diff --git a/ML_simple_project/Titanic_servival_prediction/README.md b/ML_simple_project/Titanic_servival_prediction/README.md new file mode 100644 index 0000000..d2eed3e --- /dev/null +++ b/ML_simple_project/Titanic_servival_prediction/README.md @@ -0,0 +1,24 @@ +# Titanic Survival Prediction + +## Problem statement +To train a model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc). + +## Structure of the dataset +The dataset is gathered from Kaggle. + +Data Dictionary +- Variable - Definition Key +- survival - Survival 0 = No, 1 = Yes +- pclass - Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd +- sex - Gender +- Age - Age in years +- sibsp - # of siblings / spouses aboard the Titanic +- parch - # of parents / children aboard the Titanic +- ticket - Ticket number +- fare - Passenger fare +- cabin - Cabin number +- embarked - Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) + + +## Creator +- [Yasas](https://github.com/yasas4d) diff --git a/ML_simple_project/Titanic_servival_prediction/Titanic_survival_prediction.ipynb b/ML_simple_project/Titanic_servival_prediction/Titanic_survival_prediction.ipynb new file mode 100644 index 0000000..99927b2 --- /dev/null +++ b/ML_simple_project/Titanic_servival_prediction/Titanic_survival_prediction.ipynb @@ -0,0 +1,8533 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "16084f56", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:30.646671Z", + "iopub.status.busy": "2022-09-24T14:19:30.645928Z", + "iopub.status.idle": "2022-09-24T14:19:44.502019Z", + "shell.execute_reply": "2022-09-24T14:19:44.500642Z" + }, + "papermill": { + "duration": 13.891803, + "end_time": "2022-09-24T14:19:44.505052", + "exception": false, + "start_time": "2022-09-24T14:19:30.613249", + "status": "completed" + }, + "tags": [], + "id": "16084f56", + "outputId": "33c9a3b2-f341-4988-c992-747b3bfcf789" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting plot_metric\r\n", + " Downloading plot_metric-0.0.6-py3-none-any.whl (13 kB)\r\n", + "Requirement already satisfied: pandas>=0.23.4 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (1.3.5)\r\n", + "Requirement already satisfied: colorlover>=0.3.0 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (0.3.0)\r\n", + "Requirement already satisfied: scipy>=1.1.0 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (1.7.3)\r\n", + "Requirement already satisfied: matplotlib>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (3.5.3)\r\n", + "Requirement already satisfied: numpy>=1.15.4 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (1.21.6)\r\n", + "Requirement already satisfied: scikit-learn>=0.21.2 in /opt/conda/lib/python3.7/site-packages (from plot_metric) (1.0.2)\r\n", + "Requirement already 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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install plot_metric" + ] + }, + { + "cell_type": "markdown", + "id": "aadc834c", + "metadata": { + "papermill": { + "duration": 0.032528, + "end_time": "2022-09-24T14:19:44.569171", + "exception": false, + "start_time": "2022-09-24T14:19:44.536643", + "status": "completed" + }, + "tags": [], + "id": "aadc834c" + }, + "source": [ + "**Import required libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd85de54", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:44.633280Z", + "iopub.status.busy": "2022-09-24T14:19:44.632851Z", + "iopub.status.idle": "2022-09-24T14:19:47.206201Z", + "shell.execute_reply": "2022-09-24T14:19:47.204888Z" + }, + "papermill": { + "duration": 2.609658, + "end_time": "2022-09-24T14:19:47.209255", + "exception": false, + "start_time": "2022-09-24T14:19:44.599597", + "status": "completed" + }, + "tags": [], + "id": "bd85de54" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n", + "from scipy import stats\n", + "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedShuffleSplit\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA, IncrementalPCA#, plot_roc_curve\n", + "from sklearn.inspection import permutation_importance\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n", + "from sklearn import svm\n", + "import xgboost as xgb\n", + "from sklearn.metrics import accuracy_score, log_loss, f1_score, classification_report, roc_curve, plot_roc_curve\n", + "from sklearn.metrics import confusion_matrix, precision_score, recall_score, precision_recall_curve, roc_auc_score\n", + "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", + "from plot_metric.functions import BinaryClassification\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a8b7e39", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:47.272741Z", + "iopub.status.busy": "2022-09-24T14:19:47.272310Z", + "iopub.status.idle": "2022-09-24T14:19:47.278017Z", + "shell.execute_reply": "2022-09-24T14:19:47.276705Z" + }, + "papermill": { + "duration": 0.040319, + "end_time": "2022-09-24T14:19:47.280526", + "exception": false, + "start_time": "2022-09-24T14:19:47.240207", + "status": "completed" + }, + "tags": [], + "id": "6a8b7e39" + }, + "outputs": [], + "source": [ + "#Setting max columns/rows display range\n", + "pd.set_option('display.max_columns', 100)\n", + "pd.set_option('display.max_rows', 150)" + ] + }, + { + "cell_type": "markdown", + "id": "b277828f", + "metadata": { + "papermill": { + "duration": 0.029586, + "end_time": "2022-09-24T14:19:47.339486", + "exception": false, + "start_time": "2022-09-24T14:19:47.309900", + "status": "completed" + }, + "tags": [], + "id": "b277828f" + }, + "source": [ + "**Read data**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1171f7cf", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:47.403056Z", + "iopub.status.busy": "2022-09-24T14:19:47.402328Z", + "iopub.status.idle": "2022-09-24T14:19:47.446023Z", + "shell.execute_reply": "2022-09-24T14:19:47.445206Z" + }, + "papermill": { + "duration": 0.077688, + "end_time": "2022-09-24T14:19:47.448170", + "exception": false, + "start_time": "2022-09-24T14:19:47.370482", + "status": "completed" + }, + "tags": [], + "id": "1171f7cf", + "outputId": "8bd6bf53-5fce-4f96-fd5b-85ee9be9cd72" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " Datatype Non_Null_Count Null_Count Null_Percentage \\\n", + "Cabin object 204 687 77.10 \n", + "Age float64 714 177 19.87 \n", + "Embarked object 889 2 0.22 \n", + "PassengerId int64 891 0 0.00 \n", + "Survived int64 891 0 0.00 \n", + "Pclass int64 891 0 0.00 \n", + "Name object 891 0 0.00 \n", + "Sex object 891 0 0.00 \n", + "SibSp int64 891 0 0.00 \n", + "Parch int64 891 0 0.00 \n", + "Ticket object 891 0 0.00 \n", + "Fare float64 891 0 0.00 \n", + "\n", + " Unique_Values_Count \n", + "Cabin 147 \n", + "Age 88 \n", + "Embarked 3 \n", + "PassengerId 891 \n", + "Survived 2 \n", + "Pclass 3 \n", + "Name 891 \n", + "Sex 2 \n", + "SibSp 7 \n", + "Parch 7 \n", + "Ticket 681 \n", + "Fare 248 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking detail information about train data.\n", + "def getDatasetDetail(data) : \n", + " return pd.DataFrame({\n", + " 'Datatype' : data.dtypes.astype(str), \n", + " 'Non_Null_Count': data.count(axis = 0).astype(int), \n", + " 'Null_Count': data.isnull().sum().astype(int), \n", + " 'Null_Percentage': round(data.isnull().sum()/len(data) * 100 , 2), \n", + " 'Unique_Values_Count': data.nunique().astype(int) \n", + " }).sort_values(by='Null_Percentage', ascending=False)\n", + "\n", + "getDatasetDetail(train_data)" + ] + }, + { + "cell_type": "markdown", + "id": "cf824841", + "metadata": { + "papermill": { + "duration": 0.03015, + "end_time": "2022-09-24T14:19:47.786901", + "exception": false, + "start_time": "2022-09-24T14:19:47.756751", + "status": "completed" + }, + "tags": [], + "id": "cf824841" + }, + "source": [ + "**Derive new variables**" + ] + }, + { + "cell_type": "markdown", + "id": "778d4ec6", + "metadata": { + "papermill": { + "duration": 0.03003, + "end_time": "2022-09-24T14:19:47.847375", + "exception": false, + "start_time": "2022-09-24T14:19:47.817345", + "status": "completed" + }, + "tags": [], + "id": "778d4ec6" + }, + "source": [ + "Create a new varibles wheather based in cabin information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ed1753f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:47.910575Z", + "iopub.status.busy": "2022-09-24T14:19:47.909621Z", + "iopub.status.idle": "2022-09-24T14:19:47.916848Z", + "shell.execute_reply": "2022-09-24T14:19:47.916050Z" + }, + "papermill": { + "duration": 0.041327, + "end_time": "2022-09-24T14:19:47.918942", + "exception": false, + "start_time": "2022-09-24T14:19:47.877615", + "status": "completed" + }, + "tags": [], + "id": "5ed1753f" + }, + "outputs": [], + "source": [ + "train_data[\"CabinBool\"] = (train_data[\"Cabin\"].notnull().astype('int'))" + ] + }, + { + "cell_type": "markdown", + "id": "8d5d8a37", + "metadata": { + "papermill": { + "duration": 0.029996, + "end_time": "2022-09-24T14:19:47.979168", + "exception": false, + "start_time": "2022-09-24T14:19:47.949172", + "status": "completed" + }, + "tags": [], + "id": "8d5d8a37" + }, + "source": [ + "Create a new variable using Name variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e66312e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.044502Z", + "iopub.status.busy": "2022-09-24T14:19:48.043423Z", + "iopub.status.idle": "2022-09-24T14:19:48.051915Z", + "shell.execute_reply": "2022-09-24T14:19:48.051037Z" + }, + "papermill": { + "duration": 0.043125, + "end_time": "2022-09-24T14:19:48.054357", + "exception": false, + "start_time": "2022-09-24T14:19:48.011232", + "status": "completed" + }, + "tags": [], + "id": "5e66312e" + }, + "outputs": [], + "source": [ + "train_data['Name_title'] = train_data['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f090a31", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.118526Z", + "iopub.status.busy": "2022-09-24T14:19:48.117799Z", + "iopub.status.idle": "2022-09-24T14:19:48.127194Z", + "shell.execute_reply": "2022-09-24T14:19:48.126079Z" + }, + "papermill": { + "duration": 0.043953, + "end_time": "2022-09-24T14:19:48.129334", + "exception": false, + "start_time": "2022-09-24T14:19:48.085381", + "status": "completed" + }, + "tags": [], + "id": "5f090a31", + "outputId": "a09bb991-fbdd-4d7a-ea22-28932702d53e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Mr 517\n", + "Miss 182\n", + "Mrs 125\n", + "Master 40\n", + "Dr 7\n", + "Rev 6\n", + "Mlle 2\n", + "Major 2\n", + "Col 2\n", + "the Countess 1\n", + "Capt 1\n", + "Ms 1\n", + "Sir 1\n", + "Lady 1\n", + "Mme 1\n", + "Don 1\n", + "Jonkheer 1\n", + "Name: Name_title, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data['Name_title'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ff5dfe0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.192543Z", + "iopub.status.busy": "2022-09-24T14:19:48.191440Z", + "iopub.status.idle": "2022-09-24T14:19:48.198380Z", + "shell.execute_reply": "2022-09-24T14:19:48.197326Z" + }, + "papermill": { + "duration": 0.04093, + "end_time": "2022-09-24T14:19:48.200597", + "exception": false, + "start_time": "2022-09-24T14:19:48.159667", + "status": "completed" + }, + "tags": [], + "id": "1ff5dfe0" + }, + "outputs": [], + "source": [ + "train_data['Name_title'] = train_data['Name_title'].apply(lambda a: 'Other' if a not in ['Mr', 'Miss', 'Mrs', 'Master'] else a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49f25158", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.267690Z", + "iopub.status.busy": "2022-09-24T14:19:48.266912Z", + "iopub.status.idle": "2022-09-24T14:19:48.276487Z", + "shell.execute_reply": "2022-09-24T14:19:48.275169Z" + }, + "papermill": { + "duration": 0.045426, + "end_time": "2022-09-24T14:19:48.279058", + "exception": false, + "start_time": "2022-09-24T14:19:48.233632", + "status": "completed" + }, + "tags": [], + "id": "49f25158", + "outputId": "ab461764-0b89-4db9-91c0-ec4002cad3e0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Mr 517\n", + "Miss 182\n", + "Mrs 125\n", + "Master 40\n", + "Other 27\n", + "Name: Name_title, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data['Name_title'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70cf0c17", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.347589Z", + "iopub.status.busy": "2022-09-24T14:19:48.347064Z", + "iopub.status.idle": "2022-09-24T14:19:48.352887Z", + "shell.execute_reply": "2022-09-24T14:19:48.351928Z" + }, + "papermill": { + "duration": 0.043301, + "end_time": "2022-09-24T14:19:48.355215", + "exception": false, + "start_time": "2022-09-24T14:19:48.311914", + "status": "completed" + }, + "tags": [], + "id": "70cf0c17" + }, + "outputs": [], + "source": [ + "def return_title(title, Age):\n", + " if title != 'Mr':\n", + " return title\n", + " elif (title == 'Mr') & (Age > 25.0):\n", + " return 'Mr1'\n", + " else:\n", + " return 'Mr2'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3e8f70d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.420465Z", + "iopub.status.busy": "2022-09-24T14:19:48.419735Z", + "iopub.status.idle": "2022-09-24T14:19:48.448967Z", + "shell.execute_reply": "2022-09-24T14:19:48.448011Z" + }, + "papermill": { + "duration": 0.065121, + "end_time": "2022-09-24T14:19:48.451773", + "exception": false, + "start_time": "2022-09-24T14:19:48.386652", + "status": "completed" + }, + "tags": [], + "id": "f3e8f70d" + }, + "outputs": [], + "source": [ + "#train_data[train_data['Name_title'] == 'Mr'].groupby('Age').count()['PassengerId']\n", + "train_data['Name_title'] = train_data[['Name_title', 'Age']].apply(lambda a: return_title(a.Name_title, a.Age), axis = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "359da8eb", + "metadata": { + "papermill": { + "duration": 0.034667, + "end_time": "2022-09-24T14:19:48.517925", + "exception": false, + "start_time": "2022-09-24T14:19:48.483258", + "status": "completed" + }, + "tags": [], + "id": "359da8eb" + }, + "source": [ + "Let's check title vs survival ratio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9443fbc9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.587038Z", + "iopub.status.busy": "2022-09-24T14:19:48.586582Z", + "iopub.status.idle": "2022-09-24T14:19:48.599645Z", + "shell.execute_reply": "2022-09-24T14:19:48.598530Z" + }, + "papermill": { + "duration": 0.048461, + "end_time": "2022-09-24T14:19:48.602283", + "exception": false, + "start_time": "2022-09-24T14:19:48.553822", + "status": "completed" + }, + "tags": [], + "id": "9443fbc9", + "outputId": "65a148e0-0b9d-4726-8d9a-e5864eba05be" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Name_title Survived\n", + "Master 0 17\n", + " 1 23\n", + "Miss 0 55\n", + " 1 127\n", + "Mr1 0 205\n", + " 1 51\n", + "Mr2 0 231\n", + " 1 30\n", + "Mrs 0 26\n", + " 1 99\n", + "Other 0 15\n", + " 1 12\n", + "Name: PassengerId, dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.groupby(['Name_title','Survived']).count()['PassengerId']" + ] + }, + { + "cell_type": "markdown", + "id": "4187e972", + "metadata": { + "papermill": { + "duration": 0.032306, + "end_time": "2022-09-24T14:19:48.666487", + "exception": false, + "start_time": "2022-09-24T14:19:48.634181", + "status": "completed" + }, + "tags": [], + "id": "4187e972" + }, + "source": [ + "Create a new variable using Title variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae2b15a7", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.731586Z", + "iopub.status.busy": "2022-09-24T14:19:48.730886Z", + "iopub.status.idle": "2022-09-24T14:19:48.737028Z", + "shell.execute_reply": "2022-09-24T14:19:48.736160Z" + }, + "papermill": { + "duration": 0.040326, + "end_time": "2022-09-24T14:19:48.739294", + "exception": false, + "start_time": "2022-09-24T14:19:48.698968", + "status": "completed" + }, + "tags": [], + "id": "ae2b15a7" + }, + "outputs": [], + "source": [ + "train_data['Ticket_cls'] = train_data['Ticket'].apply(lambda a: a.split(' ')[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04a508ee", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.805819Z", + "iopub.status.busy": "2022-09-24T14:19:48.805123Z", + "iopub.status.idle": "2022-09-24T14:19:48.811842Z", + "shell.execute_reply": "2022-09-24T14:19:48.810598Z" + }, + "papermill": { + "duration": 0.043658, + "end_time": "2022-09-24T14:19:48.814727", + "exception": false, + "start_time": "2022-09-24T14:19:48.771069", + "status": "completed" + }, + "tags": [], + "id": "04a508ee" + }, + "outputs": [], + "source": [ + "train_data['Ticket_cls'] = train_data['Ticket_cls'].apply(lambda a: 'Numeric' if a.isnumeric() else 'Alphabet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "569cf366", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.881300Z", + "iopub.status.busy": "2022-09-24T14:19:48.880865Z", + "iopub.status.idle": "2022-09-24T14:19:48.885904Z", + "shell.execute_reply": "2022-09-24T14:19:48.885074Z" + }, + "papermill": { + "duration": 