From 6b0d0a0734375368bc44201979119ce727400940 Mon Sep 17 00:00:00 2001
From: Satyajit Panigrahi <satyapani999@gmail.com>
Date: Sat, 21 Aug 2021 21:34:15 +0530
Subject: [PATCH] Created using Colaboratory

---
 .../Copy_of_insurance_cross_sell.ipynb        | 1351 +++++++++++++++++
 1 file changed, 1351 insertions(+)
 create mode 100644 009/solution/Copy_of_insurance_cross_sell.ipynb

diff --git a/009/solution/Copy_of_insurance_cross_sell.ipynb b/009/solution/Copy_of_insurance_cross_sell.ipynb
new file mode 100644
index 00000000..542c18a2
--- /dev/null
+++ b/009/solution/Copy_of_insurance_cross_sell.ipynb
@@ -0,0 +1,1351 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3",
+      "language": "python"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.7.6",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "colab": {
+      "name": "Copy of insurance_cross_sell.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Satyajit99p/99-ML-Learning-Projects/blob/master/009/solution/Copy_of_insurance_cross_sell.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAGvd4aTBYEe"
+      },
+      "source": [
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/009/solution/insurance_cross_sell.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "trusted": true,
+        "id": "kcCNBhh26ws-"
+      },
+      "source": [
+        "import numpy as np \n",
+        "import pandas as pd \n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import silhouette_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.cluster import KMeans\n",
+        "from sklearn.metrics import roc_auc_score,confusion_matrix, classification_report"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+        "trusted": true,
+        "id": "4Ld7_68i6wto"
+      },
+      "source": [
+        "import seaborn as sns\n",
+        "sns.set_style(\"darkgrid\")\n",
+        "plt.style.use('ggplot')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "RcMrDhBL6wt8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "f597dbca-42e1-4780-9143-dc508c9bb11d"
+      },
+      "source": [
+        "DATA_PATH='https://raw.githubusercontent.com/gimseng/99-ML-Learning-Projects/master/009/data/'\n",
+        "\n",
+        "trainData = pd.read_csv(DATA_PATH+'train.csv')\n",
+        "trainData.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Driving_License</th>\n",
+              "      <th>Region_Code</th>\n",
+              "      <th>Previously_Insured</th>\n",
+              "      <th>Vehicle_Age</th>\n",
+              "      <th>Vehicle_Damage</th>\n",
+              "      <th>Annual_Premium</th>\n",
+              "      <th>Policy_Sales_Channel</th>\n",
+              "      <th>Vintage</th>\n",
+              "      <th>Response</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>44</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>&gt; 2 Years</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>40454.0</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>217</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>76</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1-2 Year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>33536.0</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>183</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>47</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>&gt; 2 Years</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>38294.0</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>27</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>21</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>&lt; 1 Year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>28619.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>203</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>29</td>\n",
+              "      <td>1</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>&lt; 1 Year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>27496.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>39</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   id  Gender  Age  ...  Policy_Sales_Channel  Vintage  Response\n",
+              "0   1    Male   44  ...                  26.0      217         1\n",
+              "1   2    Male   76  ...                  26.0      183         0\n",
+              "2   3    Male   47  ...                  26.0       27         1\n",
+              "3   4    Male   21  ...                 152.0      203         0\n",
+              "4   5  Female   29  ...                 152.0       39         0\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "8LiwSXnJ6wur",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "a4a0c809-0400-4395-eb58-e27837f52bea"
+      },
+      "source": [
+        "testData = pd.read_csv(DATA_PATH+'test.csv')\n",
+        "testData.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Driving_License</th>\n",
+              "      <th>Region_Code</th>\n",
+              "      <th>Previously_Insured</th>\n",
+              "      <th>Vehicle_Age</th>\n",
+              "      <th>Vehicle_Damage</th>\n",
+              "      <th>Annual_Premium</th>\n",
+              "      <th>Policy_Sales_Channel</th>\n",
+              "      <th>Vintage</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>381110</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>25</td>\n",
