324941 | \n", + "18.027858 | \n", + "-0.124677 | \n", + "2.375043 | \n", + "8.911049 | \n", + "15.877401 | \n", + "-3.243134 | \n", + "
| std | \n", + "3.060380 | \n", + "4.089832 | \n", + "2.619009 | \n", + "2.066282 | \n", + "1.618390 | \n", + "7.907097 | \n", + "0.869217 | \n", + "3.422408 | \n", + "3.350265 | \n", + "1.228507 | \n", + "... | \n", + "4.572294 | \n", + "3.032355 | \n", + "1.480675 | \n", + "4.000938 | \n", + "3.162509 | \n", + "1.427808 | \n", + "5.453573 | \n", + "0.918185 | \n", + "3.019350 | \n", + "10.442345 | \n", + "
| min | \n", + "1.578200 | \n", + "-13.378500 | \n", + "2.516400 | \n", + "0.954400 | \n", + "6.012800 | \n", + "-27.098300 | \n", + "2.314800 | \n", + "6.652900 | \n", + "-8.919400 | \n", + "4.313800 | \n", + "... | \n", + "-12.900200 | \n", + "-1.295600 | \n", + "-3.340900 | \n", + "-10.697200 | \n", + "9.472600 | \n", + "-4.516700 | \n", + "-13.251900 | \n", + "6.218200 | \n", + "7.328700 | \n", + "-39.457800 | \n", + "
| 25% | \n", + "8.401725 | \n", + "-4.710650 | \n", + "8.705775 | \n", + "5.210275 | \n", + "9.858950 | \n", + "-11.260725 | \n", + "4.774725 | \n", + "13.886150 | \n", + "-2.283400 | \n", + "6.637775 | \n", + "... | \n", + "-0.104850 | \n", + "5.169025 | \n", + "0.941925 | \n", + "0.528975 | \n", + "15.661700 | \n", + "-1.153850 | \n", + "-1.858725 | \n", + "8.262350 | \n", + "13.813875 | \n", + "-11.066725 | \n", + "
| 50% | \n", + "10.547400 | \n", + "-1.649700 | \n", + "10.541350 | \n", + "6.812450 | \n", + "11.080850 | \n", + "-4.922950 | \n", + "5.377700 | \n", + "16.391400 | \n", + "0.357550 | \n", + "7.643850 | \n", + "... | \n", + "3.134700 | \n", + "7.394300 | \n", + "1.936700 | \n", + "3.482300 | \n", + "18.022800 | \n", + "-0.169750 | \n", + "2.522900 | \n", + "8.888700 | \n", + "15.966600 | \n", + "-2.733050 | \n", + "
| 75% | \n", + "12.761825 | \n", + "1.303725 | \n", + "12.449400 | \n", + "8.362650 | \n", + "12.254600 | \n", + "1.017025 | \n", + "6.008600 | \n", + "19.096700 | \n", + "2.973550 | \n", + "8.597625 | \n", + "... | \n", + "6.385200 | \n", + "9.521550 | \n", + "2.990200 | \n", + "6.172125 | \n", + "20.452025 | \n", + "0.845475 | \n", + "6.631450 | \n", + "9.579525 | \n", + "18.083525 | \n", + "4.983725 | \n", + "
| max | \n", + "20.064900 | \n", + "9.023900 | \n", + "18.277200 | \n", + "12.837200 | \n", + "15.432900 | \n", + "15.999800 | \n", + "8.021300 | \n", + "27.856800 | \n", + "8.663600 | \n", + "11.003600 | \n", + "... | \n", + "17.094600 | \n", + "16.355900 | \n", + "7.213700 | \n", + "16.241500 | \n", + "26.962100 | \n", + "4.255700 | \n", + "15.680400 | \n", + "11.559100 | \n", + "24.845300 | \n", + "24.027500 | \n", + "
8 rows × 200 columns
\n", + "\n", + " 1. There's no much difference between label means for the training and test sets, same case for standard deviation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6.3. Visual EDA\n", + "\n", + "### Distribution of the target(whether a customer made a transaction)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "# Extract the target column\n",
+ "target = santander_training.target\n",
+ "\n",
+ "# Plot a countplot\n",
+ "sns.countplot(data=santander_training, x=target)\n",
+ "\n",
+ "# Display the count on each respective bar\n",
+ "# for i, v in enumerate(target.values):\n",
+ "# plt.text(i, 2000, v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"How many customers did/didn't make a transaction?\", fontsize=20)\n",
+ "plt.xlabel(\"Made Transaction?\", fontsize=19)\n",
+ "plt.ylabel(\"Total Count\", fontsize=19)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observation\n",
+ "From the above plot, we see that:\n",
+ "1. The number of customers who didn't make a transaction are much more than those who did. This will help us in evaluating the accuracy of our predictions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Correlation\n",
+ "Here we look at the measure of relation between each variable and the other"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "corr = santander_training.corr()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "sns.heatmap(corr, annot=True, vmax=1, square=True, cmap='cubehelix')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 7. Data Preprocessing\n",
+ "\n",
+ "Here we extract the features and the target class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make a copy of the datasets\n",
+ "training = santander_training.copy()\n",
+ "test = santander_test.copy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "\u001b[1;32mc:\\users\\kasre\\pycharmprojects\\python4ds_cohort-1\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2658\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2659\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2660\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 1"
+ ]
+ }
+ ],
+ "source": [
+ "prediction_df[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
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diff --git a/notebooks/Titanic Data Exploration.ipynb b/notebooks/Titanic Data Exploration.ipynb
new file mode 100644
index 0000000..1c89e6b
--- /dev/null
+++ b/notebooks/Titanic Data Exploration.ipynb
@@ -0,0 +1,488 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
\n", + "# Introduction\n", + "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly\n", + "import seaborn as sns\n", + "import warnings\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings.filterwarnings(\"ignore\")\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "path = '../data/titanic/input/'\n", + "titanic_train = pd.read_csv(path + 'train.csv')\n", + "titanic_test = pd.read_csv(path + 'test.csv')\n", + "gender_submission = pd.read_csv(path + 'gender_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "**Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
\n", + "**Name**: Name of passenger
\n", + "**Sex**: Sex of the passenger
\n", + "**Age**: Age of the passenger (Some entries contain NaN)
\n", + "**SibSp**: Number of siblings and spouses of the passenger aboard
\n", + "**Parch**: Number of parents and children of the passenger aboard
\n", + "**Ticket**: Ticket number of the passenger
\n", + "**Fare**: Fare paid by the passenger
\n", + "**Cabin**: Cabin number of the passenger (Some entries contain NaN)
\n", + "**Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Visualisations.ipynb b/notebooks/Visualisations.ipynb new file mode 100644 index 0000000..e1815dd --- /dev/null +++ b/notebooks/Visualisations.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %maptplotlib magic command to enable interactive plots in notebooks\n", + "# inline attr to enable static pplots within the notebook\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# set default plot style\n", + "plt.style.use('seaborn-whitegrid')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### all plots start by creating a figure and an axes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "2 0 8.6093 -2.7457 12.0805 7.8928 10.5825 -9.0837 6.9427 14.6155 \n",
+ "3 0 11.0604 -2.1518 8.9522 7.1957 12.5846 -1.8361 5.8428 14.9250 \n",
+ "4 0 9.8369 -1.4834 12.8746 6.6375 12.2772 2.4486 5.9405 19.2514 \n",
+ "\n",
+ " var_8 ... var_190 var_191 var_192 var_193 var_194 var_195 var_196 \\\n",
+ "0 -4.9200 ... 4.4354 3.9642 3.1364 1.6910 18.5227 -2.3978 7.8784 \n",
+ "1 3.1468 ... 7.6421 7.7214 2.5837 10.9516 15.4305 2.0339 8.1267 \n",
+ "2 -4.9193 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 -6.5213 \n",
+ "3 -5.8609 ... 4.4666 4.7433 0.7178 1.4214 23.0347 -1.2706 -2.9275 \n",
+ "4 6.2654 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 3.9267 \n",
+ "\n",
+ " var_197 var_198 var_199 \n",
+ "0 8.5635 12.7803 -1.0914 \n",
+ "1 8.7889 18.3560 1.9518 \n",
+ "2 8.2675 14.7222 0.3965 \n",
+ "3 10.2922 17.9697 -8.9996 \n",
+ "4 9.5031 17.9974 -8.8104 \n",
+ "\n",
+ "[5 rows x 201 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Drop unwanted columns in training set\n",
+ "training_set = training.drop(columns=['ID_code'], axis=1)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 3 | \n", + "0 | \n", + "11.0604 | \n", + "-2.1518 | \n", + "8.9522 | \n", + "7.1957 | \n", + "12.5846 | \n", + "-1.8361 | \n", + "5.8428 | \n", + "14.9250 | \n", + "-5.8609 | \n", + "... | \n", + "4.4666 | \n", + "4.7433 | \n", + "0.7178 | \n", + "1.4214 | \n", + "23.0347 | \n", + "-1.2706 | \n", + "-2.9275 | \n", + "10.2922 | \n", + "17.9697 | \n", + "-8.9996 | \n", + "
| 4 | \n", + "0 | \n", + "9.8369 | \n", + "-1.4834 | \n", + "12.8746 | \n", + "6.6375 | \n", + "12.2772 | \n", + "2.4486 | \n", + "5.9405 | \n", + "19.2514 | \n", + "6.2654 | \n", + "... | \n", + "-1.4905 | \n", + "9.5214 | \n", + "-0.1508 | \n", + "9.1942 | \n", + "13.2876 | \n", + "-1.5121 | \n", + "3.9267 | \n", + "9.5031 | \n", + "17.9974 | \n", + "-8.8104 | \n", + "
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+ " \n",
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+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 var_5 var_6 var_7 \\\n",
+ "0 11.0656 7.7798 12.9536 9.4292 11.4327 -2.3805 5.8493 18.2675 \n",
+ "1 8.5304 1.2543 11.3047 5.1858 9.1974 -4.0117 6.0196 18.6316 \n",
+ "2 5.4827 -10.3581 10.1407 7.0479 10.2628 9.8052 4.8950 20.2537 \n",
+ "3 8.5374 -1.3222 12.0220 6.5749 8.8458 3.1744 4.9397 20.5660 \n",
+ "4 11.7058 -0.1327 14.1295 7.7506 9.1035 -8.5848 6.8595 10.6048 \n",
+ "\n",
+ " var_8 var_9 ... var_190 var_191 var_192 var_193 var_194 var_195 \\\n",
+ "0 2.1337 8.8100 ... -2.1556 11.8495 -1.4300 2.4508 13.7112 2.4669 \n",
+ "1 -4.4131 5.9739 ... 10.6165 8.8349 0.9403 10.1282 15.5765 0.4773 \n",
+ "2 1.5233 8.3442 ... -0.7484 10.9935 1.9803 2.1800 12.9813 2.1281 \n",
+ "3 3.3755 7.4578 ... 9.5702 9.0766 1.6580 3.5813 15.1874 3.1656 \n",
+ "4 2.9890 7.1437 ... 4.2259 9.1723 1.2835 3.3778 19.5542 -0.2860 \n",
+ "\n",
+ " var_196 var_197 var_198 var_199 \n",
+ "0 4.3654 10.7200 15.4722 -8.7197 \n",
+ "1 -1.4852 9.8714 19.1293 -20.9760 \n",
+ "2 -7.1086 7.0618 19.8956 -23.1794 \n",
+ "3 3.9567 9.2295 13.0168 -4.2108 \n",
+ "4 -5.1612 7.2882 13.9260 -9.1846 \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Drop unwanted columns in test set\n",
+ "test_set = test.drop(columns=['ID_code'], axis=1)\n",
+ "test_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 3 | \n", + "8.5374 | \n", + "-1.3222 | \n", + "12.0220 | \n", + "6.5749 | \n", + "8.8458 | \n", + "3.1744 | \n", + "4.9397 | \n", + "20.5660 | \n", + "3.3755 | \n", + "7.4578 | \n", + "... | \n", + "9.5702 | \n", + "9.0766 | \n", + "1.6580 | \n", + "3.5813 | \n", + "15.1874 | \n", + "3.1656 | \n", + "3.9567 | \n", + "9.2295 | \n", + "13.0168 | \n", + "-4.2108 | \n", + "
| 4 | \n", + "11.7058 | \n", + "-0.1327 | \n", + "14.1295 | \n", + "7.7506 | \n", + "9.1035 | \n", + "-8.5848 | \n", + "6.8595 | \n", + "10.6048 | \n", + "2.9890 | \n", + "7.1437 | \n", + "... | \n", + "4.2259 | \n", + "9.1723 | \n", + "1.2835 | \n", + "3.3778 | \n", + "19.5542 | \n", + "-0.2860 | \n", + "-5.1612 | \n", + "7.2882 | \n", + "13.9260 | \n", + "-9.1846 | \n", + "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " var_0 var_1 var_2 var_3 var_4 var_5 var_6 var_7 var_8 \\\n",
+ "0 8.9255 -6.7863 11.9081 5.0930 11.4607 -9.2834 5.1187 18.6266 -4.9200 \n",
+ "1 11.5006 -4.1473 13.8588 5.3890 12.3622 7.0433 5.6208 16.5338 3.1468 \n",
+ "2 8.6093 -2.7457 12.0805 7.8928 10.5825 -9.0837 6.9427 14.6155 -4.9193 \n",
+ "3 11.0604 -2.1518 8.9522 7.1957 12.5846 -1.8361 5.8428 14.9250 -5.8609 \n",
+ "4 9.8369 -1.4834 12.8746 6.6375 12.2772 2.4486 5.9405 19.2514 6.2654 \n",
+ "\n",
+ " var_9 ... var_190 var_191 var_192 var_193 var_194 var_195 var_196 \\\n",
+ "0 5.7470 ... 4.4354 3.9642 3.1364 1.6910 18.5227 -2.3978 7.8784 \n",
+ "1 8.0851 ... 7.6421 7.7214 2.5837 10.9516 15.4305 2.0339 8.1267 \n",
+ "2 5.9525 ... 2.9057 9.7905 1.6704 1.6858 21.6042 3.1417 -6.5213 \n",
+ "3 8.2450 ... 4.4666 4.7433 0.7178 1.4214 23.0347 -1.2706 -2.9275 \n",
+ "4 7.6784 ... -1.4905 9.5214 -0.1508 9.1942 13.2876 -1.5121 3.9267 \n",
+ "\n",
+ " var_197 var_198 var_199 \n",
+ "0 8.5635 12.7803 -1.0914 \n",
+ "1 8.7889 18.3560 1.9518 \n",
+ "2 8.2675 14.7222 0.3965 \n",
+ "3 10.2922 17.9697 -8.9996 \n",
+ "4 9.5031 17.9974 -8.8104 \n",
+ "\n",
+ "[5 rows x 200 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract the target class and drop it. Extract x.\n",
+ "y = training_set['target'].values\n",
+ "training_set.drop(columns=['target'], axis=1, inplace=True)\n",
+ "training_set.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = training_set.values\n",
+ "# print(X)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 8. Feature Engineering\n",
+ "\n",
+ "Feature engineering is important for better model performance.\n",
+ "Looking at our data, the features are not uniform scalewise. This calls for feature scaling to make sure they are uniform\n",
+ "\n",
+ "## 8.1. Scaling the Data\n",
+ "\n",
+ "We'll use scikit learn's StandardScaler class.\n",
+ "Before scaling, we need to split the training set into Folds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Instantiate the scaler\n",
+ "scaler = StandardScaler()\n",
+ "\n",
+ "# Transform training data\n",
+ "X = scaler.fit_transform(X)\n",
+ "\n",
+ "# Transform test_set\n",
+ "X_test = test_set.values\n",
+ "X_test = scaler.transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 9. Model Selection & Training\n",
+ "\n",
+ "Here we'll train several models with the training data and choose the best performing model.\n",
+ "\n",
+ "## 9.1. Choosing a performace metric\n",
+ "\n",
+ "For choosing the best model, we'll use K-fold cross validation of 10 folds and compute the mean score of each fold.\n",
+ "\n",
+ "## 9.2. Model Evaluation\n",
+ "\n",
+ "We'll compare the score of 4 models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a score collection for comparison\n",
+ "scores = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Gradient Boosting Classifier\n",
+ "We implement a Gradient Boosting classifier with a max_depth of 1 and 0 random state"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9008002205002205\n"
+ ]
+ }
+ ],
+ "source": [
+ "GBC = GradientBoostingClassifier(max_depth=1, random_state=0)\n",
+ "\n",
+ "GBC_score = cross_val_score(GBC, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(GBC_score)\n",
+ "\n",
+ "scores['GBC'] = GBC_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Random Forest Classifier\n",
+ "We'll implement another ensemble, Random Forest Classifier, with default parameters(10 estimators)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9007002207002207\n"
+ ]
+ }
+ ],
+ "source": [
+ "RFC = RandomForestClassifier(n_estimators=10)\n",
+ "\n",
+ "RFC_score = cross_val_score(RFC, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(RFC_score)\n",
+ "\n",
+ "scores['RFC'] = RFC_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### MLP Classifier\n",
+ "Here we'll implement a neural network, MLP Classifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.8533966827966829\n"
+ ]
+ }
+ ],
+ "source": [
+ "MLP = MLPClassifier(hidden_layer_sizes=(10, 10), random_state=0, max_iter=1000)\n",
+ "\n",
+ "MLP_score = cross_val_score(MLP, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(MLP_score)\n",
+ "\n",
+ "scores['MLP'] = MLP_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Kernelized SVM\n",
+ "Here we'll implement a kernelized Support vector machine with a Gaussian model kernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9040005228005228\n"
+ ]
+ }
+ ],
+ "source": [
+ "svc = SVC(kernel='rbf', random_state=0, gamma='auto')\n",
+ "\n",
+ "svc_score = cross_val_score(svc, X, y, cv=10, scoring='accuracy').mean()\n",
+ "\n",
+ "print(svc_score)\n",
+ "\n",
+ "scores['svc'] = svc_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Lets look at the score for each model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'GBC': 0.9008002205002205,\n",
+ " 'RFC': 0.9007002207002207,\n",
+ " 'MLP': 0.8533966827966829,\n",
+ " 'svc': 0.9040005228005228}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scores"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Its evident that support vector classifier performed well. We'll fine tune its parameters for optimization\n",
+ "\n",
+ "# 10. Model Evaluation and Optimization\n",
+ "\n",
+ "We'll use GridSearchCV to fine tune the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize the tuning params\n",
+ "Cs = [0.001, 0.01, 0.1, 1, 10]\n",
+ "gammas = [0.001, 0.01, 0.1, 1, 10]\n",
+ "\n",
+ "param_grid = {'C': Cs, 'gamma': gammas}\n",
+ "\n",
+ "gridscv = GridSearchCV(\n",
+ " estimator=SVC(kernel='rbf', random_state=0),\n",
+ " param_grid=param_grid,\n",
+ " scoring='accuracy',\n",
+ " n_jobs=1,\n",
+ " cv=10\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# gridscv.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# best_params = gridscv.best_params_\n",
+ "# best_score = gridscv.best_score_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 11. Prediction\n",
+ "\n",
+ "Here we fit the model with the fine tuned params and make predictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+ " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
+ " max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
+ " tol=0.001, verbose=False)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = SVC(kernel='rbf', random_state=0, gamma='auto')\n",
+ "\n",
+ "model.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "metadata": {},
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+ "source": [
+ "predictions = model.predict(X_test)\n",
+ "\n",
+ "prediction_df = pd.DataFrame(predictions)\n",
+ "\n",
+ "prediction_df.head(50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "1",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\users\\kasre\\pycharmprojects\\python4ds_cohort-1\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2655\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2656\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2657\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 1",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | 0 | \n", + "