0.041644, + "end_time": "2022-09-24T14:19:48.888312", + "exception": false, + "start_time": "2022-09-24T14:19:48.846668", + "status": "completed" + }, + "tags": [], + "id": "569cf366" + }, + "outputs": [], + "source": [ + "def return_numeric(cls, Ticket):\n", + " if cls != 'Numeric':\n", + " return cls\n", + " elif (cls == 'Numeric') & (int(Ticket) > 120000):\n", + " return 'Numeric1'\n", + " else:\n", + " return 'Numeric2'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd54aa79", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:48.954167Z", + "iopub.status.busy": "2022-09-24T14:19:48.953139Z", + "iopub.status.idle": "2022-09-24T14:19:48.983445Z", + "shell.execute_reply": "2022-09-24T14:19:48.982545Z" + }, + "papermill": { + "duration": 0.066637, + "end_time": "2022-09-24T14:19:48.986078", + "exception": false, + "start_time": "2022-09-24T14:19:48.919441", + "status": "completed" + }, + "tags": [], + "id": "dd54aa79" + }, + "outputs": [], + "source": [ + "train_data['Ticket_cls'] = train_data[['Ticket_cls', 'Ticket']].apply(lambda a: return_numeric(a.Ticket_cls, a.Ticket), axis = 1)" + ] + }, + { + "cell_type": "markdown", + "id": "70a69aab", + "metadata": { + "papermill": { + "duration": 0.033885, + "end_time": "2022-09-24T14:19:49.054515", + "exception": false, + "start_time": "2022-09-24T14:19:49.020630", + "status": "completed" + }, + "tags": [], + "id": "70a69aab" + }, + "source": [ + "Let's check title vs survival ratio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c3c8a24", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:49.122119Z", + "iopub.status.busy": "2022-09-24T14:19:49.121432Z", + "iopub.status.idle": "2022-09-24T14:19:49.133415Z", + "shell.execute_reply": "2022-09-24T14:19:49.132510Z" + }, + "papermill": { + "duration": 0.04698, + "end_time": "2022-09-24T14:19:49.135737", + "exception": false, + "start_time": "2022-09-24T14:19:49.088757", + "status": "completed" + }, + "tags": [], + "id": "4c3c8a24", + "outputId": "59b6a0b3-5d34-4425-fed6-b8b94a7efe3d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Ticket_cls Survived\n", + "Alphabet 0 142\n", + " 1 88\n", + "Numeric1 0 259\n", + " 1 100\n", + "Numeric2 0 148\n", + " 1 154\n", + "Name: PassengerId, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.groupby(['Ticket_cls','Survived']).count()['PassengerId']" + ] + }, + { + "cell_type": "markdown", + "id": "0ae1f77d", + "metadata": { + "papermill": { + "duration": 0.030537, + "end_time": "2022-09-24T14:19:49.198407", + "exception": false, + "start_time": "2022-09-24T14:19:49.167870", + "status": "completed" + }, + "tags": [], + "id": "0ae1f77d" + }, + "source": [ + "Create new variable using SibSp and Parch variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5833eb4e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:49.263126Z", + "iopub.status.busy": "2022-09-24T14:19:49.262695Z", + "iopub.status.idle": "2022-09-24T14:19:49.269355Z", + "shell.execute_reply": "2022-09-24T14:19:49.268070Z" + }, + "papermill": { + "duration": 0.04132, + "end_time": "2022-09-24T14:19:49.271811", + "exception": false, + "start_time": "2022-09-24T14:19:49.230491", + "status": "completed" + }, + "tags": [], + "id": "5833eb4e" + }, + "outputs": [], + "source": [ + "train_data['FamilySize'] = train_data['SibSp'] + train_data['Parch'] + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87ec6623", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:49.336265Z", + "iopub.status.busy": "2022-09-24T14:19:49.335854Z", + "iopub.status.idle": "2022-09-24T14:19:49.341881Z", + "shell.execute_reply": "2022-09-24T14:19:49.340966Z" + }, + "papermill": { + "duration": 0.041425, + "end_time": "2022-09-24T14:19:49.344139", + "exception": false, + "start_time": "2022-09-24T14:19:49.302714", + "status": "completed" + }, + "tags": [], + "id": "87ec6623" + }, + "outputs": [], + "source": [ + "train_data['Solo_traveller'] = train_data['FamilySize'].apply(lambda a: 'Yes' if a == 1 else 'No')" + ] + }, + { + "cell_type": "markdown", + "id": "88899cc1", + "metadata": { + "papermill": { + "duration": 0.030321, + "end_time": "2022-09-24T14:19:49.405962", + "exception": false, + "start_time": "2022-09-24T14:19:49.375641", + "status": "completed" + }, + "tags": [], + "id": "88899cc1" + }, + "source": [ + "**Data transformation**" + ] + }, + { + "cell_type": "markdown", + "id": "6775b835", + "metadata": { + "papermill": { + "duration": 0.031012, + "end_time": "2022-09-24T14:19:49.469832", + "exception": false, + "start_time": "2022-09-24T14:19:49.438820", + "status": "completed" + }, + "tags": [], + "id": "6775b835" + }, + "source": [ + "Check the skewness on Age" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99d8fcce", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:49.534372Z", + "iopub.status.busy": "2022-09-24T14:19:49.533716Z", + "iopub.status.idle": "2022-09-24T14:19:49.897593Z", + "shell.execute_reply": "2022-09-24T14:19:49.896720Z" + }, + "papermill": { + "duration": 0.399018, + "end_time": "2022-09-24T14:19:49.899825", + "exception": false, + "start_time": "2022-09-24T14:19:49.500807", + "status": "completed" + }, + "tags": [], + "id": "99d8fcce", + "outputId": "06a74c7b-9569-4abc-dcb0-8158c8a0c563" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Passenger Age Distribution')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(train_data['Age'], label = 'Skewness: %.2f'%(train_data['Age'].skew()))\n", + "plt.legend(loc = 'best')\n", + "plt.title('Passenger Age Distribution')" + ] + }, + { + "cell_type": "markdown", + "id": "fa294a00", + "metadata": { + "papermill": { + "duration": 0.033678, + "end_time": "2022-09-24T14:19:49.966649", + "exception": false, + "start_time": "2022-09-24T14:19:49.932971", + "status": "completed" + }, + "tags": [], + "id": "fa294a00" + }, + "source": [ + "**Observation:** Skewness is fine for Age column" + ] + }, + { + "cell_type": "markdown", + "id": "f9fee1d2", + "metadata": { + "papermill": { + "duration": 0.031829, + "end_time": "2022-09-24T14:19:50.030528", + "exception": false, + "start_time": "2022-09-24T14:19:49.998699", + "status": "completed" + }, + "tags": [], + "id": "f9fee1d2" + }, + "source": [ + "Check the skewness on Fare" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c40161a6", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:50.098048Z", + "iopub.status.busy": "2022-09-24T14:19:50.097213Z", + "iopub.status.idle": "2022-09-24T14:19:50.477773Z", + "shell.execute_reply": "2022-09-24T14:19:50.476507Z" + }, + "papermill": { + "duration": 0.416152, + "end_time": "2022-09-24T14:19:50.480324", + "exception": false, + "start_time": "2022-09-24T14:19:50.064172", + "status": "completed" + }, + "tags": [], + "id": "c40161a6", + "outputId": "e105ebe2-ece0-4132-c3da-10e1247c0b88" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Passenger Fare Distribution')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(train_data['Fare'], label = 'Skewness: %.2f'%(train_data['Fare'].skew()))\n", + "plt.legend(loc = 'best')\n", + "plt.ylabel('Passenger Fare Distribution')" + ] + }, + { + "cell_type": "markdown", + "id": "441c04bf", + "metadata": { + "papermill": { + "duration": 0.031177, + "end_time": "2022-09-24T14:19:50.543367", + "exception": false, + "start_time": "2022-09-24T14:19:50.512190", + "status": "completed" + }, + "tags": [], + "id": "441c04bf" + }, + "source": [ + "**Observation:** Skewness is not good in this scenarion. So, in next step correcting it using log transformation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a88c5a1a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:50.608751Z", + "iopub.status.busy": "2022-09-24T14:19:50.608338Z", + "iopub.status.idle": "2022-09-24T14:19:50.615394Z", + "shell.execute_reply": "2022-09-24T14:19:50.614310Z" + }, + "papermill": { + "duration": 0.042502, + "end_time": "2022-09-24T14:19:50.617609", + "exception": false, + "start_time": "2022-09-24T14:19:50.575107", + "status": "completed" + }, + "tags": [], + "id": "a88c5a1a" + }, + "outputs": [], + "source": [ + "train_data['norm_fare'] = np.log(train_data['Fare'] + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13cbdf85", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:50.686948Z", + "iopub.status.busy": "2022-09-24T14:19:50.686505Z", + "iopub.status.idle": "2022-09-24T14:19:51.024033Z", + "shell.execute_reply": "2022-09-24T14:19:51.022867Z" + }, + "papermill": { + "duration": 0.376923, + "end_time": "2022-09-24T14:19:51.026514", + "exception": false, + "start_time": "2022-09-24T14:19:50.649591", + "status": "completed" + }, + "tags": [], + "id": "13cbdf85", + "outputId": "d34b3db4-1f39-41b8-cda2-20cb20938271" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Passenger Fare Distribution After Log Transformation')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+nby8PI477ji+973v8eGHHx4xvaKigvPOO49LLrmE6667jt/97nd89atfZcmSJbz//vv87Gc/4+DBg0ydOpWFCxdSWlqK1+vl448/BmDhwoV85StfAWD58uXce++9rF27lq1bt/Lxxx9TU1PDTTfdxAsvvMDSpUu55ppr+NWvfgXA3XffzfLly1m5ciUPP/wwAH/605948MEHWbFiBQsXLiQ+Ph6AcePGfem9FRcXk5aW1riTz8rKYtcu/8+l//TTTxk1ahRjxozh4Ycfxuv1Mnr0aBYuXEhxcTGVlZXMnz+fvLy8Y17nlgiMMWElKSmJpUuXMnfuXDIzM7n44ot5/PHHG6fPmjWLq6++miuvvBKAt99+m7vvvptx48Yxbdo0qqqq2LlzJ1OnTmXBggV8/PHHnHPOOVRUVFBZWcm2bdsYNmwYAJMmTSIrK4uoqCjGjRvH9u3b2bBhA6tXr+bMM89k3Lhx3HXXXeTn5wMwduxYLrvsMp5++unGnfmUKVP48Y9/zP33309JSUnj+BUrVhzTepg8eTJr1qzhs88+4w9/+ANVVVWMGDGCW265henTpzNjxgzGjRuHx+M5puWAJQJjTBjyeDxMmzaNO++8kwceeIAXX3yxcdqUKVN48803G5txVJUXX3yRFStWsGLFCnbu3MmIESOYOHEiubm5jWcA48ePZ968eZxwwgmNdTU0PzUss7a2FlVl1KhRjfWtWrWKt99+G4DXX3+dG2+8kWXLljFx4kRqa2u59dZbefTRRzl06BBTpkxh/fr1zb6v9PR0SkpKqK11nuqZn59P3759W1wXI0aMICkpidWrVwNw7bXXsnTpUhYsWEC3bt047rjj2rh2vyxoiUBEHhORvSKyupnpl4nIShFZJSKfiMjxwYrFGNN5bNiwgU2bNjUOr1ixggEDBjQOz5kzh27dunHjjTcCcNZZZ/HXv/61MTEsX74cgJiYGPr168fzzz/PSSedxNSpU/nTn/7U2CzUnGHDhlFUVMSiRYsAp8uNNWvWUF9fT15eHqeddhr33HMPpaWlVFRUsGXLFsaMGcMtt9zCxIkTW0wEIsJpp53GCy+8AMATTzzBrFmzvlRu27Ztjclix44drF+/nobnsOzduxeAnTt38tJLL3HppZe2+H4CEcy7hh4HHgCebGb6NuBUVT0gIjOBucDkIMZjjDkKHX0XVkVFBTfddFNjM8uQIUOYO3fuEWXuu+8+rrnmGn7+859z5513cvPNNzN27Fjq6+sZOHAgr732GgBTp07l3XffJT4+nqlTp5Kfn8/UqVNbXH5MTAwvvPACP/jBDygtLaW2tpabb76Z4447jssvv5zS0lJUlR/84AekpaXx61//mvfff5+oqChGjRrFzJkzAecagb/moXvuuYfZs2dz2223MX78eK699loAXn31VXJzc5kzZw4fffQRd999N9HR0URFRfHQQw813jJ74YUXUlxcTHR0NA8++CBpaWnHuMaD/MxiEckGXlPV0a2U6wasVtWWz5GAnJwctQfTRCa7fbRjrFu3jhEjRoQ6DHMM/H2GIrJUVXP8lQ+XawTXAm80N1FErheRXBHJLSoq6sCwjDGm6wt5IhCR03ASwS3NlVHVuaqao6o5mZl+H7lpjDHmKIX0l8UiMhZ4FJipqsWhjMUY8wVVtY7nOqmjae4P2RmBiPQHXgKuUNWNoYrDGHOkuLg4iouLj2qHYkKr4XkEcXFxbZovaGcEIvIsMA3IEJF84DdANICqPgzcDqQDD7lHHrXNXcgwxnScrKws8vPzsetxnVPDE8raImiJQFUvaWX6t4FvB2v5xpijEx0d3aanW5nOL+QXi40xxoSWJQJjjIlwlgiMMSbCWSIwxpgIZ4nAGGMinCUCY4yJcJYIjDEmwlkiMMaYCGeJwBhjIpwlAmOMiXCWCIwxJsJZIjDGmAhnicAYYyKcJQJjjIlwlgiMMSbCWSIwxpgIZ4nAGGMinCUCY4yJcJYIjDEmwlkiMMaYCGeJwBhjIpwlAmOMiXCWCIwxJsJZIjDGmAgXtEQgIo+JyF4RWd3MdBGR+0Vks4isFJEJwYrFGGNM84J5RvA4MKOF6TOBoe7reuBvQYzFGGNMM4KWCFR1AbC/hSKzgCfVsRhIE5HewYrHGGOMf6G8RtAXyPMZznfHfYmIXC8iuSKSW1RU1CHBGWNMpOgUF4tVda6q5qhqTmZmZqjDMcaYLiWUiWAX0M9nOMsdZ4wxpgOFMhG8Clzp3j10IlCqqgUhjMcYYyKSN1gVi8izwDQgQ0Tygd8A0QCq+jAwHzgb2AxUAlcHKxZjjDHNC1oiUNVLWpmuwI3BWr4xxpjAdIqLxcYYY4LHEoExxkQ4SwTGGBPhLBEYY0yEs0RgjDERzhKBMcZEOEsExhgT4SwRGGNMhLNEYIwxEc4SgTHGRDhLBMYYE+EsERhjTISzRGCMMRHOEoExxkQ4SwTGGBPhLBEYY0yEs0RgjDERzhKBMcZEOEsExhgT4SwRGGNMhLNEYIwxEc4SgTHGRDhLBMYYE+EsERhjTIQLaiIQkRkiskFENovIrX6m9xeR90VkuYisFJGzgxmPMcaYLwtaIhARD/AgMBMYCVwiIiObFLsN+LeqjgdmAw8FKx5jjDH+BfOMYBKwWVW3qmo18Bwwq0kZBVLc/1OB3UGMxxhjjB/eINbdF8jzGc4HJjcpcwfwtojcBCQCZwQxHmOMMX6E+mLxJcDjqpoFnA08JSJfiklErheRXBHJLSoq6vAgjTGmKwtmItgF9PMZznLH+boW+DeAqi4C4oCMphWp6lxVzVHVnMzMzCCFa4wxkSmYieAzYKiIDBSRGJyLwa82KbMTOB1AREbgJAI75DfGmA4UtESgqrXA94G3gHU4dwetEZE5InK+W+wnwHUi8jnwLHCVqmqwYjLGGPNlwbxYjKrOB+Y3GXe7z/9rgSnBjMEYY0zLQn2x2BhjTIhZIjDGmAgXUCIQkZdE5Bx/t3YaY4zp3ALdsT8EXApsEpG7RWRYEGMyxhjTgQJKBKr6P1W9DJgAbAf+JyKfiMjVIhIdzACNMcYEV8BNPSKSDlwFfBtYDtyHkxjeCUpkxhhjOkRAt4+KyH+AYcBTwHmqWuBO+peI5AYrOGOMMcEX6O8I5rm/CWgkIrGqelhVc4IQlzHGmA4SaNPQXX7GLWrPQIwxxoRGi2cEItILpzvpeBEZD4g7KQVICHJsxhhjOkBrTUNn4VwgzgL+7DO+HPhlkGIyxhjTgVpMBKr6BPCEiFyoqi92UEzGGGM6UGtNQ5er6tNAtoj8uOl0Vf2zn9mMMcZ0Iq01DSW6f5OCHYgxxpjQaK1p6BH3750dE44xxpiOFminc38UkRQRiRaRd0WkSEQuD3Zwxhhjgi/Q3xFMV9Uy4FycvoaGAD8LVlDGGGM6TqCJoKEJ6RzgeVUtDVI8xhhjOligXUy8JiLrgUPAd0UkE6gKXljGGGM6SqDdUN8KnAzkqGoNcBCYFczAjDHGdIy2PLx+OM7vCXznebKd4zHGGNPBAu2G+ilgMLACqHNHK5YIjDGm0wv0jCAHGKmqGsxgjGmLgtJDbCgsR0SYMjgdr8ceqW3M0Qg0EawGegEFrRU0piNs3FPOE59sp+HIZM3uUi6fPICUeHtyqjFtFeghVAawVkTeEpFXG16tzSQiM0Rkg4hsFpFbmynzTRFZKyJrROSZtgRvIlNZVQ3P5+bRIyWWX549gssm92dv2WFeXJaPnbQa03aBnhHc0daKRcQDPAicCeQDn4nIq6q61qfMUOAXwBRVPSAiPdq6HBN53lxdSHVdPbMn9icp1suoPqmUjKzh9VUFrN5dFurwjOl0Ar199EOcXxRHu/9/BixrZbZJwGZV3aqq1cBzfPmW0+uAB1X1gLucvW2I3USgisO1rMovZWJ2d3qmxDWOP3FQOr1T43h95W4O19a1UIMxpqlA+xq6DngBeMQd1Rd4uZXZ+gJ5PsP57jhfxwHHicjHIrJYRGYEEo+JXMt2HKBOlUnZ3Y8Y74kSZozqRVlVLa+vtEtZxrRFoNcIbgSmAGUAqroJaI9mHC8wFJgGXALME5G0poVE5HoRyRWR3KKionZYrOmM6lVZsn0/2emJ9PA5G2gwpEcSmcmx/OPj7XatwJg2CDQRHHabdwBwf1TW2jdtF9DPZzjLHecrH3hVVWtUdRuwEScxHEFV56pqjqrmZGZmBhiy6Wry91ey/2A1E7O7+Z0uIpw8OJ1Vu0pZuuNAB0dnTOcVaCL4UER+ifMQ+zOB54H/tjLPZ8BQERkoIjHAbKDpnUYv45wNICIZOE1FWwOMyUSYDXsqEGB4r5Rmy4zv143kOC/PfLqz4wIzppMLNBHcChQBq4DvAPOB21qaQVVrge8DbwHrgH+r6hoRmSMi57vF3gKKRWQt8D7wM1UtbvvbMJFg455y+nVPID7G02yZGG8U54zpzVtrCqmsru3A6IzpvAK6fVRV60XkZeBlVQ24kV5V5+MkDd9xt/v8r8CP3ZcxzdpXcZhdJYc4Y0TPVst+bXxfnvssj3fW7mHWuKb3JxhjmmrxjEAcd4jIPmADsMF9OtntLc1nTHtbsNE5/hjWM7nVspOyu9MnNY6Xlze9JGWM8ae1pqEf4dwtNFFVu6tqd2AyMEVEfhT06IxxLdhYRGKsl95pX75bqKmoKGHW+L4s2LSP4orDHRCdMZ1ba4ngCuAS944eAFR1K3A5cGUwAzPG12fbDzAwI5EokYDKnzu2N3X1yttr9wQ5MmM6v9YSQbSq7ms60r1OYL17mQ5RWFrFrpJDDOieEPA8I3unkJ2ewPxV9uMyY1rTWiKoPsppxrSbZTud3wT0b0MiEBFmjunNJ1uKOXDQNlVjWtJaIjheRMr8vMqBMR0RoDFLdxwg1hsV0PUBX+eMaWgeKgxSZMZ0DS0mAlX1qGqKn1eyqlrTkOkQS3cc4PisNLxRbXvwzKg+KfTrHs/8VZYIjGmJPdLJhLWqmjrW7C5lwgD/3Uq0REQ4e0xvPt68j5JKax4ypjmWCExYW7O7lJo6ZUL/tKOa/+zRvamtV96xu4eMaZYlAhPWVu9yHjQzNivtqOYfm5VK37R4u3vImBZYIjBhbfWuUjKSYuiZEntU8zvNQ734aPM+Sg/VtHN0xnQNlghMWFu9u4xRfVKRAH9I5s/ZY3pTU6e8u86ah4zxJ9BnFhvT4apq6ti0p5yvDj+2Z1CM65dGn9Q45q8q4OsTshrHB9pV9aWT+x/T8o0Jd3ZGYMLWxj3l1NYro/ukHlM9DT8uW7BxH+VV1jxkTFOWCEzYarhQPLrvsSUCgLPH9KK6rp531+095rqM6WosEZiwtWZ3KSlxXrK6xR9zXeP7daNXSpzdPWSMH5YITNhas7uMkX1SjulCcYOoKGHG6F58sLGIisP25DJjfFkiMGGpvl7ZuKe8xecTt9U5Y3tTXVvPe+utecgYX5YITFjKP3CIyuo6hvdq/YlkgTqhfzd6JMcyf6U1Dxnjy24fNWFpfaFzoXhYOyaCqChh5uhePPdZHgeD1Dxkt6SazsjOCExY2lBYDsBxATyjuC3OGduHw7X1vLnaeiQ1poElAhOW1u8pp1/3eBJj2/ekdWJ2NwakJ/D80rx2rdeYzswSgQlLGwrLGdaz/S4UNxARLpqQxeKt+9lvTy4zBrBEYMLQ4do6tu072K4Xin19/YQsRL54BKYxkS6oiUBEZojIBhHZLCK3tlDuQhFREckJZjymc9i8t4K6em3XC8W++qbFc8qQDHK376euXoOyDGM6k6AlAhHxAA8CM4GRwCUiMtJPuWTgh8CnwYrFdC4NF4qDdUYAcOVJ2ZRV1bKuoCxoyzCmswjmGcEkYLOqblXVauA5YJafcr8F7gGqghiL6UQ27CknxhNFdkZi0Jbx1eE9SEuIZtHW4qAtw5jOIpiJoC/ge2tGvjuukYhMAPqp6utBjMN0MhsKyxmUmUi0J3ibpydKmDwwnW37DlJQeihoyzGmMwjZxWIRiQL+DPwkgLLXi0iuiOQWFRUFPzgTUhsKy4PaLNRgYnY3YjxRLNy0L+jLMiacBTMR7AL6+QxnueMaJAOjgQ9EZDtwIvCqvwvGqjpXVXNUNScz89geUmLCW2llDQWlVQxrxz6GmpMQ42XSwO6szC+xW0lNRAtmIvgMGCoiA0UkBpgNvNowUVVLVTVDVbNVNRtYDJyvqrlBjMmEuQ17gn+h2NcpQzIQERZstDNNE7mClghUtRb4PvAWsA74t6quEZE5InJ+sJZrOrcNQehjqCUp8dHkDOhG7o79FFcc7pBlGhNugtrpnKrOB+Y3GXd7M2WnBTMW0zmsLywnOc5L79S4DlvmacN7sGznAd5eu4dLJllncCby2C+LTVhxupZIbpeH0QQqJS6aU4ZksmpXKXn7KztsucaEC0sEJmyoKhv2lDO8d8c0C/n6ytAMUuK8vLxil/3a2EQcSwQmbOwuraK8qrZD7hhqKjbaw7lj+1BQWsUnW+x2UhNZLBGYsLHe7e6ho+4YampUnxRG9E7hnbV77EdmJqJYIjBhY73bx1BH3THUlIhwwfi+xMd4eHZJHodr6465znpVKqtrKauqQdWanEx4skdVmrCxobCcvmnxpMRFhyyGpFgv38zpx2MfbeO5JXlcfuKAo6on/0Ali7cWs76wnMrqusa6R/ZJ4fThPdozZGOOmSUCEzY2FJaH7GzA1+DMJM4f14dXVuzmP8t3ccmkfngD7PeosKyKN1YVsGlvBXHRUQzvlULftHhEYOf+SpZuP8CKvBIGpCcwY3TvIL8TYwJjicCEheraerYUVXD6iPA4Wp48MJ2KqlreXb+XG55eyr2zx5PUwmMziysO8+d3NvLMpzuJjY5ixqheTB7YndhoT2OZkwdD8YjDPL80n+/+cxlzzh/FFSdld8C7MaZllghMWNhSVEFtEB9GczROH9GThFgvr6/czfQ/f8gvzxnBWaN6HdErav6BSv756U6e/GQ7VbX1nDgondOH9yChmaSRnhTLtacMZOGmIn79yhoykmKZOcbODExoWSIwYeGLh9F0/K2jLTlpUDpXnZzNL19axfefWU56Ygyj+qYSHx3FjuJK1heWIwLnjOnNzWcMZcm21h9/Ge2J4oFLJ3DpvMX86N8r6Nc9gdF9Uzvg3Rjjn901ZMLC+sJyoj3CoMzgPYzmaJ0woBuv/eAUHr0yh6lDMyitrGZL0UF6psRxy4zhLPjZaTxw6QSG9Aj8bCYu2sPcK3NIjY/mh88t51D1sd+hZMzRsjMCExY2FJYxODMpqA+jORbRnijOGNmTM0b2bLc6M5Ji+X/fGMflf/+Uu99Yx52zRrdb3ca0RXh+60zEWd9BD6MJN6cMzeDqKdk8uXgHy3a23qxkTDBYIjAh1/AwmuG9w+v6QEf5yfRh9EyO49cvr6a2rj7U4ZgIZInAhFzDw2jC6Y6hjpQU6+X280ayZncZTy/eEepwTASyRGBCruFhNJHYNNRg5uheTB2awf97eyN7y6pCHY6JMJYITMitKywnJc5Lr5SOexhNuBER5swazeHaen43f12owzERxu4aMiG3obCc4b1T2u1hNM98urNd6gmm5mKcMiSDV1bsJjM5lkEZSVw62Z6YZoLPzghMSNXXK+sLyhgRwc1Cvk49LpO0hGj++/lue0CO6TCWCExIbSs+yMHqOvtlrSvGG8W5Y3qzp+wwi7cWhzocEyEsEZiQWr2rFMASgY8RvVM4rmcS/1u3h73lduHYBJ8lAhNSa3aXEeONYkiPpFCHEjZEhHPH9KG2Trn7jfWhDsdEAEsEJqRW7yplRK/ksO1aIlQykmOZOjSDl5bt4qNN9gxlE1z27TMho6qs3lXKKGsW8uu04T0YlJHIrS+t5ODh2lCHY7owSwQmZPIPHKKsqpbRfSwR+BPtieKPF41lV8kh7np9bajDMV1YUBOBiMwQkQ0isllEbvUz/ccislZEVorIuyJydA+INZ3SqsYLxZHZx1AgcrK7852vDObZJXm8tnJ3qMMxXVTQEoGIeIAHgZnASOASERnZpNhyIEdVxwIvAH8MVjwm/HyeV0KMJypi+xgK1E+mH8eE/mnc+uKqxgf4GNOegnlGMAnYrKpbVbUaeA6Y5VtAVd9X1Up3cDGQFcR4TJhZvrOEUX1TiPV6Wi8cwRqeaJYQ4+Hqfyxhj/VFZNpZMLuY6Avk+QznA5NbKH8t8Ia/CSJyPXA9QP/+9pP7rqCmrp6Vu0q4dJK1BgaiT1o8j101kYsfWcQlcxfz1Lcn0zct/qjqCrQLDuveInKERV9DInI5kAOc6m+6qs4F5gLk5OTY7+67gPUF5VTV1DO+f1qoQ+k0RvdN5YlrJnH1459x0d8+4YFLJ3DCgG5A5+hfyYSvYDYN7QL6+QxnueOOICJnAL8CzlfVw0GMx4SR5XnO07gsEbRNTnZ3/nX9SXiihG8+sog/vLGO0sqaUIdlOrlgJoLPgKEiMlBEYoDZwKu+BURkPPAIThLYG8RYTJhZvrOEzOTYo27eiGQj+6Qw/4dTuWB8X+Yu2Mop97zHS8vyWVdQRnlVDap20mzaJmhNQ6paKyLfB94CPMBjqrpGROYAuar6KvB/QBLwvNsF8U5VPT9YMZnwsXznASb0T2u3rqeDKRybXVLiovnTN47n2lMGMm/BVl5bVUDuDucsK0ogMcZLfIwHr0fwiBAlQlSUEOuNIiUumpT4aNITY+jXPYFuCdGd4nMwwRPUawSqOh+Y32Tc7T7/nxHM5ZvwtKesiu3FlVw22S4UH6sRvVP488XjOL5fGvkHDrG75BAVh2s5eLiWyuo66uqVelXqVKmvh9JDNezcX0lldV1jHemJMYzum8rE7O50T4wJ4bsxoRIWF4tNZGnoXvmkwekhjqTriPZEMTAjkYEZiQGVr6mrp6j8MDuKD7K+sJyFm4pYsLGIUX1TOX14D3pG8NPiIpElAtPhFm0pJiXOy4je9oviUIn2RNEnLZ4+afGcNDiD0kM1fLq1mE+2FrN2dymnDMnk6xP6Ehdtv/GIBNbXkOlwi7YWM2lgOp4oa5cOF6nx0Uwf1YufTR/G+P7dWLCpiJn3LbSH40QISwSmQ+0uOcSO4kprFgpTibFeLpyQxTVTBlJXr1wybzF/fnuDPTazi7NEYDpUwxHmiYO6hzgS05IhPZJ48+apXDghi/vf28xV/1hCcYX9zKerskRgOtT7G4rISIphRC+7PhDuEmK8/N9FY7n762P4dNt+zv3rRyzfeSDUYZkgsERgOkxNXT0fbNjLacN6EGXXBzoFEWH2pP689N2T8UQJFz+ymGc+3Wk/WutiLBGYDvPZ9v2UV9Vy+oieoQ7FtNHovqm8dtMpnDg4nV/+ZxW3vLiSqpq61mc0nYLdPmo6zLvr9hLjiWLq0IxQh9JphNOvmtMSYvjHVRO5938b+et7m1lfWM7fLj/BugnpAuyMwHQIVeXddXs4cXA6ibF2/NFZeaKEn0wfxrwrc9hWdJDz/voRH2/eF+qwzDGyRGA6xOpdZWwvruSsUdYs1BWcObInr3x/ChlJMVzx90/545vrOVxrTUWdlR2amQ7x4rJ8YjxRnDumT6hDMQEKpFnqkkn9WVdQxkMfbOHddXv50zeOZ0xWagdEZ9qTnRGYoKupq+fVz3dzxsgepCZEhzoc045ivR7+eNHx/OOqiZQcquZrD33MnP+utWckdDJ2RmCC7sMNRew/WM3Xx9sjqbuihjOH66cO5s01hfzj4208u2Qnp4/owWSfrkTs0ZfhyxKBCbonFm0nMzmWU4dlhjoUE0TxMR4uGN+XEwd15/VVBby2soCPNu1j6tAMcrLtl+ThzBKBCaq1u8tYuGkfPztrGNEea4mMBL1T47l2ykA27qnggw17+e/KAt7bUETxwWpmT+xHH7vdNOxYIugAgd4L3hVPnecu2EJijIfL7SE0EUVEGNYrmWG9ktm27yALNhbx1/c28cB7mzhtWA8uOiGL04b3sG6uw4QlAhM0G/eU89+VBVx9crZdJI5gDQ/MmTo0g399lse/cvN4d/1eEmM8nDGyJ2eO7MkpQzJIS7Cno4WKJQITFKrKHa+uISnWy/dOGxLqcEwY6Nc9gZ+eNYybzxjK4q37eW3lbt5cU8grK3YTJTA2K43Jg7ozLiuN4/ul0Ts1zp6l3EEsEZig+O/KAj7ZUsxvvza6xefghlMXCia4mn7WY7PSGNUnlfwDlWzaW8HmvRU8umAbdW6Hdqnx0QzKTGRwZhKDMhMZlJHE4MxEBqQnEuO1603tyRKBaXdbiyr41UurOD4rlUsndb3rHqb9eKKEAenOzv2MET2praunoLSK/AOV7Ck7TFHFYd5aU0h5VW3jPFEC3RJiyEiKZdrwTI7PSmNsVip90+LtDOIoWSIIgkPVdWwvPkhJZQ119crGPeXEeqNIjY8mOS66Sz+isaSymhueXorXIzx42YQu/V5N+/N6oujXPYF+3ROOGF9VU8e+isMUlR92/lZUs6/88BFnEMlxXgZnJrmvxCOuOXTFGzHakyWCY1RTV8/qXaUs3rqfT7cVs6GwnILSqmbLRwmkJ8bSr3sC/bsnMCA9gR7JsV3iSGZvWRVX/H0J24sr+cdVE8nqltD6TMYEIC7aQ1a3hC9tU7V19RSWVZF/4BDbiw+yaU85K/JKAMhIimFwZhJDeyRRVlVDSpzdsNAcSwRtVFevrN1dxsdb9rFoSzG52/dzsNrpbGtojyROGpTOoMxEsjMS6Z4YQ7QninfX7qGqtp6SyhpKKqspLKtifWEZy9ynPSXHehmUmYgnCk4enPGlo6Fwp6q8vXYPv/rPKiqr63j8qomcPMS6mjbB5/VENSaIEwelU6/KnrIqthQdZPPecpbvLOHTbft5ZslOju+XxtQhGUwZksG4/mnEeu3W1QYSzCcNicgM4D7AAzyqqnc3mR4LPAmcABQDF6vq9pbqzMnJ0dzc3OAE7EdVTR3rC8v5PK+ERVuKWbS1mNJDTj8qQ9wd/4mD0pk0sDuZybF+6/B3QVRVKT5YzfZ9B9lSVMGWooNUHHbaQft1j2fK4AxOGpzOhP7dyOoWnm2fVTV1vLd+L499tI3cHQcY2TuFv1w8jmG9kgOuwy4Wm2Cqra8nb/8h4qKj+GjzPj7PK6FewRslDO2ZzKg+KQzpkURWt3j6psWTmRxLUqyXxFhvl/sBpIgsVdUcv9OClQhExANsBM4E8oHPgEtUda1Pme8BY1X1BhGZDVygqhe3VG97JYKaunqqauqoqqnnUHUd+yurKXbbIHfur2RHcSVb9zmnmrX1zjrqmxbPyYPTmTIkg5MHp9MjJS6gZQWys1NVJg3szseb9/GJm3AaLpAlx3kZ0SuFQZmJZHWLJ6tbAhlJsaTGR5MaH01KvLfdrz2oKrX1SlVNHZXVTvvsvopq9pRVsbXoIKt2lbBsRwmHaurokxrHDdMGM3ti/8a7OWwHb8JJwzWC0kM1LN5azOd5JazZXcaa3WXsqzjsdx5vlBDjjSLaE4UnSvBGSePf3mnxxLrTYjxRxHi/eMVHe0iM8ZDgJpTEGA8JMV4SY52/SbFeEmI8JLp/Y71RHXKg11IiCGbT0CRgs6pudYN4DpgFrPUpMwu4w/3/BeABERENQnZ6e00hv3hplbPzr62nrr75RXijhH5u+/1pwzIZm5XK6L7BvStBxDlCGdozmaumDKSuXlmzu5TVu8pYW1DKuoJy/rduD/sqqputo2EjjfZE4fUI3qgovG5yUJSGtarg/u+Mc4a1cXxDkmxuFUV7hKE9kvlmThbTR/Vi8sDueLvY0ZPpmlLjozlrVC/OGtWrcVx5VQ27Sg6Rv/8Q81cVcLi2nsO1de7feurqlNr6emrrlTr3JUDF4Vqqa+upqaunutZ91TkHlg3NxYHy+CQZjwgej/vXHSciiMBlkwfw3WmD23mtBPeM4CJghqp+2x2+Apisqt/3KbPaLZPvDm9xy+xrUtf1wPXu4DBgQ5PFZQDh/JikcI4vnGOD8I7PYjt64RxfV41tgKr67fmxU1wsVtW5wNzmpotIbnOnPOEgnOML59ggvOOz2I5eOMcXibEF83x+F9DPZzjLHee3jIh4gVSci8bGGGM6SDATwWfAUBEZKCIxwGzg1SZlXgW+5f5/EfBeMK4PGGOMaV7QmoZUtVZEvg+8hXP76GOqukZE5gC5qvoq8HfgKRHZDOzHSRZHo9lmozARzvGFc2wQ3vFZbEcvnOOLuNiC+jsCY4wx4c/u+TPGmAhnicAYYyJcl0kEIvINEVkjIvUiEha3fonIDBHZICKbReTWUMfjS0QeE5G97m85woqI9BOR90VkrfuZ/jDUMTUQkTgRWSIin7ux3RnqmPwREY+ILBeR10Idiy8R2S4iq0RkhYh0XF8xARCRNBF5QUTWi8g6ETkp1DE1EJFh7jpreJWJyM3tVn9XuUYgIiOAeuAR4KeqGtKNLJAuNkJJRL4CVABPquroUMfjS0R6A71VdZmIJANLga+Fw7oT56fliapaISLRwEfAD1V1cYhDO4KI/BjIAVJU9dxQx9NARLYDOU1/NBoOROQJYKGqPure6ZigqiUhDutL3H3LLpwf3+5ojzq7zBmBqq5T1aa/OA6lxi42VLUaaOhiIyyo6gKcO7XCjqoWqOoy9/9yYB3QN7RROdRR4Q5Gu6+wOpoSkSzgHODRUMfSWYhIKvAVnDsZUdXqcEwCrtOBLe2VBKALJYIw1BfI8xnOJ0x2Zp2JiGQD44FPQxxKI7fZZQWwF3hHVcMmNte9wM9xzpDDjQJvi8hSt+uYcDEQKAL+4TapPSoiiaEOqhmzgWfbs8JOlQhE5H8istrPK2yOtE37EZEk4EXgZlUtC3U8DVS1TlXH4fxafpKIhE3TmoicC+xV1aWhjqUZp6jqBGAmcKPbRBkOvMAE4G+qOh44CITVdT0At8nqfOD59qy3U/Q11EBVzwh1DG0QSBcbphlu+/uLwD9V9aVQx+OPqpaIyPvADCBcLrpPAc4XkbOBOCBFRJ5W1ctDHBcAqrrL/btXRP6D04S6ILRRAc4Ze77P2d0LhGEiwEmgy1R1T3tW2qnOCDqZQLrYMH64F2T/DqxT1T+HOh5fIpIpImnu//E4NwOsD2lQPlT1F6qaparZONvce+GSBEQk0b34j9vsMp0wSaCqWgjkicgwd9TpHNllfri4hHZuFoIulAhE5AIRyQdOAl4XkbdCGY+q1gINXWysA/6tqmtCGZMvEXkWWAQME5F8Ebk21DH5mAJcAXzV53a5s0MdlKs38L6IrMRJ9u+oaljdohnGegIficjnwBLgdVV9M8Qx+boJ+Kf72Y4Dfh/acI7kJs8zgXY/Q+4yt48aY4w5Ol3mjMAYY8zRsURgjDERzhKBMcZEOEsExhgT4SwRGGNMhLNEYIwxEc4SgTEdQER+4HZt/M9Qx2JMU/Y7AmMCICJe90eCRzv/euAMVc3viOUZ0xZ2RmAihohku0fl89yHyrwtIvEiMk5EFovIShH5j4h0c8t/ICL3ug9Q+aE7/BcRyXXrmSgiL4nIJhG5q4XlPgwMAt4QkR+JyCQRWeT2cvlJQ7cGInKViLwqIu8B77pdMjwmzoNwllvniiZYLBGYSDMUeFBVRwElwIXAk8AtqjoWWAX8xqd8jKrmqOr/c4erVTUHeBh4BbgRGA1cJSLp/haoqjcAu4HTVPUvOH0TTXV7ubydI7symABcpKqnAr/C6StoEnAa8H9h3DWy6cQ6Ve+jxrSDbaq6wv1/KTAYSFPVD91xT3BkF7//ajJ/Q8eBq4A1qloAICJbcXqbLQ4ghlTgCREZitM/f7TPtHdUteGBQdNxehL9qTscB/TH6bvKmHZjicBEmsM+/9cBaa2UP9jM/PVN6qon8O/Tb4H3VfUC98E7HzSzPAEuDLMn75kuyJqGTKQrBQ6IyFR3+ArgwxbKt4dUvng2xVUtlHsLuMntlhsRGR/kuEyEskRgDHwLp/29ofvhOUFe3h+BP4jIclo+i/gtTrPRShFZ4w4b0+7s9lFjjIlwdkZgjDERzi4WG9NO3NtH3/Uz6XRVDeRuImNCwpqGjDEmwlnTkDHGRDhLBMYYE+EsERhjTISzRGCMMRHu/wOMdd7Q3gAHdAAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(train_data['norm_fare'], label = 'Skewness: %.2f'%(train_data['norm_fare'].skew()))\n", + "plt.legend(loc = 'best')\n", + "plt.title('Passenger Fare Distribution After Log Transformation')" + ] + }, + { + "cell_type": "markdown", + "id": "8e7f084d", + "metadata": { + "papermill": { + "duration": 0.033033, + "end_time": "2022-09-24T14:19:51.091821", + "exception": false, + "start_time": "2022-09-24T14:19:51.058788", + "status": "completed" + }, + "tags": [], + "id": "8e7f084d" + }, + "source": [ + "**Observation:** Skewness is fine after using log transformation" + ] + }, + { + "cell_type": "markdown", + "id": "f93b8a10", + "metadata": { + "papermill": { + "duration": 0.031878, + "end_time": "2022-09-24T14:19:51.155548", + "exception": false, + "start_time": "2022-09-24T14:19:51.123670", + "status": "completed" + }, + "tags": [], + "id": "f93b8a10" + }, + "source": [ + "**Fill missing values**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "648ce8ba", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.224044Z", + "iopub.status.busy": "2022-09-24T14:19:51.222855Z", + "iopub.status.idle": "2022-09-24T14:19:51.231786Z", + "shell.execute_reply": "2022-09-24T14:19:51.230969Z" + }, + "papermill": { + "duration": 0.046328, + "end_time": "2022-09-24T14:19:51.234270", + "exception": false, + "start_time": "2022-09-24T14:19:51.187942", + "status": "completed" + }, + "tags": [], + "id": "648ce8ba" + }, + "outputs": [], + "source": [ + "#Function to handle missing values\n", + "def fill_Missing_Values(df): \n", + " # check missing values\n", + " miss_col = df.isnull().sum()\n", + " # filter for null value columns\n", + " miss_col = miss_col[miss_col > 0]\n", + " #Loop missing columns\n", + " for column in miss_col.index:\n", + " if df[column].dtype.name == 'object':\n", + " # impute object type columns with mode\n", + " df[column].fillna(df[column].mode()[0], inplace=True)\n", + " \n", + " elif df[column].dtype.name == 'float64' or df[column].dtype.name == 'int64' or df[column].dtype.name == 'int32':\n", + " # impute numeric type columns with median\n", + " df[column] = df[column].fillna((df[column].median()))\n", + " \n", + " else:\n", + " print('Unable to handle: ', column)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b1f3514", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.300766Z", + "iopub.status.busy": "2022-09-24T14:19:51.299955Z", + "iopub.status.idle": "2022-09-24T14:19:51.310460Z", + "shell.execute_reply": "2022-09-24T14:19:51.309286Z" + }, + "papermill": { + "duration": 0.046647, + "end_time": "2022-09-24T14:19:51.313022", + "exception": false, + "start_time": "2022-09-24T14:19:51.266375", + "status": "completed" + }, + "tags": [], + "id": "5b1f3514" + }, + "outputs": [], + "source": [ + "train_data = fill_Missing_Values(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "763e624f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.380342Z", + "iopub.status.busy": "2022-09-24T14:19:51.379529Z", + "iopub.status.idle": "2022-09-24T14:19:51.406308Z", + "shell.execute_reply": "2022-09-24T14:19:51.405028Z" + }, + "papermill": { + "duration": 0.063163, + "end_time": "2022-09-24T14:19:51.408803", + "exception": false, + "start_time": "2022-09-24T14:19:51.345640", + "status": "completed" + }, + "tags": [], + "id": "763e624f", + "outputId": "f4c20619-b135-4f09-d620-e087526c8bbb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatatypeNon_Null_CountNull_CountNull_PercentageUnique_Values_Count
PassengerIdint6489100.0891
Survivedint6489100.02
Solo_travellerobject89100.02
FamilySizeint6489100.09
Ticket_clsobject89100.03
Name_titleobject89100.06
CabinBoolint6489100.02
Embarkedobject89100.03
Cabinobject89100.0147
Farefloat6489100.0248
Ticketobject89100.0681
Parchint6489100.07
SibSpint6489100.07
Agefloat6489100.088
Sexobject89100.02
Nameobject89100.0891
Pclassint6489100.03
norm_farefloat6489100.0248
\n", + "
" + ], + "text/plain": [ + " Datatype Non_Null_Count Null_Count Null_Percentage \\\n", + "PassengerId int64 891 0 0.0 \n", + "Survived int64 891 0 0.0 \n", + "Solo_traveller object 891 0 0.0 \n", + "FamilySize int64 891 0 0.0 \n", + "Ticket_cls object 891 0 0.0 \n", + "Name_title object 891 0 0.0 \n", + "CabinBool int64 891 0 0.0 \n", + "Embarked object 891 0 0.0 \n", + "Cabin object 891 0 0.0 \n", + "Fare float64 891 0 0.0 \n", + "Ticket object 891 0 0.0 \n", + "Parch int64 891 0 0.0 \n", + "SibSp int64 891 0 0.0 \n", + "Age float64 891 0 0.0 \n", + "Sex object 891 0 0.0 \n", + "Name object 891 0 0.0 \n", + "Pclass int64 891 0 0.0 \n", + "norm_fare float64 891 0 0.0 \n", + "\n", + " Unique_Values_Count \n", + "PassengerId 891 \n", + "Survived 2 \n", + "Solo_traveller 2 \n", + "FamilySize 9 \n", + "Ticket_cls 3 \n", + "Name_title 6 \n", + "CabinBool 2 \n", + "Embarked 3 \n", + "Cabin 147 \n", + "Fare 248 \n", + "Ticket 681 \n", + "Parch 7 \n", + "SibSp 7 \n", + "Age 88 \n", + "Sex 2 \n", + "Name 891 \n", + "Pclass 3 \n", + "norm_fare 248 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "getDatasetDetail(train_data)" + ] + }, + { + "cell_type": "markdown", + "id": "1f6f8124", + "metadata": { + "papermill": { + "duration": 0.032671, + "end_time": "2022-09-24T14:19:51.474584", + "exception": false, + "start_time": "2022-09-24T14:19:51.441913", + "status": "completed" + }, + "tags": [], + "id": "1f6f8124" + }, + "source": [ + "**Handle outliers**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13222d74", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.541702Z", + "iopub.status.busy": "2022-09-24T14:19:51.540909Z", + "iopub.status.idle": "2022-09-24T14:19:51.547072Z", + "shell.execute_reply": "2022-09-24T14:19:51.546245Z" + }, + "papermill": { + "duration": 0.042437, + "end_time": "2022-09-24T14:19:51.549438", + "exception": false, + "start_time": "2022-09-24T14:19:51.507001", + "status": "completed" + }, + "tags": [], + "id": "13222d74" + }, + "outputs": [], + "source": [ + "def cap_outliers(array, k=3):\n", + " upper_limit = array.mean() + k*array.std()\n", + " lower_limit = array.mean() - k*array.std()\n", + " array[arrayupper_limit] = upper_limit\n", + " return array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96e62b7d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.616758Z", + "iopub.status.busy": "2022-09-24T14:19:51.615946Z", + "iopub.status.idle": "2022-09-24T14:19:51.623951Z", + "shell.execute_reply": "2022-09-24T14:19:51.622976Z" + }, + "papermill": { + "duration": 0.044589, + "end_time": "2022-09-24T14:19:51.626537", + "exception": false, + "start_time": "2022-09-24T14:19:51.581948", + "status": "completed" + }, + "tags": [], + "id": "96e62b7d" + }, + "outputs": [], + "source": [ + "# #Get all numeric columns\n", + "numCol = train_data.select_dtypes(exclude=['object']).columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90a4218d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.696693Z", + "iopub.status.busy": "2022-09-24T14:19:51.695624Z", + "iopub.status.idle": "2022-09-24T14:19:51.746428Z", + "shell.execute_reply": "2022-09-24T14:19:51.745589Z" + }, + "papermill": { + "duration": 0.088777, + "end_time": "2022-09-24T14:19:51.748893", + "exception": false, + "start_time": "2022-09-24T14:19:51.660116", + "status": "completed" + }, + "tags": [], + "id": "90a4218d" + }, + "outputs": [], + "source": [ + "#Cap all Outliers\n", + "for col in numCol:\n", + " arr = cap_outliers(train_data[col])\n", + " train_data[col] = arr" + ] + }, + { + "cell_type": "markdown", + "id": "d0a680fa", + "metadata": { + "papermill": { + "duration": 0.031921, + "end_time": "2022-09-24T14:19:51.813180", + "exception": false, + "start_time": "2022-09-24T14:19:51.781259", + "status": "completed" + }, + "tags": [], + "id": "d0a680fa" + }, + "source": [ + "**Data Visualization**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "785a73f9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:51.880434Z", + "iopub.status.busy": "2022-09-24T14:19:51.880030Z", + "iopub.status.idle": "2022-09-24T14:19:53.055080Z", + "shell.execute_reply": "2022-09-24T14:19:53.053708Z" + }, + "papermill": { + "duration": 1.211194, + "end_time": "2022-09-24T14:19:53.057554", + "exception": false, + "start_time": "2022-09-24T14:19:51.846360", + "status": "completed" + }, + "tags": [], + "id": "785a73f9", + "outputId": "9ba75983-1f0e-46a2-e14b-ca7c8650a157" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20, 10))\n", + "rows = int(math.ceil(len(['Survived','Pclass','Sex','SibSp','Parch','Embarked', 'Ticket_cls', 'Name_title', 'CabinBool', 'Solo_traveller'])/4))\n", + "cols = 4\n", + "for i,n in enumerate(['Survived','Pclass','Sex','SibSp','Parch','Embarked', 'Ticket_cls', 'Name_title', 'CabinBool', 'Solo_traveller']):\n", + " plt.subplot(rows, cols, i+1)\n", + " sns.countplot(x=n, data= train_data)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c9fe765", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:53.126355Z", + "iopub.status.busy": "2022-09-24T14:19:53.125946Z", + "iopub.status.idle": "2022-09-24T14:19:53.861297Z", + "shell.execute_reply": "2022-09-24T14:19:53.859842Z" + }, + "papermill": { + "duration": 0.772748, + "end_time": "2022-09-24T14:19:53.864244", + "exception": false, + "start_time": "2022-09-24T14:19:53.091496", + "status": "completed" + }, + "tags": [], + "id": "4c9fe765", + "outputId": "906d3903-6644-4abf-bef9-9a77ac634802" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Heatmap\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "sns.heatmap(train_data.corr(), \n", + " xticklabels=train_data.corr().columns.values,\n", + " yticklabels=train_data.corr().columns.values,annot= True)\n", + "\n", + "bottom, top = ax.get_ylim()\n", + "ax.set_ylim(bottom + 0.5, top - 0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ee65a96e", + "metadata": { + "papermill": { + "duration": 0.035223, + "end_time": "2022-09-24T14:19:53.934915", + "exception": false, + "start_time": "2022-09-24T14:19:53.899692", + "status": "completed" + }, + "tags": [], + "id": "ee65a96e" + }, + "source": [ + "**Observation:** There are no highlily correlated variables(>95% correlation)." + ] + }, + { + "cell_type": "markdown", + "id": "36331360", + "metadata": { + "papermill": { + "duration": 0.034072, + "end_time": "2022-09-24T14:19:54.003505", + "exception": false, + "start_time": "2022-09-24T14:19:53.969433", + "status": "completed" + }, + "tags": [], + "id": "36331360" + }, + "source": [ + "**Create dummies for the categorical variables**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e999e241", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:54.075846Z", + "iopub.status.busy": "2022-09-24T14:19:54.075457Z", + "iopub.status.idle": "2022-09-24T14:19:54.084893Z", + "shell.execute_reply": "2022-09-24T14:19:54.083357Z" + }, + "papermill": { + "duration": 0.048392, + "end_time": "2022-09-24T14:19:54.087599", + "exception": false, + "start_time": "2022-09-24T14:19:54.039207", + "status": "completed" + }, + "tags": [], + "id": "e999e241", + "outputId": "58823304-6a25-4242-ac81-6828e871bb6c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked', 'Name_title',\n", + " 'Ticket_cls', 'Solo_traveller'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "categorical_cols = train_data.select_dtypes(include = \"object\").columns\n", + "print (categorical_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0b28187", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:54.158503Z", + "iopub.status.busy": "2022-09-24T14:19:54.157607Z", + "iopub.status.idle": "2022-09-24T14:19:54.182545Z", + "shell.execute_reply": "2022-09-24T14:19:54.181379Z" + }, + "papermill": { + "duration": 0.062801, + "end_time": "2022-09-24T14:19:54.184907", + "exception": false, + "start_time": "2022-09-24T14:19:54.122106", + "status": "completed" + }, + "tags": [], + "id": "f0b28187", + "outputId": "2a625b25-bdf8-46e4-eed7-1a9ab15e3c96" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatatypeNon_Null_CountNull_CountNull_PercentageUnique_Values_Count
