From 4f801e31115ea3162bcdf69be0cf871501a809cc Mon Sep 17 00:00:00 2001
From: Alan Grissett <alan.grissett@gmail.com>
Date: Wed, 11 Feb 2015 07:23:33 -0500
Subject: [PATCH] Notebook testing many methods of machine learning.

---
 spambase.ipynb | 1204 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1204 insertions(+)
 create mode 100644 spambase.ipynb

diff --git a/spambase.ipynb b/spambase.ipynb
new file mode 100644
index 0000000..531a365
--- /dev/null
+++ b/spambase.ipynb
@@ -0,0 +1,1204 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:cf4efd6286bc94cf20ff115121279da52b3af2f1b90a19860f43be7989102381"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "import numpy as np\n",
+      "import pandas as pd\n",
+      "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
+      "from sklearn.svm import SVC, LinearSVC\n",
+      "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier, AdaBoostClassifier\n",
+      "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix\n",
+      "from sklearn.cross_validation import cross_val_score, train_test_split \n",
+      "from sklearn.feature_selection import SelectKBest, chi2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "spam_data = pd.read_csv(\"spambase.data\", header=None)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def show_binary_plot(data, prediction):\n",
+      "    plt.matshow(confusion_matrix(data, prediction), cmap=plt.cm.binary, interpolation='nearest')\n",
+      "    plt.title('confusion matrix')\n",
+      "    plt.colorbar()\n",
+      "    plt.ylabel('expected label')\n",
+      "    plt.xlabel('predicted label')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def show_results(data, prediction):\n",
+      "    print(classification_report(data, prediction))\n",
+      "    print(confusion_matrix(data, prediction))\n",
+      "    print(f1_score(data, prediction))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "spam_data.info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 4601 entries, 0 to 4600\n",
+        "Data columns (total 58 columns):\n",
+        "0     4601 non-null float64\n",
+        "1     4601 non-null float64\n",
+        "2     4601 non-null float64\n",
+        "3     4601 non-null float64\n",
+        "4     4601 non-null float64\n",
+        "5     4601 non-null float64\n",
+        "6     4601 non-null float64\n",
+        "7     4601 non-null float64\n",
+        "8     4601 non-null float64\n",
+        "9     4601 non-null float64\n",
+        "10    4601 non-null float64\n",
+        "11    4601 non-null float64\n",
+        "12    4601 non-null float64\n",
+        "13    4601 non-null float64\n",
+        "14    4601 non-null float64\n",
+        "15    4601 non-null float64\n",
+        "16    4601 non-null float64\n",
+        "17    4601 non-null float64\n",
+        "18    4601 non-null float64\n",
+        "19    4601 non-null float64\n",
+        "20    4601 non-null float64\n",
+        "21    4601 non-null float64\n",
+        "22    4601 non-null float64\n",
+        "23    4601 non-null float64\n",
+        "24    4601 non-null float64\n",
+        "25    4601 non-null float64\n",
+        "26    4601 non-null float64\n",
+        "27    4601 non-null float64\n",
+        "28    4601 non-null float64\n",
+        "29    4601 non-null float64\n",
+        "30    4601 non-null float64\n",
+        "31    4601 non-null float64\n",
+        "32    4601 non-null float64\n",
+        "33    4601 non-null float64\n",
+        "34    4601 non-null float64\n",
+        "35    4601 non-null float64\n",
+        "36    4601 non-null float64\n",
+        "37    4601 non-null float64\n",
+        "38    4601 non-null float64\n",
+        "39    4601 non-null float64\n",
+        "40    4601 non-null float64\n",
+        "41    4601 non-null float64\n",
+        "42    4601 non-null float64\n",
+        "43    4601 non-null float64\n",
+        "44    4601 non-null float64\n",
+        "45    4601 non-null float64\n",