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\n", + "# Introduction\n", + "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import plotly\n", + "import seaborn as sns\n", + "import warnings\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# warnings.filterwarnings(\"ignore\")\n", + "sns.set(color_codes=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "path = '../data/titanic/input/'\n", + "titanic_train = pd.read_csv(path + 'train.csv')\n", + "titanic_test = pd.read_csv(path + 'test.csv')\n", + "gender_submission = pd.read_csv(path + 'gender_submission.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
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+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic_train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | PassengerId | \n", + "Survived | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "Braund, Mr. Owen Harris | \n", + "male | \n", + "22.0 | \n", + "1 | \n", + "0 | \n", + "A/5 21171 | \n", + "7.2500 | \n", + "NaN | \n", + "S | \n", + "
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| 2 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "Heikkinen, Miss. Laina | \n", + "female | \n", + "26.0 | \n", + "0 | \n", + "0 | \n", + "STON/O2. 3101282 | \n", + "7.9250 | \n", + "NaN | \n", + "S | \n", + "
| 3 | \n", + "4 | \n", + "1 | \n", + "1 | \n", + "Futrelle, Mrs. Jacques Heath (Lily May Peel) | \n", + "female | \n", + "35.0 | \n", + "1 | \n", + "0 | \n", + "113803 | \n", + "53.1000 | \n", + "C123 | \n", + "S | \n", + "
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+ "3 895 3 Wirz, Mr. Albert male \n",
+ "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
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+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "0 34.5 0 0 330911 7.8292 NaN Q \n",
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+ "3 27.0 0 0 315154 8.6625 NaN S \n",
+ "4 22.0 1 1 3101298 12.2875 NaN S "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic_test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "| \n", + " | PassengerId | \n", + "Pclass | \n", + "Name | \n", + "Sex | \n", + "Age | \n", + "SibSp | \n", + "Parch | \n", + "Ticket | \n", + "Fare | \n", + "Cabin | \n", + "Embarked | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "892 | \n", + "3 | \n", + "Kelly, Mr. James | \n", + "male | \n", + "34.5 | \n", + "0 | \n", + "0 | \n", + "330911 | \n", + "7.8292 | \n", + "NaN | \n", + "Q | \n", + "
| 1 | \n", + "893 | \n", + "3 | \n", + "Wilkes, Mrs. James (Ellen Needs) | \n", + "female | \n", + "47.0 | \n", + "1 | \n", + "0 | \n", + "363272 | \n", + "7.0000 | \n", + "NaN | \n", + "S | \n", + "
| 2 | \n", + "894 | \n", + "2 | \n", + "Myles, Mr. Thomas Francis | \n", + "male | \n", + "62.0 | \n", + "0 | \n", + "0 | \n", + "240276 | \n", + "9.6875 | \n", + "NaN | \n", + "Q | \n", + "
| 3 | \n", + "895 | \n", + "3 | \n", + "Wirz, Mr. Albert | \n", + "male | \n", + "27.0 | \n", + "0 | \n", + "0 | \n", + "315154 | \n", + "8.6625 | \n", + "NaN | \n", + "S | \n", + "
| 4 | \n", + "896 | \n", + "3 | \n", + "Hirvonen, Mrs. Alexander (Helga E Lindqvist) | \n", + "female | \n", + "22.0 | \n", + "1 | \n", + "1 | \n", + "3101298 | \n", + "12.2875 | \n", + "NaN | \n", + "S | \n", + "
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+ "text/plain": [
+ " PassengerId Survived\n",
+ "0 892 0\n",
+ "1 893 1\n",
+ "2 894 0\n",
+ "3 895 0\n",
+ "4 896 1"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gender_submission.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Dataset Feature Description\n",
+ "**Survived**: Outcome of survival (0 = No; 1 = Yes)| \n", + " | PassengerId | \n", + "Survived | \n", + "
|---|---|---|
| 0 | \n", + "892 | \n", + "0 | \n", + "
| 1 | \n", + "893 | \n", + "1 | \n", + "
| 2 | \n", + "894 | \n", + "0 | \n", + "
| 3 | \n", + "895 | \n", + "0 | \n", + "
| 4 | \n", + "896 | \n", + "1 | \n", + "
\n", + "**Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
\n", + "**Name**: Name of passenger
\n", + "**Sex**: Sex of the passenger
\n", + "**Age**: Age of the passenger (Some entries contain NaN)
\n", + "**SibSp**: Number of siblings and spouses of the passenger aboard
\n", + "**Parch**: Number of parents and children of the passenger aboard
\n", + "**Ticket**: Ticket number of the passenger
\n", + "**Fare**: Fare paid by the passenger
\n", + "**Cabin**: Cabin number of the passenger (Some entries contain NaN)
\n", + "**Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Visualisations.ipynb b/notebooks/Visualisations.ipynb new file mode 100644 index 0000000..e1815dd --- /dev/null +++ b/notebooks/Visualisations.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %maptplotlib magic command to enable interactive plots in notebooks\n", + "# inline attr to enable static pplots within the notebook\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# set default plot style\n", + "plt.style.use('seaborn-whitegrid')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### all plots start by creating a figure and an axes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Figure\n",
+ "fig = plt.figure()\n",
+ "\n",
+ "# axes\n",
+ "ax = plt.axes()\n",
+ "\n",
+ "# this creates a figure in its simplest for"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A simple sinusoid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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pDVVn7Nxpgm7KXpePSSErMSrovslKkj8He0cXrxdWUJCXRmp8pNHheEx2UjTzxiTz2s7jcgNWnNNL24+TkRDJVeMCv5uyV4jVwm3Ts9lcVhdUN2AlyZ/D6uJKTrd2cOdlxi/Q7Wl3zBjKyUY7Gw/IDVjxaeUNrXx4oJbbpw8NqNndF+Oz07KxWuDVneUXfrJJmOsd9KDXP65gaFI0s0b6b814Vy3MTSM5NoKXtwfPB11cvDcKK7BY4NZpQ4wOxePSEyK5alwar39cHjQ3YCXJD6DiVCsfHarnlqlDsJrghmt//3cDtprqM3ajwxF+xOFwsnxXBXNHJ5OZGGV0OF5xx2XZ1DW3s94WHDdgJckPoHcB7JumZBkciffcMnUIDidBXbhJfNr2Iw1UnDrLLVPNdxXf64qxqaTFR7B8V3AMPpAk34/T6eSNwgpmjxrMkEHmGDo2kJEpsUwZmsjyXRU4nXIDVnR7vbCcuIhQrs4L3BmuFxJitXDj5Cw26FrqmtuMDsfrJMn3s/PoKY43tJr6SqbXTVOGcKC6mf0nzxgdivADzW2drC6q4tqJmUSGBfYM1wu5afIQOh1O3tpz0uhQvE6SfD9vFJYTEx4S0LU6LtZ1EzIJD7HyRmFwfG0V57eqqJKzHV1BcYGj0uPIz4rnzd3m/+xLku+jtb2Td/ZVcs2EDKLD3VkZMTAkRIexMDeVt/aepKMrOEYaiHN7o7CCkckxTBmaaHQoPnHzlCEUnziDrmoyOhSvkiTfx5riKlrau7hlqnlmuF7IzVOG0NDSzgZda3QowkDH6lvYcaSBm6cOCdiSwpfq+omZhFotvGnyG7AuX64qpazAH4CJQBtwj9a6rM/+3wBzgN4/kzcAYcDLQBRwElimtW7FT7xR2D02PhAXR3DV5WNTGBwTzpu7KliUm2Z0OMIgb+46gcVi7hFl/Q2OjWC+SmXF7hP859XKdBO/ernTqhuBSK31LLrXbn283/4pwNVa6/k9/zUCPwFe1lrPA3YDX3Xj/B5V1Whn6+F6PjM5K2iuZKB7zPwNk7JYb6vhdGu70eEIAzidTt7ae5JZIweTkWDOsfHncvOULGqa2thi4lWj3Enyc4E1AFrrbcC03h09V/ljgD8rpbYope7ufwywGljoxvk96u19J3E6u1eRCTY3TcmivcvByn2VRociDFB0opEjdS1cPzH4PvtX5aSSEBXGchMPPnDn7mI80Njn5y6lVKjWuhOIAX4LPAGEAB8opT7ud0wTkDDQC9tsNpcCstvtLh/72rYTjE4Kp72uHFudSy9hCHfa3MvqdDI0IYzXPipjWoLf9J6dkyfaHGi82ea/7qwn1Aojw8/41b+rr97nOdmRvLu/kt37wokMM7bLxhttdifJnwHi+vxs7UnwAK3AU7397Uqp9+nuu+895mzP/08P9MI5OTkuBWSz2Vw69khdCwfrD/PDpePIyRnl0rmN4mqb+7ulMown1h0gMXO4339l91SbA4m32tzlcLJlxXrmqzRmTMr3+Ou7w1fv8xciUll1YBsVzkFcl2Pstxl32lxYWDjgdnf+bG0BlgIopWYCRX32jQU2K6VClFJhdHfT7Op7DLAE2OTG+T1m5d6TWCxwXRB+Xe3V2/Z3pMsmqOw40kD1mTZuCMJuyl7ThyeRFh/BW3vNOTHKnSS/ArArpT4CngQeUEo9qJS6XmttA14CtgEbgb9prfcDjwC3K6W2ALOA37kXvvucTif/2nOC6cOT/P4K1ptGJMcwPivBtB90MbC39p4gOjyEhTnBO7IqxGrhmvGZbNS1NJ7tMDocj3O5u0Zr7QC+1m9zaZ/9vwR+2e+YamCxq+f0hpLKMxyqbWHZnBFGh2K46yZm8PNVpRyta2F4cozR4Qgva+90sKqoioLcNKLCzV3G4EKum5jB81uOsHZ/FbeaaCU4kMlQvLXnJKFWC0vHZxgdiuGundD9lX2lXM0HhQ8PdF+53jApeMbGn8uk7ESyk6JMOcIsqJO8w+Fk5d6TzBuTTFJMuNHhGC4zMYrpwwexcp8k+WDwr70nGRQdxtwxyUaHYjiLxcJ1EzLZUlZHvckqUwZ1ki88foqTjfagHBt/LtdPzORAdTOlVVKZ0sxa2zt5r6SapeMzCDPpTM9Ldd3ETLocTlYVVxkdikcF9bv79t6TRIRaWZRr/oqTF2vJ+AxCrBbpsjG590trONvR9e8uOgHj0uMYkxprus9+0CZ5h8PJ6uIqrlSpxEaYv+LkxUqOjWD2qMGs3Fspi4mY2OqiKpJjI5gxIsnoUPyGxWLhuomZ7DzaQGXjWaPD8ZigTfKFx09R09TGkvFyFd/fdRMyOd7QStGJxgs/WQScs+1dvF9aw+L8NEJMuIaxO66bmInTaa75IkGb5FcVVRIeauWqcalGh+J3CvLSCLVaWG2yvknRbYPu7qpZmi8jyvobkRxDTka8qT77QZnkHQ4na4qruHxMCnGRYUaH43cSo8OZNWowq4uky8aMVhVXkRQTLl0157A0P53CY6eoarQbHYpHBGWS31NxmspGO0ulq+acluRncLS+FVuluVfNCTb2ji7et1VzdV6aaeunu6u3C/fd/ea4mg/Kd3l1USVhIRYWyiIZ51SQl4bVAmuKzdM3KWDjgVpa2rtk8t95jE7tHmWz2iSf/aBL8k6nk1VFVcwbk0K8dNWcU3JsBJeNGGy6McPBbnVRJYnRYcwcOdjoUPzakvEZ7DjSQJ0JJkYFXZLfV9HIidNnWZIvXTUXsmR8OmU1zRysli4bM2jr7OI9Ww0FuWkyAeoCluSn43DC2v3VRofitqB7p1cVVxJqtch6phfh6rx0LBZMNdIgmG0+WEdzWydLpKvmgsalxzEiOcYUXTZBleSdTieri6qYPTqZxGipVXMhafGRTBs2iFVFgf9BF/BOUSXxkaHMGSW1ai7EYrGwOD+djw7Vc6olsNc+Dqokv//kGY43tLJUumou2uL8DEqrmjhS12J0KMIN7Z0O1pVUsyg3nfDQoPq1d9nS/Ay6HE7W2QK7yyao3u3VxZWEWC0U5EmSv1iLe/4gmuFrazDbcqiOJnunDBu+BPlZ8QwZFMWaAO+uDJok3zuqZtbIwVJW+BJkJUYxMTuR1UWB/UEPdquLKomLCJWywpfAYrGwJD+dTQdrOWMP3BWjXKrMpZSyAn+ge3HuNuAerXVZn/0PALf3/LhKa/2/SikLUAEc7Nm+VWv9A5cjv0QHqps5UtfCl+fKClCXaml+Or9YXUp5QyvZSdFGhyMuUWdXd1fNleNSiQgN7hWgLtXi/Aye2XSE92013Dg5MBdXcfVK/kYgUms9C3gIeLx3h1JqJHAnMJvudVwLlFITgFHALq31/J7/fJbgAdaVdF+JFsiomku2pKfGSaB/bQ1WhcdOcaq1g6ulm/KSTc5OJD0+MqAHH7ia5OcCawC01tuAaX32lQOLtdZdPevAhgF2YCqQpZT6QCm1Siml3Ij7kq0tqWby0ERS4yN9eVpTGDo4mnHpcawrCewbUMFqbUk14SFWrlApRocScKxWCwV5aXx4sJaz7V1Gh+MSVwupxwN969B2KaVCtdadWusOoK6ne+ZXwG6t9QGlVDrwC63160qpucCLwPSBXtxms7kUlN1uH/DY2pZO9lU0smxKksuv7a/O1WZPm5IWwqv7Gti6q5jEKGO/8vuqzf7E1TY7nU7e2VPOxPQIyg8fvPABfsRf3udxsW3YOxy8+sFuZg717gL33mizq0n+DBDX52er1rqz9welVCTwPNAEfL1n88dAJ4DWerNSKkspZdFaf6rMYU5OjktB2Wy2AY/dufUoAHddOZ5RKbEuvba/OlebPe2O+EZe3ruZ8q4EZuUYu5q9r9rsT1xtc2nVGaqaj/CtRTnk5Az1QmTe4y/v8+ixDh7dtI6SM2Es83I87rS5sLBwwO2udtdsAZYCKKVmAkW9O3qu4P8F7NVaf1Vr3fsd57+Bb/c8ZyJwfKAE7w1r91czMiXGdAnel/Iy48lKjGJtifTLB5K1+6uxWGBhrqyb4KqwECsLctJ4z1ZNZ5fD6HAumatX8iuARUqpjwALsEwp9SBQBoQAVwARSqklPc//AfAo8KJS6hq6r+i/5E7gF6uxtYNth+u5Z95IX5zOtCyW7r7Jl7Yfp6WtkxhZMjEgrC2pYnJ2Iqlxci/KHQW5aazYfYKPj50KuOJuLv2m9txQ/Vq/zaV9Hp/rE3WNK+dzxwe6hk6Hk4I8GVXjroLcdP6y5SgfHqiV+icB4MTpsxSfOMNDS8YZHUrAu3xsCuGhVtburw64JG/6yVBrS6pIiYtg0pBEo0MJeNOHDyIxOoy1MsomILzX8z7JsGH3xUSEMm90MmtLqgJutTRTJ3l7RxcbdS2LctOwyoLFbgsNsbJgXBrrbdV0BGDfZLBZW1LFqJQYRsq9KI8oyEuj4tTZgFstzdRJfuuhelrau+RKxoMK8tI4Y+9kx5EGo0MR59F9L6pB6jR50IKcNCwWAm7wgamT/NqSKmIjQpk1KrD60PzZ5WNSiAyzstYk61+a1fu6mi6HUy5wPCg5NoJpwwYF3EIipk3yXQ4n60qqma9SpF6HB0WFhzBvTAprS6oDrm8ymKwrqSY1LoKJci/Kowpy0ympPEN5Q6vRoVw00yb5PeWnqGtul6+rXnB1XjqVjXaKTjRe+MnC5+wdXWyQe1Fe0buiXCCV+DBtkl+7v5qwEAvzpV6Hxy0Yl4rVYo71L83oo0N1tLZ3yQWOFwxPjkGlxQVUv7wpk7zT6eTd/VXMHDmY+Mgwo8MxnUEx4cwYkRRQH/RgsnZ/NbERocwcmWR0KKZUkJfGjiMNAbMsoCmTfFlNM0frW+VKxosKctP/XaNf+I8uh5P3bHIvypsKctNxOOG9AFkW0JRJvneyzqIcGVngLf/XNylX8/5E7kV5X35WPBkJkQEzKdC0SX5idiLpCVKvw1uyk6LJzYiXfnk/I/eivM9isVCQm8amAKkxb7okX9VoZ2/5aRkf7AMFeWkUHj9FbVOb0aEIuu9FrS2pZtaoZLkX5WUFeenYOxx8eLDW6FAuyHRJfl1PP9nVUpDM6wpy03E6YX2A9E2a3aHa7nski+QCx+tmjEgiPjKUdwNgUqDpkvza/VWMTJba8b6QkxHHkEFRAdM3aXbv7pd7Ub7SW2P+/dIav68xb6ok39zexdZD9SzKS8NikUkg3tbdN5nO5rI6mts6L3yA8Cq5F+VbBblpnG7tYOfRU0aHcl6mSvIfV5ztrh2fKyMLfKUgL432TgcfHvD/vkkzk3tRvtdbY97fZ7+6vLyPUsoK/AGYCLQB92ity/rsvxf4Kt2rQD2itX5bKZUMvAxEASeBZVprjxWB2FreQnJsBJOzpV6Hr0wbNohB0WGsK6lmqSwkYhi5F+V7MRGhzO2pMf9f1+b4be+BO1fyNwKRWutZwEPA4707lFLpwLeAOcDVwC+UUhHAT4CXtdbzgN10/xHwiLbOLnaeaGVRbqrU6/Ch0BArV0mNecOtK6lmhNyL8rmCXP+vMe9Okp8LrAHQWm8DpvXZNwPYorVu01o30r3264S+xwCrgYVunP8Tth6q52yHdNUYQWrMG+uMvYOth+ooyJV7Ub4WCDXm3Uny8UDfMoRdSqnQc+xrAhL6be/d5hFFFY3EhFmldrwBpMa8sTboWjq6ZB1jI6TERTB1qH/XmHe5Tx44A8T1+dmqte48x7444HSf7Wf7bPsUm812ycHMSnYwalEyR8oOXPKxgcxut7v07+Vpk9MjWbWvgtvGWL1+Nekvbfal87X5ja3VDIoMIaK5CpuJ5iwEyvs8MdnCc4Vn2LBzH2mx7k1C80ab3UnyW4DrgH8opWYCRX327QB+ppSKBCKAHKC455ilwF+BJcCmgV44JyfHpYBsNpvLxwYqf2nzTS2x/Ocb+3AkZJGf5bEvaAPylzb70rna3NbZxa5Xj3PdxEzy83INiMx7AuV9viulhecKN3CkPY75OSPcei132lxYWDjgdne6a1YAdqXUR8CTwANKqQeVUtdrrauA39CdxN8HfqS1tgOPALcrpbYAs4DfuXF+4UcW5KT11JiXLhtf2na4gea2TpnlaqARyTGMSY312y4bl6/ktdYO4GugfSvKAAAXy0lEQVT9Npf22f8M8Ey/Y6qBxa6eU/ivpJhwpg1PYm1JNQ8WKKPDCRpr91cRHR7C7FHJRocS1Ary0vjjxsOcbm0nMTrc6HA+wVSToYSxCnLTKK1q4li91Jj3BUefdYwjw6R2vJEW5abT5XDyfmmN0aF8iiR54TG9w1f9fQagWew70UhNU5sMG/YDE7ISSIuP8MsuG0nywmOGDo5mXHqcFCzzkbX7qwixWrhSpRodStCzWi0syk1j44Fa7B3+VWNekrzwqIK8dD4+2kB9s9SY97a1JdXMHJlEQrTUjvcHBbnpnO3oYvPBOqND+QRJ8sKjCnLTcDhhvR/2TZrJodpmymqapavGj8wcOZi4iFC/m/0qSV54VF5mPFmJUX7ZN2kmvfc9ZOik/wgPtTJ/XCrrbTV0OZxGh/NvkuSFR1ks3X2Tmw7W0touNea9Ze3+KvKz4slMjDI6FNFHQW4a9S3t7DruPzXmJckLjyvITaOt08EmP+ubNIuaJju7y09LV40fmq9SCAux+NWkQEnywuOmj0giISpMumy8ZL2tBqcTKUjmh+Iiw5g9Kpm1JdU4nf7RZSNJXnhcWIiVBeNSWV9a7ffrXwaitfurGJoUjUqLu/CThc8V5KVxrL6VgzXNRocCSJIXXlKQFxjrXwaa5rZOtpTVS+14P7awZyF1f+mykSQvvGLemO71L/1tOFmg26hrae9yyKgaP5YWH8mk7ES/mRQoSV54RUxEKPNGJ7POj/omzWBdSRVJMeFMHTbI6FDEeRTkpbGvopHKxrNGhyJJXnhPQZ7/r38ZSDq6HKwvrWHBuFRCQ+RX15/1jnx6zw+u5uWTIrwmENa/DCTbDzfQZO+kIE+GTvq70amxjEyO8YsuG0nywmuSYyOYNsy/178MJGtLqogMszJ3tNSODwSL8tLYeqiexrMdhsYhSV54VUFuOiWVZyhvaDU6lIDmdHbXjr98TApR4VI7PhAU5KbT6XCyQRtbx8mllaGUUlHAi0Aq0AR8UWtd2+85vwLm9pzjz1rrZ5RSScAButd7BVihtX7K1eCF/1uUm8bPVtl4z1bNsjnurX8ZzMoa2qlstPMdWXUrYEzOTiQ5trvG/A2TsgyLw9Ur+fuAIq31POBvwI/77lRKXQmM1lrPojvRf18pNQiYAryitZ7f858keJMbnhzD2DT/Xf8yUGw93oLVAgvGSe34QNFbY36DrqGt07ga864m+bnAmp7Hq4GF/fZvBe7ueewEQoAOYCowRSm1USn1ulIqw8XziwBSkJvOjqMNnGppNzqUgLX1eAvThycxKMa/1g8V51eQm0ZLexcfHao3LIYLdtcopb4MPNBvczXQ2PO4CUjou1NrbQfsSqkw4AW6u2ualVKlQKHW+j2l1J3Ab4Fb+p/TZrNdckMA7Ha7y8cGqkBo85joNrocTl78YA8LR7k/FT8Q2uxJFY3tHD3dweKxlqBqtxne56QuB1GhFv6xpZR0x4UTvTfafMEkr7V+Dniu7zal1JtA729rHHC6/3E93TNvABu01r/o2fw+0HsHbgXw8EDnzMnJuZjYP8Vms7l8bKAKhDaPczr5xaZ6ik9Z+aYHYg2ENnvS+x+UAfDFBRPJSAie0sJmeZ+vymljx9EGlBqH1Xr+UhTutLmwsHDA7a5212wBlvY8XgJs6ruz58bseuB5rfVP++x6Fri55/ECYOCohKlYLBYK8tL48ECd361/GQhWF1eikiOCKsGbSUFeGrVNbeyp+NS1sE+4NLoGeBp4QSm1GWgH7gBQSv2S7qv3OcBI4F6l1L09xywDHgKeV0p9HWgB7nEjdhFAFuWm8betx9h8sI6FUnflopU3tFJ84gxfnppkdCjCRfNVKqFWC2v3VzNlqO/LUbiU5LXWrcCtA2z/Xs/DHcCT5zj8SlfOKQLbZSMGExcZyrv7qyTJX4I1xd2zhecMizE4EuGqhKgwZo0azJriSr6/WPm8eqhMhhI+ER7aXWN+na2aDqkxf9FWF1eSlxlPRlyY0aEINyzJz+BofashdZwkyQufWTo+g9OtHWw7bNxwskBS2XiWXcdPsyRfatUEuoK8NKwWWFVU6fNzS5IXPnP52BRiwkMM+aAHond7umoW58t0kkCXHBvBzJGDWVVU6fPS25Lkhc9EhoWwICeNd/fLsoAXY3VxFWNSYxmdGmt0KMIDlozP4HBdC7rat102kuSFTy0dn0FDSzvbDjcYHYpfq2tuY+fRBumqMZHFeendXTb7fPtNVpK88Kn5KoXo8BBWFUuXzfms3V+Nw9l99SfMISUughkjklhV7Nv1FSTJC5+KDAvhqnGpvFtcJV0257G6uJLhg6MZl+5+GQjhP5aOz6CsppkDPuyykSQvfO6a8RnUt7Sz44h02QzkdGs7Ww/Vszg/w+djqoV3Lc5Px+LjUTaS5IXPzVepRIVJl825rCupptPhlP54E0qNi2T68CRJ8sLcosK7u2zWFFfT5fDtcLJA8Pa+SoYMimLCkIQLP1kEnKX56RyobqasxjddNpLkhSGWjs+grrlNumz6aWhpZ3NZHddNzJSuGpPqvZm+qsg3N2AlyQtDXDkuhcgwq0yM6md1cSVdDifXTcg0OhThJWnxkUwbNshnn31J8sIQ0eGhXDUuldXFVdJl08fKvScZlRJDToaMqjGzpeMzKK1q4lBts9fPJUleGObaCZnUNbexXWrZAFB9xs72Iw3SVRMElo7PwGLp/qPubZLkhWGuGpdKbEQo/9rj/Q96IHhnXyVOZ/cfP2Fu6QmRXDYiibf2nPR6LRtJ8sIwkWEhXJ2XzqriSkNXs/cXK/edJDcjXmrVBIkbJmVxuK6F4hNnvHoeSfLCUNdPyqTJ3skGXWt0KIYqb2hl9/HTXDtRyhgEiyX56YSFWPjXnhNePY9LK0P1rOH6IpAKNAFf1PqTv6VKqbeAwUAHcFZrvUQpNRr4K+AEioFvaK1lbnsQmzNqMINjwnlrz0muzgveyT/v9Iy0kFE1wSMxOpwrxqayct9JfrA0h5ALLPLtKlev5O8DirTW84C/AT8e4Dmjgbla6/la6yU9254AftxznAW4wcXzC5MIDbFy7YQM3rNV02TvMDocw6zce5JJ2YlkJ0UbHYrwoRsmZVJ9po3tR7w3+MDVJD8XWNPzeDWwsO9OpVQakAisVEptVkpd27NrKrDxXMeJ4HT9pCzaOh2s3V9tdCiGKKtpYv/JM1w3Ua7ig83CnDRiwkN4y4uDDy7YXaOU+jLwQL/N1UBjz+MmoP/863DgceApIAnYopTaAVi01s7zHAeAzWa7qOD7s9vtLh8bqMzQ5iink7TYUF7ecoDc6AtP9TZDm/v6664GrBYYF918znaZrc0XI1jaPHNIFG/vPcHnVCiOjjaPt/mCSV5r/RzwXN9tSqk3gd7ZGnHA6X6HVQF/1Fp3AjVKqd2AAvr2vw90HAA5OTkXFXx/NpvN5WMDlVnafEu5lT9uPEzykJGkxEWc97lmaTOAw+Fk0z/f5/KxKcyZMv6czzNTmy9WsLT589bBrP/LTqosSQyNPOVymwsLCwfc7mp3zRZgac/jJcCmfvsXAv8AUErFAvmADditlJp/nuNEkLphUhZdDifv7AuuMfPbjtRzstHOTVOGGB2KMMic0cn/HnzgDa4m+aeBPKXUZuArwP8CKKV+qZSaobVeDRxUSm0D1gI/1FrXAd8B/lcptZXuLp033G6BMIWxaXHkZMSzYrd3h5P5mzd3nSAuIpSC3DSjQxEGCQuxck3P4IOWds8PNnRpCKXWuhW4dYDt3+vz+NsD7D8AXOHKOYX53Twli0fesXGguomxaeav3dLa3snqokqunZBJZFiI0eEIA902PZsVu05w6mynx19bJkMJv/GZyVmEWi28/nG50aH4xNr91bS0d3HTlCyjQxEGy8tMYNdPFjEkIdzjry1JXviNwbERXDUulRW7T9IRBOu/Lt9VQVZiFNOHJxkdivADYSHeSceS5IVfuXVaNnXNbWw0eZmD6jN2tpTVcdOULKxemukoBEiSF35mvkohOTac1wvN3WWzYvcJHM7uLiohvEmSvPArYSFWbpyUxXpbDfXNbUaH4xVOp5PXdpYzffggRqZIxUnhXZLkhd+5dVo2nQ6naevMbzvcwJG6Fm6fPtToUEQQkCQv/I5Kj2PCkAReL6zw+oIKRnh153HiI0O5ZoKUFRbeJ0le+KVbp2VjqzzD3orGCz85gJxqaWd1URWfmZwlY+OFT0iSF37pxkmZRIeH8OK2Y0aH4lFv7j5Be5eD22dIV43wDUnywi/FRYZx4+QsVu49yenWdqPD8Qin08mrO44zMTuRnIx4o8MRQUKSvPBbd102jLZOB28UVhgdikfsOn6KgzXN3DEj2+hQRBCRJC/8Vm5mPFOHDeKl7cdxOAL/BuyL244TGxHKtbLEn/AhSfLCr901cyhH6lr46JD3lkfzhZomO2/vO8ktU4cQE+FSXUAhXCJJXvi1JfkZDIoOC/gbsC9tO06nw8mXZg83OhQRZCTJC78WGRbCZ6dls85WzYnTZ40OxyVtnV28tP04V6pUhifHGB2OCDKS5IXf+0LP1e9ftxwxNhAXrSqqpK65Ta7ihSEkyQu/l5UYxdLxGby6o5wme4fR4VwSp9PJX7YcZVRKDPPGJBsdjghCLt0BUkpFAS8CqUAT8EWt/682rFJqMfBQz48WYC7d67xGASuBgz37ntZav+Za6CKY3DtvBCv3nuS1neXMCaBcWXjsFPsqGvnpDXlYLFJSWPieq1fy9wFFWut5wN+AH/fdqbVeo7Wer7WeD7wNPKa1tgFTgCd690mCFxdrwpBELhuRxPObj9AZQMMpn95wiEHRYdw8VRbqFsZwNcnPBdb0PF4NLBzoSUqpIcDn6VnoG5gKXKOU+lAp9ZxSyvwLeQqPuXfeSE422tl8tMXoUC5KadUZ1pfW8KXZI4gOl2GTwhgX/OQppb4MPNBvczXQWzmqCUg4x+EPAk9qrXsLg+8AntVaFyqlfgT8N/Dd/gfZbLaLCP3T7Ha7y8cGqmBqcwZOhsSH8XrRKa4YUeL33R+/3FRDZKiFWcltbr9HwfQ+95I2e8YFk7zW+jngub7blFJvAr1X4XHA6f7HKaWswLXAj/psXqG17n3uCuC3A50zJyfngoEPxGazuXxsoAq2Nt/fGsd/vrGPkwxmYU6a0eGcU3lDKx8ePcKy2cOZMSnX7dcLtvcZpM2XqrCwcMDtrnbXbAGW9jxeAmwa4Dn5QKnWuu/g5neVUjN6Hi8ABo5KiHP4zOQs0mND+fX6A35da/5PHx7CaoF75o00OhQR5FxN8k8DeUqpzcBX6OlzV0r9sk8SV8DhfsfdB/xaKbUBmAM84uL5RZAKDbFy+4REik+c4f3SGqPDGVB5Qyuv7Szn1mnZpCdEGh2OCHIu3Q3SWrcCtw6w/Xt9Hr8OvN5v/y5gtivnFKLXglFxLC9t4an1B7lqXKrf9c0/tf4gFouFb1412uhQhJDJUCLwhFot/MeVo9lX0ci6kmqjw/mEspom3txVwednDiMjIcrocISQJC8C001ThjAqJYZH15TS0eUwOpx/e3LdQSLDQrhv/iijQxECkCQvAlRYiJUfLMnhcG0Lr+44bnQ4AOwpP807RZXcPWcEybERRocjBCBJXgSwBTmpzBo5mCffO8gZg2vaOBxO/uet/STHRvDVK2REjfAfkuRFwLJYLPzomhwaWtr5/QdlhsayYvcJ9pSf5vuLFXGRYYbGIkRfkuRFQMvPSuDWqUN4btMRdFWTITE0t3Xy2JpSJmYncvMUqVEj/IskeRHwfrg0h/ioMH64osiQtWAfW11KbXMb/3NdLlarfw3nFEKSvAh4g2LC+dHSHAqPneJlH9+E3Xa4nr9vO8ay2SOYPHSQT88txMWQJC9M4aYpWcwZPZhfrLJxtM43VSrPtnfx/eX7GJoUzXevHuuTcwpxqSTJC1OwWCz86paJhFgt3P/qbp+Mnf/pOyUcq2/l0ZvHSylh4bckyQvTyEyM4tGbJ7C3opEn1h3w6rn+tecEL28/zteuGMXsUQG0VJUIOpLkhaksHZ/B7dOzeXrDIVYXVXrlHAeqm/jhm0VMGzaI7xRIN43wb5Lkhen8z/V5TB6ayAP/2EPxicYLH3AJas7YWfaXncREhPLbOyYTFiK/QsK/ySdUmE5kWAh//vw0BsdE8KW/7KSsptkjr9vY2sGyv+7kVGs7z39puhQgEwFBkrwwpZS4CF64u3tpg889s83tRN/Q0s4dz27jYHUzv79jCvlZ51rxUgj/IklemNbo1FheufcynE4nNz/9EZsP1rn0Oodrm/nsn7ZSVtPMn78wlSvHpXo4UiG8x61xX0qpzwC3aq3vGGDfvcBXgU7gEa3120qpZOBlIAo4CSzrWYBECK8YkxbHm/fN4Z6/7eSLf9nB/QvGcN/8URfVl+50OvnnnhP85J/7CQu18sLdM5g5crAPohbCc1y+kldKPQX8YqDXUEqlA9+ie4m/q4FfKKUigJ8AL2ut5wG76f4jIIRXDR0czfL7ZnPN+AyeWHeAa36ziXf2VdJ1jhIITqeT7YfrufPZ7Tzw2l5Gp8Xy1n/MkQQvApI7V/IfAf9k4EQ9A9iitW4D2pRSZcAEYC7w857nrO55/KQbMQhxUeIiw/jN5yZz3cRMfrHaxjde3kVybDhXqlRyMuJJiAqjtaOLQzXNbNA1HK1vJTk2nIdvyOPOy4YRIjVpRIC6YJJXSn0ZeKDf5mVa69eUUvPPcVg80HfsWhOQ0G9777ZPsdlsFwprQHa73eVjA5W0+dIMscBvFqeyrbyVjUeaWVN8ktcLK/69PyLUQl5qJNfPSuaqkbFEhJ7lgC71VOguk/c5OHijzRdM8lrr54DnLvF1zwBxfX6OA0732X62z7ZPycnJucTTdbPZbC4fG6ikza7Jz4N76O6aqWtu52x7F2GhFtLiIv2ykqS8z8HBnTYXFhYOuN1bBTd2AD9TSkUCEUAOUAxsAZYCfwWWAJu8dH4hLorFYiElTpbqE+bl0SGUSqkHlVLXa62rgN/QncTfB36ktbYDjwC3K6W2ALOA33ny/EIIIT7JrSt5rfUGYEOfn5/o8/gZ4Jl+z68GFrtzTiGEEBdPJkMJIYSJSZIXQggTkyQvhBAmJkleCCFMTJK8EEKYmMXpHLh+h1EKCwv9KyAhhAgQU6dO/dRMPr9L8kIIITxHumuEEMLEJMkLIYSJeat2jU8ppazAH4CJQBtwj9a6zNiovEspFQY8Dwynuz7QI1rrtwwNygeUUqlAIbBIaz8oD+llSqkfANcD4cAfegoGmlbP5/oFuj/XXcC9Zn6flVKXAY9precrpUbTXdfLSXetr29orR3unsMsV/I3ApFa61nAQ8DjBsfjC3cB9T0LsCwhCOoA9SSAP9FdxdT0ekp5z6Z78Z0rgGxDA/KNpUCo1no28DDwM4Pj8Rql1PeAZ4HInk1PAD/u+Z22ADd44jxmSfJzgTUAWuttwDRjw/GJ14H/6vNzp1GB+ND/A/5I99KRweBqoAhYAawE3jY2HJ84AIT2fDuPBzoMjsebDgE39fl5KrCx5/FqYKEnTmKWJN9/kZIupZQpuqLORWvdrLVuUkrFAW8APzY6Jm9SSn0JqNVav2t0LD6UTPcFy63A14CXlFL+V+zes5rp7qoppbvA4W8MjcaLtNbL+eQfMYvWune44zkXVbpUZkny/RcpsWqtTX9lq5TKBj4A/q61ftnoeLzsbmCRUmoDMAn4W89awmZWD7yrtW7XWmvADqQYHJO3PUB3m8fSfY/thZ51KYJB3/73cy6qdKnMkuR7FyNBKTWT7q+4pqaUSgPWAt/XWj9vdDzeprW+XGt9hdZ6PrAH+ELPugVmthlYrJSyKKUygRi6E7+ZneL/vpU3AGFAiHHh+NTuPkuqemxRJbN0aayg+yrvI7pvWCwzOB5f+CEwCPgvpVRv3/wSrXVQ3JQMBlrrt5VSl9O90pqV7tEWXQaH5W1PAs8rpTbRPaLoh1rrFoNj8pXvAM8opcIBG93dsG6TGa9CCGFiZumuEUIIMQBJ8kIIYWKS5IUQwsQkyQshhIlJkhdCCBOTJC+EECYmSV4IIUxMkrwQQpjY/wcpHelOBPdoewAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# The data\n",