+              "      <td>1</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>&lt; 1 Year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>35786.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>53</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>381111</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>40</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1-2 Year</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>33762.0</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>381112</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>47</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1-2 Year</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>40050.0</td>\n",
+              "      <td>124.0</td>\n",
+              "      <td>199</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>381113</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>24</td>\n",
+              "      <td>1</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>&lt; 1 Year</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>37356.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>187</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>381114</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>27</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>&lt; 1 Year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>59097.0</td>\n",
+              "      <td>152.0</td>\n",
+              "      <td>297</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       id Gender  Age  ...  Annual_Premium  Policy_Sales_Channel  Vintage\n",
+              "0  381110   Male   25  ...         35786.0                 152.0       53\n",
+              "1  381111   Male   40  ...         33762.0                   7.0      111\n",
+              "2  381112   Male   47  ...         40050.0                 124.0      199\n",
+              "3  381113   Male   24  ...         37356.0                 152.0      187\n",
+              "4  381114   Male   27  ...         59097.0                 152.0      297\n",
+              "\n",
+              "[5 rows x 11 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "hw66ZUsN6wu4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a769f6e7-814c-4b58-fbcc-c4c0fe88c21e"
+      },
+      "source": [
+        "trainData = trainData.drop(['id'], axis = 1)\n",
+        "trainData.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 381109 entries, 0 to 381108\n",
+            "Data columns (total 11 columns):\n",
+            " #   Column                Non-Null Count   Dtype  \n",
+            "---  ------                --------------   -----  \n",
+            " 0   Gender                381109 non-null  object \n",
+            " 1   Age                   381109 non-null  int64  \n",
+            " 2   Driving_License       381109 non-null  int64  \n",
+            " 3   Region_Code           381109 non-null  float64\n",
+            " 4   Previously_Insured    381109 non-null  int64  \n",
+            " 5   Vehicle_Age           381109 non-null  object \n",
+            " 6   Vehicle_Damage        381109 non-null  object \n",
+            " 7   Annual_Premium        381109 non-null  float64\n",
+            " 8   Policy_Sales_Channel  381109 non-null  float64\n",
+            " 9   Vintage               381109 non-null  int64  \n",
+            " 10  Response              381109 non-null  int64  \n",
+            "dtypes: float64(3), int64(5), object(3)\n",
+            "memory usage: 32.0+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "dIe4Vd3W6wu8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c3444468-829a-48a0-d8de-150281bee7a6"
+      },
+      "source": [
+        "testData = testData.drop(['id'], axis = 1)\n",
+        "testData.info()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 127037 entries, 0 to 127036\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column                Non-Null Count   Dtype  \n",
+            "---  ------                --------------   -----  \n",
+            " 0   Gender                127037 non-null  object \n",
+            " 1   Age                   127037 non-null  int64  \n",
+            " 2   Driving_License       127037 non-null  int64  \n",
+            " 3   Region_Code           127037 non-null  float64\n",
+            " 4   Previously_Insured    127037 non-null  int64  \n",
+            " 5   Vehicle_Age           127037 non-null  object \n",
+            " 6   Vehicle_Damage        127037 non-null  object \n",
+            " 7   Annual_Premium        127037 non-null  float64\n",
+            " 8   Policy_Sales_Channel  127037 non-null  float64\n",
+            " 9   Vintage               127037 non-null  int64  \n",
+            "dtypes: float64(3), int64(4), object(3)\n",
+            "memory usage: 9.7+ MB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "_3s8lcZt6wu_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "622f027e-e072-44a9-ddf3-f8c05367d7ed"
+      },
+      "source": [
+        "#Checking for null values\n",
+        "print(trainData.isnull().sum())\n",
+        "print(\" \")\n",
+        "print(testData.isnull().sum())"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Gender                  0\n",
+            "Age                     0\n",
+            "Driving_License         0\n",
+            "Region_Code             0\n",
+            "Previously_Insured      0\n",
+            "Vehicle_Age             0\n",
+            "Vehicle_Damage          0\n",
+            "Annual_Premium          0\n",
+            "Policy_Sales_Channel    0\n",
+            "Vintage                 0\n",
+            "Response                0\n",
+            "dtype: int64\n",
+            " \n",
+            "Gender                  0\n",
+            "Age                     0\n",