+        "46    4601 non-null float64\n",
+        "47    4601 non-null float64\n",
+        "48    4601 non-null float64\n",
+        "49    4601 non-null float64\n",
+        "50    4601 non-null float64\n",
+        "51    4601 non-null float64\n",
+        "52    4601 non-null float64\n",
+        "53    4601 non-null float64\n",
+        "54    4601 non-null float64\n",
+        "55    4601 non-null int64\n",
+        "56    4601 non-null int64\n",
+        "57    4601 non-null int64\n",
+        "dtypes: float64(55), int64(3)\n",
+        "memory usage: 2.1 MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results = spam_data[57]\n",
+      "spam_data = spam_data.drop(57, axis=1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "train_data, test_data, train_results, test_results = train_test_split(spam_data, results, \n",
+      "                                                                      test_size=0.6, random_state=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Gaussian Naive Bayes"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\"\"\"Create Gaussian Naive Bayes learner and apply it to test data.  Then make predictions on test data\"\"\"\n",
+      "gaussian_learner = GaussianNB()\n",
+      "gaussian_learner.fit(train_data, train_results)\n",
+      "gaussian_predictions = gaussian_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, gaussian_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.95      0.75      0.84      1633\n",
+        "          1       0.73      0.94      0.82      1128\n",
+        "\n",
+        "avg / total       0.86      0.83      0.83      2761\n",
+        "\n",
+        "[[1231  402]\n",
+        " [  64 1064]]\n",
+        "0.820354664611\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, gaussian_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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YQEH3rMeWwGTgsbx8EunEq/c1t2lmnavdAkXFodeImBoRBwHLgX0jYnJETCbdNKOqq1bM\nrG/tds/MasYo3hkRpQtMiIhfSXpXE9tk1vE6cXr0sXzZ6nWk8YoTAJ/CbdaAwdRbqEY1geI0UhKg\nc/PrX5BukmFmdeq4QBERqyX9G/DTiHhyI7TJrOO1W6CoJgHQ0cA84Of59T6Sbm52w8w6WScOZk4D\n9gfuAoiIeQOZKayOe2hYjSZMmNDqJlgvgykIVKOaQLE2Il7t9YfVlBPAzHrqxECxQNKJwGaSJgDn\n4DtcmTWk3aZHq2nt2aQ7cP+BdDPP3wF/38xGmXW6dhujqCZQHB4RF0TEu/PjC6R8AmZWp0YChaTt\nJP1Q0hOSHpe0v6TtJc2S9LSk2yVtV7Z9w7lHqwkUF1S5zMyq1GCP4puk0xXeRbr26klSis9ZETER\nuDO/7p179FDgcqWcpTXp7+rRw4DDgdGS/oXuvIZbA2trrcjMutV7WJFTZkyJiFMAImId8Fo+jeHA\nvNk1wGxSsGhu7lHShV9zc0Vz6b7c/HXg07VUYmY9NTD+sDPwoqSrSIm55pLGDHeMiBV5mxXAjvn5\ngOQe7e+emY8Cj0q6CVgVEesBJA0F3lJrRWbWrYFAsRmwL/CpiHhI0jfIhxklERGS+jtBqSkJgG4n\nZSX6fX49HLgNOKDWyswsqTQ9umjRIhYtWtTfrkuBpRHxUH79Q+DzwPOS3hoRz0saRcroBxsx9+iw\niCgFCSLidUnDa63IzLpV6lGMHz+e8ePHb3h9xx139FifA8ESSRMj4mnSj/iC/DgFuCj/+5O8y0bL\nPbpK0uSImAsg6d3A6lorMrNuDZ4jcTZwvaQtgEWkK7yHAjMlnQEsBj4MGzf36N8DN0oq3dVqFGm6\nxczq1GDu0UeB9/Sx6uAK22+U3KMPSdoV2JU08/FknmoxszoNprMuq1EYKHKK9c8AO0XExyVNkLRr\nRPxn85tn1pnaLVBUc4bWVcAbdM9yLAf+uWktMtsEdOK1HuMi4iJSsCAiVjW3SWadb8iQIVU9Botq\nBjP/IGnL0gtJ40hXkppZnQZTb6Ea1d7h6ufA2yXNAN4PnNrENpl1vI4LFBFxu6SHSbfDE3BORLzU\n9JaZdbCOCxRKf9GBwAdI54hvDvy4ye0y62gdFyiAy4FxpLtbCThT0oci4qymtsysg3VioDgI2C0i\n3gSQdDXpdFAzq9NgmtGoRjWtXQjsVPZ6p7zMzOrUbudRVNOj2AZ4QtKDpDGK/YCHJN1CuvT96GY2\n0KwTDaYgUI1qAsUX+1gWdN/xysxq1ImB4oWI6DEmIWlqRMxuTpPMOl+7BYpqxihmSjpPyXBJlwEX\nNrthZp2s3cYoqgkU+5NupXU/6c44z+Hb4Jk1pN0CRTWHHutId7TaEhgG/KY0VWpm9enE6dEHgTXA\nu4EpwAmSbmxqq8w6XCf2KP6m7I6/zwFHSzqpiW0y63iDKQhUo5oexVxJJ0n6IoCknYCnm9sss87W\nbj2KagLF5cD7gBPy698D/9q0FpltAhoNFJKGSpqXT3xE0jRJS/OyeUopQUvbNpykuJpDj/0jYh9J\n8wAiYqWkzeupzMySAegtnEu65mrr/DqASyPi0l71lCcpHg3coZQTpKYJiWp6FG8opREsVbwD4FkP\nswY00qOQ9HZSAvEr6U4errLn5TYkKY6IxaTrtPartb3VBIrLSPef+DNJXwXuBb5Wa0Vm1q3Be2Z+\nHfgcPX+wAzhb0qOSvitpu7z8baQ0hCV1JSkuDBQRcR1wHik4LAeOiYiZtVZkZt3q7VFIOpJ0WcU8\nevYgriBlOt+bNDt5ST/VNyVTGBHxBPBErYWbWd8qHVYsWLCABQsW9LfrAaRTFA4nnQC5jaRrI+Lk\nsrKvBG7JLzdakmIzG2CVAsUee+zBHnvsseH1jTf2PLcxIi4ALshlHAj8Q0ScLGlURDyXNzsOmJ+f\nb7QkxXWTNB04gtRV2rOZdZm1kwE6R6L8Vg//X9Je+fUzwJmwcZMUN+Iq0mDotU2ux6ytDESgyLd6\nmJ2fVzxbeqMkKW5ERNwtaWwz6zBrR4PprMtqeIzCrAXa7epRBwqzFnCPokbTpk3b8Hzq1KlMnTq1\nZW0xq1ZXVxerV6+ue38HihqVBwqzdjF8+HCGDx++4fUrr7xS0/7tFiiaeqAk6QbgPmCipCWSTmtm\nfWbtot0uM2/2rMfxzSzfrF0NpiBQjZYfephtihwozKyQp0fNrJB7FGZWyIHCzAo5UJhZIQcKMyvk\nQGFmhTzrYWaF3KMws0IOFGZWyIHCzAo5UJhZoXYLFO019GrWIRpIADRM0gOSHpH0uKSv5eXbS5ol\n6WlJt5dlChuQJMUOFGYtUG9KwYhYAxwUEXsDk4CDJH0AOB+YFRETgTvz695Jig8FLpdU8/fegcKs\nBRq5cU1EdOWnWwBDgVeAo4Fr8vJrgGPz842WpNjMBliD2cyHSHoEWAHcFRELgB0jYkXeZAWwY34+\nIEmKPZhp1gKNDGZGxJvA3pK2BW6TdFCv9SGpv2xggy5TmJn1oVKgmDNnDnPnzq2qjIh4TdKtwGRg\nhaS3RsTzkkYBL+TNBiRJsepIQzhgJNWTBtFqNGHChFY3oeMtXLiQiKiqmyApqg0GkydP7lGupJHA\nuoh4VdKWwG3Al4D/DbwcERdJOh/YLiLOz4OZM0jjEqOBO4DxtX7x3KMwa4EGDj1GAdfkmYshwPci\n4k5J84CZks4AFgMfhvZJUmxmfaj36tGImA/s28fylcDBFfYZ3EmKzaxv7XZmpgOFWQs4UJhZIQcK\nMyvkQGFmhRwozKyQA4WZFfLNdc2skHsUZlbIgcLMCjlQmFkhBwozK+RAYWaFHCjMrFC7TY+2V2sH\ngdmzZ7e6CR2vq6ureKM218g9M1vBgaJGDhTNt3r16lY3oenaLVD40MOsBQZTEKiGA4VZC7RboGj5\nzXVbVrnZAKvl5rrLllV3I+zRo0dXXW4ztbRHMRjeALNWaLcehQczzVqg3tyjkqZLWiFpftmyaZKW\nSpqXH4eVrWs4QTF4jMKsJRroUVwFXAZcW7YsgEsj4tJedZQnKB4N3CFpYs40VhP3KMxaoN7p0Yi4\nm5SU+I+K7GPZgCQoBgcKs5ZownkUZ0t6VNJ3JW2Xlw1IgmLwocegJ2kq8NmIOErSUcBuEXFRhW23\nBU6IiCtqrGMa8HpEXFLN8l7bXA3cEhE/qrKusXn7PWtpY6epFATuuece7r333lqLuwL4cn7+FeAS\n4IwK29Y10+hA0SKShtR6rBgRtwC39LPJnwBnkT44NRVd4/Jat7FeKgWKKVOmMGXKlA2vL7744sKy\nIqKUkBhJV9L9GRmQBMXgQ48BJ2lsHmG+TtLjkm7MyWSRtFjShZLmAn8t6RBJ90maK2mmpBF5u0Ml\nPZG3O66s7FMlXZaf7yjpx5IeyY/3ARcC4/LI90V5u89JejB3S6eVlfUFSU9JuhvYtYq/6+O5nEck\n/bD0N2UHS3ool3dE3n6opIvL6v5Eg29tR6l31qMvStnLS44DSjMiNwMflbSFpJ2BCcCD9bTXPYrm\nmAicFhH3S/ou6Vf+EtKv70sRMVkpK/WPgD+PiNWSzgM+I+li4NvAQRGxSNIP6PtX+1+AuyLiOKWE\ntVsB5wG7R8Q+AHk6bHxE7Je3+Q9JU4Au0mj4XsDmwMPAnIK/6UcR8Z1c7ldIXdtvkQbR3hER75E0\nHrgr/3sK8Gqu+y3APZJur/md7FD1znpIugE4EBgpaQnwT8BUSXuTPifPAGfCwCUoBgeKZlkSEffn\n59cB55ACBcAP8r/vJU1b3Zc/NFsA95F+3Z+JiEVl+/f1a3wQ8DGAfAjzO0nb99rmEOAQpUzXACNI\nvypbAzdFxBpgjaSb6XvUvNyekv4fsC0pKP08Lw9gZm7HQkm/Ad6Z695T0l/l7bYBxpNG3jd59QaK\niDi+j8XT+9m+4QTF4EDRLOVRW71eryp7PisiTijfUdJevcrq7xNVzaftaxHx7V51nNtr3/7KKbX9\nauDoiJgv6RRgahX7fCoiZvWqe2xxkztfA+dRtITHKJpjJ0nvzc9PAO7uY5sHgPdLGgcgaYSkCcCT\nwFhJu+Tt+voFAbgT+Lu871BJ2wCvk3oLJbcBp5eNfYyWtAPwC+BYScMkbQ0cSeVBydIneivgeUmb\nk3oyUbb+r5WMA3bJf8NtwFmSNst1T5Q0vEIdm5wmTI82lXsUzfEU8ElJ04EFdM9CbPgyRsSLkk4F\nbsjH8ABfiIhf54G/WyV1kYLMiLL9S2WcC3xb0hnAeuBvI+IBSfcqnd7704g4T9K7gPvzh+514GMR\nMS+PfTwKvED/A1yl+v4vKbi9mP/dqmz9s7mMbYAzI+KNPPo+FnhYqfIXgGN7vw+bqsEUBKrR0qtH\nO5F8noAVkBSrVq0q3hAYMWLEoLh40j2K5nD0tX612z0z3aMw28gkxZo1a6radtiwYe5RmG2q2m2M\nwoHCrAUcKMyskAOFmRVyoDCzQg4UZlao3aZHHSjMWqDdehQ+j8LMCrVX/8fMWsKBwswKOVCYWSEH\nCjMr5EBhZoX+B0RkRZDESjQmAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10fbd5da0>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Multinominal Naive Bayes"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\"\"\"Create Gaussian Naive Bayes learner and apply it to test data.  Then make predictions on test data\"\"\"\n",
+      "multinomial_learner = MultinomialNB()\n",
+      "multinomial_learner.fit(train_data, train_results)\n",
+      "multinomial_predictions = multinomial_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, multinomial_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.82      0.88      0.85      1633\n",
+        "          1       0.80      0.72      0.76      1128\n",
+        "\n",
+        "avg / total       0.81      0.81      0.81      2761\n",
+        "\n",
+        "[[1434  199]\n",
+        " [ 314  814]]\n",
+        "0.760392340028\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, multinomial_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UZXc9uswM5rseZrVrm0BBx12PzYBJwB/T+g+SDbz6UGOrZta+Wi1QVOx6jYgpEbE/8BKw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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1151e3e80>"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Support Vector Machines"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "svc_learner = SVC()\n",
+      "svc_learner.fit(train_data, train_results)\n",