+ "x = np.linspace(0, 10, 1000)\n",
+ "plt.plot(x, np.sin(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Multiple lines\n",
+ "plt.plot(x, np.sin(x))\n",
+ "plt.plot(x, np.cos(x))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Labelled axes with custom colors and legend"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot fn with x data, ydata, line color(and style) and label\n",
+ "# Sinusoid\n",
+ "plt.plot(x, np.sin(x), color='green', label='sin x', linestyle='solid')\n",
+ "\n",
+ "# Cosine\n",
+ "plt.plot(x, np.cos(x), color='blue', linestyle='dotted', label='cos x')\n",
+ "\n",
+ "# Set title, x-axis label and y-axis label\n",
+ "plt.title('Sine and cosine waves')\n",
+ "plt.xlabel('x')\n",
+ "# plt.axis('equal')\n",
+ "\n",
+ "# Set y-limits\n",
+ "plt.ylim(-2, 2)\n",
+ "\n",
+ "# Include a legend at the lower-center\n",
+ "plt.legend(loc='lower center', ncol=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Histogram"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 10., 28., 83., 179., 227., 219., 154., 70., 22., 8.]),\n",
+ " array([-2.9255609 , -2.33289716, -1.74023341, -1.14756966, -0.55490591,\n",
+ " 0.03775784, 0.63042159, 1.22308534, 1.81574908, 2.40841283,\n",
+ " 3.00107658]),\n",
+ " )"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Data\n",
+ "data = np.random.randn(1000)\n",
+ "\n",
+ "plt.hist(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/full_kiva_loans_analysis.ipynb b/notebooks/full_kiva_loans_analysis.ipynb
new file mode 100644
index 0000000..891f149
--- /dev/null
+++ b/notebooks/full_kiva_loans_analysis.ipynb
@@ -0,0 +1,2372 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Contents\n",
+ "1. [Introduction](#intro)\n",
+ "2. [Retreiving the Data](#retreiving-data)
\n", + " 2.1. [Loading the Libraries](#load-libraries)
\n", + " 2.2. [Loading the datasets](#load-datasets)\n", + "3. [Taking a glimpse of the data](#data-glimpse)
\n", + " 3.1. [General overview of tables](#gen-overview)
\n", + " 3.2. [Statistical overview of the data](#stat-overview)\n", + "4. [Data cleaning and preparation](#data-prep)
\n", + " 4.1. [Handling Missing Data](#missing-data)\n", + "5. [Data Exploration and Visualization](#data-exploration)
\n", + " 5.1. [Loan counts per sector](#per-sector)
\n", + " 5.2. [Loan counts per world region](#per-region)
\n", + " 5.3. [World Region MPIs](#region-mpis)
\n", + " 5.4. [Repayment Intervals](#repayment)
\n", + " 5.5. [Gender Comparisons](#genders)
\n", + " 5.6. [Loan amounts per gender](#loans-per-gender)
\n", + " 5.7. [Total loan amounts per country](#loans-per-country)
\n", + " 5.8. [Total Loans distribution across the world](#loan-dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world.The kiva datasets contain data on money funded to borrowers from different countries.
\n", + "My main objective is to pair the Kiva's datasets and do some analysis to help them understand their borrowers and improve their efficiency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Retreiving the Data\n", + "\n", + "\n", + "### 2.1. Loading the Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import the desired libraries\n", + "\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import os\n", + "\n", + "# Set default seaborn settings\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Loading in the datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading the main dataset\n", + "kiva_loans = pd.read_csv('../data/kiva/kiva_loans.csv')\n", + "\n", + "# Kiva mpi region locations\n", + "mpi_region_locations = pd.read_csv('../data/kiva/kiva_mpi_region_locations.csv')\n", + "\n", + "# Loan theme ids\n", + "loan_theme_ids = pd.read_csv('../data/kiva/loan_theme_ids.csv')\n", + "\n", + "# Loan themes by region\n", + "loan_themes_region = pd.read_csv('../data/kiva/loan_themes_by_region.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Taking a glimpse of the data\n", + "\n", + "\n", + "### 3.1. General overview of tables\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "We'll therefore deal with only the available data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Data Exploration and Visualization\n", + "\n", + "\n", + "### 5.1. Loan counts per sector\n", + "\n", + "\n", + "#### Top sectors in which more loans were given" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + " 2.1. [Loading the Libraries](#load-libraries)
\n", + " 2.2. [Loading the datasets](#load-datasets)\n", + "3. [Taking a glimpse of the data](#data-glimpse)
\n", + " 3.1. [General overview of tables](#gen-overview)
\n", + " 3.2. [Statistical overview of the data](#stat-overview)\n", + "4. [Data cleaning and preparation](#data-prep)
\n", + " 4.1. [Handling Missing Data](#missing-data)\n", + "5. [Data Exploration and Visualization](#data-exploration)
\n", + " 5.1. [Loan counts per sector](#per-sector)
\n", + " 5.2. [Loan counts per world region](#per-region)
\n", + " 5.3. [World Region MPIs](#region-mpis)
\n", + " 5.4. [Repayment Intervals](#repayment)
\n", + " 5.5. [Gender Comparisons](#genders)
\n", + " 5.6. [Loan amounts per gender](#loans-per-gender)
\n", + " 5.7. [Total loan amounts per country](#loans-per-country)
\n", + " 5.8. [Total Loans distribution across the world](#loan-dist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Introduction\n", + "\n", + "Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world.The kiva datasets contain data on money funded to borrowers from different countries.
\n", + "My main objective is to pair the Kiva's datasets and do some analysis to help them understand their borrowers and improve their efficiency" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Retreiving the Data\n", + "\n", + "\n", + "### 2.1. Loading the Libraries\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import the desired libraries\n", + "\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import folium\n", + "import os\n", + "\n", + "# Set default seaborn settings\n", + "sns.set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Loading in the datasets\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading the main dataset\n", + "kiva_loans = pd.read_csv('../data/kiva/kiva_loans.csv')\n", + "\n", + "# Kiva mpi region locations\n", + "mpi_region_locations = pd.read_csv('../data/kiva/kiva_mpi_region_locations.csv')\n", + "\n", + "# Loan theme ids\n", + "loan_theme_ids = pd.read_csv('../data/kiva/loan_theme_ids.csv')\n", + "\n", + "# Loan themes by region\n", + "loan_themes_region = pd.read_csv('../data/kiva/loan_themes_by_region.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Taking a glimpse of the data\n", + "\n", + "\n", + "### 3.1. General overview of tables\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# kiva loans overview\n",
+ "kiva_loans.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " LocationName ISO country region world_region MPI \\\n",
+ "0 Badakhshan, Afghanistan AFG Afghanistan Badakhshan South Asia 0.387 \n",
+ "1 Badghis, Afghanistan AFG Afghanistan Badghis South Asia 0.466 \n",
+ "2 Baghlan, Afghanistan AFG Afghanistan Baghlan South Asia 0.300 \n",
+ "3 Balkh, Afghanistan AFG Afghanistan Balkh South Asia 0.301 \n",
+ "4 Bamyan, Afghanistan AFG Afghanistan Bamyan South Asia 0.325 \n",
+ "\n",
+ " geo lat lon \n",
+ "0 (36.7347725, 70.81199529999999) 36.734772 70.811995 \n",
+ "1 (35.1671339, 63.7695384) 35.167134 63.769538 \n",
+ "2 (35.8042947, 69.2877535) 35.804295 69.287754 \n",
+ "3 (36.7550603, 66.8975372) 36.755060 66.897537 \n",
+ "4 (34.8100067, 67.8212104) 34.810007 67.821210 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# mpi regions overview\n",
+ "mpi_region_locations.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | LocationName | \n", + "ISO | \n", + "country | \n", + "region | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Badakhshan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badakhshan | \n", + "South Asia | \n", + "0.387 | \n", + "(36.7347725, 70.81199529999999) | \n", + "36.734772 | \n", + "70.811995 | \n", + "
| 1 | \n", + "Badghis, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badghis | \n", + "South Asia | \n", + "0.466 | \n", + "(35.1671339, 63.7695384) | \n", + "35.167134 | \n", + "63.769538 | \n", + "
| 2 | \n", + "Baghlan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Baghlan | \n", + "South Asia | \n", + "0.300 | \n", + "(35.8042947, 69.2877535) | \n", + "35.804295 | \n", + "69.287754 | \n", + "
| 3 | \n", + "Balkh, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Balkh | \n", + "South Asia | \n", + "0.301 | \n", + "(36.7550603, 66.8975372) | \n", + "36.755060 | \n", + "66.897537 | \n", + "
| 4 | \n", + "Bamyan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Bamyan | \n", + "South Asia | \n", + "0.325 | \n", + "(34.8100067, 67.8212104) | \n", + "34.810007 | \n", + "67.821210 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id Loan Theme ID Loan Theme Type Partner ID\n",
+ "0 638631 a1050000000skGl General 151.0\n",
+ "1 640322 a1050000000skGl General 151.0\n",
+ "2 641006 a1050000002X1ij Higher Education 160.0\n",
+ "3 641019 a1050000002X1ij Higher Education 160.0\n",
+ "4 641594 a1050000002VbsW Subsistence Agriculture 336.0"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Loan theme by ids overview\n",
+ "loan_theme_ids.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "Loan Theme ID | \n", + "Loan Theme Type | \n", + "Partner ID | \n", + "
|---|---|---|---|---|
| 0 | \n", + "638631 | \n", + "a1050000000skGl | \n", + "General | \n", + "151.0 | \n", + "
| 1 | \n", + "640322 | \n", + "a1050000000skGl | \n", + "General | \n", + "151.0 | \n", + "
| 2 | \n", + "641006 | \n", + "a1050000002X1ij | \n", + "Higher Education | \n", + "160.0 | \n", + "
| 3 | \n", + "641019 | \n", + "a1050000002X1ij | \n", + "Higher Education | \n", + "160.0 | \n", + "
| 4 | \n", + "641594 | \n", + "a1050000002VbsW | \n", + "Subsistence Agriculture | \n", + "336.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Partner ID Field Partner Name sector \\\n",
+ "0 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "1 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "2 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "3 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "4 9 KREDIT Microfinance Institution General Financial Inclusion \n",
+ "\n",
+ " Loan Theme ID Loan Theme Type country forkiva \\\n",
+ "0 a1050000000slfi Higher Education Cambodia No \n",
+ "1 a10500000068jPe Vulnerable Populations Cambodia No \n",
+ "2 a1050000000slfi Higher Education Cambodia No \n",
+ "3 a10500000068jPe Vulnerable Populations Cambodia No \n",
+ "4 a1050000002X1Uu Sanitation Cambodia No \n",
+ "\n",
+ " region geocode_old ISO ... amount \\\n",
+ "0 Banteay Meanchey (13.75, 103.0) KHM ... 450 \n",
+ "1 Battambang Province NaN KHM ... 20275 \n",
+ "2 Battambang Province NaN KHM ... 9150 \n",
+ "3 Kampong Cham Province (12.0, 105.5) KHM ... 604950 \n",
+ "4 Kampong Cham Province (12.0, 105.5) KHM ... 275 \n",
+ "\n",
+ " LocationName geocode \\\n",
+ "0 Banteay Meanchey, Cambodia [(13.6672596, 102.8975098)] \n",
+ "1 Battambang Province, Cambodia [(13.0286971, 102.989615)] \n",
+ "2 Battambang Province, Cambodia [(13.0286971, 102.989615)] \n",
+ "3 Kampong Cham Province, Cambodia [(12.0982918, 105.3131185)] \n",
+ "4 Kampong Cham Province, Cambodia [(12.0982918, 105.3131185)] \n",
+ "\n",
+ " names geo lat \\\n",
+ "0 Banteay Meanchey Province; Cambodia (13.6672596, 102.8975098) 13.667260 \n",
+ "1 Battambang Province; Cambodia (13.0286971, 102.989615) 13.028697 \n",
+ "2 Battambang Province; Cambodia (13.0286971, 102.989615) 13.028697 \n",
+ "3 Kampong Cham Province; Cambodia (12.0982918, 105.3131185) 12.098292 \n",
+ "4 Kampong Cham Province; Cambodia (12.0982918, 105.3131185) 12.098292 \n",
+ "\n",
+ " lon mpi_region mpi_geo \\\n",
+ "0 102.897510 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "1 102.989615 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "2 102.989615 Banteay Mean Chey, Cambodia (13.6672596, 102.8975098) \n",
+ "3 105.313119 Kampong Cham, Cambodia (11.9924294, 105.4645408) \n",
+ "4 105.313119 Kampong Cham, Cambodia (11.9924294, 105.4645408) \n",
+ "\n",
+ " rural_pct \n",
+ "0 90.0 \n",
+ "1 90.0 \n",
+ "2 90.0 \n",
+ "3 90.0 \n",
+ "4 90.0 \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Loan themes by region\n",
+ "loan_themes_region.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2. Statistical overview\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Simple overview of all numerical values for the main dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Partner ID | \n", + "Field Partner Name | \n", + "sector | \n", + "Loan Theme ID | \n", + "Loan Theme Type | \n", + "country | \n", + "forkiva | \n", + "region | \n", + "geocode_old | \n", + "ISO | \n", + "... | \n", + "amount | \n", + "LocationName | \n", + "geocode | \n", + "names | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "mpi_region | \n", + "mpi_geo | \n", + "rural_pct | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000000slfi | \n", + "Higher Education | \n", + "Cambodia | \n", + "No | \n", + "Banteay Meanchey | \n", + "(13.75, 103.0) | \n", + "KHM | \n", + "... | \n", + "450 | \n", + "Banteay Meanchey, Cambodia | \n", + "[(13.6672596, 102.8975098)] | \n", + "Banteay Meanchey Province; Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "13.667260 | \n", + "102.897510 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 1 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a10500000068jPe | \n", + "Vulnerable Populations | \n", + "Cambodia | \n", + "No | \n", + "Battambang Province | \n", + "NaN | \n", + "KHM | \n", + "... | \n", + "20275 | \n", + "Battambang Province, Cambodia | \n", + "[(13.0286971, 102.989615)] | \n", + "Battambang Province; Cambodia | \n", + "(13.0286971, 102.989615) | \n", + "13.028697 | \n", + "102.989615 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 2 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000000slfi | \n", + "Higher Education | \n", + "Cambodia | \n", + "No | \n", + "Battambang Province | \n", + "NaN | \n", + "KHM | \n", + "... | \n", + "9150 | \n", + "Battambang Province, Cambodia | \n", + "[(13.0286971, 102.989615)] | \n", + "Battambang Province; Cambodia | \n", + "(13.0286971, 102.989615) | \n", + "13.028697 | \n", + "102.989615 | \n", + "Banteay Mean Chey, Cambodia | \n", + "(13.6672596, 102.8975098) | \n", + "90.0 | \n", + "
| 3 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a10500000068jPe | \n", + "Vulnerable Populations | \n", + "Cambodia | \n", + "No | \n", + "Kampong Cham Province | \n", + "(12.0, 105.5) | \n", + "KHM | \n", + "... | \n", + "604950 | \n", + "Kampong Cham Province, Cambodia | \n", + "[(12.0982918, 105.3131185)] | \n", + "Kampong Cham Province; Cambodia | \n", + "(12.0982918, 105.3131185) | \n", + "12.098292 | \n", + "105.313119 | \n", + "Kampong Cham, Cambodia | \n", + "(11.9924294, 105.4645408) | \n", + "90.0 | \n", + "
| 4 | \n", + "9 | \n", + "KREDIT Microfinance Institution | \n", + "General Financial Inclusion | \n", + "a1050000002X1Uu | \n", + "Sanitation | \n", + "Cambodia | \n", + "No | \n", + "Kampong Cham Province | \n", + "(12.0, 105.5) | \n", + "KHM | \n", + "... | \n", + "275 | \n", + "Kampong Cham Province, Cambodia | \n", + "[(12.0982918, 105.3131185)] | \n", + "Kampong Cham Province; Cambodia | \n", + "(12.0982918, 105.3131185) | \n", + "12.098292 | \n", + "105.313119 | \n", + "Kampong Cham, Cambodia | \n", + "(11.9924294, 105.4645408) | \n", + "90.0 | \n", + "
5 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount partner_id \\\n",
+ "count 6.712050e+05 671205.000000 671205.000000 657698.000000 \n",
+ "mean 9.932486e+05 785.995061 842.397107 178.199616 \n",
+ "std 1.966113e+05 1130.398941 1198.660073 94.247581 \n",
+ "min 6.530470e+05 0.000000 25.000000 9.000000 \n",
+ "25% 8.230720e+05 250.000000 275.000000 126.000000 \n",
+ "50% 9.927800e+05 450.000000 500.000000 145.000000 \n",
+ "75% 1.163653e+06 900.000000 1000.000000 204.000000 \n",
+ "max 1.340339e+06 100000.000000 100000.000000 536.000000 \n",
+ "\n",
+ " term_in_months lender_count \n",
+ "count 671205.000000 671205.000000 \n",
+ "mean 13.739022 20.590922 \n",
+ "std 8.598919 28.459551 \n",
+ "min 1.000000 0.000000 \n",
+ "25% 8.000000 7.000000 \n",
+ "50% 13.000000 13.000000 \n",
+ "75% 14.000000 24.000000 \n",
+ "max 158.000000 2986.000000 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kiva_loans.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Simple overview of categorical(object) values(non-numerical)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "partner_id | \n", + "term_in_months | \n", + "lender_count | \n", + "
|---|---|---|---|---|---|---|
| count | \n", + "6.712050e+05 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "657698.000000 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "
| mean | \n", + "9.932486e+05 | \n", + "785.995061 | \n", + "842.397107 | \n", + "178.199616 | \n", + "13.739022 | \n", + "20.590922 | \n", + "
| std | \n", + "1.966113e+05 | \n", + "1130.398941 | \n", + "1198.660073 | \n", + "94.247581 | \n", + "8.598919 | \n", + "28.459551 | \n", + "
| min | \n", + "6.530470e+05 | \n", + "0.000000 | \n", + "25.000000 | \n", + "9.000000 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "8.230720e+05 | \n", + "250.000000 | \n", + "275.000000 | \n", + "126.000000 | \n", + "8.000000 | \n", + "7.000000 | \n", + "
| 50% | \n", + "9.927800e+05 | \n", + "450.000000 | \n", + "500.000000 | \n", + "145.000000 | \n", + "13.000000 | \n", + "13.000000 | \n", + "
| 75% | \n", + "1.163653e+06 | \n", + "900.000000 | \n", + "1000.000000 | \n", + "204.000000 | \n", + "14.000000 | \n", + "24.000000 | \n", + "
| max | \n", + "1.340339e+06 | \n", + "100000.000000 | \n", + "100000.000000 | \n", + "536.000000 | \n", + "158.000000 | \n", + "2986.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " activity sector \\\n",
+ "count 671205 671205 \n",
+ "unique 163 15 \n",
+ "top Farming Agriculture \n",
+ "freq 72955 180302 \n",
+ "\n",
+ " use country_code \\\n",
+ "count 666973 671197 \n",
+ "unique 424912 86 \n",
+ "top to buy a water filter to provide safe drinking... PH \n",
+ "freq 5217 160441 \n",
+ "\n",
+ " country region currency posted_time \\\n",
+ "count 671205 614405 671205 671205 \n",
+ "unique 87 12695 67 667399 \n",
+ "top Philippines Kaduna PHP 2017-05-15 00:00:00+00:00 \n",
+ "freq 160441 10000 160440 25 \n",
+ "\n",
+ " disbursed_time funded_time tags \\\n",
+ "count 668809 622874 499789 \n",
+ "unique 5719 498007 86719 \n",
+ "top 2017-02-01 08:00:00+00:00 2016-09-21 13:03:24+00:00 user_favorite \n",
+ "freq 2800 33 27088 \n",
+ "\n",
+ " borrower_genders repayment_interval date \n",
+ "count 666984 671205 671205 \n",
+ "unique 11298 4 1298 \n",
+ "top female monthly 2017-03-20 \n",
+ "freq 426502 342717 1308 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kiva_loans.describe(include=[\"O\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Data cleaning and preparation\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1. Handling Missing Data\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Taking a look at the total counts of all missing data in our main datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# General method to check for missing values and their percentages of the total values\n",
+ "def check_missing_data(dataset):\n",
+ " \"\"\"\n",
+ " Method to check for total missing values in a dataset\n",
+ " \n",
+ " @params: dataset: the dataset(DataFrame) to be checked\n",
+ " \n",
+ " returns: A DataFrame with sums of missing values and their percentage to the total missing values\n",
+ " \n",
+ " \"\"\"\n",
+ " # Extract total null values\n",
+ " null_values = dataset.isnull().sum()\n",
+ "\n",
+ " # sort them\n",
+ " total = null_values.sort_values(ascending=False)\n",
+ "\n",
+ " # percentage of missing values sum to total null values\n",
+ " percentage = null_values / (dataset.isnull().count().sort_values(ascending=False))\n",
+ "\n",
+ " # merge the total and percentage into one dataframe\n",
+ " missing_data = pd.concat([total, percentage], axis='columns', keys=['Total', 'Percentage'], sort=False)\n",
+ "\n",
+ " return missing_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in kiva_loans data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "671205 | \n", + "671205 | \n", + "666973 | \n", + "671197 | \n", + "671205 | \n", + "614405 | \n", + "671205 | \n", + "671205 | \n", + "668809 | \n", + "622874 | \n", + "499789 | \n", + "666984 | \n", + "671205 | \n", + "671205 | \n", + "
| unique | \n", + "163 | \n", + "15 | \n", + "424912 | \n", + "86 | \n", + "87 | \n", + "12695 | \n", + "67 | \n", + "667399 | \n", + "5719 | \n", + "498007 | \n", + "86719 | \n", + "11298 | \n", + "4 | \n", + "1298 | \n", + "
| top | \n", + "Farming | \n", + "Agriculture | \n", + "to buy a water filter to provide safe drinking... | \n", + "PH | \n", + "Philippines | \n", + "Kaduna | \n", + "PHP | \n", + "2017-05-15 00:00:00+00:00 | \n", + "2017-02-01 08:00:00+00:00 | \n", + "2016-09-21 13:03:24+00:00 | \n", + "user_favorite | \n", + "female | \n", + "monthly | \n", + "2017-03-20 | \n", + "
| freq | \n", + "72955 | \n", + "180302 | \n", + "5217 | \n", + "160441 | \n", + "160441 | \n", + "10000 | \n", + "160440 | \n", + "25 | \n", + "2800 | \n", + "33 | \n", + "27088 | \n", + "426502 | \n", + "342717 | \n", + "1308 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "tags 171416 0.255385\n",
+ "region 56800 0.084624\n",
+ "funded_time 48331 0.072006\n",
+ "partner_id 13507 0.020124\n",
+ "use 4232 0.006305\n",
+ "borrower_genders 4221 0.006289\n",
+ "disbursed_time 2396 0.003570\n",
+ "country_code 8 0.000012\n",
+ "date 0 0.000000\n",
+ "funded_amount 0 0.000000\n",
+ "loan_amount 0 0.000000\n",
+ "activity 0 0.000000\n",
+ "sector 0 0.000000\n",
+ "currency 0 0.000000\n",
+ "country 0 0.000000\n",
+ "repayment_interval 0 0.000000\n",
+ "posted_time 0 0.000000\n",
+ "term_in_months 0 0.000000\n",
+ "lender_count 0 0.000000\n",
+ "id 0 0.000000"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(kiva_loans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in mpi regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| tags | \n", + "171416 | \n", + "0.255385 | \n", + "
| region | \n", + "56800 | \n", + "0.084624 | \n", + "
| funded_time | \n", + "48331 | \n", + "0.072006 | \n", + "
| partner_id | \n", + "13507 | \n", + "0.020124 | \n", + "
| use | \n", + "4232 | \n", + "0.006305 | \n", + "
| borrower_genders | \n", + "4221 | \n", + "0.006289 | \n", + "
| disbursed_time | \n", + "2396 | \n", + "0.003570 | \n", + "
| country_code | \n", + "8 | \n", + "0.000012 | \n", + "
| date | \n", + "0 | \n", + "0.000000 | \n", + "
| funded_amount | \n", + "0 | \n", + "0.000000 | \n", + "
| loan_amount | \n", + "0 | \n", + "0.000000 | \n", + "
| activity | \n", + "0 | \n", + "0.000000 | \n", + "
| sector | \n", + "0 | \n", + "0.000000 | \n", + "
| currency | \n", + "0 | \n", + "0.000000 | \n", + "
| country | \n", + "0 | \n", + "0.000000 | \n", + "
| repayment_interval | \n", + "0 | \n", + "0.000000 | \n", + "
| posted_time | \n", + "0 | \n", + "0.000000 | \n", + "
| term_in_months | \n", + "0 | \n", + "0.000000 | \n", + "
| lender_count | \n", + "0 | \n", + "0.000000 | \n", + "
| id | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "lon 1880 0.678211\n",
+ "lat 1880 0.678211\n",
+ "MPI 1788 0.645022\n",
+ "region 1788 0.645022\n",
+ "LocationName 1788 0.645022\n",
+ "world_region 1764 0.636364\n",
+ "country 1764 0.636364\n",
+ "ISO 1764 0.636364\n",
+ "geo 0 0.000000"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(mpi_region_locations)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in id loan themes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| lon | \n", + "1880 | \n", + "0.678211 | \n", + "
| lat | \n", + "1880 | \n", + "0.678211 | \n", + "
| MPI | \n", + "1788 | \n", + "0.645022 | \n", + "
| region | \n", + "1788 | \n", + "0.645022 | \n", + "
| LocationName | \n", + "1788 | \n", + "0.645022 | \n", + "
| world_region | \n", + "1764 | \n", + "0.636364 | \n", + "
| country | \n", + "1764 | \n", + "0.636364 | \n", + "
| ISO | \n", + "1764 | \n", + "0.636364 | \n", + "
| geo | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "Partner ID 14813 0.019013\n",
+ "Loan Theme Type 14813 0.019013\n",
+ "Loan Theme ID 14813 0.019013\n",
+ "id 0 0.000000"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(loan_theme_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Missing data in regions loan theme"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| Partner ID | \n", + "14813 | \n", + "0.019013 | \n", + "
| Loan Theme Type | \n", + "14813 | \n", + "0.019013 | \n", + "
| Loan Theme ID | \n", + "14813 | \n", + "0.019013 | \n", + "
| id | \n", + "0 | \n", + "0.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Total Percentage\n",
+ "geocode_old 14536 0.923742\n",
+ "mpi_geo 6065 0.385422\n",
+ "names 2075 0.131863\n",
+ "lon 2074 0.131800\n",
+ "lat 2074 0.131800\n",
+ "geocode 2074 0.131800\n",
+ "rural_pct 1392 0.088460\n",
+ "mpi_region 14 0.000890\n",
+ "ISO 14 0.000890\n",
+ "country 0 0.000000\n",
+ "Field Partner Name 0 0.000000\n",
+ "sector 0 0.000000\n",
+ "Loan Theme ID 0 0.000000\n",
+ "Loan Theme Type 0 0.000000\n",
+ "number 0 0.000000\n",
+ "forkiva 0 0.000000\n",
+ "region 0 0.000000\n",
+ "amount 0 0.000000\n",
+ "LocationName 0 0.000000\n",
+ "geo 0 0.000000\n",
+ "Partner ID 0 0.000000"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "check_missing_data(loan_themes_region)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the percentages, it's clear that there are relatively bigger chunks of missing data hence filling them in would lead to false/misguided conclusions. | \n", + " | Total | \n", + "Percentage | \n", + "
|---|---|---|
| geocode_old | \n", + "14536 | \n", + "0.923742 | \n", + "
| mpi_geo | \n", + "6065 | \n", + "0.385422 | \n", + "
| names | \n", + "2075 | \n", + "0.131863 | \n", + "
| lon | \n", + "2074 | \n", + "0.131800 | \n", + "
| lat | \n", + "2074 | \n", + "0.131800 | \n", + "
| geocode | \n", + "2074 | \n", + "0.131800 | \n", + "
| rural_pct | \n", + "1392 | \n", + "0.088460 | \n", + "
| mpi_region | \n", + "14 | \n", + "0.000890 | \n", + "
| ISO | \n", + "14 | \n", + "0.000890 | \n", + "
| country | \n", + "0 | \n", + "0.000000 | \n", + "
| Field Partner Name | \n", + "0 | \n", + "0.000000 | \n", + "
| sector | \n", + "0 | \n", + "0.000000 | \n", + "
| Loan Theme ID | \n", + "0 | \n", + "0.000000 | \n", + "
| Loan Theme Type | \n", + "0 | \n", + "0.000000 | \n", + "
| number | \n", + "0 | \n", + "0.000000 | \n", + "
| forkiva | \n", + "0 | \n", + "0.000000 | \n", + "
| region | \n", + "0 | \n", + "0.000000 | \n", + "
| amount | \n", + "0 | \n", + "0.000000 | \n", + "
| LocationName | \n", + "0 | \n", + "0.000000 | \n", + "
| geo | \n", + "0 | \n", + "0.000000 | \n", + "
| Partner ID | \n", + "0 | \n", + "0.000000 | \n", + "
\n", + "We'll therefore deal with only the available data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Data Exploration and Visualization\n", + "\n", + "\n", + "### 5.1. Loan counts per sector\n", + "\n", + "\n", + "#### Top sectors in which more loans were given" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sectors = kiva_loans['sector'].value_counts()\n",
+ "# print(sectors)\n",
+ "\n",
+ "# Inistantiate the figure object\n",
+ "fig = plt.figure(figsize=(15,8))\n",
+ "\n",
+ "# Plot a barplot of The sectors and their loan counts\n",
+ "bar_plot = sns.barplot(x=sectors.index, y=sectors.values)\n",
+ "\n",
+ "# Set labels and title\n",
+ "plt.title(\"Loan Counts per Sector\", fontsize=20)\n",
+ "plt.ylabel(\"Sector\", fontsize=15)\n",
+ "plt.xlabel(\"Count\", fontsize=15)\n",
+ "\n",
+ "labels = bar_plot.get_xticklabels()\n",
+ "bar_plot.set_xticklabels(labels, rotation=45)\n",
+ "\n",
+ "# show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above chart, **Agriculture** was the sector with the highest loan counts, followed by **Food**, then **Retail**.
\n", + "**Wholesale** had the least loan counts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Loan counts per world region\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + "**Wholesale** had the least loan counts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Loan counts per world region\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "world_regions = mpi_region_locations['world_region'].value_counts()\n",
+ "# print(world_regions)\n",
+ "fig = plt.figure(figsize=(16, 9))\n",
+ "\n",
+ "sns.barplot(x=world_regions.values, y=world_regions.index)\n",
+ "\n",
+ "plt.title(\"Loan Counts Per world Region\", fontsize=30)\n",
+ "\n",
+ "for i, v in enumerate(world_regions.values):\n",
+ " plt.text(10,i,v,color='k',fontsize=19)\n",
+ "\n",
+ "plt.xlabel(\"Count\", fontsize=25)\n",
+ "plt.ylabel(\"World Region\", fontsize=25)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Sub-Saharan Africa** has the highest number of loans followed by **Latin America and Carribean** region, while **Europe and Central Asia** has the least number of loans.\n",
+ "
\n", + "
\n", + "To account for this trend, we take a look at each region's mean poverty index.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "
\n", + "To account for this trend, we take a look at each region's mean poverty index.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Sub-Saharan Africa 0.337128\n",
+ "South Asia 0.219630\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Arab States 0.115287\n",
+ "Latin America and Caribbean 0.063665\n",
+ "Europe and Central Asia 0.025273"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "poverty_index_table = pd.pivot_table(mpi_region_locations, aggfunc=[np.mean], index=['world_region'], values=['MPI'])['mean'].sort_values(by=['MPI'], ascending=False)\n",
+ "poverty_index_table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Arab States | \n", + "0.115287 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "poverty_index = poverty_index_table['MPI'].round(2)\n",
+ "# print(poverty_index['MPI'])\n",
+ "fig = plt.figure(figsize=(15, 8))\n",
+ "\n",
+ "sns.barplot(x=poverty_index.values, y=poverty_index.index)\n",
+ "\n",
+ "for i, v in enumerate(poverty_index.values):\n",
+ " plt.text(0.01, i, v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"Region Poverty Indexes\", fontsize=30)\n",
+ "plt.ylabel(\"World Region\", fontsize=25)\n",
+ "plt.xlabel(\"MPI\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above charts, its clear that **Sub-Saharan Africa** has the highest Multidimensional Poverty Index while **Europe and Central ASia** has the lowest. This explains why the two regions have the highest and lowest loan counts respectively.\n",
+ "
\n", + "
\n", + "A [study](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa) conducted by The World Bank shows that the number of extremely poor people continues to rise in Sub-Saharan africa while it falls in other regions ([World bank Data Blog](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa)).\n", + "
\n", + "
\n", + "From this study, it is expected that the number of loan counts from **Sub-Saharan Africa** will always be high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.4. Repayment Intervals\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
\n", + "
\n", + "A [study](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa) conducted by The World Bank shows that the number of extremely poor people continues to rise in Sub-Saharan africa while it falls in other regions ([World bank Data Blog](https://blogs.worldbank.org/opendata/number-extremely-poor-people-continues-rise-sub-saharan-africa)).\n", + "
\n", + "
\n", + "From this study, it is expected that the number of loan counts from **Sub-Saharan Africa** will always be high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.4. Repayment Intervals\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "intervals = kiva_loans['repayment_interval'].value_counts()\n",
+ "# print(intervals)\n",
+ "plt.figure(figsize=(15,8))\n",
+ "\n",
+ "plt.pie(intervals.values, labels=intervals.index, shadow=True, autopct='%1.1f%%', startangle=90)\n",
+ "\n",
+ "plt.axis('equal')\n",
+ "plt.title('Repayment Intervals', fontsize=30)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a percentage of **51.1%**, majority of the loans are repaid **monthly**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.5. Gender Comparisons\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "genders = []\n",
+ "\n",
+ "for gender in kiva_loans['borrower_genders'].values:\n",
+ " if str(gender) != \"nan\":\n",
+ " genders.extend([lst.strip() for lst in gender.split(\",\")])\n",
+ " \n",
+ "# print(genders)\n",
+ "# Add a new genders column with the simplified data\n",
+ "kiva_loans['genders'] = pd.Series(np.array(genders))\n",
+ "\n",
+ "# Dropping redundant borrower_genders column\n",
+ "kiva_loans.drop(columns=['borrower_genders'], inplace=True)\n",