+            "Driving_License         0\n",
+            "Region_Code             0\n",
+            "Previously_Insured      0\n",
+            "Vehicle_Age             0\n",
+            "Vehicle_Damage          0\n",
+            "Annual_Premium          0\n",
+            "Policy_Sales_Channel    0\n",
+            "Vintage                 0\n",
+            "dtype: int64\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n-4uw_WDxFNN"
+      },
+      "source": [
+        "No value is null"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HY3Kgauhvgxv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "dab03373-0a99-4907-8fa3-c886532225da"
+      },
+      "source": [
+        "# checking for consistency in numerical feature columns\n",
+        "trainData.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Driving_License</th>\n",
+              "      <th>Region_Code</th>\n",
+              "      <th>Previously_Insured</th>\n",
+              "      <th>Annual_Premium</th>\n",
+              "      <th>Policy_Sales_Channel</th>\n",
+              "      <th>Vintage</th>\n",
+              "      <th>Response</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "      <td>381109.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>38.822584</td>\n",
+              "      <td>0.997869</td>\n",
+              "      <td>26.388807</td>\n",
+              "      <td>0.458210</td>\n",
+              "      <td>30564.389581</td>\n",
+              "      <td>112.034295</td>\n",
+              "      <td>154.347397</td>\n",
+              "      <td>0.122563</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>15.511611</td>\n",
+              "      <td>0.046110</td>\n",
+              "      <td>13.229888</td>\n",
+              "      <td>0.498251</td>\n",
+              "      <td>17213.155057</td>\n",
+              "      <td>54.203995</td>\n",
+              "      <td>83.671304</td>\n",
+              "      <td>0.327936</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2630.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>25.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>24405.000000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>82.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>36.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>28.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>31669.000000</td>\n",
+              "      <td>133.000000</td>\n",
+              "      <td>154.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>49.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>39400.000000</td>\n",
+              "      <td>152.000000</td>\n",
+              "      <td>227.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>85.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>540165.000000</td>\n",
+              "      <td>163.000000</td>\n",
+              "      <td>299.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                 Age  Driving_License  ...        Vintage       Response\n",
+              "count  381109.000000    381109.000000  ...  381109.000000  381109.000000\n",
+              "mean       38.822584         0.997869  ...     154.347397       0.122563\n",
+              "std        15.511611         0.046110  ...      83.671304       0.327936\n",
+              "min        20.000000         0.000000  ...      10.000000       0.000000\n",
+              "25%        25.000000         1.000000  ...      82.000000       0.000000\n",
+              "50%        36.000000         1.000000  ...     154.000000       0.000000\n",
+              "75%        49.000000         1.000000  ...     227.000000       0.000000\n",
+              "max        85.000000         1.000000  ...     299.000000       1.000000\n",
+              "\n",
+              "[8 rows x 8 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OOncPIVbxHW8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "outputId": "185dca4b-cad1-4c4a-d4dc-d46bb49f1df3"
+      },
+      "source": [
+        "# Outlier Analysis\n",
+        "df = trainData[trainData.columns]\n",
+        "ax = sns.boxplot(data=df,orient = 'h')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZiYuQ-4Ux-gT"
+      },
+      "source": [
+        "# from the box plot and statistical description table we realise that the Annual premium insurance\n",
+        "# has high variability (min = 2630 & max = 540165) but the proximity of mean and 50 percentile\n",
+        "# values show that the distribution can be considered stable."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "ibt_A8jd6wvO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "outputId": "ed468e2c-598f-4b8d-9e48-f0f0a459271c"
+      },
+      "source": [
+        "trainData[\"Response\"].value_counts().plot.pie(colormap = \"summer\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc064342790>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Nyd1No9M3A-N",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 600
+        },
+        "outputId": "2b0d84e6-6d4d-48e9-e777-1276a3548d9a"
+      },
+      "source": [
+        "# checking correlation to eliminate highly corelated features\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "sns.heatmap(trainData.corr(), annot=True,vmin=-1, vmax=1, center= 0, cmap= 'coolwarm', linewidths=3, linecolor='black')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc06437ef10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JdXvti813RCr"
+      },
+      "source": [
+        "# As there are completely independent features, we can consider them all."