+      "svc_learner_predictions = svc_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, svc_learner_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.81      0.84      0.83      1633\n",
+        "          1       0.76      0.72      0.74      1128\n",
+        "\n",
+        "avg / total       0.79      0.79      0.79      2761\n",
+        "\n",
+        "[[1371  262]\n",
+        " [ 317  811]]\n",
+        "0.736937755566\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, svc_learner_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x115afa9b0>"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "linear_svc = LinearSVC()\n",
+      "linear_svc.fit(train_data, train_results)\n",
+      "linear_svc_predictions = linear_svc.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, linear_svc_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.85      0.95      0.90      1633\n",
+        "          1       0.91      0.75      0.82      1128\n",
+        "\n",
+        "avg / total       0.87      0.87      0.87      2761\n",
+        "\n",
+        "[[1550   83]\n",
+        " [ 280  848]]\n",
+        "0.823700825644\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, linear_svc_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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RsTrvthoYmZf3ICUmrniWlIe0JmUCxW8kfQzYTtIESVfiGa7MeqXeOTMljQc+C4wlBYGd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\nI0CWrwMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115bb8da0>"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Random Forest Classifier"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "random_forest_learner = RandomForestClassifier(n_estimators=500)\n",
+      "random_forest_learner.fit(train_data, train_results)\n",
+      "random_forest_predictions = random_forest_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, random_forest_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.94      0.97      0.95      1633\n",
+        "          1       0.96      0.90      0.93      1128\n",
+        "\n",
+        "avg / total       0.94      0.94      0.94      2761\n",
+        "\n",
+        "[[1588   45]\n",
+        " [ 110 1018]]\n",
+        "0.929256047467\n"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, random_forest_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x115bfb7f0>"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Gradient Boosting Classifier"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gradient_learner = GradientBoostingClassifier(n_estimators=500, learning_rate=1, max_depth=1, random_state=0)\n",
+      "gradient_learner.fit(train_data, train_results)\n",
+      "gradient_predictions = gradient_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 316
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, gradient_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.94      0.95      0.95      1363\n",
+        "          1       0.93      0.91      0.92       938\n",
+        "\n",
+        "avg / total       0.94      0.94      0.94      2301\n",
+        "\n",
+        "[[1301   62]\n",
+        " [  84  854]]\n",
+        "0.921251348436\n"
+       ]
+      }
+     ],
+     "prompt_number": 317
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, gradient_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x1158ef668>"
+       ]
+      }
+     ],
+     "prompt_number": 318
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Extra Tree Classifier"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "extra_tree = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=1, random_state=0)\n",
+      "extra_tree.fit(train_data, train_results)\n",
+      "extra_tree_predictions = extra_tree.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 319
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, extra_tree_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.94      0.96      0.95      1363\n",
+        "          1       0.94      0.91      0.93       938\n",
+        "\n",