+ "# print(kiva_loans.head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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0ITgjm81HtVyZT2pZVoMx5nic5akG4MypPTWx09o0nLV683Buy3lsGsWK4qzVWtLs+qqMMRexaYRqqjHmfpwRvyqcZdIuY9M0jm9oNuplWdZyY8xUnLVxB+Hs2vYPnBIxGGc3q8Nw1jYtaov7oRXJ8zOvM8bcBngTG0R0OMuyNhhjfobz4soDPGWMOSnx+Tqc++IynCXowDkY64Z2ymIbYy7GOcgwG+fnfCfwJs4BleOBq9l8FzO72XWkyu25Hmcr7SLgemPMvjjlew3OCijX4LwzU0cLo66WZcWMMefi3BcB4B/GmENw1gdejnMg50/YtM3zerYy0isim1PBFdnST7fzfC/j7D+/0a9wRu9+g3Pgyc0tXMbGect0s13MLMtqNMacg1PosoGDEh/NfQM8RGJ7WJx1dF3ZktOyrMWJucMP4Czg3wu4KvHRksXAhYnF71u6vueNMWfhlIRc4PLER3Nv4+wWV9vC136GUxj64+w89USzr0/HKVAfbuWm7aq3gRqcOcqnJz4ixpi8xEYVHc6yrGcSG5LcjzM/+ezER3NfAidux1rEu5LlI2PMj3GKXDbOY6H5rn734BzAuAebj4hvvA7Xb49lWXXGmINxDrocg/MYOLHZ2d7AOQCtxQNDLcv62BhzHE4574FTZo9r4awrgFM6YttnkUyhKQoibcSyrLhlWb/FWcD9PpwyWo9Tdr4E7gLGWJZ1ZUs7lVmW9W7isg/ilMFw4mMVTmn6Gc4T6X8S1wubtgt1hWVZayzLOgnnbdrbcApkOc40hAacEejnceZbDm+t3CZd39M4I2J/xZmLvAFnh66FONMzjgOObmVDAxKbB4zEGbGbi3M/VeFsDnEZzouGditviQyrcZZ9ejfxvZtwfoauHtxmWdYTOJsE3Iiz+cB6nBH1Cpw55JcA+1qW1ZbLabWW5RmcUc57cEbUG3F+Lu8AJ1mWdRnOqCZsvtlC8nW4fnsS2wQfAFyEs5pIBc6I7SzgFziFN9bqFTjX8Q7O7bgG54VXOc7t2JC4zquBEZZltTo3X0S25LFtLZ0nIiKpJTFNKB94NfEiSkRku2mKgoiIdAhjzOE489QXAndaltXi+rGJqS8bt7+e00HxRCSDqOCKiEhHWQ+cnDidhVN2N5PYrjp56bkXOiCXiGQYTVEQEZEOY4z5Ati4VvRLOLv2LcM5sHAEzjbUG5fFusuyrC1KsIjItmgEV0REOtLpwGScpd9OSXy05D84K5KIiOwwjeCKiEiHSuwodz7wA5zl3LrjrD6wCmdN6EfcWjdYRDKDCq6IiIiIZBStgysiIiIiGUUFV0REREQyigquiIiIiGQUFVwRERERySgquCIiIiKSUVRwRURERCSjqOCKiIiISEZRwRURERGRjKKCKyIiIiIZRQVXRERERDKK3+0AIiIZKBsoAHw4f2d9QBCIA1EglvRvNdDgTkwRkcykgisisn2ygWHAHkAvoGdDU7R/NBrvZ2P38Xg8Pf0+b9eA31sAeMORWDhu23Y8jh23bTs3y5vts2PYkaYGvF4PHo/H4/F5PYFgANuO2dFIjR2Prse2y/F41nh8gZXeYGgVUA6sBb4BvgXCbt0BIiLpwmPbttsZRERSSXdgT2B4OBIb3RiO7ef3eYZlBXw9Kqsb61esrbUrqhr962saQ1W1Td6q2jDVdRs/mqiuC9MYjm1xpbdeMcHuuehNT9X0F7f4micYwpddgC8nH29OQfLpuD+vW6O/oGc00KO/x5fXNceONK2zY1HLEwzN8PqD84GvgQXAhna+X0RE0oZGcEWkM8sG9o/G4hPrG6PHZgW8e3o8nqyyivqGJaur/YtXVeUsX1PjWbG2lrKKOmJxO3/nv1Xrgwl2uJFouJFo1drmX/ICOZs+8xPo1qcw0LN/YbDHgIODfQbXBXsPigW69smx7XiDHQ3P92blvOHx+qYBnwNNO59XRCR9qeCKSGfSBZgQjsQOawrHjsnO8g9bsa62YZa1NnteaXmgdEUVldWN4MyXbVs2nl2+jniUSMVKIhUrqafEA+Rt/JIvv3sgq98e47MHjRqbvfvejYFuhaF4uOFrbyA02eMPTAU+wZnvKyKS8VRwRSSTBYFDG5qip8Ri8aOygr4Bi1ZWNc601ubNL63wWsvW0xSOBdwO2RZiNZXUWyXUWyVBIOgJhgj1N3uFdhsxMqdozCXBXoNy4pGmpR5/YLI3kPUsMB3nIDcRkYyjObgikmkKgONq68NnBwO+I1auq418OHtl3tyF5d7SFRuIxtz5m3fr5ePpuXgKLc3B7RA+P1l9i8gZMiaWN+rgel9etzjx+IverOwngGlAxJ1gIiJtTyO4IpIJ8oCTauvDFwYDvglfL60MT5u5Mr/kqzI21DRlux0uJcSiNK2waFph+dZ/8Ey+v1tfcocfcG7+6IO/7+9a6CMWec0byn0CeBtodDuuiMiuUMEVkXTlA46pbYhcHvR7D7WWrg+/8/my/E/nr6a+MZrldrhUF12/mqrpL3qrpr9Y4MvvQe7wA07PH33IscFeA4PxaGSKL5R7BzCVrR0dJyKSolRwRSTd9IpG4xdFYvFfr1tfH3r5g0X50+etoqY+olK7k2I1FVR//rqn+vPXC3y5Xcjdc/xJXfY/8XBfdv4GTyB0m8frnYSWIRORNKI5uCKSDjzA/nUNkd/4/d4Tps9dZb/y4aLsb5enT+dyfQ7uTggNHEGX4uPrsov29RGPPu/NyrkdmOF2rp1hjFkCTLQsa4m7SUSkI2gEV0RSWS5wVl1D5JqmcKzvi9MWZr/7+TJvTb2Oh+oIjcu/onH5V7m+3C7k7334mV2KTzgVn3+ZL5T7D+AZtMWwiKQoFVwRSUXdmyKxq4HLv1xUYb80bWHe7G/WoTec3BGrq2LDJy/4Nkx/KSe7aMzwrvufdGdW/2F3ejzef3j8gTuA2o7IYYyZCFyLs13x7sArie99Cs4o/3HAacA5OC+OwsBZlmVZSdfhA/4JTMSZxz3JsqzbOyK/iHQcr9sBRESSdAlHYjc2hWPLPpy18peX3fJe7vX3T8+bZancpgQ7TsPCmax+4ob8lQ9fnV9fOvMP8UjTSjsWvQanUHaE/YFLgLHA5cA6y7LGAnOBM3HK7kTLskYBryXOk+ynAJZl7QsUAycbYw7qoOwi0kE0gisiqSAvEo39Kh63r/l0/mr/429+HSqrqHc7k2xFpHwFa567JSfQcyDdD/3Rddm77/V7j9f3V4/Pfw/Qnj+8+ZZlLQcwxpQD7yb+fynQDTgbONMYMww4Bpjd7PJHAGOMMYclPs8DRgMftmNmEelgKrgi4qacSDR+eSwev3bGgjWBxyZ/nb1yXYe82y1tJFK+nDX/+3tusPcguk/80Q2hQSOv9fgDf/Z4fffRPnN0w80+jyadHoizQ9tdwGSgDNin2fl9wNWWZb0AYIzpSQdNsRCRjqOCKyJuCMRi8Z9HYvEb5nyzLuu/kxdkLyurcTuT7ILw2qWUPXtzbrDPYLof+uO/hgbu+SePP3CNx+t7CIh3UIxxwELLsm43xmQDfwGWNzvPe8BPjTGvAlnARzhTHqZ2UEYR6QAquCLS0Q6sb4z8t3TFhj4PvDw/Z/GqarfzSBsKr1lC2dN/zQ32Lcrtddyl/xfo1ucKb1bOuWw5VaA9vAXsbYz5Cuegs2nAqGbnuRcYCszCeQ58xLKsqR2QTUQ6kNbBFZGO0rO+MXJHNBo/9Z7n52Z/PHeV23k6VDqug7vrPOTvfZjd48jzG/F4/usNZl8DVLmdSkQyn0ZwRaS9eWNx+4JIJHb7OyVLQ4+/afkbmqLbvpRkAJuaOe966r75LLvH4eedl7vn+DM8geBlHo/3KbQFsIi0IxVcEWlPo+saIv8tq6g1dz47J3vRSg3edUbxhlrWvXZ3qHrmW6FeJ15xvz+v2y+9oZzzgQVuZxORzKR1cEWkPeQ2NkXvqGuIlDzy2pd7//pfH6jcCk2rvmXF/b/KrZz21Nh4uGFGPNx4CxB0O5eIZB6N4IpIW9uvvjHy8hcLynrd/9L8YFVt81WdpFOz41R/8Ya3bsEn2b1OvPyy0ABzsjcr51TgK7ejiUjm0AiuiLQVX1M4el1dQ/jjfz87u/8/H5+pciutitVtoOzpv+ZUvPPoHvFw4+d2NPJLnJUPRER2mUZwRaQtDKypa3xl2ZqaEbc9MSu4bkN7rO8vmahm9jvehqXzc/r84Lc3Bbr2/r43K+c0YK3buUQkvWkEV0R21Qn1jZEFL0wt3esP93yicis7LLq+jJUP/Ta3esaUA+Lhxq+BQ93OJCLpTQVXRHZWoKau8d+VVfXP//nBT3Ofe2+hN66Fn2Rn2XEq3388uOa5W7rFGmpfj0eabsLZVldEZIep4IrIzuhbVdMw21q6/uIrbpsW/Gpxpdt5JEM0LJ7Divt+mR0uW/TLeFP9B0A3tzOJSPpRwRWRHRKJRveqrWv86tWPFg//y8Mlgeo6HUgmbStWt4FVj/0pt2betP3i4YbZQJHbmUQkvajgish2q1xfdWJTOFZyz/Pzuj7zzrde7fQt7caOUzHlwazK9x4bEI80zQQOcjuSiKQPFVwR2S5layt+6/UHX/jzQ59lfThnldtxpJOonjHFu+a5WwriTQ1T7HjsXLfziEh6UMEVkW3xrSorfzRG4Obf/vsj/9dL1rudRzqZhkWzWTnp99mx+ur/xCNN/0DPXSKyDfojISJbk1u2bv2H5TWxs39z50f+sop6t/NIJxUpX86KB67MiVSsvCzeVP8ykO12JhFJXSq4ItKixsamfuXraxbMW7Sh+PoHPvPXNUTcjiSdXLy+mlWT/pBbv2jO4fGmhhKgu9uZRCQ1qeCKyBZWrl4zqKEpOn/y9OX973x2ri8a09FkkhrsWIS1L9yaXTPn3WHxpobpQA+3M4lI6lHBFZHNfFoya49QKGfui9MWdX323W/1N0JSUsXbjwSrZ789OFFye7qdR0RSi568ROQ7b749bXhR0ZCZL3+4JO/FaYs8bucR2ZrKdx4NVs96a5BKrog0p4IrIgC8MeV9s9++Y0pe+2R57ovTFulvg6SFynf/G6ye8eZu8aaGT4FebucRkdSgJzER4Y0p75txY/f9/LVPlue+MLVUfxckrVS+/3iwasbkgfFww2dAb7fziIj79EQm0sl9V26nq9xK+lr//hPBqpLXByRKbh+384iIu/RkJtKJvfL6O0PHjd3389enL8994X2VW0lv66c9Faj67LX+8aaGT4AubucREffoCU2kk7rh5jsK99ln7+lTSlbmPq9yKxli/QdPB2rnf9A/3lT/BhBwO4+IuENPaiKd0OEn/LjLD045/mNrRV3XZ97RUmCSWcqnPJjVuPLbMfGmhkcArQYi0gnpiU2kkyksKs6+5spLp3iC+bvd/dxcn9t5RNqcHWfNc7fkRGsqTolHwn90O46IdDwVXJFOpLCo2HfbzX98ZNSo0fvdNGmGXzuUSaayI42sfuKGXDvc8Hvbjp/pdh4R6VgquCKdRGFRsed3V15yw/HHHvHDGx4q8dc2RNyOJNKuYrXrWf3kn7PtSNNDwAS384hIx1HBFekkfnbBWef85Nwzf/f3x2b6VpfXuR1HpEOE1y5lzfO35sQjTW8Ae7idR0Q6hgquSCdw+W+uP/Dyi39y70OvLvB9uajC7TgiHaph0Wwq3n4kLx5umAp0dTuPiLQ/FVyRDFd8yCm7XXHphS++P6ss6/0ZK3REuXRKNbPe9tbO/7BnvKn+CbSygkjGU8EVyWCFRcVdrr3miucaYsGuT0yx9HiXTq3irYezorUbDrFj0UvcziIi7UtPeCIZqrCoOPjTn5z5r4MmHLDPbU/O9ttaMEE6OTsWYc2zf8u1Y9FbgdFu5xGR9qOCK5KBCouKPcVj977oyit+dvatT8z2b6htcjuSSEqIVK6i/M0HsuNNDa8CuW7nEZH2oYIrkoFCoax9b7r+mhsmf7rcP6+03O04Iimldt5UT33pzN7xpvr73M4iIu1DBVckwxQWFfe+9reX3xXI7qJteEVase71e7LjTfWn2nb8LLeziEjb05OfSAYpLCoOHHXYQX8644cnjb31ydmBeFwTb0VaYocbKfvf33PsaOQBoMjtPCLGFRAEAAAgAElEQVTStlRwRTJIr57dT7nphmvOfejVBb41lfVuxxFJaeGyxVS+/0Qo3lT/EuBzO4+ItB0VXJEMUVhUPPS63/3y+hUV4dB7XyzXOp8i26H68zd8kcrVu9vx2KVuZxGRtqOCK5IBCouKc8fvv+/vjz3qUHPvi18G3M4jkj5s1r5yZ64di/4d6Od2GhFpGyq4ImmusKjY4/V6z7zx+qtPeOqtb3yV1Y1uRxJJK5HyFVR//npQqyqIZA4VXJH0Zy658OzzcvK6dX39kyWamiCyE9Z/+L9APNx4GHCs21lEZNep4IqkscKi4mBhn14XX3HphcV3PzdPqyZIq75eV8/VUxYBsLCigV++XspVby7ins9WEW+2zV1NU5Tr3l3CbyYv4s/vLWVDQxSAJ+as5ddvlPL0vLUAxOI2f526jFgG/N7Z0TDrXrs7Jx5ufATIcTuPiOwaFVyR9Hb4TTdcfVzJV2t91rL1bmeRFPW/+eu4Y/pKwjGniN756UouHteXW48ZQm7Qx9TFVZud/+l56xjZO4fbjh3CScN7MGnWGgBmra7l9uOKmLGyFoA3vqnk6KHd8Hkz442DhkWzaVg0Oz8eafqL21lEZNeo4IqkqcKi4l6HHfy9nx34veIhj76xwO92HkldffOD/HHibt99Xl4XZURvZ5ByRO8cvlxbt9n5l1U1MbZf/hZf93s9RGJxvB4PdeEYX62tZ1z//A66FR2j/M0HcrDjPwdGuZ1FRHaeCq5IGiosKvYEA4Gzb7zhmkMeeX2Br6Y+4nYkSWEHDuqCP2mUtTA/wNwyp7R+tryGxmh8s/MXdQvx2YoaAD5dXkNT1Bn5PXF4D26atpxTR/Tg2fnr+MHInjw0o4x7PlvF+sQ0hnQXq9tA5buPZcWb6v8LZMbQtEgnpIIrkp72uuLS88+I2ME8rXkrO+rK8QN4dv46/vTuErqGfBRkbf4GwOmje7GmNszv31rMuvoIvXKdlecm7FbADYcNYnDXEPWROFWNUbqE/BxR1I2Xv65w46a0i+pZb3tj9TVD0QFnImlLBVckzRQWFWfn5mRfeOF5Z+7z4CtfBez0P75HOljJyhp+Pb4/fzl8MNVNMfbpm7fZ1+evqefwoq787ajdKcwLfDedYaOn5q3lrNG9aIzZ+Dzg8UBjZPNR4LRmx6l499G8eFP9v9DzpEha0gNXJP0c+8vLLhi7eHWt11qqA8tkx/XLD/Knd5dy5eRScoI+igc482j/8PZiIrE4AwqCPDprDVdOLmXakirOHN3ru8suWFdP79wg3XMC7Ns3l89W1HDPZ6s4emg3t25Ou6i3PiNaU9EX+L7bWURkx3lsDf+IpI3CouIeubk5t33x4Wun3/jIjOxvl29wO5Jsp1svH0/PxVOomv6i21FkO2UPGUOf7/9mmTcrZwgQczuPiGw/jeCKpJdjrvrFT4ctXFHtV7kVaV8Ni2YTqVzdHTjb7SwismNUcEXSRGFRca/8vNwjzz7j1H2emPJNwO08Ip1BxduT8uLhhn8CesyJpBEVXJH0cezVv75k+IIl632lK6u2fW4R2WWNy7+iqWxxrh2PX+h2FhHZfiq4ImmgsKi4d5eC/CNO/8GJYzR6K9KxKt99NM+Ohm8CQm5nEZHto4Irkh6Ov+bKS4bNX1TpXbK62u0sIp1K06qFNK60smzbPs/tLCKyfVRwRVJcYVFxYSiUdcgPTjl+zFNvafRWxA0bPnkx1w43/A7tbiaSFlRwRVLf8Recc/qA5WtqPUvLatzOItIpNS6ZR6yxridwsNtZRGTbVHBFUlhhUXFf4MCzzjh15OufLA26nUekM6ua/lJurLH2GrdziMi2qeCKpLaDx++/b16vnj16fjp/tdtZRDq1mnlTPR5f4FBgoNtZRGTrVHBFUlRhUXE2cOjll/xkjzenL/VGY9p1UMRNdriRmrnve+KRpsvcziIiW6eCK5K69unWtSB3/3FjRk35bKkeqyIpoLrk9Szg52jJMJGUpidNkRRUWFTsAY694pLz+84vLbfLNzS6HUlEgEjlKppWlwKc5nYWEWmdCq5Iatrd4/EMOPnEY/d9/eOlWhpMJIVUffpyfqyx7vdu5xCR1qngiqSmiccffWg3nz+YN/vbdW5nEZEk9QtnQjw2CBjtdhYRaZkKrkiKKSwqLgDG/+TcM4omT1/qt3VsmUhqsePUzJ0aiEfC57gdRURapoIrknrGhUJZgTGjR5gPZ6/SrkkiKah2/gcB7Nj5aGczkZSkgiuSQgqLir3AsWefdlLuirU18XUbGtyOJCItCK9ZTLyxLhs4wO0sIrIlFVyR1DII6H7i8UcP/2D2au1cJpKigr0HEakqz41GIjrYTCQF+d0OICKb2TcUyrL3GmWG3fvGVL31KZJCgr0HkTNiAvmjJ+LNymF5eYNdGPcc7HcGi+Ju5xORTVRwRVJEYnrCwWf98MS8FWtq4pXVWvtWxG3B3oPIHTHBzhs90ePJymHx6hr70deXet77YjmA9+HrjvKFsvxjgRKXo4pIEhVckdSxO5B/7NGHDZ0+f42mJ4i4ZMtSW8uk15eSKLWbvbMybeaK0IkHDTktK+BTwRVJISq4IqljH4/HE9tr1J57PvWfTzU9QaQD7UipTfbxnFX+48YPPpuA72pAi/qJpAgVXJEUkNiad/yxR00MNjTFvCvW1rodSSTj7WypTbZwxQaiMbsrMBT4pj3zisj2U8EVSQ39ga4nH3/k4Onz1+hxKdJOtii1ZTteapub8+06+6Ax/SeigiuSMvREKpIaRgL2nnsOH/rku8u1fJ9IG2qPUptsprU2d8ywXsfn5wTv3/W0ItIWVHBFUsOE7OxQzW79+/T+eskct7OIpL32LrXJ5peWE/B5D05cr+bhiqQAFVwRlxUWFRcAA487amKsrKI2WtcY9bmdSSQddWSpTVZWUU84Gg+GstgD+La9vo+IbD8VXBH37QbED55QPGh+aYUekyI7oKVS++jrS3m3nUttc/MWrrMn7N3/EFRwRVKCnkxF3LcHYI8YMWLoG59XaPRWZBtSpdQmm2mty917aK/j83KCD7rx/UVkcyq4Iu7b2+fzVg8Z1L/fl8/oIGyRlqRiqU02r7Qcn+bhiqQMFVwRFxUWFYeAQRMPOqCxtiFsr69pcjuSSMoI9h5E7p4T7Ly9UrPUJltdXkcsFs8GhgClbucR6exUcEXctRtgHz5xwm7zF1Wk1BO2iBvSqdQ2N3dheXz8Xv0OQQVXxHUquCLu2h3w7DV61B6fWOsDbocRcUM6l9pkXy2uzN3H9C7OzvI/7HYWkc5OBVfEXXsBNQP6F/b99p3ZbmcR6TDfldrRh3i8oVwWpWmpTbZsTTWRaHxsdpbbSUREBVfEJYVFxX5gaCiUta5Ht4K8letq3Y4k0q6al9rFZbU8+saytC61yZavqSEY8O7hdg4RUcEVcVNfwLvfPqML1lfVRyPReNDtQCJtLdNLbbLyDY14PZ5soDtQ6XYekc5MBVfEPb0Azz57jei5fG2NlhWSjNGZSm1zqyvqGgYVFowAPnI7i0hnpoIr4p5CgGF77N5z2Zo6PRYlrQV67UbeiAM7ZalNtmhlVWBQYcFIVHBFXKUnVRH3DAYaBg4c2PeLxXXawUzSTkul9r+Tl/PO58ugE5XaZItWVuV8b1TfMaEsPb2KuEmPQBH3DATqC/v07r3iM21fL+lBpXbrlpXVEI7Gx4a0koKIq1RwRVyQWEGht8fjWdG7V7euK9ZqBQVJXSq122/ZmmqCAe9Qt3OIdHYquCLu6A5QtPtuObaNp7ou7HYekc0Eeu1G7ogJdv7oiSq1O6B8QyM+rycHKACq3c4j0lmp4Iq4oydgj91ndI9V62pi6LEoKaClUvuYSu0Oq22INHXL9/VGBVfENXpSFXFHT8DXt2/vnMrqRrezSCeWKLXkj56ISm3bqK4Nx7rlh3oDC93OItJZqeCKuGMQ0NSjW7ee1XURr9thpHNpodTaj01e7lGpbRvraxo9g/oW9HY7h0hnpoIr4o5CoLFbty45VbURPQ6l3W0qtYfgDeWp1LajiqpGP6CCK+IiPbGKuKMLEOnSpUvespqwyoW0i+aldklZrf3Y5BUqte2ssroxZNt2b49Hd7GIW1RwRdyRD9Tl5efnV5dpBQVpOyq17ltf0+RtDMd2y9ZmDyKu0aNPpIMVFhV7gDxgQ35ebl51ndbAlV2jUptaqmqbiETjA7K12YOIa1RwRTpeFk7psHNzc3K0Bq7sjECvgeSOOFClNgVtqG0C7EK3c4h0Ziq4Ih0vB7ABcnOyQyq4sr1aKrWPv7nC83aJSm0qqappwuv19nI7h0hnpoIr0vGSCm4oq0YFV7ZCpTb91DVG8Hk9uW7nEOnMVHBFOl7OdydCQX99Y8TNLJKCVGrTWzRm4/Xo+VXETXoAinS8bMDj8/k8AHHb5TSSEgK9BpK75wTy95qoUpvmYrE4Ho/H53YOkc5MBVek4+UAnkDA74nF425nERe1Vmrf/WIZ8bhKbbqKxmwVXBGXqeCKdDw/4PH7fJ543LbR6Fyn4fGo1HYG0Vgcr1fPryJu0gNQpON5AE8gEPDatgpuZxG3oetBp9Nl/A9UajOcbYMe1iLuUsEV6XheEqsoSOdx86NfsN/wPrw/c7lKbYZzdujVa1cRN6nginQ8D+BpCofjXq82q+8sNtSGefeL5W7HkA6Q6Ld6ESviIq/bAUQ6IS9gNzWF416vVwVXJMN4vB5svUsj4ioVXJGOFwc8sVjM9no8aAxXJLMkHtIquCIuUsEV6XjfrQ0Wi8VtnwZxRTJKdpafaCze6HYOkc5MBVek48VJjO40RaKxUFBT4UUySX5OkFjMrnY7h0hnpoIr0vG+G8GtrWto6pKX5WYWEWlj+blBYnF7g9s5RDozFVyRjhcjMYJbV9dQ3yUv6HIcEWlL+TkBwC53O4dIZ6aCK9LxGkgU3JraurqCXI3gimSSvJwgXo9nnds5RDozFVyRjldHouDW1tbWagRXJLPk5wQJBHyr3M4h0pmp4Ip0vPqNJ6qrq6sLclVwRTJJQW4wlhXwrXU7h0hnpoIr0vHqSCyVuX7DhtouuYGYy3lEpA11zctqAirdziHSmanginS8ehKPvYqK9fVd84IquCIZpGt+VhSocDuHSGemgivSwcpKSyJAGPCtLa+o65IX1I5HIhmka36WjQquiKtUcEXcUQ0EVq9eW9dV6+CKZJTC7jkBYKHbOUQ6MxVcEXdUAYFZc79aX9gzP+B2GBFpG9lZfkJZ/gCwwu0sIp2ZCq6IOzYAwRUrVzeGI9FIz64ht/OISBvo1yuXxqboSpJ2LBSRjqeCK+KOdUAWQNnaisqBvfNdjiMibWFArzziNl+7nUOks1PBFXHHciAAsHp1WdmAPiq4Ipmgf++8eE7IP9PtHCKdnQquiDvWATGAJUuXlu3WJzfqch4RaQO79+1S5/d5F7idQ6SzU8EVccc6Eps9LPh64bpBffK0Fq5IBhhYmG8D37idQ6SzU8EVcUcVEAV8n30xe92APgU+twOJyK7r3S07G7DcziHS2angirigrLTExllGKOfrbxbV+nxeCnKDbscSkV3QvSBELG434LyAFREXqeCKuGcZkGvbNmvWVW4Y0DvP7TwisguG9O9COBLX6K1IClDBFXHPYhJLha1cuXpVUf+uLscRkV0xakiPaHaWf4rbOUREBVfETd+tpDBr9tzSUUO6hl3OIyK7YIzpVRfwe6e5nUNEVHBF3FROYiWFyW9NXTqyqKcejyJpyu/zslufghzgM7eziIgKroibKoBGIDBzzpdVsWgs0r+X5uGKpKM9BnahMRxdCtS4nUVEVHBFXFNWWhIHvgS6Any7aMmSkUN6uBtKRHbKiN172H6f9123c4iIQwVXxF1zgGyAOXPmLRxd1C3ich4R2QljhvWqzc7yv+d2DhFxqOCKuGspYANMeffDpaOLerocR0R2xvBB3QPAx27nEBGHCq6Iu1YDEcD/yaczKgJ+b7x3t2y3M4nIDhjQOw9saoCVbmcREYcKroiLykpLYsACoItt2yxctGz5yCEaxRVJJ6OG9CBm2x+6nUNENlHBFXHfXCAXYO68+d/svUd3zcMVSSMT9u5Xk5cdeM7tHCKyiQquiPsWk5iH+8LLby4cO6KPx+txOZGIbJesgI8Ru/fIAt50O4uIbKKCK+K+lTg7mvlKZsxZX1tbX2sGdXc7k4hsh72G9qQpHJsHrHc7i4hsooIr4rKy0pII8DWJ9XBnzJoz93uj+sTcTSUi22PCXv0acrMDT7qdQ0Q2p4Irkho+AfIAXnr1zQXj9+qrgiuS4rweOGBUX7xez8tuZxGRzangiqSGrxP/eia/Na0s4CM6uG+Bq4FEZOtGDumJ7UwxKnU7i4hsTgVXJAWUlZZsABYBBbZt8/mMOXMOGtMv7nYuEWndIfv2bwwFfZPcziEiW1LBFUkdHwFdAJ55/pW5E/ftr2kKIinK64ED9+5v+33eZ9zOIiJbUsEVSR3zAQ/A5LemlWHHmoYO7OpyJBFpSWJ6wipgodtZRGRLKrgiKaKstGQdsJTErmaflcycdfCYfhrFFUlBx00YXJ8d9P3H7Rwi0jIVXJHUMpXENIVHHnt29mFjB9oBvx6mIqmkIDdI8YhCr8/nfcTtLCLSMj1ziqSWuTjTFDyffDajctXqNasP3Lu/25lEJMnh4wbGI7H4q0Cl21lEpGUquCIppKy0pBKwgG4Az7346scnHjgo7G4qEdnI44GTDy5qyA0Fbnc7i4i0TgVXJPW8BRQA3Pfwk9/0KAhG9xigg81EUsFee/QiFPSvAT51O4uItE4FVyT1zANqgFAkErXfnfrRpydMGBR1O5SIwEkHD6nLDvn/CdhuZxGR1qngiqSYstKSCDAF6A3wr7semvG90X3Jzwm4G0ykk+teEGLM0F5er8fzhNtZRGTrVHBFUtP0xL/e0sXL6ud99e03R4wbqBEjERcdWbxbLBqLP4vzDouIpDAVXJEUVFZaUgF8QWIU99HHn/nk+AmDox6Pu7lEOiuf18OJBw1pygkF7nA7i4hsmwquSOp6F8gCePHVt1aGmxqq9xvex+VIIp3TxP0G2H6f90tglttZRGTbVHBFUte3wBog37Zt/vfCq1PPPnKolgwT6WA+r4dzjxtRn5sduMrtLCKyfVRwRVJUWWlJHHgD6A5w250PfpkX8tSP3VOjuCId6dD9BthZAd+XwAduZxGR7aOCK5LavgCagKxYLGZPevyZKeceazSKK9JBfF4P5xw3okGjtyLpRQVXJIWVlZbUA68ChQB3/mfS135PtOZ7o/u6G0ykk0iM3s4DPnQ7i4hsPxVckdQ3FWgEQrZt89CkJ6acc8ywsDfDVlSw4zFWz3ySZR/fzfJP7iFcu5ZwXTnLPr6H5Z/cw5p5L2Db8RYvW7N6PqtnPvnd51XLSlj20V2smffid/+3euaTxCKN7X47JHNo9FYkfangiqS4stKSOuBFoA/AfQ8/9W0s0rjhwDH93Q3WxurWfo1tx9ltwmV0H3oE5V+/ybqvXqWnOZqB438Otk1t2VdbXG7t/Jcp/3oyyRtLVa+YwcAJPyfaWEUsXE/tmgVkd98dXyDUgbdI0t2hYwfaWQHfXOAjt7OIyI5RwRVJDx8CdUC2bdvc9+B/p/z46GERbwYN4wZye4Idx7bjxKNNeLw+GjesJLvHEAByew+nvvzbLS6X3X0QfUafutn/eXxB7HgUOx4Dj4fq5Z/TZbfiDrkdkhl8Xg/nHrunRm9F0pQKrkgaKCstaQBeILHxw6Qnnl9UV1tdPnHfAe4Ga0NefxaR+kqWTL2VNXOfo+vgCYCNJ7G7hdefRTy65RSD/H5jgM2Lfo+hh7F65pPk9x1FzcpZFAwcR2XpNNbMe4Fw7doOuDWS7o4+YJAdDPjmAB+7nUVEdpwKrkj6+BioBnJs2+bu+x5588fHDItkBXxu52oT6xd9SE4vw+6HXs2gg39N2exnnBHYhHi0Ca8/e7uuK7v77vQfdz55ffemoXIxwdweRBur6WmOpuKbd9rrJkiG6JIX5LzjRzTmZgcudjuLiOwcFVyRNFFWWtIEPAf0Anjqf68uW7Z0+aLTjxga2/ol04MvkP3dHFlfIAfbjhPq0p/68lLAmaOb02PwDl1n5cL36VZ0KPFY5LuR4HhMq6zJ1v3khBFNHo/nIWCe21lEZOeo4Iqkl8+A9UAewFV/+Mtrx35vUHxgn3x3U7WBbkMOorFqJcs/uYcVn95Hz+HH0HvUKVR88xbLProL246R13cvAFZ8+gB2PLrV64vUVxKPNhDq0o+sgr5EGjawsuRhug4e3xE3R9LUnoO7M36v/vXZWf5r3c4iIjvPY9v2ts8lIimjsKi4GLgcWATw979cUzzugIMO/90904PuJhNJb16vh7uvmtgwoE/BBcDTbucRkZ2nEVyR9PMFzlunfQCuu/G2z/NDVB+23wC9WhXZBceNH2x375I9D3jG7SwismtUcEXSTFlpSRx4HMgCApFI1L7xb7e/cMGJI2L5OQGX04mkp275WZxz7J5NOaHA+SQvqiwiaUkFVyQNlZWWrMbZ/KEfwKuT3109Y9bc2ecfv+fWJ6aKSIsuOmlkk9fruQdY4HYWEdl1Krgi6estoBzoAnDlNX9+p3hE7+ieg7u7m0okzYwu6sm4kX1rQ0H/9W5nEZG2oYIrkqYSy4Y9AnQHPGVry5seeOSJV684ba+I36eHtsj2yAn5uerH+zZmZ/nPA2rdziMibUPPgiLpbQHwCdAX4P/+/dBXlRVrl5577PCMWBtXpL1dcuqoSHbQ/xTwuttZRKTtqOCKpLGy0hIbeBbnoJiQbdtcfPnVLxy6X7/w3kN7uZxOJLWN36svxSP7lmeHAle4nUVE2pYKrkiaKystWQ88SeKAsyXLVjb847a7n7vyrDERraog0rLuBSGuOG3vptzs4KlAndt5RKRtqeCKZIYPgZlAf4BHHn9u0RczZs385Rl7R9yNJZJ6vB64+px9wwG/7+84uwOKSIZRwRXJAIm1cScBTSS28f35r659u2+3QNVJB+2uNT1Fkpx+xND4wN75c7OC/hvdziIi7UMFVyRDlJWWbADuA3oBvtq6+tjPf/WHJ848cmhk2G7dXE4nkhpGDenBqYfsUV+QFzoJ0MGYIhlKBVckg5SVlswDpgADAL6YOXfDXfdOevF35+4bycvWfFzp3LrmZ3H1OfuFc7KDpwGr3c4jIu1HBVck8zwHLAV6A9xxzyNfz5335ZyrfrRPxOtxN5iIW4J+L9dfMC4S8HtuBd50O4+ItC8VXJEMU1ZaEgb+A/iAHIALL71qcn4wsuaik0bqLVnpdDwe+PVZY6LdCrLeyssJ/dHtPCLS/lRwRTJQWWnJGuB+oBDwNTY2xc86/7Inxu3Zo/aEA3XQmXQuZx05LD58UNfFPbrm/RBnzWgRyXAquCKZaybwBjAQ8Kxctabxwkt/M+nMI/ZoKh5Z6HI0kY5xyD797ePGD6rp2S3/QKDR7Twi0jFUcEUyVGKXs+eAGSQOOpsxa/6Ga/548+O/OmPvSNGALq7mE2lvwwd345JTR4WDfu9BwFq384hIx1HBFclgZaUlUeBBYBnOdAVeeu2tlffcP+nFP11QHO3ZNeRqPpH20rtbNteeNzYSCTednp0dmud2HhHpWCq4IhmurLSkAfg30AB0B7j9rocXvPXO+1NvuGj/SHaW39V8Im0tJ+Tn+ouKo+Gmxuu7devyitt5RKTjeWxb8+1FOoPCouKBwHVANVDr8Xh49rH/nJzbre/IGx/+PBCN6W+BpL/sLD9//mlxtGuO95m+fXqcgw4qE+mUNIIr0kmUlZYsB+4AegJZtm3z4wt+8WrArl9+zTn7Rfw+LZIr6S0U9HHDheOiOYHY63379DgXlVuRTksFV6QTKSst+RJ4BOgP+JrC4fjJp1/wZK6vYfnvzx0bVcmVdJUV9HH9heOiPrth2qABhd8H4m5nEhH3qOCKdD4fAK8CgwBvfX1D7OQzLnwy5K1b+vvzxkb8Pv1ZkPSSFfBx/QXjop5Y3afffLPwKFRuRTo9zcEV6YQKi4q9wFnA0cASIJ6Tk+178ekHzwx7cgfdPGlGIBpTR5DUlxXwcd0FY6NBGkrWr688ePz++2m3PhFRwRXprBIl90zgGBIlNxTK8r70zENnRr35g29+9ItAJKqSK6kr6Pdy3QXjolmexi/Wr684UOVWRDbSe5EinVRZaUkceBqYDAwmsaXvKWdc+LQ/Vr3k2vPHRgJ+/YmQ1JQV8HHt+WNjIW/jLJVbEWlOz14inVii5D4DvI4zJ9cpuWde9LQnUrXougvGRUJBn7shRZrpmpfFzZceEA15G7/4dmHpBJVbEWlOBVekk0uU3P8Br5FUck8946Jn6zesXvDPKyZEtOOZpIoBvfO49YoJsbKVSybf/u/7x5947OERtzOJSOrRHFwRAaCwqNgDnAaciDMnN+bxeLj9H3+acNQRhx5y48OfBxau2OBqRuncRhf15Opz9om9+dZ7D//q6r9cknhxJiKyBRVcEflOouR+HzgZWAGEAX5x6fnDr7j0gu/f+eycwKfzy9yMKJ3UofsNsC88cc/YY0/+7y9/veXum1RuRWRrVHBFZDOJknsI8BNgHVALcPwxh/W99eY/nvPKh0uynnu/VNObpMOcddSw+JFj+4XvffC/F153zRVPup1HRFKfCq6ItKiwqHgk8AucUdwKgFEjhuVPuv/2875eXtfl7ufm+qMx/f2Q9uP3efjFaXtHB/UJ1Nxz36STbr3pDx+5nUlE0oMKroi0qrCouD/wa6AAWAXQs0f34FOT7jrDm1Uw8OZHZwSq68KuZpTM1LNriGt+vG+0obZyxT9vv+fwJx++Y5HbmUQkfajgishWFRYVdwEuA4YCywA7EHQC+2YAAA1SSURBVPB77r/rH0cVj913v1senxn4anGluyElo+w3vDe/OmPv2NvvffDezf+864xZH7+23u1MIpJeVHBFZJsKi4qzgHOBg3BKbhTg0ot+NPTXV/zsBy9/sCjw3HsLvXH9OZFd4PV6+NHRJnb4fv2it91x790PP/a/a8tKSxrdziUi6UcFV0S2S2Jr3+NxlhJbA9QBjB5pCu698+9n1ob9PW97clZgfU2TmzElTfXuls1vf7RPJFy/Yf21N/zjms9nzn2srLREGziIyE5RwRWRHVJYVLw3cAngAcoAsoJB77//78aJhxx4wPfuem6e/9P5q13NKOnl4H36c/EpI2PPv/R6yY1/v/PiRfM/mOd2JhFJbyq4IrLDCouKewI/AwywnMSUhR+ecuyAG679zRmzvq0MPfDyl/6GpqibMSXF5YT8XHrqqOiwgXlNN/z1tkmvvPHO9WWlJRVu5xKR9KeCKyI7pbCo2A+chLMpRCVQBdC7V4/gvXf+7YSioqLhdz47NzB3YbmbMSVFHTimHz89aWT08y9mLbn2z7dct2Ll6ufLSku07a6ItAkVXBHZJYVFxcOBi4F8YCVgA1z2s3OGXXbx+SfPX7Q+8OArX2lurgBQ2COHn39/dKRnga/xplvumPzSq2/9qay0xHI7l4hkFhVcEdllhUXFecDZwIE483LrAbp17RK49W9/nHjwhP2Ln377G99rHy/xxLXUQqfk93n4/sSi+KkTi+LPPP/qvJtuueuh+vqG/5aVltS4nU1EMo8KrkgaMcY8DEwErrUs66k2vu5JwFTLsibtzOUTW/yOA84HgjgbQ8QBDp5Q3POv1199clZ2QZ+7n58XWLBE6+Z2JiOH9ODyH46OlK8tK7/mupvfmjNvwb+BmWWlJXoCEpF2oYIrkkaMMXEgZFlWm28ftqsFd6PExhCn4hTxapz5uXg8nv9v795jo6ryOIB/586rj5lO6Wum0JY603oB5VGQASoPFXBRWNT1hQsRRde3rG7UZNddo4kmxkSTzW5ifCzsaiKurBG0QSxbFrcCMvJody3MbR1g+pyZttPHdIbSzmP/OBfs7hpSFGln+H6Sk7nNbe+cTNPJt2d+9/zw9BMPXLnh7jUrD7o7dZurjunYBS21ZWUacO+qqdE5cl7s1d+/sX/Tu1vfisfjH/s8roGxnhsRpTYGXKIkIcvyxwB+CqAewGsAngAgATgE4FFFUQZlWfYB2AZgHkSpwCYAGwEUAbhHUZTPZVleAuAlABkAsgE8qSjK9pEBV5blu7/r+uczX5vDWQZgPYBiAB0ATgOArSDP+OrLzy2dO2dWxZZdjdrPvvRqhqPx7//C0LiTkabDTYvt8dWL7PE9tfs9zz7/yjZ/oOtNn8fFdrtEdFFIYz0BIhodRVFWq4drAfwCQKWiKLMABAA8pZ6zAvhUUZQKAGkAblEUZRGA5yECKwA8DuB+RVFmA7gfwIsjn0eW5SvOcf1R83lc3wB4AcA7AHIATAKg8QW6Tq/dsHHHY7969u2rp5q8b/9m6fANlaUJnZZvR8nOaNDi1msd8bd+vTRaZD518ra1D/7t/keeedQf6Potwy0RXUy6sZ4AEZ23awGUA/hSlmVA1LseHnH+U