PassengerIdint6489100.0891
Survivedint6489100.02
Solo_travellerobject89100.02
FamilySizefloat6489100.07
Ticket_clsobject89100.03
Name_titleobject89100.06
CabinBoolint6489100.02
Embarkedobject89100.03
Cabinobject89100.0147
Farefloat6489100.0241
Ticketobject89100.0681
Parchfloat6489100.04
SibSpfloat6489100.05
Agefloat6489100.084
Sexobject89100.02
Nameobject89100.0891
Pclassint6489100.03
norm_farefloat6489100.0248
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" + ], + "text/plain": [ + " Datatype Non_Null_Count Null_Count Null_Percentage \\\n", + "PassengerId int64 891 0 0.0 \n", + "Survived int64 891 0 0.0 \n", + "Solo_traveller object 891 0 0.0 \n", + "FamilySize float64 891 0 0.0 \n", + "Ticket_cls object 891 0 0.0 \n", + "Name_title object 891 0 0.0 \n", + "CabinBool int64 891 0 0.0 \n", + "Embarked object 891 0 0.0 \n", + "Cabin object 891 0 0.0 \n", + "Fare float64 891 0 0.0 \n", + "Ticket object 891 0 0.0 \n", + "Parch float64 891 0 0.0 \n", + "SibSp float64 891 0 0.0 \n", + "Age float64 891 0 0.0 \n", + "Sex object 891 0 0.0 \n", + "Name object 891 0 0.0 \n", + "Pclass int64 891 0 0.0 \n", + "norm_fare float64 891 0 0.0 \n", + "\n", + " Unique_Values_Count \n", + "PassengerId 891 \n", + "Survived 2 \n", + "Solo_traveller 2 \n", + "FamilySize 7 \n", + "Ticket_cls 3 \n", + "Name_title 6 \n", + "CabinBool 2 \n", + "Embarked 3 \n", + "Cabin 147 \n", + "Fare 241 \n", + "Ticket 681 \n", + "Parch 4 \n", + "SibSp 5 \n", + "Age 84 \n", + "Sex 2 \n", + "Name 891 \n", + "Pclass 3 \n", + "norm_fare 248 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "getDatasetDetail(train_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a6ef393", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:54.255973Z", + "iopub.status.busy": "2022-09-24T14:19:54.255460Z", + "iopub.status.idle": "2022-09-24T14:19:54.274812Z", + "shell.execute_reply": "2022-09-24T14:19:54.273490Z" + }, + "papermill": { + "duration": 0.057745, + "end_time": "2022-09-24T14:19:54.277271", + "exception": false, + "start_time": "2022-09-24T14:19:54.219526", + "status": "completed" + }, + "tags": [], + "id": "3a6ef393" + }, + "outputs": [], + "source": [ + "# Creating dummies for categorical variables\n", + "categorical_cols = ['Sex', 'Name_title', 'Ticket_cls', 'Embarked', 'Solo_traveller', 'CabinBool', 'Parch', 'SibSp', 'Pclass']\n", + "dummies = pd.get_dummies(train_data[categorical_cols],drop_first=True)\n", + "# Drop Categorical columns\n", + "train_data.drop(categorical_cols,axis=1,inplace=True)\n", + "# Concatenate original dataframe and dummies\n", + "train_data = pd.concat([train_data,dummies],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00fef0bf", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:54.348697Z", + "iopub.status.busy": "2022-09-24T14:19:54.348288Z", + "iopub.status.idle": "2022-09-24T14:19:54.371224Z", + "shell.execute_reply": "2022-09-24T14:19:54.370364Z" + }, + "papermill": { + "duration": 0.061435, + "end_time": "2022-09-24T14:19:54.373550", + "exception": false, + "start_time": "2022-09-24T14:19:54.312115", + "status": "completed" + }, + "tags": [], + "id": "00fef0bf", + "outputId": "159788c7-cfd9-4631-84ca-c585b65b1d9a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedNameAgeTicketFareCabinFamilySizenorm_fareCabinBoolParchSibSpPclassSex_maleName_title_MissName_title_Mr1Name_title_Mr2Name_title_MrsName_title_OtherTicket_cls_Numeric1Ticket_cls_Numeric2Embarked_QEmbarked_SSolo_traveller_Yes
010Braund, Mr. Owen Harris22.0A/5 211717.2500B96 B982.02.11021300.01.0310010000010
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.0PC 1759971.2833C852.04.28059310.01.0100001000000
231Heikkinen, Miss. Laina26.0STON/O2. 31012827.9250B96 B981.02.18885600.00.0301000000011
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.011380353.1000C1232.03.99083410.01.0100001001010
450Allen, Mr. William Henry35.03734508.0500B96 B981.02.20276500.00.0310100010011
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Agefloat6489100.084
Embarked_Suint889100.02
Embarked_Quint889100.02
Ticket_cls_Numeric2uint889100.02
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Name_title_Missuint889100.02
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Parchfloat6489100.04
CabinBoolint6489100.02
norm_farefloat6489100.0248
Solo_traveller_Yesuint889100.02
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3-0.885142-0.631295-0.5383820.894146-0.5285960.829568-1.346933-0.496926-0.633699-0.6424162.377857-0.1743291.238470-0.715316-0.2893330.611978-1.249460
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Agenorm_fareCabinBoolParchSibSpPclassSex_maleName_title_MissName_title_Mr1Name_title_Mr2Name_title_MrsName_title_OtherTicket_cls_Numeric1Ticket_cls_Numeric2Embarked_QEmbarked_SSolo_traveller_Yes
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Accuracy Score\n", + " acc_sc = round(accuracy_score(actual, pred)*100,2)\n", + " \n", + " # Precision score\n", + " prec_sc = round(precision_score(actual, pred)*100,2)\n", + " \n", + " # Recall score\n", + " rec_sc = round(recall_score(actual, pred)*100,2)\n", + " \n", + " # Confusion matrix\n", + " confusion_m = confusion_matrix(actual, pred)\n", + " \n", + " TP = confusion_m[1,1] # true positive \n", + " TN = confusion_m[0,0] # true negatives\n", + " FP = confusion_m[0,1] # false positives\n", + " FN = confusion_m[1,0] # false negatives\n", + " # Specificity\n", + " Specificity= round(TN / float(TN+FP),2)\n", + " # ROC AUC score\n", + " roc_score = round(recall_score(actual, pred)*100,2)\n", + " #F1-Score\n", + " f1_score=round(2*((prec_sc*rec_sc)/(prec_sc+rec_sc)),2)\n", + " return {\"TP\":TP,\"TN\":TN,\"FP\":FP,\"FN\":FN,\"Recall\":rec_sc,\"Precision\":prec_sc,\"Specificity\":Specificity,\"ROC/AUC Score\":roc_score,\"F1-Score\":f1_score, \"Accuracy\": acc_sc}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48619ed5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:55.669198Z", + "iopub.status.busy": "2022-09-24T14:19:55.668525Z", + "iopub.status.idle": "2022-09-24T14:19:55.705074Z", + "shell.execute_reply": "2022-09-24T14:19:55.703205Z" + }, + "papermill": { + "duration": 0.078705, + "end_time": "2022-09-24T14:19:55.708882", + "exception": false, + "start_time": "2022-09-24T14:19:55.630177", + "status": "completed" + }, + "tags": [], + "id": "48619ed5" + }, + "outputs": [], + "source": [ + "# Build Logistic Regression using important features\n", + "# Initialize the Logistic regression\n", + "model_lg = LogisticRegression(random_state=42)\n", + "# fit the training data\n", + "model_lg.fit(X_train_scaled, y_train)\n", + "# predict the training data\n", + "Y_train_pred = model_lg.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = model_lg.predict(X_test_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6a7f1a3", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:55.852037Z", + "iopub.status.busy": "2022-09-24T14:19:55.851630Z", + "iopub.status.idle": "2022-09-24T14:19:55.872897Z", + "shell.execute_reply": "2022-09-24T14:19:55.872050Z" + }, + "papermill": { + "duration": 0.070832, + "end_time": "2022-09-24T14:19:55.875468", + "exception": false, + "start_time": "2022-09-24T14:19:55.804636", + "status": "completed" + }, + "tags": [], + "id": "e6a7f1a3" + }, + "outputs": [], + "source": [ + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b54b7410", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:55.950885Z", + "iopub.status.busy": "2022-09-24T14:19:55.950113Z", + "iopub.status.idle": "2022-09-24T14:19:55.966317Z", + "shell.execute_reply": "2022-09-24T14:19:55.965083Z" + }, + "papermill": { + "duration": 0.056433, + "end_time": "2022-09-24T14:19:55.969095", + "exception": false, + "start_time": "2022-09-24T14:19:55.912662", + "status": "completed" + }, + "tags": [], + "id": "b54b7410", + "outputId": "25b5831b-9ff1-43bf-faea-a5946e68db11" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data205384556875.0978.850.8775.0976.9282.72
test_data5396141676.8179.100.8776.8177.9483.24
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" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 205 384 55 68 75.09 78.85 0.87 75.09 \n", + "test_data 53 96 14 16 76.81 79.10 0.87 76.81 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 76.92 82.72 \n", + "test_data 77.94 83.24 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "314bfb6d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:56.044116Z", + "iopub.status.busy": "2022-09-24T14:19:56.043712Z", + "iopub.status.idle": "2022-09-24T14:19:56.353369Z", + "shell.execute_reply": "2022-09-24T14:19:56.352254Z" + }, + "papermill": { + "duration": 0.349873, + "end_time": "2022-09-24T14:19:56.356070", + "exception": false, + "start_time": "2022-09-24T14:19:56.006197", + "status": "completed" + }, + "tags": [], + "id": "314bfb6d", + 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95c109c8", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:56.432203Z", + "iopub.status.busy": "2022-09-24T14:19:56.431755Z", + "iopub.status.idle": "2022-09-24T14:19:58.640705Z", + "shell.execute_reply": "2022-09-24T14:19:58.639175Z" + }, + "papermill": { + "duration": 2.251419, + "end_time": "2022-09-24T14:19:58.644917", + "exception": false, + "start_time": "2022-09-24T14:19:56.393498", + "status": "completed" + }, + "tags": [], + "id": "95c109c8", + "outputId": "3afe1cc8-8795-4f6b-bcd0-a6ddda207fda" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 10 folds for each of 6 candidates, totalling 60 fits\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(cv=KFold(n_splits=10, random_state=4, shuffle=True),\n", + " estimator=LogisticRegression(),\n", + " param_grid={'C': [0.01, 0.1, 1, 10, 100, 1000]},\n", + " return_train_score=True, scoring='recall', verbose=1)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating KFold object with 10 splits\n", + "folds = KFold(n_splits=10, shuffle=True, random_state=4)\n", + "\n", + "# Specify params\n", + "params = {\"C\": [0.01, 0.1, 1, 10, 100, 1000]}\n", + "\n", + "# Specifing score as recall as we are more focused on acheiving the higher sensitivity than the accuracy\n", + "model_cv = GridSearchCV(estimator = LogisticRegression(),\n", + " param_grid = params, \n", + " scoring= 'recall', \n", + " cv = folds, \n", + " verbose = 1,\n", + " return_train_score=True) \n", + "\n", + "# Fit the model\n", + "model_cv.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9a26329", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:58.784480Z", + "iopub.status.busy": "2022-09-24T14:19:58.784081Z", + "iopub.status.idle": "2022-09-24T14:19:58.821628Z", + "shell.execute_reply": "2022-09-24T14:19:58.820490Z" + }, + "papermill": { + "duration": 0.082498, + "end_time": "2022-09-24T14:19:58.824412", + "exception": false, + "start_time": "2022-09-24T14:19:58.741914", + "status": "completed" + }, + "tags": [], + "id": "f9a26329", + "outputId": "3e84fd5f-e808-49ee-b494-53a625b68b69" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_Cparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoresplit5_test_scoresplit6_test_scoresplit7_test_scoresplit8_test_scoresplit9_test_scoremean_test_scorestd_test_scorerank_test_scoresplit0_train_scoresplit1_train_scoresplit2_train_scoresplit3_train_scoresplit4_train_scoresplit5_train_scoresplit6_train_scoresplit7_train_scoresplit8_train_scoresplit9_train_scoremean_train_scorestd_train_score
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10.0187850.0004260.0055080.0002050.1{'C': 0.1}0.850.6764710.7826090.781250.7777780.6060610.7500000.656250.7500000.7083330.7338750.06845650.7470360.7364020.7480.7427390.7520330.7541670.7510040.7551870.7510040.7550200.7492590.005662
20.0224700.0006190.0052960.0002751{'C': 1}0.900.7352940.7826090.781250.7777780.6363640.7916670.656250.7500000.7083330.7519540.07117910.7470360.7447700.7440.7385890.7601630.7833330.7590360.7551870.7670680.7630520.7562230.012648
30.0264780.0010430.0055630.00018310{'C': 10}0.850.7058820.7826090.781250.7407410.6363640.7916670.687500.7083330.7083330.7392680.05911720.7470360.7405860.7400.7344400.7601630.7708330.7510040.7510370.7590360.7429720.7497110.010532
40.0283500.0018340.0056370.000263100{'C': 100}0.850.7058820.7826090.781250.7407410.6363640.7916670.687500.7083330.7083330.7392680.05911720.7470360.7405860.7400.7344400.7560980.7708330.7429720.7510370.7590360.7429720.7485010.010343
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\n", + "
" + ], + "text/plain": [ + " mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n", + "0 0.013272 0.001253 0.005451 0.000101 0.01 \n", + "1 0.018785 0.000426 0.005508 0.000205 0.1 \n", + "2 0.022470 0.000619 0.005296 0.000275 1 \n", + "3 0.026478 0.001043 0.005563 0.000183 10 \n", + "4 0.028350 0.001834 0.005637 0.000263 100 \n", + "5 0.031621 0.001411 0.005740 0.000327 1000 \n", + "\n", + " params split0_test_score split1_test_score split2_test_score \\\n", + "0 {'C': 0.01} 0.85 0.617647 0.739130 \n", + "1 {'C': 0.1} 0.85 0.676471 0.782609 \n", + "2 {'C': 1} 0.90 0.735294 0.782609 \n", + "3 {'C': 10} 0.85 0.705882 0.782609 \n", + "4 {'C': 100} 0.85 0.705882 0.782609 \n", + "5 {'C': 1000} 0.85 0.705882 0.782609 \n", + "\n", + " split3_test_score split4_test_score split5_test_score split6_test_score \\\n", + "0 0.71875 0.777778 0.545455 0.708333 \n", + "1 0.78125 0.777778 0.606061 0.750000 \n", + "2 0.78125 0.777778 0.636364 0.791667 \n", + "3 0.78125 0.740741 0.636364 0.791667 \n", + "4 0.78125 0.740741 0.636364 0.791667 \n", + "5 0.78125 0.740741 0.636364 0.791667 \n", + "\n", + " split7_test_score split8_test_score split9_test_score mean_test_score \\\n", + "0 0.53125 0.625000 0.666667 0.678001 \n", + "1 0.65625 0.750000 0.708333 0.733875 \n", + "2 0.65625 0.750000 0.708333 0.751954 \n", + "3 0.68750 0.708333 0.708333 0.739268 \n", + "4 0.68750 0.708333 0.708333 0.739268 \n", + "5 0.68750 0.708333 0.708333 0.739268 \n", + "\n", + " std_test_score rank_test_score split0_train_score split1_train_score \\\n", + "0 0.095677 6 0.687747 0.690377 \n", + "1 0.068456 5 0.747036 0.736402 \n", + "2 0.071179 1 0.747036 0.744770 \n", + "3 0.059117 2 0.747036 0.740586 \n", + "4 0.059117 2 0.747036 0.740586 \n", + "5 0.059117 2 0.747036 0.740586 \n", + "\n", + " split2_train_score split3_train_score split4_train_score \\\n", + "0 0.684 0.672199 0.695122 \n", + "1 0.748 0.742739 0.752033 \n", + "2 0.744 0.738589 0.760163 \n", + "3 0.740 0.734440 0.760163 \n", + "4 0.740 0.734440 0.756098 \n", + "5 0.740 0.734440 0.756098 \n", + "\n", + " split5_train_score split6_train_score split7_train_score \\\n", + "0 0.716667 0.694779 0.701245 \n", + "1 0.754167 0.751004 0.755187 \n", + "2 0.783333 0.759036 0.755187 \n", + "3 0.770833 0.751004 0.751037 \n", + "4 0.770833 0.742972 0.751037 \n", + "5 0.770833 0.742972 0.751037 \n", + "\n", + " split8_train_score split9_train_score mean_train_score std_train_score \n", + "0 0.706827 0.690763 0.693973 0.011707 \n", + "1 0.751004 0.755020 0.749259 0.005662 \n", + "2 0.767068 0.763052 0.756223 0.012648 \n", + "3 0.759036 0.742972 0.749711 0.010532 \n", + "4 0.759036 0.742972 0.748501 0.010343 \n", + "5 0.759036 0.746988 0.748903 0.010198 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# results