+        "avg / total       0.94      0.94      0.94      2301\n",
+        "\n",
+        "[[1312   51]\n",
+        " [  87  851]]\n",
+        "0.925\n"
+       ]
+      }
+     ],
+     "prompt_number": 320
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, extra_tree_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ey8vOFGbWquq59ZA0jNSR+ZmyzRdK2oV0W/EccAoMQKYwM2ucOjOFrQRGdtt2XC/HNy5T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\nVfX/Ada03+dDL4NGAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x11611c828>"
+       ]
+      }
+     ],
+     "prompt_number": 321
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Finding and Removing Less Useful Columns +  Bagging"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "new_data = SelectKBest(chi2, k=50).fit_transform(spam_data, results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 322
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "train_data, test_data, train_results, test_results = train_test_split(new_data, results, \n",
+      "                                                                      test_size=0.4, random_state=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 323
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "random_forest_learner = RandomForestClassifier(n_estimators=500)\n",
+      "random_forest_learner.fit(train_data, train_results)\n",
+      "random_forest_predictions = random_forest_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 324
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, random_forest_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.95      0.97      0.96      1097\n",
+        "          1       0.95      0.92      0.94       744\n",
+        "\n",
+        "avg / total       0.95      0.95      0.95      1841\n",
+        "\n",
+        "[[1064   33]\n",
+        " [  61  683]]\n",
+        "0.935616438356\n"
+       ]
+      }
+     ],
+     "prompt_number": 325
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, random_forest_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAADvCAYAAAAKNZpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncqAws1wOFGaW6/8DiBiyNvXPqA4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115194b38>"
+       ]
+      }
+     ],
+     "prompt_number": 326
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gradient_learner = GradientBoostingClassifier(n_estimators=70, learning_rate=1, max_depth=1, random_state=0)\n",
+      "gradient_learner.fit(train_data, train_results)\n",
+      "gradient_predictions = gradient_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 327
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, gradient_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.94      0.96      0.95      1097\n",
+        "          1       0.94      0.91      0.93       744\n",
+        "\n",
+        "avg / total       0.94      0.94      0.94      1841\n",
+        "\n",
+        "[[1055   42]\n",
+        " [  64  680]]\n",
+        "0.927694406548\n"
+       ]
+      }
+     ],
+     "prompt_number": 328
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, gradient_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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oVqJYvXo1q1ev7unQNcCaiLg/rd8InA+skzQ8ItZJGkHWOwnZw6bKeylHp7SaOFCYNUG1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\nQGFmuRwozCyXA4WZ5XKgMLNc/x+uCKv+07TW6QAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115a90400>"
+       ]
+      }
+     ],
+     "prompt_number": 329
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "extra_tree = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=1, random_state=0)\n",
+      "extra_tree.fit(train_data, train_results)\n",
+      "extra_tree_predictions = extra_tree.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 112
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, extra_tree_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.93      0.97      0.95      1097\n",
+        "          1       0.96      0.90      0.93       744\n",