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "WEMvDu0M6wve"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score,recall_score\n",
+        "from sklearn.preprocessing import OrdinalEncoder, StandardScaler, MinMaxScaler\n",
+        "\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "\n",
+        "from sklearn import preprocessing"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "_ZYlST0W6wvh"
+      },
+      "source": [
+        "# encoding the numerical columns using Standard Scaler\n",
+        "numCols = ['Age','Region_Code', 'Policy_Sales_Channel', 'Vintage' ]\n",
+        "scaler = StandardScaler()\n",
+        "trainData[numCols] = scaler.fit_transform(trainData[numCols])\n",
+        "\n",
+        "#using MinMax encoding for Annual premium feature due to high difference btween maximum and minimum value\n",
+        "scalerM = MinMaxScaler()\n",
+        "trainData[[\"Annual_Premium\"]] = scalerM.fit_transform(trainData[[\"Annual_Premium\"]])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jMEd8w516RU8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8c431342-e1ed-477a-f235-670878773972"
+      },
+      "source": [
+        "# encoding the categorical columns using OneHotEncoder\n",
+        "\n",
+        "s = (trainData.dtypes == 'object')\n",
+        "object_cols = ['Gender', 'Vehicle_Age', 'Vehicle_Damage']\n",
+        "print(object_cols)\n",
+        "\n",
+        "encoder = OrdinalEncoder()\n",
+        "trainData[object_cols] = encoder.fit_transform(trainData[object_cols])\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['Gender', 'Vehicle_Age', 'Vehicle_Damage']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "WPQxQFIU6wvm"
+      },
+      "source": [
+        "x = trainData.drop(['Response'], axis = 1)\n",
+        "y = trainData['Response']"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KP2HCCMk8OEc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "4c674a59-3b1f-4871-dd06-6cafc7405c1d"
+      },
+      "source": [
+        "x.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Driving_License</th>\n",
+              "      <th>Region_Code</th>\n",
+              "      <th>Previously_Insured</th>\n",
+              "      <th>Vehicle_Age</th>\n",
+              "      <th>Vehicle_Damage</th>\n",
+              "      <th>Annual_Premium</th>\n",
+              "      <th>Policy_Sales_Channel</th>\n",
+              "      <th>Vintage</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.333777</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.121784</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.070366</td>\n",
+              "      <td>-1.587234</td>\n",
+              "      <td>0.748795</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.396751</td>\n",
+              "      <td>1</td>\n",
+              "      <td>-1.767879</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.057496</td>\n",
+              "      <td>-1.587234</td>\n",
+              "      <td>0.342443</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.527181</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.121784</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.066347</td>\n",
+              "      <td>-1.587234</td>\n",
+              "      <td>-1.521998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-1.148985</td>\n",
+              "      <td>1</td>\n",
+              "      <td>-1.163187</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.048348</td>\n",
+              "      <td>0.737321</td>\n",
+              "      <td>0.581474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.633242</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.104409</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.046259</td>\n",
+              "      <td>0.737321</td>\n",
+              "      <td>-1.378580</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Gender       Age  ...  Policy_Sales_Channel   Vintage\n",
+              "0     1.0  0.333777  ...             -1.587234  0.748795\n",
+              "1     1.0  2.396751  ...             -1.587234  0.342443\n",
+              "2     1.0  0.527181  ...             -1.587234 -1.521998\n",
+              "3     1.0 -1.148985  ...              0.737321  0.581474\n",
+              "4     0.0 -0.633242  ...              0.737321 -1.378580\n",
+              "\n",
+              "[5 rows x 10 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 69
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S5h_pwJB8Rnh",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1e57b49c-7f3f-4d29-a176-20e0c88e9d09"
+      },
+      "source": [
+        "y.head()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    1\n",
+              "1    0\n",
+              "2    1\n",
+              "3    0\n",
+              "4    0\n",
+              "Name: Response, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "zxBMI2WF6wvp"
+      },
+      "source": [
+        "xTrain, xValid, yTrain, yValid = train_test_split(x,y,test_size = 0.4, random_state = 42 )"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1Fc6OpxO8avM"
+      },
+      "source": [
+        "#calculating silhoutte score to determine optimum number of clusters\n",
+        "def s_score(neighbours = 11):\n",
+        "  model = KMeans(n_clusters=neighbours)\n",
+        "  model.fit_predict(xTrain)\n",
+        "  return silhouette_score(xTrain, model.labels_, metric = 'euclidean')\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AIkY6cWGBtKE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6795819d-b87a-41fc-8645-9343d40a8664"