of grid search CV\n", + "cv_results = pd.DataFrame(model_cv.cv_results_)\n", + "cv_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7ba5ef5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:19:58.902236Z", + "iopub.status.busy": "2022-09-24T14:19:58.901804Z", + "iopub.status.idle": "2022-09-24T14:20:00.040658Z", + "shell.execute_reply": "2022-09-24T14:20:00.039856Z" + }, + "papermill": { + "duration": 1.180577, + "end_time": "2022-09-24T14:20:00.042967", + "exception": false, + "start_time": "2022-09-24T14:19:58.862390", + "status": "completed" + }, + "tags": [], + "id": "a7ba5ef5", + "outputId": "092e8209-2cb7-4b9a-a04b-76e18b65f352" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot of C versus train and validation scores\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(cv_results['param_C'], cv_results['mean_test_score'])\n", + "plt.plot(cv_results['param_C'], cv_results['mean_train_score'])\n", + "plt.xlabel('C')\n", + "plt.ylabel('sensitivity')\n", + "plt.legend(['test result', 'train result'], loc='upper left')\n", + "plt.xscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7e86103", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:00.126092Z", + "iopub.status.busy": "2022-09-24T14:20:00.124958Z", + "iopub.status.idle": "2022-09-24T14:20:00.131949Z", + "shell.execute_reply": "2022-09-24T14:20:00.130359Z" + }, + "papermill": { + "duration": 0.051352, + "end_time": "2022-09-24T14:20:00.134504", + "exception": false, + "start_time": "2022-09-24T14:20:00.083152", + "status": "completed" + }, + "tags": [], + "id": "d7e86103", + "outputId": "3043df8a-b9eb-47e9-f446-505873866836" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The highest test sensitivity is 0.7519544227440647 at C = 1\n" + ] + } + ], + "source": [ + "# Best score with best C\n", + "best_score = model_cv.best_score_\n", + "best_C = model_cv.best_params_['C']\n", + "\n", + "print(\" The highest test sensitivity is {0} at C = {1}\".format(best_score, best_C))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b654ff87", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:00.216880Z", + "iopub.status.busy": "2022-09-24T14:20:00.216138Z", + "iopub.status.idle": "2022-09-24T14:20:00.252771Z", + "shell.execute_reply": "2022-09-24T14:20:00.251104Z" + }, + "papermill": { + "duration": 0.081662, + "end_time": "2022-09-24T14:20:00.257386", + "exception": false, + "start_time": "2022-09-24T14:20:00.175724", + "status": "completed" + }, + "tags": [], + "id": "b654ff87" + }, + "outputs": [], + "source": [ + "# Instantiate the model with best C\n", + "logistic_1 = LogisticRegression(class_weight='balanced',C=best_C)\n", + "\n", + "# Fit the model on the train set\n", + "log_1_model = logistic_1.fit(X_train_scaled, y_train)\n", + "\n", + "# predict the training data\n", + "Y_train_pred = log_1_model.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = log_1_model.predict(X_test_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50f42fec", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:00.404752Z", + "iopub.status.busy": "2022-09-24T14:20:00.404315Z", + "iopub.status.idle": "2022-09-24T14:20:00.423754Z", + "shell.execute_reply": "2022-09-24T14:20:00.422543Z" + }, + "papermill": { + "duration": 0.066978, + "end_time": "2022-09-24T14:20:00.426379", + "exception": false, + "start_time": "2022-09-24T14:20:00.359401", + "status": "completed" + }, + "tags": [], + "id": "50f42fec" + }, + "outputs": [], + "source": [ + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c971e23", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:00.505673Z", + "iopub.status.busy": "2022-09-24T14:20:00.505292Z", + "iopub.status.idle": "2022-09-24T14:20:00.520302Z", + "shell.execute_reply": "2022-09-24T14:20:00.519019Z" + }, + "papermill": { + "duration": 0.057104, + "end_time": "2022-09-24T14:20:00.522592", + "exception": false, + "start_time": "2022-09-24T14:20:00.465488", + "status": "completed" + }, + "tags": [], + "id": "7c971e23", + "outputId": "0175f428-2771-4f8e-e461-ec2aca9bc0fa" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data222357825181.3273.030.8181.3276.9581.32
test_data5889211184.0673.420.8184.0678.3882.12
\n", + "
" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 222 357 82 51 81.32 73.03 0.81 81.32 \n", + "test_data 58 89 21 11 84.06 73.42 0.81 84.06 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 76.95 81.32 \n", + "test_data 78.38 82.12 " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04256c67", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:00.602042Z", + "iopub.status.busy": "2022-09-24T14:20:00.601578Z", + "iopub.status.idle": "2022-09-24T14:20:00.893975Z", + "shell.execute_reply": "2022-09-24T14:20:00.892785Z" + }, + "papermill": { + "duration": 0.335587, + "end_time": "2022-09-24T14:20:00.896452", + "exception": false, + "start_time": "2022-09-24T14:20:00.560865", + "status": "completed" + }, + "tags": [], + "id": "04256c67", + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from plot_metric.functions import BinaryClassification\n", + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "269cd45b", + "metadata": { + "papermill": { + "duration": 0.040614, + "end_time": "2022-09-24T14:20:00.978129", + "exception": false, + "start_time": "2022-09-24T14:20:00.937515", + "status": "completed" + }, + "tags": [], + "id": "269cd45b" + }, + "source": [ + "**Decision Tree**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e90328b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.060407Z", + "iopub.status.busy": "2022-09-24T14:20:01.059978Z", + "iopub.status.idle": "2022-09-24T14:20:01.204878Z", + "shell.execute_reply": "2022-09-24T14:20:01.203585Z" + }, + "papermill": { + "duration": 0.188992, + "end_time": "2022-09-24T14:20:01.208033", + "exception": false, + "start_time": "2022-09-24T14:20:01.019041", + "status": "completed" + }, + "tags": [], + "id": "1e90328b", + "outputId": "fa28d12f-ec43-440c-ff00-c4a64699e25c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5,\n", + " estimator=DecisionTreeClassifier(class_weight='balanced',\n", + " random_state=42),\n", + " param_grid={'max_depth': range(10, 20, 10),\n", + " 'min_samples_leaf': range(50, 150, 50),\n", + " 'min_samples_split': range(50, 150, 50)},\n", + " scoring='recall', verbose=1)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create the parameter grid \n", + "param_grid = {\n", + " 'max_depth': range(10,20,10),\n", + " 'min_samples_leaf': range(50, 150, 50),\n", + " 'min_samples_split': range(50, 150, 50),\n", + "}\n", + "\n", + "\n", + "# Instantiate the grid search model\n", + "dtree = DecisionTreeClassifier(class_weight='balanced',random_state=42)\n", + "\n", + "grid_search = GridSearchCV(estimator = dtree, \n", + " param_grid = param_grid, \n", + " scoring= 'recall',\n", + " cv = 5, \n", + " verbose = 1)\n", + "\n", + "# Fit the grid search to the data\n", + "grid_search.fit(X_train_scaled,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2b3be75", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.292037Z", + "iopub.status.busy": "2022-09-24T14:20:01.291419Z", + "iopub.status.idle": "2022-09-24T14:20:01.314068Z", + "shell.execute_reply": "2022-09-24T14:20:01.312940Z" + }, + "papermill": { + "duration": 0.067639, + "end_time": "2022-09-24T14:20:01.316613", + "exception": false, + "start_time": "2022-09-24T14:20:01.248974", + "status": "completed" + }, + "tags": [], + "id": "d2b3be75", + "outputId": "58821f9b-3fce-49bc-8bbe-97710eecc517" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_max_depthparam_min_samples_leafparam_min_samples_splitparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoremean_test_scorestd_test_scorerank_test_score
00.0036470.0002900.0024180.000130105050{'max_depth': 10, 'min_samples_leaf': 50, 'min...0.8000000.7272730.8363640.8888890.8518520.8208750.0548131
10.0036550.0001270.0024070.0000511050100{'max_depth': 10, 'min_samples_leaf': 50, 'min...0.8000000.7272730.8363640.8888890.8518520.8208750.0548131
20.0031460.0000420.0022700.0000171010050{'max_depth': 10, 'min_samples_leaf': 100, 'mi...0.7090910.5454550.7636360.7037040.7222220.6888220.0746983
30.0031790.0000420.0023310.00002910100100{'max_depth': 10, 'min_samples_leaf': 100, 'mi...0.7090910.5454550.7636360.7037040.7222220.6888220.0746983
\n", + "
" + ], + "text/plain": [ + " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", + "0 0.003647 0.000290 0.002418 0.000130 \n", + "1 0.003655 0.000127 0.002407 0.000051 \n", + "2 0.003146 0.000042 0.002270 0.000017 \n", + "3 0.003179 0.000042 0.002331 0.000029 \n", + "\n", + " param_max_depth param_min_samples_leaf param_min_samples_split \\\n", + "0 10 50 50 \n", + "1 10 50 100 \n", + "2 10 100 50 \n", + "3 10 100 100 \n", + "\n", + " params split0_test_score \\\n", + "0 {'max_depth': 10, 'min_samples_leaf': 50, 'min... 0.800000 \n", + "1 {'max_depth': 10, 'min_samples_leaf': 50, 'min... 0.800000 \n", + "2 {'max_depth': 10, 'min_samples_leaf': 100, 'mi... 0.709091 \n", + "3 {'max_depth': 10, 'min_samples_leaf': 100, 'mi... 0.709091 \n", + "\n", + " split1_test_score split2_test_score split3_test_score split4_test_score \\\n", + "0 0.727273 0.836364 0.888889 0.851852 \n", + "1 0.727273 0.836364 0.888889 0.851852 \n", + "2 0.545455 0.763636 0.703704 0.722222 \n", + "3 0.545455 0.763636 0.703704 0.722222 \n", + "\n", + " mean_test_score std_test_score rank_test_score \n", + "0 0.820875 0.054813 1 \n", + "1 0.820875 0.054813 1 \n", + "2 0.688822 0.074698 3 \n", + "3 0.688822 0.074698 3 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cv results\n", + "cv_results = pd.DataFrame(grid_search.cv_results_)\n", + "cv_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db10cf79", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.398943Z", + "iopub.status.busy": "2022-09-24T14:20:01.398525Z", + "iopub.status.idle": "2022-09-24T14:20:01.405080Z", + "shell.execute_reply": "2022-09-24T14:20:01.403801Z" + }, + "papermill": { + "duration": 0.050135, + "end_time": "2022-09-24T14:20:01.407569", + "exception": false, + "start_time": "2022-09-24T14:20:01.357434", + "status": "completed" + }, + "tags": [], + "id": "db10cf79", + "outputId": "8336b9b5-17b9-4978-db2e-efcaae6158a1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best score:- 0.820875420875421\n", + "DecisionTreeClassifier(class_weight='balanced', max_depth=10,\n", + " min_samples_leaf=50, min_samples_split=50,\n", + " random_state=42)\n" + ] + } + ], + "source": [ + "# Printing the optimal sensitivity score and hyperparameters\n", + "print(\"Best score:-\", grid_search.best_score_)\n", + "print(grid_search.best_estimator_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00be4999", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.488735Z", + "iopub.status.busy": "2022-09-24T14:20:01.488312Z", + "iopub.status.idle": "2022-09-24T14:20:01.515852Z", + "shell.execute_reply": "2022-09-24T14:20:01.515006Z" + }, + "papermill": { + "duration": 0.071037, + "end_time": "2022-09-24T14:20:01.518421", + "exception": false, + "start_time": "2022-09-24T14:20:01.447384", + "status": "completed" + }, + "tags": [], + "id": "00be4999" + }, + "outputs": [], + "source": [ + "# Instantiate the model with best hyperparameters\n", + "dt = DecisionTreeClassifier(class_weight='balanced', criterion = \"gini\",max_depth=10,\n", + " min_samples_leaf=50, min_samples_split=50,\n", + " random_state=42)\n", + "\n", + "# Fit the model on the train set\n", + "dt_model = dt.fit(X_train_scaled, y_train)\n", + "\n", + "# predict the training data\n", + "Y_train_pred = dt_model.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = dt_model.predict(X_test_scaled)\n", + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7cb032e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.599636Z", + "iopub.status.busy": "2022-09-24T14:20:01.598734Z", + "iopub.status.idle": "2022-09-24T14:20:01.614081Z", + "shell.execute_reply": "2022-09-24T14:20:01.613204Z" + }, + "papermill": { + "duration": 0.057793, + "end_time": "2022-09-24T14:20:01.616253", + "exception": false, + "start_time": "2022-09-24T14:20:01.558460", + "status": "completed" + }, + "tags": [], + "id": "d7cb032e", + "outputId": "88520ab2-c68f-49f7-94c5-132d3d0eb01c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data205366736875.0973.740.8375.0974.4180.20
test_data4783272268.1263.510.7568.1265.7372.63
\n", + "
" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 205 366 73 68 75.09 73.74 0.83 75.09 \n", + "test_data 47 83 27 22 68.12 63.51 0.75 68.12 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 74.41 80.20 \n", + "test_data 65.73 72.63 " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba3d6076", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:01.698065Z", + "iopub.status.busy": "2022-09-24T14:20:01.697191Z", + "iopub.status.idle": "2022-09-24T14:20:01.976940Z", + "shell.execute_reply": "2022-09-24T14:20:01.975773Z" + }, + "papermill": { + "duration": 0.323497, + "end_time": "2022-09-24T14:20:01.979321", + "exception": false, + "start_time": "2022-09-24T14:20:01.655824", + "status": "completed" + }, + "tags": [], + "id": "ba3d6076", + "outputId": "bc7d1fb3-ec1c-4ed7-f25c-8bae5675308a" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "773529ad", + "metadata": { + "papermill": { + "duration": 0.040755, + "end_time": "2022-09-24T14:20:02.061444", + "exception": false, + "start_time": "2022-09-24T14:20:02.020689", + "status": "completed" + }, + "tags": [], + "id": "773529ad" + }, + "source": [ + "**RandomForest Model**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01f144d9", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:02.143511Z", + "iopub.status.busy": "2022-09-24T14:20:02.143099Z", + "iopub.status.idle": "2022-09-24T14:20:08.913066Z", + "shell.execute_reply": "2022-09-24T14:20:08.912263Z" + }, + "papermill": { + "duration": 6.813689, + "end_time": "2022-09-24T14:20:08.915171", + "exception": false, + "start_time": "2022-09-24T14:20:02.101482", + "status": "completed" + }, + "tags": [], + "id": "01f144d9", + "outputId": "de8dc3eb-2c62-41c4-8e07-916ee13f6a6d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(cv=3, estimator=RandomForestClassifier(class_weight='balanced'),\n", + " param_grid={'max_depth': range(10, 20, 10), 'max_features': [7],\n", + " 'min_samples_leaf': range(50, 150, 50),\n", + " 'min_samples_split': range(50, 150, 50),\n", + " 'n_estimators': [300]},\n", + " return_train_score=True, scoring='recall', verbose=1)" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create the parameter grid \n", + "param_grid = {\n", + " 'max_depth': range(10,20,10),\n", + " 'min_samples_leaf': range(50, 150, 50),\n", + " 'min_samples_split': range(50, 150, 50),\n", + " 'max_features': [7],\n", + " 'n_estimators':[300]\n", + "}\n", + "# Create a based model\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "# Instantiate the grid search model\n", + "grid_search = GridSearchCV(estimator = rf, \n", + " param_grid = param_grid, \n", + " cv = 3,\n", + " verbose = 1, \n", + " scoring='recall',\n", + " return_train_score=True)\n", + "\n", + "# Fit the model\n", + "grid_search.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b29f7be", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:08.999045Z", + "iopub.status.busy": "2022-09-24T14:20:08.998379Z", + "iopub.status.idle": "2022-09-24T14:20:09.004403Z", + "shell.execute_reply": "2022-09-24T14:20:09.002871Z" + }, + "papermill": { + "duration": 0.051441, + "end_time": "2022-09-24T14:20:09.007402", + "exception": false, + "start_time": "2022-09-24T14:20:08.955961", + "status": "completed" + }, + "tags": [], + "id": "0b29f7be", + "outputId": "71cc571b-4c4b-4274-c1c8-f01f23cf41ea" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Score 0.7362637362637363\n", + "Best Params {'max_depth': 10, 'max_features': 7, 'min_samples_leaf': 50, 'min_samples_split': 50, 'n_estimators': 300}\n" + ] + } + ], + "source": [ + "print(\"Best Score\",grid_search.best_score_)\n", + "print(\"Best Params\",grid_search.best_params_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16511ce3", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:09.090855Z", + "iopub.status.busy": "2022-09-24T14:20:09.090192Z", + "iopub.status.idle": "2022-09-24T14:20:09.689173Z", + "shell.execute_reply": "2022-09-24T14:20:09.688218Z" + }, + "papermill": { + "duration": 0.644021, + "end_time": "2022-09-24T14:20:09.691923", + "exception": false, + "start_time": "2022-09-24T14:20:09.047902", + "status": "completed" + }, + "tags": [], + "id": "16511ce3" + }, + "outputs": [], + "source": [ + "# Instantiate the model with best hyperparameters\n", + "rfc = RandomForestClassifier(class_weight='balanced',\n", + " max_depth=10,\n", + " min_samples_leaf=50, \n", + " min_samples_split=100,\n", + " max_features=7,\n", + " n_estimators=300,\n", + " random_state=42)\n", + "\n", + "# Fit the model on the train set\n", + "rfc_model = rfc.fit(X_train_scaled, y_train)\n", + "\n", + "# predict the training data\n", + "Y_train_pred = rfc_model.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = rfc_model.predict(X_test_scaled)\n", + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8781916d", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:09.776939Z", + "iopub.status.busy": "2022-09-24T14:20:09.775747Z", + "iopub.status.idle": "2022-09-24T14:20:09.791112Z", + "shell.execute_reply": "2022-09-24T14:20:09.790203Z" + }, + "papermill": { + "duration": 0.059589, + "end_time": "2022-09-24T14:20:09.793416", + "exception": false, + "start_time": "2022-09-24T14:20:09.733827", + "status": "completed" + }, + "tags": [], + "id": "8781916d", + "outputId": "f1c0b903-0c52-4a46-90f4-76397cb8f695" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data208364756576.1973.500.8376.1974.8280.34