+        "\n",
+        "avg / total       0.94      0.94      0.94      1841\n",
+        "\n",
+        "[[1066   31]\n",
+        " [  77  667]]\n",
+        "0.925104022191\n"
+       ]
+      }
+     ],
+     "prompt_number": 113
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, extra_tree_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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rycysmB70aOwAHAZ8LiIelnQ1FXdJR0RI6u5L3pMUm7WCaiWKlStXsnLlyu4OXQWsioiH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\n1hQOFGaWy4HCzHI5UJhZLgcKM8v1/wGVacfQTKAvqwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1151a3278>"
+       ]
+      }
+     ],
+     "prompt_number": 114
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gaussian_learner = GaussianNB()\n",
+      "gaussian_learner.fit(train_data, train_results)\n",
+      "gaussian_predictions = gaussian_learner.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 115
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, gaussian_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.95      0.73      0.83      1097\n",
+        "          1       0.70      0.94      0.81       744\n",
+        "\n",
+        "avg / total       0.85      0.82      0.82      1841\n",
+        "\n",
+        "[[804 293]\n",
+        " [ 45 699]]\n",
+        "0.805299539171\n"
+       ]
+      }
+     ],
+     "prompt_number": 116
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, extra_tree_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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rycysmB70aOwAHAZ8LiIelnQ1FXdJR0RI6u5L3pMUm7WCaiWKlStXsnLlyu4OXQWsioiH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\n1hQOFGaWy4HCzHI5UJhZLgcKM8v1/wGVacfQTKAvqwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1151c38d0>"
+       ]
+      }
+     ],
+     "prompt_number": 117
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ada_boost = AdaBoostClassifier()\n",
+      "ada_boost.fit(train_data, train_results)\n",
+      "ada_predictions = ada_boost.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 118
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, ada_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.92      0.95      0.94      1097\n",
+        "          1       0.93      0.88      0.91       744\n",
+        "\n",
+        "avg / total       0.93      0.93      0.93      1841\n",
+        "\n",
+        "[[1046   51]\n",
+        " [  86  658]]\n",
+        "0.905712319339\n"
+       ]
+      }
+     ],
+     "prompt_number": 119
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_binary_plot(test_results, ada_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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HaW5+tSxdvwOFWQmanEfxeeCanMP2KeB4YChwvaQTgYXAX0FK1y+pK13/mwx0uv6KUY8+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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115aee5f8>"
+       ]
+      }
+     ],
+     "prompt_number": 120
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Machine Learning Democracy - Bootstrap Aggregation"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "democratic_predictions = (random_forest_predictions + gradient_predictions + extra_tree_predictions \n",
+      "                          + gaussian_predictions + ada_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 121
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "democratic_results = []\n",
+      "\n",
+      "for element in democratic_predictions:\n",
+      "    if element >= 3:\n",
+      "        democratic_results.append(1)\n",
+      "    else:\n",
+      "        democratic_results.append(0)\n",
+      "\n",
+      "        \n",
+      "democratic_predictions = np.array(democratic_results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 122
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, democratic_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.95      0.97      0.96      1097\n",
+        "          1       0.95      0.92      0.94       744\n",