/XRC+CLEccT1ON1AFbJsnw7gPkATOd5/VHzeVxRALttDmcdRAe0SgA9AHqra2r91TW1f7l51fWTNj5y3/I7l1438f1dTbq/f9Wsicb4yVIy0eskrJg/OXHHsvJYU9Px1nUbHq9zHaqvAfC+z+PiPnFEdNEx4BIlHy2ADxRF2QgAsiybMOJv+X/qc7+r00ItgH8A2AOgBsB753P978PncQVtDuebAD6HKFsoBdANILStqrptW1X1n2+9aUXRYw9vWH7nsvLCLbsa9TVftSDGHRfGNa2kwbK5JYm7ri+PtrS2+R549Om6f+511QH4AEADbyIjorHCgEuUfPYAeEqW5RcBdAJ4HYAHogzhnGRZzgFwOYBFEDWxL0ME2gty/XNRw47b5nD+DsAsAHdgRND9cPvO1g+379x8+y03Fj/20L3L1ywrt723q0m/51ArojHW6I4n6UYdrruqKHHLEnu0u6ur88lnnjtcXVP7NYC/AviXz+PiL4yIxhQDLlGSURSlXpblFwDshqijr4MIqqP52aAsy38C0ABgWL1GhizLmRfi+qOhli0cVMsW5gC4DSOC7taPdrRs/WjHpjW3rip56IF7lt+zcopt14EWaeeXXskfjFyoadD3UGw1Y2Xl5Og1s4twrNHT/OxzLylVO3c3QATbIz6PKzbWcyQiAriLAhGNMbXl71UQQTcPQBeAs9tILaqcm/vgfevmzXdWzGr09iSq9nkNh475weqFi0OSNHBOs2H1wslDJTZzYveevUde+8NbzU2ek+0QpQgH2WKXiMYbBlwiGhdsDqce3wbdXIzYQxcALBaz7pcP33vljSuWV5rNpuwd+7zaalez1MsWwD8Ki8mA650l8ZVXl8b6+vqD72/9qP7NzVt8g4OngxBb0e33eVzcyJiIxiUGXCIaV9SgOwPACgBlEDfKBSBKKgAAN/7kGtt96++aXzFj2hVHGjsTNQdb9UeUTtbq/kBGgxZzp1lxTcXEoemOPOlwfYP7j69vbtzzxYF+AMcA7ARwVC0zISIatxhwiWhcUlv/TgKwEGLrMgOAXgB9Z76n0FZgfPyh9dMXL6ycU2jLz3c1dMRr6zr0RxoZdkfLqNeiQs7H4lmFw3Om2KSTLe3tn+3a3bDpnQ+6O7uCEYidNmp9Hlf7WM+ViGi0GHCJaNyzOZzpACoA3ADRlW0IYlX37E1N06aUm9ev/dnUhZULZtuseXmH3f74gQa//qA7gPAploiOlJmuh3OaFZXTrUMzywu0Le0Bf23t3vo3Nm0JNLe2JwD4AVQBOOzzuHhnHxElHQZcIkoa6qruZQCWQDSN0AIYhNiB4WzYlcvtpp/fsbq8csH8GWX2omJPS0/0oLvLcPREUNPU0nvJre6mGbSYWpqDK+258ellOcOlEy26Jk9zc+0X+/+96d2tXW3tPgmiBMQFsU9yE7f6IqJkxoBLRElJXdWdAhF0KyDC7imIG9PO1ohOyLbo16252b5g3lVl5WV2e35udvbxtp7hhuM9+oYTQcl9MojwYGqVlGak6USgdeTGZ5blDJfYLLrWjs4ut7uxqXafq3nbJ9WR3r5+LYA4gCMA9gJw+zyuwbGdORHRhcGAS0RJz+ZwZkCEXSdE2NVDrOh2Q6zwnlWQn2tYteK6ogXz5pTK8uVlJZOsBf7gQPRrT1B7zNura/GH0NY5gNNDybGlqyldj2KrGcVWMybbTLHp9pzoxAKzztvqD7jdSlPtPteJ7VW7gv2hATPEPwHDEK2XXQAUliAQUSpiwCWilGJzOA0A7ABmApgPwKKeikLcoBYe+f1paUZpxbIlhYsXOkumyLLdas3Pz8/JMofCp2NtgVC82T+ga+2MaNs6B9AaCKG7bxBj8baZbTKiyGpCidWMYqspWmozxYqtWVqjQavxBXp6Onx+f3Nzc/v+A4dbP/m0pi8cOWWCaNShgehIVwegHsA33N6LiFIdAy4RpSy1ZjcHQAlEi+IZAArV0wmIvXZDEB/Vn6XX6zQzp0+zzJ51RZ5cdlleSUmJzWYtsBbk50wwGvT6YN+paChyOt4fHtL0h4c1/ZFhXX94WApFhhAKD6E/MoSByDCGo3HEEwkkEgkkEkA8kYBW0kCnlSCpj3qthKxMAyxmIyyZBlhMhni2yRDNNhkS2SYjLGajZM5I0w1Fo1F/INjT3uHzeb3ejqPubzpdh+o7vz7aGEokEiYAWfi27XIHRJh1A/D6PK4+EBFdQhhwieiSYnM4TRCB1wEReO0QYVejjkEAEXX8341WhbYC45TL7Vk2a356fl5uRm5Odnq2JSvdYrFkms1ms8lkMmVmZGRkZqalS5JW0migkTQaaDQajSRpNLF4IhGLxeLxeCIei8Xj0Vg0Fg5HIv2hgVB/X19fb2/vQFd3T7izqzvS3hEIt7Z1hE94WyM9vX0xABnqSFPnfEYbRKBVIAJt6Ed7AYmIkgADLhFd0tSShnyINsH5ACarwwaxIpqA+Kg/BlG/OnJEMWL3hh9AgqgbNqiPZ47PPK9GnUc7AC+A4xBlB50Autl4gYjovzHgEhF9B5vDKUHU7+apoxDABIhSAAsAszr0ECu953oz1ZzjvBYiKPdD1Aj3QDS0CKpfd0ME2R6fx5Ucd74REY0xBlwioh9AbS2cASBdfTSqpzQjBiBCcBxiRTYO0awirI4hn8fFN2MioguEAZeIiIiIUoo01hMgIiIiIrqQGHCJiIiIKKUw4BIRERFRSmHAJSIiIqKUwoBLRERERCmFAZeIiIiIUgoDLhERERGlFAZcIiIiIkopDLhERERElFIYcImIiIgopTDgEhEREVFKYcAlIiIiopTCgEtEREREKYUBl4iIiIhSCgMuEREREaUUBlwiIiIiSikMuERERESUUhhwiYiIiCilMOASERERUUphwCUiIiKilMKAS0REREQp5T+Pm92jfXg7tAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "kiva_genders = kiva_loans['genders'].value_counts()\n",
+ "# print(kiva_genders)\n",
+ "\n",
+ "plt.figure(figsize=(12, 5))\n",
+ "\n",
+ "plt.pie(kiva_genders.values, labels=kiva_genders.index, shadow=True, autopct='%1.1f%%', startangle=90)\n",
+ "\n",
+ "plt.axis('equal')\n",
+ "plt.title('Loan Count Per gender', fontsize=30)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the above chart, it is clear that majority of the loans were given to **Females**, with a percentage of around **80%**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.6. Total Loan amounts per gender\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gender_loans = pd.pivot_table(kiva_loans, values=['funded_amount'], aggfunc=[np.sum], index=['genders'])\n",
+ "gender_loan_sums = gender_loans['sum']['funded_amount']\n",
+ "#print(gender_loan_sums)\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "\n",
+ "sns.barplot(x=gender_loan_sums.index, y=gender_loan_sums.values)\n",
+ "\n",
+ "for i, v in enumerate(gender_loan_sums.values):\n",
+ " plt.text(i, (0.1e8), v, color='k', fontsize=19)\n",
+ "\n",
+ "plt.title(\"Total loans per gender\", fontsize=30)\n",
+ "plt.xlabel(\"Gender\", fontsize=25)\n",
+ "plt.ylabel(\"Total Loan amount(USD)\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.7. Total Loan amounts per Country\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " funded_amount country world_region MPI \\\n",
+ "0 600.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "1 1000.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "2 250.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "4 800.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "5 800.0 Nicaragua Latin America and Caribbean 0.031 \n",
+ "\n",
+ " geo lat lon c_code \n",
+ "0 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "1 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "2 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "4 (28.3998551, 83.6895693) 28.399855 83.689569 NI \n",
+ "5 (28.3998551, 83.6895693) 28.399855 83.689569 NI "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# merge the two datasets\n",
+ "loan_regions = pd.merge(kiva_loans, mpi_region_locations).dropna()\n",
+ "\n",
+ "# print(loan_regions.head())\n",
+ "labels = [loan_regions['funded_amount'], loan_regions['country'], loan_regions['world_region'], loan_regions['MPI'], loan_regions['geo'], loan_regions['lat'], loan_regions['lon'], loan_regions['country_code']]\n",
+ "keys = ['funded_amount', 'country', 'world_region', 'MPI', 'geo', 'lat', 'lon', 'c_code']\n",
+ "countries = pd.concat(labels, ignore_index=True, axis=1)\n",
+ "countries.columns = keys\n",
+ "countries.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | funded_amount | \n", + "country | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "c_code | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "600.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 1 | \n", + "1000.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 2 | \n", + "250.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 4 | \n", + "800.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
| 5 | \n", + "800.0 | \n", + "Nicaragua | \n", + "Latin America and Caribbean | \n", + "0.031 | \n", + "(28.3998551, 83.6895693) | \n", + "28.399855 | \n", + "83.689569 | \n", + "NI | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "country_loans = pd.pivot_table(countries, index=['country'], aggfunc=[np.sum], values=['funded_amount'])\n",
+ "country_loan_sums = country_loans['sum'].sort_values(ascending=False, by=['funded_amount']).head(5)\n",
+ "# print(country_loan_sums.head())\n",
+ "funded_amts = country_loan_sums['funded_amount']\n",
+ "\n",
+ "plt.figure(figsize=(15, 8))\n",
+ "\n",
+ "sns.barplot(x=funded_amts.values, y=funded_amts.index)\n",
+ "\n",
+ "plt.title(\"Total loan amounts per Country(Top 5)\", fontsize=30)\n",
+ "plt.xlabel(\"Total Loan amount(USD)\", fontsize=25)\n",
+ "plt.ylabel(\"Country\", fontsize=25)\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Nicaragua** had the largest vamount of loans borrowed, followed by **Peru** then **Guatemala**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.8. Total Loans Distribution across the world\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "amts = country_loans['sum']['funded_amount'].dropna()\n",
+ "# states = pd.Series(countries['country'].unique()).sort_values()\n",
+ "country_totals = pd.DataFrame({\n",
+ " 'countries': amts.index,\n",
+ " 'loan_totals': amts.values\n",
+ "})\n",
+ "\n",
+ "# Chloropleth map\n",
+ "m = folium.Map()\n",
+ "\n",
+ "world_geo = '../data/geodata/world-countries.json'\n",
+ "\n",
+ "bins = list(pd.Series(amts.values).quantile([0, 0.25, 0.5, 0.75, 1]))\n",
+ "\n",
+ "folium.Choropleth(\n",
+ " geo_data=world_geo,\n",
+ " data=country_totals,\n",
+ " columns=['countries', 'loan_totals'],\n",
+ " key_on='feature.properties.name',\n",
+ " fill_color='YlOrRd',\n",
+ " highlight=True,\n",
+ " fill_opacity=0.7,\n",
+ " line_opacity=0.2,\n",
+ " bins=bins,\n",
+ " reset=True,\n",
+ " legend_name='Loan Totals',\n",
+ ").add_to(m)\n",
+ "\n",
+ "display(m)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/intro_to_ML.ipynb b/notebooks/intro_to_ML.ipynb
new file mode 100644
index 0000000..d377361
--- /dev/null
+++ b/notebooks/intro_to_ML.ipynb
@@ -0,0 +1,687 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Learning how to do data modelling with Scikit-Learn Algorithms"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import folium\n",
+ "\n",
+ "sns.set(color_codes=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ " iris = sns.load_dataset('iris')\n",
+ "iris = iris.dropna(axis=0)\n",
+ "iris.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | sepal_length | \n", + "sepal_width | \n", + "petal_length | \n", + "petal_width | \n", + "species | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 1 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 2 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "setosa | \n", + "
| 3 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "setosa | \n", + "
| 4 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(iris, hue=\"species\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Supervised Learning\n",
+ "## Classification\n",
+ "### K-Neighbors Algorithm\n",
+ "This algorithm uses labelled data to predict where other certain new labelled data belongs to."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split, cross_val_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction values: \n",
+ " ['virginica' 'versicolor' 'virginica' 'versicolor' 'virginica' 'virginica'\n",
+ " 'versicolor' 'versicolor' 'setosa' 'virginica' 'setosa' 'setosa'\n",
+ " 'virginica' 'virginica' 'setosa' 'virginica' 'versicolor' 'setosa'\n",
+ " 'setosa' 'setosa' 'versicolor' 'setosa' 'versicolor' 'virginica'\n",
+ " 'virginica' 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'setosa'\n",
+ " 'virginica' 'virginica' 'versicolor' 'setosa' 'virginica' 'setosa'\n",
+ " 'setosa' 'setosa' 'setosa' 'versicolor' 'versicolor' 'setosa'\n",
+ " 'versicolor' 'virginica' 'versicolor']\n",
+ "Score: 0.9777777777777777\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Step 1: Import a class of model\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "\n",
+ "# Step 2: Initiate model with heper params\n",
+ "model = KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ "# step 3.0: Modify data into desired feature and target matrices\n",
+ "X = iris.drop('species', axis=1).values\n",
+ "y = iris['species'].values\n",
+ "\n",
+ "# Step 3.1: Divide the dataset into training and test sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+ "\n",
+ "# step 4: Fit/train the model with the training sets\n",
+ "model.fit(X_train, y_train)\n",
+ "\n",
+ "# Step 5: # Predict outputs of training set\n",
+ "prediction = model.predict(X_test)\n",
+ "print(\"Prediction values: \\n\", prediction)\n",
+ "\n",
+ "# Compute the accuracy score\n",
+ "score = model.score(X_test, y_test)\n",
+ "print(\"Score: \", score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparing score values for different values of K"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "k_values = np.arange(1,11)\n",
+ "scores = []\n",
+ "# print(k_values)\n",
+ "for value in k_values:\n",
+ " model = KNeighborsClassifier(n_neighbors=value)\n",
+ " \n",
+ " X = iris.drop('species', axis=1).values\n",
+ " y = iris['species'].values\n",
+ " \n",
+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+ " \n",
+ " model.fit(X_train, y_train)\n",
+ " \n",
+ " prediction = model.predict(X_test)\n",
+ " \n",
+ " score = model.score(X_test, y_test)\n",
+ " \n",
+ " scores.append(score)\n",
+ " \n",
+ "# Plot score vs k\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "plt.plot(k_values, scores)\n",
+ "plt.title('Score Values for different k values')\n",
+ "plt.xlabel('K')\n",
+ "plt.ylabel('Score')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cross validation: A better way of measuring accuracy\n",
+ "This tests on several sets of the data which are auto-generated accordingly\n",
+ "### Cross_Validation_Score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "scores: [0.96666667 1. 1. 0.93333333 1. ]\n",
+ "Mean: 0.9800000000000001\n"
+ ]
+ }
+ ],
+ "source": [
+ "# We already have our model, feature and target ready\n",
+ "scores = cross_val_score(model, X, y, cv=5) # imported from sklearn.model_selection\n",
+ "print(\"scores: \", scores)\n",
+ "print(\"Mean: \", scores.mean())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NAIVE BAYES CLASSIFICATION\n",
+ "### Gaussian NB model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['virginica', 'versicolor', 'versicolor', 'versicolor', 'virginica',\n",
+ " 'virginica', 'versicolor', 'versicolor', 'setosa', 'virginica',\n",
+ " 'setosa', 'setosa', 'virginica', 'virginica', 'setosa',\n",
+ " 'virginica', 'versicolor', 'setosa', 'setosa', 'setosa',\n",
+ " 'versicolor', 'setosa', 'versicolor', 'virginica', 'virginica',\n",
+ " 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'setosa',\n",
+ " 'versicolor', 'virginica', 'versicolor', 'setosa', 'virginica',\n",
+ " 'setosa', 'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
+ " 'setosa', 'versicolor', 'virginica', 'versicolor'], dtype='\n",
+ "Aims at minimizing cost functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "\n",
+ "# prepare data\n",
+ "# X, predictor var, house size in K sq feet \n",
+ "X = np.arange(1, 11)\n",
+ "# print(X)\n",
+ "# y, observed response, price of hose in $\n",
+ "y = np.array([300, 350, 500, 700, 800, 850, 900, 900, 1000, 1200])\n",
+ "# print y\n",
+ "\n",
+ "model = LinearRegression()\n",
+ "\n",
+ "X = X[:, np.newaxis]\n",
+ "\n",
+ "model.fit(X, y)\n",
+ "\n",
+ "predictions = model.predict(X)\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.scatter(X, y, color='red')\n",
+ "plt.plot(X, predictions, color='blue')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.945818181818182"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.score(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([92.72727273])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.coef_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "239.9999999999999"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.intercept_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hence, our regression formular is **y=240 + 92.7x**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Logistic Regression\n",
+ "#### Using the iris dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "RangeIndex: 150 entries, 0 to 149\n",
+ "Data columns (total 5 columns):\n",
+ "sepal_length 150 non-null float64\n",
+ "sepal_width 150 non-null float64\n",
+ "petal_length 150 non-null float64\n",
+ "petal_width 150 non-null float64\n",
+ "species 150 non-null object\n",
+ "dtypes: float64(4), object(1)\n",
+ "memory usage: 5.3+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "iris_dataset.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iris_dataset = sns.load_dataset('iris')\n",
+ "iris_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | sepal_length | \n", + "sepal_width | \n", + "petal_length | \n", + "petal_width | \n", + "species | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 1 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
| 2 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "setosa | \n", + "
| 3 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "setosa | \n", + "
| 4 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "setosa | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(iris_dataset, hue='species', height=2.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/kiva_analytics.ipynb b/notebooks/kiva_analytics.ipynb
new file mode 100644
index 0000000..e6ca2d7
--- /dev/null
+++ b/notebooks/kiva_analytics.ipynb
@@ -0,0 +1,2388 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# KIVA ANALYTICS\n",
+ "The kiva datasets contain data on money funded to borrowers from different countries.\n",
+ "A lot of information can be deduced from the datasets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importing the packages\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Taking a look at the main dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n", + "The main columns with missing data are disbursed_time column, funded_time column and borrower_genders column.
\n", + "Dropping these columns would lead to loss of other valuable data and filling them in with other values would lead to misguided(false) conclusions, hence we are going to work with only the available data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
while the maximum amount was **100000.00 USD**.\n", + "6. The minimum loan amount disbursed by the field agent to a borrower was **25.00 USD** while