test_data5084261972.4665.790.7672.4668.9674.86
\n", + "
" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 208 364 75 65 76.19 73.50 0.83 76.19 \n", + "test_data 50 84 26 19 72.46 65.79 0.76 72.46 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 74.82 80.34 \n", + "test_data 68.96 74.86 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fec9763", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:09.878037Z", + "iopub.status.busy": "2022-09-24T14:20:09.877333Z", + "iopub.status.idle": "2022-09-24T14:20:10.159604Z", + "shell.execute_reply": "2022-09-24T14:20:10.158450Z" + }, + "papermill": { + "duration": 0.327115, + "end_time": "2022-09-24T14:20:10.162246", + "exception": false, + "start_time": "2022-09-24T14:20:09.835131", + "status": "completed" + }, + "tags": [], + "id": "0fec9763", + "outputId": "45d37a61-fd86-4e11-9ae8-3a2750fa57df" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ee8746a0", + "metadata": { + "papermill": { + "duration": 0.04162, + "end_time": "2022-09-24T14:20:10.245308", + "exception": false, + "start_time": "2022-09-24T14:20:10.203688", + "status": "completed" + }, + "tags": [], + "id": "ee8746a0" + }, + "source": [ + "**SVC**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bf10552", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:10.330931Z", + "iopub.status.busy": "2022-09-24T14:20:10.329640Z", + "iopub.status.idle": "2022-09-24T14:20:38.856748Z", + "shell.execute_reply": "2022-09-24T14:20:38.855295Z" + }, + "papermill": { + "duration": 28.572904, + "end_time": "2022-09-24T14:20:38.859196", + "exception": false, + "start_time": "2022-09-24T14:20:10.286292", + "status": "completed" + }, + "tags": [], + "id": "0bf10552", + "outputId": "fe7b8de0-65fb-4d65-81e3-4a21f79868e7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n", + "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.613 total time= 0.0s\n", + "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 1/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.811 total time= 0.0s\n", + "[CV 3/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.831 total time= 0.0s\n", + "[CV 5/5] END .....C=0.1, gamma=1, kernel=linear;, score=0.796 total time= 0.0s\n", + "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.776 total time= 0.0s\n", + "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.831 total time= 0.0s\n", + "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 1/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.811 total time= 0.0s\n", + "[CV 3/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END ...C=0.1, gamma=0.1, 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"[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.613 total time= 0.0s\n", + "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 1/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.811 total time= 0.0s\n", + "[CV 3/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.831 total time= 0.0s\n", + "[CV 5/5] END .C=0.1, gamma=0.001, kernel=linear;, score=0.796 total time= 0.0s\n", + "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.615 total time= 0.0s\n", + "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.613 total time= 0.0s\n", + "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.620 total time= 0.0s\n", + "[CV 1/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.811 total time= 0.0s\n", + "[CV 3/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.831 total time= 0.0s\n", + "[CV 5/5] END C=0.1, gamma=0.0001, kernel=linear;, score=0.796 total time= 0.0s\n", + "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.734 total time= 0.0s\n", + "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.720 total time= 0.0s\n", + "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 4/5] END 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score=0.831 total time= 0.1s\n", + "[CV 4/5] END .......C=9, gamma=1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END .......C=9, gamma=1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END ........C=9, gamma=0.1, kernel=rbf;, score=0.797 total time= 0.0s\n", + "[CV 2/5] END ........C=9, gamma=0.1, kernel=rbf;, score=0.769 total time= 0.0s\n", + "[CV 3/5] END ........C=9, gamma=0.1, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END ........C=9, gamma=0.1, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 5/5] END ........C=9, gamma=0.1, kernel=rbf;, score=0.789 total time= 0.0s\n", + "[CV 1/5] END .....C=9, gamma=0.1, kernel=linear;, score=0.797 total time= 0.1s\n", + "[CV 2/5] END .....C=9, gamma=0.1, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END .....C=9, gamma=0.1, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END .....C=9, gamma=0.1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END 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time= 0.0s\n", + "[CV 2/5] END ......C=9, gamma=0.001, kernel=rbf;, score=0.762 total time= 0.0s\n", + "[CV 3/5] END ......C=9, gamma=0.001, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 4/5] END ......C=9, gamma=0.001, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 5/5] END ......C=9, gamma=0.001, kernel=rbf;, score=0.775 total time= 0.0s\n", + "[CV 1/5] END ...C=9, gamma=0.001, kernel=linear;, score=0.797 total time= 0.1s\n", + "[CV 2/5] END ...C=9, gamma=0.001, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ...C=9, gamma=0.001, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ...C=9, gamma=0.001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ...C=9, gamma=0.001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END .....C=9, gamma=0.0001, kernel=rbf;, score=0.790 total time= 0.0s\n", + "[CV 2/5] END .....C=9, gamma=0.0001, kernel=rbf;, score=0.762 total time= 0.0s\n", + "[CV 3/5] END .....C=9, gamma=0.0001, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 4/5] END .....C=9, gamma=0.0001, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 5/5] END .....C=9, gamma=0.0001, kernel=rbf;, score=0.789 total time= 0.0s\n", + "[CV 1/5] END ..C=9, gamma=0.0001, kernel=linear;, score=0.797 total time= 0.1s\n", + "[CV 2/5] END ..C=9, gamma=0.0001, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ..C=9, gamma=0.0001, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ..C=9, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ..C=9, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.706 total time= 0.0s\n", + "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.699 total time= 0.0s\n", + "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.768 total time= 0.0s\n", + "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.768 total time= 0.0s\n", + "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.746 total time= 0.0s\n", + "[CV 1/5] END ......C=10, gamma=1, kernel=linear;, score=0.797 total time= 0.2s\n", + "[CV 2/5] END ......C=10, gamma=1, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ......C=10, gamma=1, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ......C=10, gamma=1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ......C=10, gamma=1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.797 total time= 0.0s\n", + "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.769 total time= 0.0s\n", + "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.824 total time= 0.0s\n", + "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.789 total time= 0.0s\n", + "[CV 1/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.797 total time= 0.2s\n", + "[CV 2/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ....C=10, gamma=0.1, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.832 total time= 0.0s\n", + "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.825 total time= 0.0s\n", + "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.831 total time= 0.0s\n", + "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.817 total time= 0.0s\n", + "[CV 1/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.797 total time= 0.2s\n", + "[CV 2/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ...C=10, gamma=0.01, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.804 total time= 0.0s\n", + "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.762 total time= 0.0s\n", + "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.817 total time= 0.0s\n", + "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.775 total time= 0.0s\n", + "[CV 1/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.797 total time= 0.2s\n", + "[CV 2/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END ..C=10, gamma=0.001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.790 total time= 0.0s\n", + "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.762 total time= 0.0s\n", + "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.803 total time= 0.0s\n", + "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.789 total time= 0.0s\n", + "[CV 1/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.797 total time= 0.2s\n", + "[CV 2/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.825 total time= 0.1s\n", + "[CV 3/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.831 total time= 0.1s\n", + "[CV 4/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 5/5] END .C=10, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.1s\n", + "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.685 total time= 0.0s\n", + "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.692 total time= 0.0s\n", + "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.775 total time= 0.0s\n", + "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.711 total time= 0.0s\n", + "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.754 total time= 0.0s\n", + "[CV 1/5] END .....C=100, gamma=1, kernel=linear;, score=0.797 total time= 0.9s\n", + "[CV 2/5] END .....C=100, gamma=1, kernel=linear;, score=0.825 total time= 0.7s\n", + "[CV 3/5] END .....C=100, gamma=1, kernel=linear;, score=0.831 total time= 0.7s\n", + "[CV 4/5] END .....C=100, gamma=1, kernel=linear;, score=0.824 total time= 0.6s\n", + "[CV 5/5] END .....C=100, gamma=1, kernel=linear;, score=0.824 total time= 0.5s\n", + "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.734 total time= 0.0s\n", + "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.797 total time= 0.0s\n", + "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.782 total time= 0.0s\n", + "[CV 1/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.797 total time= 0.9s\n", + "[CV 2/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.825 total time= 0.7s\n", + "[CV 3/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.831 total time= 0.7s\n", + "[CV 4/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.824 total time= 0.6s\n", + "[CV 5/5] END ...C=100, gamma=0.1, kernel=linear;, score=0.824 total time= 0.5s\n", + "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.797 total time= 0.0s\n", + "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.811 total time= 0.0s\n", + "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.824 total time= 0.0s\n", + "[CV 1/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.797 total time= 0.9s\n", + "[CV 2/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.825 total time= 0.7s\n", + "[CV 3/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.831 total time= 0.7s\n", + "[CV 4/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.824 total time= 0.6s\n", + "[CV 5/5] END ..C=100, gamma=0.01, kernel=linear;, score=0.824 total time= 0.5s\n", + "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.811 total time= 0.0s\n", + "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.825 total time= 0.0s\n", + "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.838 total time= 0.0s\n", + "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.824 total time= 0.0s\n", + "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.817 total time= 0.0s\n", + "[CV 1/5] END .C=100, gamma=0.001, kernel=linear;, score=0.797 total time= 0.9s\n", + "[CV 2/5] END .C=100, gamma=0.001, kernel=linear;, score=0.825 total time= 0.7s\n", + "[CV 3/5] END .C=100, gamma=0.001, kernel=linear;, score=0.831 total time= 0.7s\n", + "[CV 4/5] END .C=100, gamma=0.001, kernel=linear;, score=0.824 total time= 0.6s\n", + "[CV 5/5] END .C=100, gamma=0.001, kernel=linear;, score=0.824 total time= 0.5s\n", + "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.797 total time= 0.0s\n", + "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.762 total time= 0.0s\n", + "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.810 total time= 0.0s\n", + "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.817 total time= 0.0s\n", + "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.768 total time= 0.0s\n", + "[CV 1/5] END C=100, gamma=0.0001, kernel=linear;, score=0.797 total time= 0.9s\n", + "[CV 2/5] END C=100, gamma=0.0001, kernel=linear;, score=0.825 total time= 0.7s\n", + "[CV 3/5] END C=100, gamma=0.0001, kernel=linear;, score=0.831 total time= 0.7s\n", + "[CV 4/5] END C=100, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.6s\n", + "[CV 5/5] END C=100, gamma=0.0001, kernel=linear;, score=0.824 total time= 0.5s\n" + ] + }, + { + "data": { + "text/plain": [ + "GridSearchCV(estimator=SVC(),\n", + " param_grid={'C': [0.1, 1, 8, 9, 10, 100],\n", + " 'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n", + " 'kernel': ['rbf', 'linear']},\n", + " verbose=3)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# defining parameter range\n", + "param_grid = {'C': [0.1, 1, 8, 9, 10, 100], \n", + " 'gamma': [1, 0.1 , 0.01, 0.001, 0.0001],\n", + " 'kernel': ['rbf', 'linear']} \n", + "\n", + "svc = svm.SVC()\n", + "grid_search = GridSearchCV(svc, param_grid, refit = True, verbose = 3)\n", + " \n", + "# fitting the model for grid search\n", + "grid_search.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5db5b5b8", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:38.952549Z", + "iopub.status.busy": "2022-09-24T14:20:38.952130Z", + "iopub.status.idle": "2022-09-24T14:20:38.957435Z", + "shell.execute_reply": "2022-09-24T14:20:38.956346Z" + }, + "papermill": { + "duration": 0.054571, + "end_time": "2022-09-24T14:20:38.959824", + "exception": false, + "start_time": "2022-09-24T14:20:38.905253", + "status": "completed" + }, + "tags": [], + "id": "5db5b5b8", + "outputId": "8685467c-2ba0-4eb7-cdc1-4805deab5e6b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Score 0.8300502314586822\n", + "Best Params {'C': 8, 'gamma': 0.01, 'kernel': 'rbf'}\n" + ] + } + ], + "source": [ + "print(\"Best Score\",grid_search.best_score_)\n", + "print(\"Best Params\",grid_search.best_params_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e18bcbb7", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:39.053748Z", + "iopub.status.busy": "2022-09-24T14:20:39.053330Z", + "iopub.status.idle": "2022-09-24T14:20:39.112171Z", + "shell.execute_reply": "2022-09-24T14:20:39.111062Z" + }, + "papermill": { + "duration": 0.108669, + "end_time": "2022-09-24T14:20:39.114679", + "exception": false, + "start_time": "2022-09-24T14:20:39.006010", + "status": "completed" + }, + "tags": [], + "id": "e18bcbb7" + }, + "outputs": [], + "source": [ + "# Instantiate the model with best hyperparameters\n", + "svc = svm.SVC(C=8, gamma = 0.01, kernel = 'rbf', random_state=42)\n", + "\n", + "# Fit the model on the train set\n", + "svc_model = svc.fit(X_train_scaled, y_train)\n", + "\n", + "# predict the training data\n", + "Y_train_pred = svc_model.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = svc_model.predict(X_test_scaled)\n", + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ef7abbd", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:39.208471Z", + "iopub.status.busy": "2022-09-24T14:20:39.208036Z", + "iopub.status.idle": "2022-09-24T14:20:39.223915Z", + "shell.execute_reply": "2022-09-24T14:20:39.222750Z" + }, + "papermill": { + "duration": 0.065308, + "end_time": "2022-09-24T14:20:39.226237", + "exception": false, + "start_time": "2022-09-24T14:20:39.160929", + "status": "completed" + }, + "tags": [], + "id": "4ef7abbd", + "outputId": "d8703ba2-d5b2-4f70-c12b-fa6072dc9bec" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data204398416974.7383.270.9174.7378.7784.55