+      },
+      "source": [
+        "for i in range(2,12):\n",
+        "  print(f'neigbours ={i}  silhoutte score ={s_score(i)}')\n",
+        "  print()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "neigbours =2  silhoutte score =0.2502787385949678\n",
+            "\n",
+            "neigbours =3  silhoutte score =0.20706154635311733\n",
+            "\n",
+            "neigbours =4  silhoutte score =0.20996236642009017\n",
+            "\n",
+            "neigbours =5  silhoutte score =0.19611837108096966\n",
+            "\n",
+            "neigbours =6  silhoutte score =0.19822826171657373\n",
+            "\n",
+            "neigbours =7  silhoutte score =0.19925417237391735\n",
+            "\n",
+            "neigbours =8  silhoutte score =0.19643446556601207\n",
+            "\n",
+            "neigbours =9  silhoutte score =0.20165828743190414\n",
+            "\n",
+            "neigbours =10  silhoutte score =0.19936428581165078\n",
+            "\n",
+            "neigbours =11  silhoutte score =0.20476190961584428\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "trusted": true,
+        "id": "uNNeG8xm6wvr"
+      },
+      "source": [
+        "range_k = range(1,15)\n",
+        "scores = {}\n",
+        "scores_list = []\n",
+        "for k in range_k:\n",
+        "   classifier = KNeighborsClassifier(n_neighbors=k)\n",
+        "   classifier.fit(xTrain, yTrain)\n",
+        "   y_pred = classifier.predict(xValid)\n",
+        "   scores[k] = accuracy_score(yValid,y_pred)\n",
+        "   scores_list.append(accuracy_score(yValid,y_pred))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NPKILmspB1I8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "add6b1e3-b0d3-40dc-f8e3-42e938c38b54"
+      },
+      "source": [
+        "scores"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{1: 0.8253981790034374,\n",
+              " 2: 0.8643173886804335,\n",
+              " 3: 0.8470323528640025,\n",
+              " 4: 0.8663771614494503,\n",
+              " 5: 0.8560782976043662,\n",
+              " 6: 0.867689118627168,\n",
+              " 7: 0.8611293327385794,\n",
+              " 8: 0.8692175487392092,\n",
+              " 9: 0.8644157854687623,\n",
+              " 10: 0.8706279027052557,\n",
+              " 11: 0.8669019443205374,\n",
+              " 12: 0.8712904410800032,\n",
+              " 13: 0.8683975755031356,\n",
+              " 14: 0.8720841751725223}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 104
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "efIz5uKOCjkE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 299
+        },
+        "outputId": "5cd2d752-95f6-4997-a2d5-c273356609af"
+      },
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.plot(range_k,scores_list)\n",
+        "plt.xlabel(\"Value of K\")\n",
+        "plt.ylabel(\"Accuracy\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Accuracy')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 107
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wtyb0SlPCw_B",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9dd1e660-dd39-4a62-b544-b7b7eb3bb57e"
+      },
+      "source": [
+        "# since our moel performs well for larger even number of neighbours we keep our n_neighbours as 12.\n",
+        "classifier = KNeighborsClassifier(n_neighbors=12)\n",
+        "classifier.fit(xTrain, yTrain)\n",
+        "y_pred = classifier.predict(xValid)\n",
+        "accuracy_score(yValid,y_pred)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8712904410800032"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 108
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VlubyHJJB8vy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2f25d338-5a9f-4e91-e4f7-9baeeba9fe6f"
+      },
+      "source": [
+        "result_cm = confusion_matrix(yValid, y_pred)\n",
+        "print(\"CONFUSION MATRIX:\")\n",
+        "print(result_cm)\n",
+        "print()\n",
+        "result_cr = classification_report(yValid, y_pred)\n",
+        "print(\"CLASSIFICATION REPORT:\")\n",
+        "print (result_cr)\n",
+        "print()\n",
+        "roc_auc = roc_auc_score(yValid, pred_labels, average = 'weighted')\n",
+        "print('ROC AUC SCORE')\n",
+        "print(roc_auc)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "CONFUSION MATRIX:\n",
+            "[[131636   1942]\n",
+            " [ 17679   1187]]\n",
+            "\n",
+            "CLASSIFICATION REPORT:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.99      0.93    133578\n",
+            "           1       0.38      0.06      0.11     18866\n",
+            "\n",
+            "    accuracy                           0.87    152444\n",
+            "   macro avg       0.63      0.52      0.52    152444\n",
+            "weighted avg       0.82      0.87      0.83    152444\n",
+            "\n",
+            "\n",
+            "ROC AUC SCORE\n",
+            "0.5463492154622864\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HcyApLOQDzOQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a403d5b2-f779-4bfc-8204-cfe346786b20"
+      },
+      "source": [
+        "# FInally predicting on our test data\n",
+        "testData[object_cols] = encoder.fit_transform(testData[object_cols])\n",
+        "print(classifier.predict(testData))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[0 0 0 ... 0 0 0]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JBq3SxeSEAAK"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file