while the maximum amount was **100,000.00 USD**. \n", + "7. The minimum duration for which a loan was disbursed was **1 month** while the maximum duration was **158 months**.\n", + "8. There were circumstance(s) when no lender contributed to a certain loan (0 lender count) while the maximum number of lenders a particular loan received was **2986 lenders**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loans borrowed and disbursed per country\n", + "To achieve this, we create a pivot table with the countries as the indices and a total summation of the funded and loaned amounts as the values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "Separating the funded and loan amount columns into Series objects for easier manipulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Selecting the individual summation columns\n", + "funded_amts = loans_per_country_summation['funded_amount']\n", + "loan_amts = loans_per_country_summation['loan_amount']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we select the countries at minimum and maximum values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Country at minimum funded amount: \n", + " country\n", + "Virgin Islands 0.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at maximum funded amount: \n", + " country\n", + "Philippines 54476375.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at minimum loan amount: \n", + " country\n", + "Guam 4300.0\n", + "Name: loan_amount, dtype: float64\n", + "Country at maximum loan amount: \n", + " country\n", + "Philippines 55342225.0\n", + "Name: loan_amount, dtype: float64\n" + ] + } + ], + "source": [ + "# Countries at minimum and maximum funded amount\n", + "min_funded_amt_country = funded_amts[(funded_amts == funded_amts.min())]\n", + "max_funded_amt_country = funded_amts[(funded_amts == funded_amts.max())]\n", + "\n", + "# Countries at minimum and maximum loan amount\n", + "min_loan_amt_country = loan_amts[(loan_amts == loan_amts.min())]\n", + "max_loan_amt_country = loan_amts[(loan_amts == loan_amts.max())]\n", + "\n", + "print(\"Country at minimum funded amount: \\n\", min_funded_amt_country)\n", + "print(\"Country at maximum funded amount: \\n\", max_funded_amt_country)\n", + "print(\"Country at minimum loan amount: \\n\", min_loan_amt_country)\n", + "print(\"Country at maximum loan amount: \\n\", max_loan_amt_country)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the above per country analysis, we can see that:\n", + "1. The country that received the largest amount of loan for all sectors was **Philippines**, receiving
a total of **55,342,225.00 USD**.\n", + "2. The country that received the smallest amount of loan for all sectors was **Guam**, receiving
a total of **4,300.00 USD**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loans borrowed per sector across all countries\n", + "To achieve this, we create a pivot table with the sectors as the indices and the summation of
\n", + "loan amounts as the column." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read the kiva_loans.csv file\n",
+ "kiva_main_dataset = pd.read_csv('F:/isaka/python4DS/kiva_loans.csv')\n",
+ "\n",
+ "# Display the first 5 entries\n",
+ "kiva_main_dataset.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Handling Missing Data\n",
+ "The very first thing before starting on any analytics is to take care of all missing data in the relevant columns(key interest points) by either dropping them or filling them in.| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n", + "The main columns with missing data are disbursed_time column, funded_time column and borrower_genders column.
\n", + "Dropping these columns would lead to loss of other valuable data and filling them in with other values would lead to misguided(false) conclusions, hence we are going to work with only the available data." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency partner_id posted_time \\\n",
+ "0 Lahore PKR 247.0 2014-01-01 06:12:39+00:00 \n",
+ "1 Lahore PKR 247.0 2014-01-01 06:51:08+00:00 \n",
+ "2 Maynaguri INR 334.0 2014-01-01 09:58:07+00:00 \n",
+ "3 Lahore PKR 247.0 2014-01-01 08:03:11+00:00 \n",
+ "4 Abdul Hakeem PKR 245.0 2014-01-01 11:53:19+00:00 \n",
+ "\n",
+ " disbursed_time funded_time term_in_months \\\n",
+ "0 2013-12-17 08:00:00+00:00 2014-01-02 10:06:32+00:00 12.0 \n",
+ "1 2013-12-17 08:00:00+00:00 2014-01-02 09:17:23+00:00 11.0 \n",
+ "2 2013-12-17 08:00:00+00:00 2014-01-01 16:01:36+00:00 43.0 \n",
+ "3 2013-12-24 08:00:00+00:00 2014-01-01 13:00:00+00:00 11.0 \n",
+ "4 2013-12-17 08:00:00+00:00 2014-01-01 19:18:51+00:00 14.0 \n",
+ "\n",
+ " lender_count tags borrower_genders \\\n",
+ "0 12 NaN female \n",
+ "1 14 NaN female, female \n",
+ "2 6 user_favorite, user_favorite female \n",
+ "3 8 NaN female \n",
+ "4 16 NaN female \n",
+ "\n",
+ " repayment_interval date \n",
+ "0 irregular 2014-01-01 \n",
+ "1 irregular 2014-01-01 \n",
+ "2 bullet 2014-01-01 \n",
+ "3 irregular 2014-01-01 \n",
+ "4 monthly 2014-01-01 "
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return all columns and rows without a missing value\n",
+ "kiva_available_data = kiva_main_dataset[kiva_main_dataset.notnull()]\n",
+ "\n",
+ "kiva_available_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic derivations\n",
+ "Now we have a clean dataset without any missing values. The first thing is to look at the basic derivations/computations of the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "partner_id | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:12:39+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 10:06:32+00:00 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 06:51:08+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 09:17:23+00:00 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "334.0 | \n", + "2014-01-01 09:58:07+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 16:01:36+00:00 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "247.0 | \n", + "2014-01-01 08:03:11+00:00 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 13:00:00+00:00 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "245.0 | \n", + "2014-01-01 11:53:19+00:00 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 19:18:51+00:00 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount partner_id \\\n",
+ "count 6.712050e+05 671205.000000 671205.000000 657698.000000 \n",
+ "mean 9.932486e+05 785.995061 842.397107 178.199616 \n",
+ "std 1.966113e+05 1130.398941 1198.660073 94.247581 \n",
+ "min 6.530470e+05 0.000000 25.000000 9.000000 \n",
+ "25% 8.230720e+05 250.000000 275.000000 126.000000 \n",
+ "50% 9.927800e+05 450.000000 500.000000 145.000000 \n",
+ "75% 1.163653e+06 900.000000 1000.000000 204.000000 \n",
+ "max 1.340339e+06 100000.000000 100000.000000 536.000000 \n",
+ "\n",
+ " term_in_months lender_count \n",
+ "count 671205.000000 671205.000000 \n",
+ "mean 13.739022 20.590922 \n",
+ "std 8.598919 28.459551 \n",
+ "min 1.000000 0.000000 \n",
+ "25% 8.000000 7.000000 \n",
+ "50% 13.000000 13.000000 \n",
+ "75% 14.000000 24.000000 \n",
+ "max 158.000000 2986.000000 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return the basic derivations of the dataset\n",
+ "kiva_available_data.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### From the above table, we can see that:\n",
+ "1. A total of **671,205** loans were given out during that specific period.\n",
+ "2. An average of **842.40 USD** was disbursed to the borrowers by the field agent.\n",
+ "3. An average of **786.00 USD** was disbursed to the field agent by Kiva.\n",
+ "4. The loans were disburded for an average duration of **14 months**.\n",
+ "5. The minimum loan amount disbursed by Kiva to the field agent was **0.00 USD** while | \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "partner_id | \n", + "term_in_months | \n", + "lender_count | \n", + "
|---|---|---|---|---|---|---|
| count | \n", + "6.712050e+05 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "657698.000000 | \n", + "671205.000000 | \n", + "671205.000000 | \n", + "
| mean | \n", + "9.932486e+05 | \n", + "785.995061 | \n", + "842.397107 | \n", + "178.199616 | \n", + "13.739022 | \n", + "20.590922 | \n", + "
| std | \n", + "1.966113e+05 | \n", + "1130.398941 | \n", + "1198.660073 | \n", + "94.247581 | \n", + "8.598919 | \n", + "28.459551 | \n", + "
| min | \n", + "6.530470e+05 | \n", + "0.000000 | \n", + "25.000000 | \n", + "9.000000 | \n", + "1.000000 | \n", + "0.000000 | \n", + "
| 25% | \n", + "8.230720e+05 | \n", + "250.000000 | \n", + "275.000000 | \n", + "126.000000 | \n", + "8.000000 | \n", + "7.000000 | \n", + "
| 50% | \n", + "9.927800e+05 | \n", + "450.000000 | \n", + "500.000000 | \n", + "145.000000 | \n", + "13.000000 | \n", + "13.000000 | \n", + "
| 75% | \n", + "1.163653e+06 | \n", + "900.000000 | \n", + "1000.000000 | \n", + "204.000000 | \n", + "14.000000 | \n", + "24.000000 | \n", + "
| max | \n", + "1.340339e+06 | \n", + "100000.000000 | \n", + "100000.000000 | \n", + "536.000000 | \n", + "158.000000 | \n", + "2986.000000 | \n", + "
while the maximum amount was **100000.00 USD**.\n", + "6. The minimum loan amount disbursed by the field agent to a borrower was **25.00 USD** while
while the maximum amount was **100,000.00 USD**. \n", + "7. The minimum duration for which a loan was disbursed was **1 month** while the maximum duration was **158 months**.\n", + "8. There were circumstance(s) when no lender contributed to a certain loan (0 lender count) while the maximum number of lenders a particular loan received was **2986 lenders**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loans borrowed and disbursed per country\n", + "To achieve this, we create a pivot table with the countries as the indices and a total summation of the funded and loaned amounts as the values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " sum \n",
+ " funded_amount loan_amount\n",
+ "country \n",
+ "Afghanistan 14000.0 14000.0\n",
+ "Albania 2490000.0 2666500.0\n",
+ "Armenia 11186675.0 12915400.0\n",
+ "Azerbaijan 2699575.0 2888700.0\n",
+ "Belize 114025.0 114025.0\n",
+ "Benin 516825.0 518950.0\n",
+ "Bhutan 15625.0 20000.0\n",
+ "Bolivia 18276200.0 19843250.0\n",
+ "Brazil 661025.0 662200.0\n",
+ "Burkina Faso 2909975.0 2972700.0"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a per_country pivot table\n",
+ "loans_per_country = pd.pivot_table(kiva_available_data, index=['country'], values=['funded_amount', 'loan_amount'], aggfunc=[np.sum])\n",
+ "\n",
+ "loans_per_country.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | sum | \n", + "|
|---|---|---|
| \n", + " | funded_amount | \n", + "loan_amount | \n", + "
| country | \n", + "\n", + " | \n", + " |
| Afghanistan | \n", + "14000.0 | \n", + "14000.0 | \n", + "
| Albania | \n", + "2490000.0 | \n", + "2666500.0 | \n", + "
| Armenia | \n", + "11186675.0 | \n", + "12915400.0 | \n", + "
| Azerbaijan | \n", + "2699575.0 | \n", + "2888700.0 | \n", + "
| Belize | \n", + "114025.0 | \n", + "114025.0 | \n", + "
| Benin | \n", + "516825.0 | \n", + "518950.0 | \n", + "
| Bhutan | \n", + "15625.0 | \n", + "20000.0 | \n", + "
| Bolivia | \n", + "18276200.0 | \n", + "19843250.0 | \n", + "
| Brazil | \n", + "661025.0 | \n", + "662200.0 | \n", + "
| Burkina Faso | \n", + "2909975.0 | \n", + "2972700.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " funded_amount loan_amount\n",
+ "country \n",
+ "Afghanistan 14000.0 14000.0\n",
+ "Albania 2490000.0 2666500.0\n",
+ "Armenia 11186675.0 12915400.0\n",
+ "Azerbaijan 2699575.0 2888700.0\n",
+ "Belize 114025.0 114025.0"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Formatting to a more clear dataframe\n",
+ "loans_per_country_summation = loans_per_country['sum']\n",
+ "\n",
+ "loans_per_country_summation.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we have a clear DataFrame with the countries as indices and the summations of loaned and funded amounts as values. | \n", + " | funded_amount | \n", + "loan_amount | \n", + "
|---|---|---|
| country | \n", + "\n", + " | \n", + " |
| Afghanistan | \n", + "14000.0 | \n", + "14000.0 | \n", + "
| Albania | \n", + "2490000.0 | \n", + "2666500.0 | \n", + "
| Armenia | \n", + "11186675.0 | \n", + "12915400.0 | \n", + "
| Azerbaijan | \n", + "2699575.0 | \n", + "2888700.0 | \n", + "
| Belize | \n", + "114025.0 | \n", + "114025.0 | \n", + "
\n", + "Separating the funded and loan amount columns into Series objects for easier manipulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Selecting the individual summation columns\n", + "funded_amts = loans_per_country_summation['funded_amount']\n", + "loan_amts = loans_per_country_summation['loan_amount']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we select the countries at minimum and maximum values." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Country at minimum funded amount: \n", + " country\n", + "Virgin Islands 0.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at maximum funded amount: \n", + " country\n", + "Philippines 54476375.0\n", + "Name: funded_amount, dtype: float64\n", + "Country at minimum loan amount: \n", + " country\n", + "Guam 4300.0\n", + "Name: loan_amount, dtype: float64\n", + "Country at maximum loan amount: \n", + " country\n", + "Philippines 55342225.0\n", + "Name: loan_amount, dtype: float64\n" + ] + } + ], + "source": [ + "# Countries at minimum and maximum funded amount\n", + "min_funded_amt_country = funded_amts[(funded_amts == funded_amts.min())]\n", + "max_funded_amt_country = funded_amts[(funded_amts == funded_amts.max())]\n", + "\n", + "# Countries at minimum and maximum loan amount\n", + "min_loan_amt_country = loan_amts[(loan_amts == loan_amts.min())]\n", + "max_loan_amt_country = loan_amts[(loan_amts == loan_amts.max())]\n", + "\n", + "print(\"Country at minimum funded amount: \\n\", min_funded_amt_country)\n", + "print(\"Country at maximum funded amount: \\n\", max_funded_amt_country)\n", + "print(\"Country at minimum loan amount: \\n\", min_loan_amt_country)\n", + "print(\"Country at maximum loan amount: \\n\", max_loan_amt_country)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From the above per country analysis, we can see that:\n", + "1. The country that received the largest amount of loan for all sectors was **Philippines**, receiving
a total of **55,342,225.00 USD**.\n", + "2. The country that received the smallest amount of loan for all sectors was **Guam**, receiving
a total of **4,300.00 USD**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loans borrowed per sector across all countries\n", + "To achieve this, we create a pivot table with the sectors as the indices and the summation of
\n", + "loan amounts as the column." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "sector \n",
+ "Agriculture 143067875.0\n",
+ "Arts 12240325.0\n",
+ "Clothing 37300925.0\n",
+ "Construction 6695150.0\n",
+ "Education 30968525.0\n",
+ "Entertainment 1389150.0\n",
+ "Food 121606150.0\n",
+ "Health 9843400.0\n",
+ "Housing 23663950.0\n",
+ "Manufacturing 5447900.0\n",
+ "Personal Use 14955350.0\n",
+ "Retail 98122900.0\n",
+ "Services 48057450.0\n",
+ "Transportation 11066900.0\n",
+ "Wholesale 995200.0"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# return loan amount totals per sector\n",
+ "loans_per_sector = pd.pivot_table(kiva_available_data, index=['sector'], values=['loan_amount'], aggfunc=[np.sum])\n",
+ "loans_per_sector_summation = loans_per_sector['sum']\n",
+ "loans_per_sector_summation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Basic analysis of the sectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| sector | \n", + "\n", + " |
| Agriculture | \n", + "143067875.0 | \n", + "
| Arts | \n", + "12240325.0 | \n", + "
| Clothing | \n", + "37300925.0 | \n", + "
| Construction | \n", + "6695150.0 | \n", + "
| Education | \n", + "30968525.0 | \n", + "
| Entertainment | \n", + "1389150.0 | \n", + "
| Food | \n", + "121606150.0 | \n", + "
| Health | \n", + "9843400.0 | \n", + "
| Housing | \n", + "23663950.0 | \n", + "
| Manufacturing | \n", + "5447900.0 | \n", + "
| Personal Use | \n", + "14955350.0 | \n", + "
| Retail | \n", + "98122900.0 | \n", + "
| Services | \n", + "48057450.0 | \n", + "
| Transportation | \n", + "11066900.0 | \n", + "
| Wholesale | \n", + "995200.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "count 1.500000e+01\n",
+ "mean 3.769474e+07\n",
+ "std 4.587355e+07\n",
+ "min 9.952000e+05\n",
+ "25% 8.269275e+06\n",
+ "50% 1.495535e+07\n",
+ "75% 4.267919e+07\n",
+ "max 1.430679e+08"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return the basic sector analytics\n",
+ "loans_per_sector_summation.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table we see that:\n",
+ "1. The borrowed loans were used in a total of **15** different sectors across all countries.\n",
+ "2. The average borrowed loan amount used per sector in all countries was **37,694,740 USD**.\n",
+ "3. The minimum amount used in a particular sector was **995,200 USD** while the maximum amount was **143,067,900 USD**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Additional per-sector analysis\n",
+ "Here we return the sectors with the minimum and maximum amounts of loan used on them, respectively "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Agriculture 143067875.0\n",
+ "Arts 12240325.0\n",
+ "Clothing 37300925.0\n",
+ "Construction 6695150.0\n",
+ "Education 30968525.0\n",
+ "Entertainment 1389150.0\n",
+ "Food 121606150.0\n",
+ "Health 9843400.0\n",
+ "Housing 23663950.0\n",
+ "Manufacturing 5447900.0\n",
+ "Personal Use 14955350.0\n",
+ "Retail 98122900.0\n",
+ "Services 48057450.0\n",
+ "Transportation 11066900.0\n",
+ "Wholesale 995200.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# return a series object of all loan amount totals per sector.\n",
+ "loan_amts_per_sector = loans_per_sector_summation['loan_amount']\n",
+ "loan_amts_per_sector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Wholesale 995200.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return sector with minimum amount\n",
+ "loan_amts_per_sector[loan_amts_per_sector == loan_amts_per_sector.min()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sector\n",
+ "Agriculture 143067875.0\n",
+ "Name: loan_amount, dtype: float64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Return sector with maximum amount\n",
+ "loan_amts_per_sector[loan_amts_per_sector == loan_amts_per_sector.max()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above, \n",
+ "1. Most of the funds borrowed were used on **Agriculture**, with a total of **143,067,900 USD** used on it.\n",
+ "2. The sector with the smallest amount used on it was **Wholesale**, with a total of **995,200 USD** used on it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Date/Time based Analytics\n",
+ "Since the dates provided in the dataset are all objects, we first convert them to date/time values for easier manipulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Extract all time columns and convert them to datetime values\n",