test_data5197131873.9179.690.8873.9176.6982.68
\n", + "
" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 204 398 41 69 74.73 83.27 0.91 74.73 \n", + "test_data 51 97 13 18 73.91 79.69 0.88 73.91 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 78.77 84.55 \n", + "test_data 76.69 82.68 " + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd9786c0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:39.320590Z", + "iopub.status.busy": "2022-09-24T14:20:39.320166Z", + "iopub.status.idle": "2022-09-24T14:20:39.610488Z", + "shell.execute_reply": "2022-09-24T14:20:39.609291Z" + }, + "papermill": { + "duration": 0.340257, + "end_time": "2022-09-24T14:20:39.613029", + "exception": false, + "start_time": "2022-09-24T14:20:39.272772", + "status": "completed" + }, + "tags": [], + "id": "dd9786c0", + "outputId": "1177e438-fd4d-4939-c486-7fda23966923" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data231420194284.6292.400.9684.6288.3491.43
test_data4496142563.7775.860.8763.7769.2978.21
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" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 231 420 19 42 84.62 92.40 0.96 84.62 \n", + "test_data 44 96 14 25 63.77 75.86 0.87 63.77 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 88.34 91.43 \n", + "test_data 69.29 78.21 " + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6ae99c2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:40.384384Z", + "iopub.status.busy": "2022-09-24T14:20:40.383963Z", + "iopub.status.idle": "2022-09-24T14:20:40.670101Z", + "shell.execute_reply": "2022-09-24T14:20:40.668715Z" + }, + "papermill": { + "duration": 0.339116, + "end_time": "2022-09-24T14:20:40.674158", + "exception": false, + "start_time": "2022-09-24T14:20:40.335042", + "status": "completed" + }, + "tags": [], + "id": "a6ae99c2", + "outputId": "1e4cd57b-5a44-405f-a9b2-536b86ed6a0c" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "56d92ad2", + "metadata": { + "papermill": { + "duration": 0.048569, + "end_time": "2022-09-24T14:20:40.773345", + "exception": false, + "start_time": "2022-09-24T14:20:40.724776", + "status": "completed" + }, + "tags": [], + "id": "56d92ad2" + }, + "source": [ + "**Adaboost**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "120a8f71", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:40.872865Z", + "iopub.status.busy": "2022-09-24T14:20:40.871864Z", + "iopub.status.idle": "2022-09-24T14:20:41.250734Z", + "shell.execute_reply": "2022-09-24T14:20:41.249421Z" + }, + "papermill": { + "duration": 0.43116, + "end_time": "2022-09-24T14:20:41.253363", + "exception": false, + "start_time": "2022-09-24T14:20:40.822203", + "status": "completed" + }, + "tags": [], + "id": "120a8f71", + "outputId": "b5d5b8c9-832a-4dff-85d3-dc085b9eda08" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AdaBoostClassifier(n_estimators=200, random_state=1)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adaboost = AdaBoostClassifier(n_estimators=200, random_state=1)\n", + "adaboost.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d99f195", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:41.351835Z", + "iopub.status.busy": "2022-09-24T14:20:41.350452Z", + "iopub.status.idle": "2022-09-24T14:20:41.464861Z", + "shell.execute_reply": "2022-09-24T14:20:41.463890Z" + }, + "papermill": { + "duration": 0.166588, + "end_time": "2022-09-24T14:20:41.467680", + "exception": false, + "start_time": "2022-09-24T14:20:41.301092", + "status": "completed" + }, + "tags": [], + "id": "1d99f195" + }, + "outputs": [], + "source": [ + "# predict the training data\n", + "Y_train_pred = adaboost.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = adaboost.predict(X_test_scaled)\n", + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68f6cf25", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:41.568306Z", + "iopub.status.busy": "2022-09-24T14:20:41.567919Z", + "iopub.status.idle": "2022-09-24T14:20:41.583624Z", + "shell.execute_reply": "2022-09-24T14:20:41.582442Z" + }, + "papermill": { + "duration": 0.067192, + "end_time": "2022-09-24T14:20:41.585918", + "exception": false, + "start_time": "2022-09-24T14:20:41.518726", + "status": "completed" + }, + "tags": [], + "id": "68f6cf25", + "outputId": "b6067085-4d81-4b80-d95b-437609d59f2f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data218397425579.8583.850.9079.8581.8086.38
test_data4692182366.6771.880.8466.6769.1877.09
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" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 218 397 42 55 79.85 83.85 0.90 79.85 \n", + "test_data 46 92 18 23 66.67 71.88 0.84 66.67 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 81.80 86.38 \n", + "test_data 69.18 77.09 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d73f3fae", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:41.754583Z", + "iopub.status.busy": "2022-09-24T14:20:41.753927Z", + "iopub.status.idle": "2022-09-24T14:20:42.041110Z", + "shell.execute_reply": "2022-09-24T14:20:42.039465Z" + }, + "papermill": { + "duration": 0.340459, + "end_time": "2022-09-24T14:20:42.044170", + "exception": false, + "start_time": "2022-09-24T14:20:41.703711", + "status": "completed" + }, + "tags": [], + "id": "d73f3fae", + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cadfd798", + "metadata": { + "papermill": { + "duration": 0.048199, + "end_time": "2022-09-24T14:20:42.142666", + "exception": false, + "start_time": "2022-09-24T14:20:42.094467", + "status": "completed" + }, + "tags": [], + "id": "cadfd798" + }, + "source": [ + "**Neural network**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "165daa10", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:42.242318Z", + "iopub.status.busy": "2022-09-24T14:20:42.241888Z", + "iopub.status.idle": "2022-09-24T14:20:49.373755Z", + "shell.execute_reply": "2022-09-24T14:20:49.372516Z" + }, + "papermill": { + "duration": 7.185213, + "end_time": "2022-09-24T14:20:49.376495", + "exception": false, + "start_time": "2022-09-24T14:20:42.191282", + "status": "completed" + }, + "tags": [], + "id": "165daa10" + }, + "outputs": [], + "source": [ + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout, BatchNormalization, Activation, GRU, Embedding\n", + "from keras import regularizers\n", + "from tensorflow.keras.optimizers import SGD, Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b230222", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:49.475812Z", + "iopub.status.busy": "2022-09-24T14:20:49.475055Z", + "iopub.status.idle": "2022-09-24T14:20:49.618868Z", + "shell.execute_reply": "2022-09-24T14:20:49.616824Z" + }, + "papermill": { + "duration": 0.196248, + "end_time": "2022-09-24T14:20:49.621572", + "exception": false, + "start_time": "2022-09-24T14:20:49.425324", + "status": "completed" + }, + "tags": [], + "id": "7b230222", + "outputId": "8988dff2-b9df-42a9-9558-6b3ed6d8b06a" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-09-24 14:20:49.514954: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(32, activation = 'relu', input_dim = 17))\n", + "model.add(Dense(64, activation = 'relu'))\n", + "model.add(Dense(1))\n", + "#Complile model\n", + "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54655121", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:49.740508Z", + "iopub.status.busy": "2022-09-24T14:20:49.739625Z", + "iopub.status.idle": "2022-09-24T14:20:49.744941Z", + "shell.execute_reply": "2022-09-24T14:20:49.744081Z" + }, + "papermill": { + "duration": 0.073266, + "end_time": "2022-09-24T14:20:49.748282", + "exception": false, + "start_time": "2022-09-24T14:20:49.675016", + "status": "completed" + }, + "tags": [], + "id": "54655121" + }, + "outputs": [], + "source": [ + "#model.fit(X_train_scaled, y_train, batch_size = 10, epochs = 100, validation_data=(X_test_scaled, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4d5f0a2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:20:49.863880Z", + "iopub.status.busy": "2022-09-24T14:20:49.861640Z", + "iopub.status.idle": "2022-09-24T14:21:03.925096Z", + "shell.execute_reply": "2022-09-24T14:21:03.923435Z" + }, + "papermill": { + "duration": 14.12158, + "end_time": "2022-09-24T14:21:03.927703", + "exception": false, + "start_time": "2022-09-24T14:20:49.806123", + "status": "completed" + }, + "tags": [], + "id": "d4d5f0a2", + "outputId": "247e7c57-1f09-43e4-b205-49885b710735" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-09-24 14:20:49.966264: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "72/72 [==============================] - 1s 2ms/step - loss: 1.1816 - accuracy: 0.7065\n", + "Epoch 2/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8963 - accuracy: 0.8062\n", + "Epoch 3/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8179 - accuracy: 0.8202\n", + "Epoch 4/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8222 - accuracy: 0.8216\n", + "Epoch 5/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8292 - accuracy: 0.8287\n", + "Epoch 6/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8009 - accuracy: 0.8329\n", + "Epoch 7/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.7862 - accuracy: 0.8511\n", + "Epoch 8/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8609 - accuracy: 0.8062\n", + "Epoch 9/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8635 - accuracy: 0.8258\n", + "Epoch 10/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.7243 - accuracy: 0.8497\n", + "Epoch 11/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.7089 - accuracy: 0.8511\n", + "Epoch 12/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.7250 - accuracy: 0.8567\n", + "Epoch 13/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6764 - accuracy: 0.8581\n", + "Epoch 14/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6669 - accuracy: 0.8581\n", + "Epoch 15/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6421 - accuracy: 0.8581\n", + "Epoch 16/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6376 - accuracy: 0.8680\n", + "Epoch 17/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6481 - accuracy: 0.8652\n", + "Epoch 18/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6591 - accuracy: 0.8483\n", + "Epoch 19/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6325 - accuracy: 0.8666\n", + "Epoch 20/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6240 - accuracy: 0.8750\n", + "Epoch 21/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6244 - accuracy: 0.8722\n", + "Epoch 22/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6204 - accuracy: 0.8694\n", + "Epoch 23/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6212 - accuracy: 0.8680\n", + "Epoch 24/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6663 - accuracy: 0.8132\n", + "Epoch 25/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4850 - accuracy: 0.8441\n", + "Epoch 26/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4185 - accuracy: 0.8778\n", + "Epoch 27/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4117 - accuracy: 0.8722\n", + "Epoch 28/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4031 - accuracy: 0.8764\n", + "Epoch 29/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4262 - accuracy: 0.8680\n", + "Epoch 30/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4179 - accuracy: 0.8680\n", + "Epoch 31/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3996 - accuracy: 0.8764\n", + "Epoch 32/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4040 - accuracy: 0.8722\n", + "Epoch 33/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3762 - accuracy: 0.8764\n", + "Epoch 34/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3724 - accuracy: 0.8848\n", + "Epoch 35/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3741 - accuracy: 0.8848\n", + "Epoch 36/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3828 - accuracy: 0.8876\n", + "Epoch 37/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3903 - accuracy: 0.8806\n", + "Epoch 38/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.5887 - accuracy: 0.8525\n", + "Epoch 39/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.5107 - accuracy: 0.8624\n", + "Epoch 40/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4900 - accuracy: 0.8820\n", + "Epoch 41/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4355 - accuracy: 0.8806\n", + "Epoch 42/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4415 - accuracy: 0.8834\n", + "Epoch 43/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.5472 - accuracy: 0.8694\n", + "Epoch 44/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.5330 - accuracy: 0.8638\n", + "Epoch 45/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4712 - accuracy: 0.8820\n", + "Epoch 46/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4071 - accuracy: 0.8820\n", + "Epoch 47/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4028 - accuracy: 0.8890\n", + "Epoch 48/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4029 - accuracy: 0.8919\n", + "Epoch 49/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4032 - accuracy: 0.8919\n", + "Epoch 50/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3962 - accuracy: 0.8904\n", + "Epoch 51/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4003 - accuracy: 0.8947\n", + "Epoch 52/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 1.0306 - accuracy: 0.7514\n", + "Epoch 53/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.8158 - accuracy: 0.8567\n", + "Epoch 54/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4710 - accuracy: 0.8539\n", + "Epoch 55/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.4422 - accuracy: 0.8750\n", + "Epoch 56/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3849 - accuracy: 0.8834\n", + "Epoch 57/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3784 - accuracy: 0.8904\n", + "Epoch 58/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3670 - accuracy: 0.8947\n", + "Epoch 59/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3669 - accuracy: 0.8862\n", + "Epoch 60/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3620 - accuracy: 0.8989\n", + "Epoch 61/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3579 - accuracy: 0.8890\n", + "Epoch 62/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8975\n", + "Epoch 63/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8975\n", + "Epoch 64/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8961\n", + "Epoch 65/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3506 - accuracy: 0.8933\n", + "Epoch 66/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3633 - accuracy: 0.8919\n", + "Epoch 67/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3460 - accuracy: 0.8989\n", + "Epoch 68/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3731 - accuracy: 0.8989\n", + "Epoch 69/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.6266 - accuracy: 0.8638\n", + "Epoch 70/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3766 - accuracy: 0.8876\n", + "Epoch 71/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3592 - accuracy: 0.8933\n", + "Epoch 72/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3537 - accuracy: 0.8933\n", + "Epoch 73/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3634 - accuracy: 0.8989\n", + "Epoch 74/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3462 - accuracy: 0.8947\n", + "Epoch 75/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3415 - accuracy: 0.8975\n", + "Epoch 76/100\n", + "72/72 [==============================] - 0s 3ms/step - loss: 0.3399 - accuracy: 0.8975\n", + "Epoch 77/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3370 - accuracy: 0.9003\n", + "Epoch 78/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3384 - accuracy: 0.8975\n", + "Epoch 79/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3551 - accuracy: 0.8961\n", + "Epoch 80/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3510 - accuracy: 0.9031\n", + "Epoch 81/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3361 - accuracy: 0.9017\n", + "Epoch 82/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3513 - accuracy: 0.9003\n", + "Epoch 83/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3307 - accuracy: 0.9017\n", + "Epoch 