+        "\n",
+        "avg / total       0.95      0.95      0.95      1841\n",
+        "\n",
+        "[[1059   38]\n",
+        " [  56  688]]\n",
+        "0.936054421769\n"
+       ]
+      }
+     ],
+     "prompt_number": 123
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Bagging With Different Random Samples"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "train_data, test_data, train_results, test_results = train_test_split(new_data, results, \n",
+      "                                                                      test_size=0.5, random_state=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 306
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "train_data1, test_data1, train_results1, test_results1 = train_test_split(train_data, train_results, test_size=0.2, random_state=3)\n",
+      "train_data2, test_data2, train_results2, test_results2 = train_test_split(train_data, train_results, test_size=0.2, random_state=7)\n",
+      "train_data3, test_data3, train_results3, test_results3 = train_test_split(train_data, train_results, test_size=0.2, random_state=11)\n",
+      "train_data4, test_data4, train_results4, test_results4 = train_test_split(train_data, train_results, test_size=0.2, random_state=13)\n",
+      "train_data5, test_data5, train_results5, test_results5 = train_test_split(train_data, train_results, test_size=0.2, random_state=17)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 307
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "random_forest_learner = RandomForestClassifier(n_estimators=100)\n",
+      "random_forest_learner.fit(train_data1, train_results1)\n",
+      "random_forest_predictions = random_forest_learner.predict(test_data)\n",
+      "\n",
+      "gradient_learner = GradientBoostingClassifier(n_estimators=100, learning_rate=1, max_depth=1)\n",
+      "gradient_learner.fit(train_data2, train_results2)\n",
+      "gradient_predictions = gradient_learner.predict(test_data)\n",
+      "\n",
+      "extra_tree = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=1)\n",
+      "extra_tree.fit(train_data3, train_results3)\n",
+      "extra_tree_predictions = extra_tree.predict(test_data)\n",
+      "\n",
+      "gaussian_learner = GaussianNB()\n",
+      "gaussian_learner.fit(train_data4, train_results4)\n",
+      "gaussian_predictions = gaussian_learner.predict(test_data)\n",
+      "\n",
+      "ada_boost = AdaBoostClassifier()\n",
+      "ada_boost.fit(train_data5, train_results5)\n",
+      "ada_predictions = ada_boost.predict(test_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 312
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "democratic_predictions = (random_forest_predictions + gradient_predictions + extra_tree_predictions \n",
+      "                          + gaussian_predictions + ada_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 313
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "democratic_results = []\n",
+      "\n",
+      "for element in democratic_predictions:\n",
+      "    if element >= 3:\n",
+      "        democratic_results.append(1)\n",
+      "    else:\n",
+      "        democratic_results.append(0)\n",
+      "\n",
+      "        \n",
+      "democratic_predictions = np.array(democratic_results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 314
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "show_results(test_results, democratic_predictions)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "             precision    recall  f1-score   support\n",
+        "\n",
+        "          0       0.95      0.96      0.96      1363\n",
+        "          1       0.95      0.92      0.93       938\n",
+        "\n",
+        "avg / total       0.95      0.95      0.95      2301\n",
+        "\n",
+        "[[1313   50]\n",
+        " [  72  866]]\n",
+        "0.934196332255\n"
+       ]
+      }
+     ],
+     "prompt_number": 315
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file