+ "kiva_available_data['posted_time'] = pd.to_datetime(kiva_available_data['posted_time'])\n",
+ "kiva_available_data['funded_time'] = pd.to_datetime(kiva_available_data['funded_time'])\n",
+ "\n",
+ "# converting to YYYY-mm-dd formats then back to datetime values\n",
+ "kiva_available_data['posted_time'] = pd.to_datetime(kiva_available_data['posted_time'].dt.strftime(\"%Y-%m-%d\"))\n",
+ "kiva_available_data['funded_time'] = pd.to_datetime(kiva_available_data['funded_time'].dt.strftime(\"%Y-%m-%d\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Next we create a column containing the time difference between when the loan was funded and when it was posted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| count | \n", + "1.500000e+01 | \n", + "
| mean | \n", + "3.769474e+07 | \n", + "
| std | \n", + "4.587355e+07 | \n", + "
| min | \n", + "9.952000e+05 | \n", + "
| 25% | \n", + "8.269275e+06 | \n", + "
| 50% | \n", + "1.495535e+07 | \n", + "
| 75% | \n", + "4.267919e+07 | \n", + "
| max | \n", + "1.430679e+08 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "0 653051 300.0 300.0 Fruits & Vegetables Food \n",
+ "1 653053 575.0 575.0 Rickshaw Transportation \n",
+ "2 653068 150.0 150.0 Transportation Transportation \n",
+ "3 653063 200.0 200.0 Embroidery Arts \n",
+ "4 653084 400.0 400.0 Milk Sales Food \n",
+ "\n",
+ " use country_code country \\\n",
+ "0 To buy seasonal, fresh fruits to sell. PK Pakistan \n",
+ "1 to repair and maintain the auto rickshaw used ... PK Pakistan \n",
+ "2 To repair their old cycle-van and buy another ... IN India \n",
+ "3 to purchase an embroidery machine and a variet... PK Pakistan \n",
+ "4 to purchase one buffalo. PK Pakistan \n",
+ "\n",
+ " region currency ... posted_time disbursed_time \\\n",
+ "0 Lahore PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "1 Lahore PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "2 Maynaguri INR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "3 Lahore PKR ... 2014-01-01 2013-12-24 08:00:00+00:00 \n",
+ "4 Abdul Hakeem PKR ... 2014-01-01 2013-12-17 08:00:00+00:00 \n",
+ "\n",
+ " funded_time term_in_months lender_count tags \\\n",
+ "0 2014-01-02 12.0 12 NaN \n",
+ "1 2014-01-02 11.0 14 NaN \n",
+ "2 2014-01-01 43.0 6 user_favorite, user_favorite \n",
+ "3 2014-01-01 11.0 8 NaN \n",
+ "4 2014-01-01 14.0 16 NaN \n",
+ "\n",
+ " borrower_genders repayment_interval date funding_period \n",
+ "0 female irregular 2014-01-01 1 days \n",
+ "1 female, female irregular 2014-01-01 1 days \n",
+ "2 female bullet 2014-01-01 0 days \n",
+ "3 female irregular 2014-01-01 0 days \n",
+ "4 female monthly 2014-01-01 0 days \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a funding period column: Difference between time posted and time funded for a loan\n",
+ "kiva_available_data['funding_period'] = (kiva_available_data['funded_time'] - kiva_available_data['posted_time'])\n",
+ "\n",
+ "kiva_available_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "... | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "funding_period | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "653051 | \n", + "300.0 | \n", + "300.0 | \n", + "Fruits & Vegetables | \n", + "Food | \n", + "To buy seasonal, fresh fruits to sell. | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 | \n", + "12.0 | \n", + "12 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "1 days | \n", + "
| 1 | \n", + "653053 | \n", + "575.0 | \n", + "575.0 | \n", + "Rickshaw | \n", + "Transportation | \n", + "to repair and maintain the auto rickshaw used ... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-02 | \n", + "11.0 | \n", + "14 | \n", + "NaN | \n", + "female, female | \n", + "irregular | \n", + "2014-01-01 | \n", + "1 days | \n", + "
| 2 | \n", + "653068 | \n", + "150.0 | \n", + "150.0 | \n", + "Transportation | \n", + "Transportation | \n", + "To repair their old cycle-van and buy another ... | \n", + "IN | \n", + "India | \n", + "Maynaguri | \n", + "INR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "43.0 | \n", + "6 | \n", + "user_favorite, user_favorite | \n", + "female | \n", + "bullet | \n", + "2014-01-01 | \n", + "0 days | \n", + "
| 3 | \n", + "653063 | \n", + "200.0 | \n", + "200.0 | \n", + "Embroidery | \n", + "Arts | \n", + "to purchase an embroidery machine and a variet... | \n", + "PK | \n", + "Pakistan | \n", + "Lahore | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-24 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "11.0 | \n", + "8 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-01-01 | \n", + "0 days | \n", + "
| 4 | \n", + "653084 | \n", + "400.0 | \n", + "400.0 | \n", + "Milk Sales | \n", + "Food | \n", + "to purchase one buffalo. | \n", + "PK | \n", + "Pakistan | \n", + "Abdul Hakeem | \n", + "PKR | \n", + "... | \n", + "2014-01-01 | \n", + "2013-12-17 08:00:00+00:00 | \n", + "2014-01-01 | \n", + "14.0 | \n", + "16 | \n", + "NaN | \n", + "female | \n", + "monthly | \n", + "2014-01-01 | \n", + "0 days | \n", + "
5 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id funded_amount loan_amount activity sector \\\n",
+ "42344 1080950 125.0 125.0 Services Services \n",
+ "\n",
+ " use country_code country region currency ... \\\n",
+ "42344 add more stock to my shop KE Kenya NaN KES ... \n",
+ "\n",
+ " posted_time disbursed_time funded_time term_in_months \\\n",
+ "42344 2014-04-08 2015-06-09 14:40:00+00:00 2015-06-03 6.0 \n",
+ "\n",
+ " lender_count tags borrower_genders repayment_interval date \\\n",
+ "42344 5 NaN female irregular 2014-04-08 \n",
+ "\n",
+ " funding_period \n",
+ "42344 421 days \n",
+ "\n",
+ "[1 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Extract the funding period column where there are no null values\n",
+ "funding_period = kiva_available_data[kiva_available_data['funding_period'].notnull()]['funding_period']\n",
+ "\n",
+ "# return the maximum funding period\n",
+ "min_funding_period = funding_period.max()\n",
+ "\n",
+ "#Fetch the data at that period\n",
+ "kiva_available_data.query('funding_period == @min_funding_period')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### From the data above, we can see that:\n",
+ " 1. The maximum amount it took for a loan from the time it was posted on kiva to the time it was funded was **421 days**.\n",
+ " 2. This loan also took 6 days to be disbursed to the borrower from the time it was funded.\n",
+ " 3. The loan, a total of **125 USD**, was loaned to a female in kenya, which she used to add more stock to her shop."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More date/time analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "count 622874\n",
+ "mean 14 days 12:29:57.844186\n",
+ "std 14 days 10:18:20.006715\n",
+ "min -18 days +00:00:00\n",
+ "25% 5 days 00:00:00\n",
+ "50% 9 days 00:00:00\n",
+ "75% 22 days 00:00:00\n",
+ "max 421 days 00:00:00\n",
+ "Name: funding_period, dtype: object"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "funding_period.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table, we see that:\n",
+ "1. It took an average of **14 days** for a loan to be funded from the time of posting.\n",
+ "2. Some loans were funded the same day they were posted."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Miscellaneous Analytics\n",
+ "### Here we look at other general trends from the dataset\n",
+ "First we create a pivot table to compare the average loan amounts borrowed and their respective repayment intervals."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "funded_amount | \n", + "loan_amount | \n", + "activity | \n", + "sector | \n", + "use | \n", + "country_code | \n", + "country | \n", + "region | \n", + "currency | \n", + "... | \n", + "posted_time | \n", + "disbursed_time | \n", + "funded_time | \n", + "term_in_months | \n", + "lender_count | \n", + "tags | \n", + "borrower_genders | \n", + "repayment_interval | \n", + "date | \n", + "funding_period | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 42344 | \n", + "1080950 | \n", + "125.0 | \n", + "125.0 | \n", + "Services | \n", + "Services | \n", + "add more stock to my shop | \n", + "KE | \n", + "Kenya | \n", + "NaN | \n", + "KES | \n", + "... | \n", + "2014-04-08 | \n", + "2015-06-09 14:40:00+00:00 | \n", + "2015-06-03 | \n", + "6.0 | \n", + "5 | \n", + "NaN | \n", + "female | \n", + "irregular | \n", + "2014-04-08 | \n", + "421 days | \n", + "
1 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean\n",
+ " loan_amount\n",
+ "repayment_interval \n",
+ "bullet 896.558294\n",
+ "irregular 691.401979\n",
+ "monthly 945.586446\n",
+ "weekly 234.676080"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a per repayment_interval pivot table\n",
+ "repayment_interval = pd.pivot_table(kiva_available_data, index=['repayment_interval'], values=['loan_amount'], aggfunc=[np.mean])\n",
+ "\n",
+ "repayment_interval"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Restack the dataframe for easier manipulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | mean | \n", + "
|---|---|
| \n", + " | loan_amount | \n", + "
| repayment_interval | \n", + "\n", + " |
| bullet | \n", + "896.558294 | \n", + "
| irregular | \n", + "691.401979 | \n", + "
| monthly | \n", + "945.586446 | \n", + "
| weekly | \n", + "234.676080 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " loan_amount\n",
+ "repayment_interval \n",
+ "bullet 896.558294\n",
+ "irregular 691.401979\n",
+ "monthly 945.586446\n",
+ "weekly 234.676080"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "repayment_interval_mean = repayment_interval['mean']\n",
+ "repayment_interval_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## From the above table, we see that:\n",
+ "1. Majority of the loans were repaid on a monthly basis, followed by the bullet method, then irregularly.\n",
+ "2. Loan borrowers didn't prefer repaying on weekly basis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MORE DATASETS\n",
+ "Taking a look at the regions dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | loan_amount | \n", + "
|---|---|
| repayment_interval | \n", + "\n", + " |
| bullet | \n", + "896.558294 | \n", + "
| irregular | \n", + "691.401979 | \n", + "
| monthly | \n", + "945.586446 | \n", + "
| weekly | \n", + "234.676080 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " LocationName ISO country region world_region MPI \\\n",
+ "0 Badakhshan, Afghanistan AFG Afghanistan Badakhshan South Asia 0.387 \n",
+ "1 Badghis, Afghanistan AFG Afghanistan Badghis South Asia 0.466 \n",
+ "2 Baghlan, Afghanistan AFG Afghanistan Baghlan South Asia 0.300 \n",
+ "3 Balkh, Afghanistan AFG Afghanistan Balkh South Asia 0.301 \n",
+ "4 Bamyan, Afghanistan AFG Afghanistan Bamyan South Asia 0.325 \n",
+ "\n",
+ " geo lat lon \n",
+ "0 (36.7347725, 70.81199529999999) 36.734772 70.811995 \n",
+ "1 (35.1671339, 63.7695384) 35.167134 63.769538 \n",
+ "2 (35.8042947, 69.2877535) 35.804295 69.287754 \n",
+ "3 (36.7550603, 66.8975372) 36.755060 66.897537 \n",
+ "4 (34.8100067, 67.8212104) 34.810007 67.821210 "
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load the regions dataset into memory\n",
+ "regions = pd.read_csv('F:/isaka/kiva/kiva_mpi_region_locations.csv')\n",
+ "regions.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Average poverty index per region"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | LocationName | \n", + "ISO | \n", + "country | \n", + "region | \n", + "world_region | \n", + "MPI | \n", + "geo | \n", + "lat | \n", + "lon | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Badakhshan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badakhshan | \n", + "South Asia | \n", + "0.387 | \n", + "(36.7347725, 70.81199529999999) | \n", + "36.734772 | \n", + "70.811995 | \n", + "
| 1 | \n", + "Badghis, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Badghis | \n", + "South Asia | \n", + "0.466 | \n", + "(35.1671339, 63.7695384) | \n", + "35.167134 | \n", + "63.769538 | \n", + "
| 2 | \n", + "Baghlan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Baghlan | \n", + "South Asia | \n", + "0.300 | \n", + "(35.8042947, 69.2877535) | \n", + "35.804295 | \n", + "69.287754 | \n", + "
| 3 | \n", + "Balkh, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Balkh | \n", + "South Asia | \n", + "0.301 | \n", + "(36.7550603, 66.8975372) | \n", + "36.755060 | \n", + "66.897537 | \n", + "
| 4 | \n", + "Bamyan, Afghanistan | \n", + "AFG | \n", + "Afghanistan | \n", + "Bamyan | \n", + "South Asia | \n", + "0.325 | \n", + "(34.8100067, 67.8212104) | \n", + "34.810007 | \n", + "67.821210 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean\n",
+ " MPI\n",
+ "world_region \n",
+ "Arab States 0.115287\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Europe and Central Asia 0.025273\n",
+ "Latin America and Caribbean 0.063665\n",
+ "South Asia 0.219630\n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# create an MPI pivot table.\n",
+ "mpi_avg = pd.pivot_table(regions, index=['world_region'], values=['MPI'], aggfunc=[np.mean])\n",
+ "mpi_avg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | mean | \n", + "
|---|---|
| \n", + " | MPI | \n", + "
| world_region | \n", + "\n", + " |
| Arab States | \n", + "0.115287 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Arab States 0.115287\n",
+ "East Asia and the Pacific 0.136266\n",
+ "Europe and Central Asia 0.025273\n",
+ "Latin America and Caribbean 0.063665\n",
+ "South Asia 0.219630\n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Refactoring the dataframe\n",
+ "mpi_avg_mean = mpi_avg['mean']\n",
+ "mpi_avg_mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Arab States | \n", + "0.115287 | \n", + "
| East Asia and the Pacific | \n", + "0.136266 | \n", + "
| Europe and Central Asia | \n", + "0.025273 | \n", + "
| Latin America and Caribbean | \n", + "0.063665 | \n", + "
| South Asia | \n", + "0.219630 | \n", + "
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Sub-Saharan Africa 0.337128"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Region with maximum poverty index\n",
+ "# Extract the maximum p_index\n",
+ "max_mpi = mpi_avg_mean['MPI'].max()\n",
+ "\n",
+ "# Query the region at that particular index\n",
+ "mpi_avg_mean.query('MPI == @max_mpi')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Sub-Saharan Africa | \n", + "0.337128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MPI\n",
+ "world_region \n",
+ "Europe and Central Asia 0.025273"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Region with minimum poverty index\n",
+ "# Extract the minimum p_index\n",
+ "min_mpi = mpi_avg_mean['MPI'].min()\n",
+ "\n",
+ "# Query the region at that particular index\n",
+ "mpi_avg_mean.query('MPI == @min_mpi')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# From the above data, we can say that\n",
+ "1. **Sub-Saharan Africa** region has the highest poverty index, with a value of **0.337**.\n",
+ "2. **Europe and Centarl Asia** region has the lowest poverty index, with a value of **0.025**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/tests.ipynb b/notebooks/tests.ipynb
new file mode 100644
index 0000000..bafa168
--- /dev/null
+++ b/notebooks/tests.ipynb
@@ -0,0 +1,72 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Testing of Miscellaneous code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "import datetime"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1554891014.4681349"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ct = time.time()\n",
+ "ct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notes.txt b/notes.txt
new file mode 100644
index 0000000..73a672a
--- /dev/null
+++ b/notes.txt
@@ -0,0 +1,24 @@
+### Ipython Magical commands ###
+ - Preceded by %(for single-line commands) or %%(For multiline)
+ # %paste
+ -- Paste copied code on Ipython without syntax errors
+ # %cpaste
+ -- Paste multiple copied codes. '--' alone or Ctrl-D to stop.
+ # %run
+ -- Run s python script in Ipython shell. The run script will be available for use in the cells.
+
+
+### ERROR DEBUGGING ###
+ %xmode will switch to Exception mode. It can either be Plain, Context(default) and Verbose.(In order of detailness)
+ %debug to launch debug mode
+ E.g
+ %xmode Plain
+ %pdb on
+ func(args)
+
+
+### NUMPY ###
+ - The difference between python list arrays and numpy arrays is that for python each item can be of different type,
+ while numpy is restricted to a certain data type.
+ - Numpy uses vectorization in repeated operations, which is way faster than python loops.
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..49bf452
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,64 @@
+backcall==0.1.0
+bleach==3.1.0
+branca==0.3.1
+certifi==2019.3.9
+chardet==3.0.4
+colorama==0.4.1
+cycler==0.10.0
+decorator==4.3.0
+defusedxml==0.5.0
+entrypoints==0.3
+folium==0.8.2
+idna==2.8
+imageio==2.5.0
+ipykernel==5.1.0
+ipython==7.2.0
+ipython-genutils==0.2.0
+ipywidgets==7.4.2
+jedi==0.13.2
+Jinja2==2.10
+jsonschema==2.6.0
+jupyter==1.0.0
+jupyter-client==5.2.4
+jupyter-console==6.0.0
+jupyter-core==4.4.0
+kiwisolver==1.0.1
+MarkupSafe==1.1.0
+matplotlib==3.0.3
+mglearn==0.1.7
+mistune==0.8.4
+nbconvert==5.4.0
+nbformat==4.4.0
+notebook==5.7.4
+numpy==1.16.0
+pandas==0.24.1
+pandocfilters==1.4.2
+parso==0.3.1
+pickleshare==0.7.5
+Pillow==6.0.0
+plotly==3.7.1
+prometheus-client==0.5.0
+prompt-toolkit==2.0.7
+Pygments==2.3.1
+pyparsing==2.3.1
+python-dateutil==2.7.5
+pytz==2018.9
+pywinpty==0.5.5
+pyzmq==17.1.2
+qtconsole==4.4.3
+requests==2.21.0
+retrying==1.3.3
+scikit-learn==0.20.3
+scipy==1.2.1
+seaborn==0.9.0
+Send2Trash==1.5.0
+six==1.12.0
+terminado==0.8.1
+testpath==0.4.2
+tornado==5.1.1
+traitlets==4.3.2
+urllib3==1.24.1
+wcwidth==0.1.7
+webencodings==0.5.1
+widgetsnbextension==3.4.2
+xlrd==1.2.0
| \n", + " | MPI | \n", + "
|---|---|
| world_region | \n", + "\n", + " |
| Europe and Central Asia | \n", + "0.025273 | \n", + "