84/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3336 - accuracy: 0.9031\n", + "Epoch 85/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3319 - accuracy: 0.8989\n", + "Epoch 86/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3269 - accuracy: 0.9059\n", + "Epoch 87/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3279 - accuracy: 0.9031\n", + "Epoch 88/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3267 - accuracy: 0.9003\n", + "Epoch 89/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3350 - accuracy: 0.9031\n", + "Epoch 90/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3214 - accuracy: 0.9031\n", + "Epoch 91/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3283 - accuracy: 0.9031\n", + "Epoch 92/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3227 - accuracy: 0.9031\n", + "Epoch 93/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3351 - accuracy: 0.8989\n", + "Epoch 94/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3778 - accuracy: 0.8750\n", + "Epoch 95/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3347 - accuracy: 0.8975\n", + "Epoch 96/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3319 - accuracy: 0.9017\n", + "Epoch 97/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3250 - accuracy: 0.9059\n", + "Epoch 98/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3283 - accuracy: 0.9031\n", + "Epoch 99/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3214 - accuracy: 0.9003\n", + "Epoch 100/100\n", + "72/72 [==============================] - 0s 2ms/step - loss: 0.3237 - accuracy: 0.9031\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train_scaled, y_train, batch_size = 10, epochs = 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8de5211", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:04.080363Z", + "iopub.status.busy": "2022-09-24T14:21:04.079928Z", + "iopub.status.idle": "2022-09-24T14:21:04.335554Z", + "shell.execute_reply": "2022-09-24T14:21:04.334245Z" + }, + "papermill": { + "duration": 0.335183, + "end_time": "2022-09-24T14:21:04.338593", + "exception": false, + "start_time": "2022-09-24T14:21:04.003410", + "status": "completed" + }, + "tags": [], + "id": "d8de5211" + }, + "outputs": [], + "source": [ + "# predict the training data\n", + "Y_train_pred = model.predict(X_train_scaled)\n", + "# predict the testing data\n", + "Y_test_pred = model.predict(X_test_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d3571fc", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:04.489878Z", + "iopub.status.busy": "2022-09-24T14:21:04.489463Z", + "iopub.status.idle": "2022-09-24T14:21:04.496722Z", + "shell.execute_reply": "2022-09-24T14:21:04.495650Z" + }, + "papermill": { + "duration": 0.085916, + "end_time": "2022-09-24T14:21:04.498796", + "exception": false, + "start_time": "2022-09-24T14:21:04.412880", + "status": "completed" + }, + "tags": [], + "id": "2d3571fc" + }, + "outputs": [], + "source": [ + "Y_train_pred = [1 if a[0]> 0.50 else 0 for a in Y_train_pred]\n", + "Y_test_pred = [1 if a[0]> 0.50 else 0 for a in Y_test_pred]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42328c4f", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:04.653284Z", + "iopub.status.busy": "2022-09-24T14:21:04.652008Z", + "iopub.status.idle": "2022-09-24T14:21:04.673220Z", + "shell.execute_reply": "2022-09-24T14:21:04.672044Z" + }, + "papermill": { + "duration": 0.100179, + "end_time": "2022-09-24T14:21:04.675963", + "exception": false, + "start_time": "2022-09-24T14:21:04.575784", + "status": "completed" + }, + "tags": [], + "id": "42328c4f" + }, + "outputs": [], + "source": [ + "# Model evaluation\n", + "train_eval=pd.DataFrame([evaluate_model(y_train, Y_train_pred)])\n", + "test_eval=pd.DataFrame([evaluate_model(y_test, Y_test_pred)])\n", + "df_eval=pd.concat([train_eval,test_eval])\n", + "df_eval['data']=['train_data','test_data']\n", + "df_eval.set_index('data',inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2123e041", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:04.828957Z", + "iopub.status.busy": "2022-09-24T14:21:04.828261Z", + "iopub.status.idle": "2022-09-24T14:21:04.842551Z", + "shell.execute_reply": "2022-09-24T14:21:04.841388Z" + }, + "papermill": { + "duration": 0.093355, + "end_time": "2022-09-24T14:21:04.845000", + "exception": false, + "start_time": "2022-09-24T14:21:04.751645", + "status": "completed" + }, + "tags": [], + "id": "2123e041", + "outputId": "495773a3-b8cd-42a3-d232-80f63373ea12" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPTNFPFNRecallPrecisionSpecificityROC/AUC ScoreF1-ScoreAccuracy
data
train_data227422174683.1593.030.9683.1587.8191.15
test_data4996142071.0177.780.8771.0174.2481.01
\n", + "
" + ], + "text/plain": [ + " TP TN FP FN Recall Precision Specificity ROC/AUC Score \\\n", + "data \n", + "train_data 227 422 17 46 83.15 93.03 0.96 83.15 \n", + "test_data 49 96 14 20 71.01 77.78 0.87 71.01 \n", + "\n", + " F1-Score Accuracy \n", + "data \n", + "train_data 87.81 91.15 \n", + "test_data 74.24 81.01 " + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5acdc905", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:04.999756Z", + "iopub.status.busy": "2022-09-24T14:21:04.998910Z", + "iopub.status.idle": "2022-09-24T14:21:05.297722Z", + "shell.execute_reply": "2022-09-24T14:21:05.296499Z" + }, + "papermill": { + "duration": 0.379398, + "end_time": "2022-09-24T14:21:05.300418", + "exception": false, + "start_time": "2022-09-24T14:21:04.921020", + "status": "completed" + }, + "tags": [], + "id": "5acdc905", + "outputId": "ed0b88a2-ce4f-401b-eab1-3cefd1eb4970" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualisation with plot_metric\n", + "bc = BinaryClassification(y_test, Y_test_pred, labels=[\"Class 1\", \"Class 2\"])\n", + "\n", + "# Figures\n", + "plt.figure(figsize=(5,5))\n", + "bc.plot_roc_curve()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "55e067b9", + "metadata": { + "papermill": { + "duration": 0.076528, + "end_time": "2022-09-24T14:21:05.455415", + "exception": false, + "start_time": "2022-09-24T14:21:05.378887", + "status": "completed" + }, + "tags": [], + "id": "55e067b9" + }, + "source": [ + "**Model Summaries**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bb44a82", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:05.608272Z", + "iopub.status.busy": "2022-09-24T14:21:05.607743Z", + "iopub.status.idle": "2022-09-24T14:21:05.615897Z", + "shell.execute_reply": "2022-09-24T14:21:05.614820Z" + }, + "papermill": { + "duration": 0.087779, + "end_time": "2022-09-24T14:21:05.618216", + "exception": false, + "start_time": "2022-09-24T14:21:05.530437", + "status": "completed" + }, + "tags": [], + "id": "1bb44a82" + }, + "outputs": [], + "source": [ + "df=pd.DataFrame({\"Experiment No\":[1,2,3,4,5,6,7,8],\"Model\":[\"Logistic\", \"Logistic with Grid search\", \"Decision Tree\", \"Random Forest\", \"Support Vector Classifier\", \"Gradient bossting\", \"Adaboost\", \"Neural Networks\"],\n", + " \"Train Accuracy\":[82.72, 81.32, 80.20, 80.34, 84.55, 91.43, 86.38, 90.31],\"Validation Accuracy\":[83.24, 82.12, 72.63, 74.86, 82.68, 78.21, 77.09, 78.77],\n", + " \"Observation\":[\"Eventhough model was given better accuracy but not given good result on unseen data provided by kaggle\",\"Eventhough model was given better accuracy but not given good result on unseen data provided by kaggle\", \n", + " \"ot producing good results on unseen data provided by kaggle\", \"Not producing good results on unseen data provided by kaggle\", \"Produced good resutls with good accuracy on train and test datasets\",\"Overfitting\", \"Overfitting\",\"Overfitting\"]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1f02cb2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:05.771559Z", + "iopub.status.busy": "2022-09-24T14:21:05.770613Z", + "iopub.status.idle": "2022-09-24T14:21:05.846946Z", + "shell.execute_reply": "2022-09-24T14:21:05.846079Z" + }, + "papermill": { + "duration": 0.154974, + "end_time": "2022-09-24T14:21:05.849272", + "exception": false, + "start_time": "2022-09-24T14:21:05.694298", + "status": "completed" + }, + "tags": [], + "id": "b1f02cb2", + "outputId": "0d3f87f3-1645-4519-9db4-0526b487face" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Experiment NoModelTrain AccuracyValidation AccuracyObservation
01Logistic82.72000083.240000Eventhough model was given better accuracy but not given good result on unseen data provided by kaggle
12Logistic with Grid search81.32000082.120000Eventhough model was given better accuracy but not given good result on unseen data provided by kaggle
23Decision Tree80.20000072.630000ot producing good results on unseen data provided by kaggle
34Random Forest80.34000074.860000Not producing good results on unseen data provided by kaggle
45Support Vector Classifier84.55000082.680000Produced good resutls with good accuracy on train and test datasets
56Gradient bossting91.43000078.210000Overfitting
67Adaboost86.38000077.090000Overfitting
78Neural Networks90.31000078.770000Overfitting
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" + ], + "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": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data = pd.read_csv('../input/titanic/test.csv')\n", + "test_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58cc8505", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:06.503848Z", + "iopub.status.busy": "2022-09-24T14:21:06.503431Z", + "iopub.status.idle": "2022-09-24T14:21:06.508841Z", + "shell.execute_reply": "2022-09-24T14:21:06.507704Z" + }, + "papermill": { + "duration": 0.086879, + "end_time": "2022-09-24T14:21:06.511124", + "exception": false, + "start_time": "2022-09-24T14:21:06.424245", + "status": "completed" + }, + "tags": [], + "id": "58cc8505" + }, + "outputs": [], + "source": [ + "test = test_data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae22ddf7", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:06.667751Z", + "iopub.status.busy": "2022-09-24T14:21:06.667336Z", + "iopub.status.idle": "2022-09-24T14:21:06.676783Z", + "shell.execute_reply": "2022-09-24T14:21:06.675592Z" + }, + "papermill": { + "duration": 0.091696, + "end_time": "2022-09-24T14:21:06.679356", + "exception": false, + "start_time": "2022-09-24T14:21:06.587660", + "status": "completed" + }, + "tags": [], + "id": "ae22ddf7" + }, + "outputs": [], + "source": [ + "test_data[\"CabinBool\"] = (test_data[\"Cabin\"].notnull().astype('int'))\n", + "test_data['Name_title'] = test_data['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())\n", + "test_data['Name_title'] = test_data['Name_title'].apply(lambda a: 'Other' if a not in ['Mr', 'Miss', 'Mrs', 'Master'] else a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3717da0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:06.833750Z", + "iopub.status.busy": "2022-09-24T14:21:06.833339Z", + "iopub.status.idle": "2022-09-24T14:21:06.853209Z", + "shell.execute_reply": "2022-09-24T14:21:06.852004Z" + }, + "papermill": { + "duration": 0.099987, + "end_time": "2022-09-24T14:21:06.855568", + "exception": false, + "start_time": "2022-09-24T14:21:06.755581", + "status": "completed" + }, + "tags": [], + "id": "e3717da0" + }, + "outputs": [], + "source": [ + "test_data['Name_title'] = test_data[['Name_title', 'Age']].apply(lambda a: return_title(a.Name_title, a.Age), axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98a8dd7e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.010255Z", + "iopub.status.busy": "2022-09-24T14:21:07.009271Z", + "iopub.status.idle": "2022-09-24T14:21:07.032490Z", + "shell.execute_reply": "2022-09-24T14:21:07.031222Z" + }, + "papermill": { + "duration": 0.103745, + "end_time": "2022-09-24T14:21:07.035199", + "exception": false, + "start_time": "2022-09-24T14:21:06.931454", + "status": "completed" + }, + "tags": [], + "id": "98a8dd7e" + }, + "outputs": [], + "source": [ + "test_data['Ticket_cls'] = test_data['Ticket'].apply(lambda a: a.split(' ')[0])\n", + "test_data['Ticket_cls'] = test_data['Ticket_cls'].apply(lambda a: 'Numeric' if a.isnumeric() else 'Alphabet')\n", + "test_data['Ticket_cls'] = test_data[['Ticket_cls', 'Ticket']].apply(lambda a: return_numeric(a.Ticket_cls, a.Ticket), axis = 1)\n", + "test_data['FamilySize'] = test_data['SibSp'] + test_data['Parch'] + 1\n", + "test_data['Solo_traveller'] = test_data['FamilySize'].apply(lambda a: 'Yes' if a == 1 else 'No')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0043844", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.187438Z", + "iopub.status.busy": "2022-09-24T14:21:07.187013Z", + "iopub.status.idle": "2022-09-24T14:21:07.193513Z", + "shell.execute_reply": "2022-09-24T14:21:07.192747Z" + }, + "papermill": { + "duration": 0.085015, + "end_time": "2022-09-24T14:21:07.195515", + "exception": false, + "start_time": "2022-09-24T14:21:07.110500", + "status": "completed" + }, + "tags": [], + "id": "a0043844" + }, + "outputs": [], + "source": [ + "#test_data['norm_fare'] = test_data['Fare'].map(lambda x: np.log(x) if x > 0 else 0)\n", + "\n", + "test_data['norm_fare'] = np.log(test_data['Fare'] + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea6525ed", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.350553Z", + "iopub.status.busy": "2022-09-24T14:21:07.349577Z", + "iopub.status.idle": "2022-09-24T14:21:07.360436Z", + "shell.execute_reply": "2022-09-24T14:21:07.359251Z" + }, + "papermill": { + "duration": 0.090966, + "end_time": "2022-09-24T14:21:07.363278", + "exception": false, + "start_time": "2022-09-24T14:21:07.272312", + "status": "completed" + }, + "tags": [], + "id": "ea6525ed" + }, + "outputs": [], + "source": [ + "test_data = fill_Missing_Values(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dce22a7", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.517234Z", + "iopub.status.busy": "2022-09-24T14:21:07.516567Z", + "iopub.status.idle": "2022-09-24T14:21:07.532309Z", + "shell.execute_reply": "2022-09-24T14:21:07.531222Z" + }, + "papermill": { + "duration": 0.095576, + "end_time": "2022-09-24T14:21:07.534682", + "exception": false, + "start_time": "2022-09-24T14:21:07.439106", + "status": "completed" + }, + "tags": [], + "id": "4dce22a7" + }, + "outputs": [], + "source": [ + "# Creating dummies for categorical variables\n", + "categorical_cols = ['Sex', 'Name_title', 'Ticket_cls', 'Embarked', 'Solo_traveller', 'CabinBool', 'Parch', 'SibSp', 'Pclass']\n", + "dummies = pd.get_dummies(test_data[categorical_cols],drop_first=True)\n", + "# Drop Categorical columns\n", + "test_data.drop(categorical_cols,axis=1,inplace=True)\n", + "# Concatenate original dataframe and dummies\n", + "test_data = pd.concat([test_data,dummies],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3ad7fd0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.689011Z", + "iopub.status.busy": "2022-09-24T14:21:07.688341Z", + "iopub.status.idle": "2022-09-24T14:21:07.694263Z", + "shell.execute_reply": "2022-09-24T14:21:07.693468Z" + }, + "papermill": { + "duration": 0.084613, + "end_time": "2022-09-24T14:21:07.696466", + "exception": false, + "start_time": "2022-09-24T14:21:07.611853", + "status": "completed" + }, + "tags": [], + "id": "c3ad7fd0" + }, + "outputs": [], + "source": [ + "# Drop previously dropped columns\n", + "test_data.drop(['PassengerId', 'Cabin', 'Ticket', 'Name', 'Fare', 'FamilySize'], axis = 1, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b05193c", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:07.853695Z", + "iopub.status.busy": "2022-09-24T14:21:07.852752Z", + "iopub.status.idle": "2022-09-24T14:21:07.859816Z", + "shell.execute_reply": "2022-09-24T14:21:07.859044Z" + }, + "papermill": { + "duration": 0.089275, + "end_time": "2022-09-24T14:21:07.862168", + "exception": false, + "start_time": "2022-09-24T14:21:07.772893", + "status": "completed" + }, + "tags": [], + "id": "3b05193c" + }, + "outputs": [], + "source": [ + "test_data_sc = scaler.transform(test_data)\n", + "test_data_scaled = pd.DataFrame(test_data_sc, columns=test_data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3ded88b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-09-24T14:21:08.017242Z", + "iopub.status.busy": "2022-09-24T14:21:08.016104Z", + "iopub.status.idle": "2022-09-24T14:21:08.037978Z", + "shell.execute_reply": "2022-09-24T14:21:08.036748Z" + }, + "papermill": { + "duration": 0.103016, + "end_time": "2022-09-24T14:21:08.040443", + "exception": false, + "start_time": "2022-09-24T14:21:07.937427", + "status": "completed" + }, + "tags": [], + "id": "f3ded88b", + "outputId": "b9b2899b-4b43-43db-e732-3af2ad931b65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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