Health_Insurance.ipynb

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  "metadata": {
    "colab": {
      "name": "Health_Insurance.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN+YOzC3jSIJwkwmqbk0IQa"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfEbsFjfpDkp"
      },
      "source": [
        "# Health Insurance Cross Sell Prediction\n",
        "\n",
        "**Predict Health Insurance Owners' who will be interested in Vehicle Insurance**\n",
        "\n",
        "Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the customers from past year will also be interested in Vehicle Insurance provided by the company.\n",
        "\n",
        "**Objective**\n",
        "\n",
        "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wTCP2P7wo4A"
      },
      "source": [
        "# 1. Prepare the problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LvJU89zPYJ2"
      },
      "source": [
        "### Importing  Required Libreries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKwCXcbjPG07"
      },
      "source": [
        "# import libraries\n",
        "import pandas  as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from collections import Counter\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import precision_score,recall_score,accuracy_score,f1_score,confusion_matrix,roc_auc_score,classification_report\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "sns.set_theme(style=\"darkgrid\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liVmCOyFvrlJ"
      },
      "source": [
        "### Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUjxnNEhv2ri"
      },
      "source": [
        "data=pd.read_csv(\"/content/health_insurance.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3pGtQuyw24j"
      },
      "source": [
        "# 2. Summarize Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9M5F5uLw1qo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "446a064c-82ad-4848-f357-c4daa93c769c"
      },
      "source": [
        "#showing first 5 rows\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUdPKue0xY_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "5236fe54-74af-422f-a73c-c0d602a909e8"
      },
      "source": [
        "\n",
        "# showing last 5 rows\n",
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <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>381104</th>\n",
              "      <td>381105</td>\n",
              "      <td>Male</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>30170.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>88</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381105</th>\n",
              "      <td>381106</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>40016.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381106</th>\n",
              "      <td>381107</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 1 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>35118.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381107</th>\n",
              "      <td>381108</td>\n",
              "      <td>Female</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>&gt; 2 Years</td>\n",
              "      <td>Yes</td>\n",
              "      <td>44617.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381108</th>\n",
              "      <td>381109</td>\n",
              "      <td>Male</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1-2 Year</td>\n",
              "      <td>No</td>\n",
              "      <td>41777.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>237</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  Gender  Age  ...  Policy_Sales_Channel  Vintage  Response\n",
              "381104  381105    Male   74  ...                  26.0       88         0\n",
              "381105  381106    Male   30  ...                 152.0      131         0\n",
              "381106  381107    Male   21  ...                 160.0      161         0\n",
              "381107  381108  Female   68  ...                 124.0       74         0\n",
              "381108  381109    Male   46  ...                  26.0      237         0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dS4Qcnb7x0a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c678eec-e49d-4a58-de6d-38f18d9e9596"
      },
      "source": [
        "# check datatypes, shape,null values\n",
        "print(data.info())\n",
        "print()\n",
        "print(data.isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 381109 entries, 0 to 381108\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   id                    381109 non-null  int64  \n",
            " 1   Gender                381109 non-null  object \n",
            " 2   Age                   381109 non-null  int64  \n",
            " 3   Driving_License       381109 non-null  int64  \n",
            " 4   Region_Code           381109 non-null  float64\n",
            " 5   Previously_Insured    381109 non-null  int64  \n",
            " 6   Vehicle_Age           381109 non-null  object \n",
            " 7   Vehicle_Damage        381109 non-null  object \n",
            " 8   Annual_Premium        381109 non-null  float64\n",
            " 9   Policy_Sales_Channel  381109 non-null  float64\n",
            " 10  Vintage               381109 non-null  int64  \n",
            " 11  Response              381109 non-null  int64  \n",
            "dtypes: float64(3), int64(6), object(3)\n",
            "memory usage: 34.9+ MB\n",
            "None\n",
            "\n",
            "id                      0\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",
            "Response                0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPNo_ErcuNuk"
      },
      "source": [
        "**Dataset has 381109 rows and 12 columns.**\n",
        "**There is no missing values present in dataset.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CacKir_BzIl-"
      },
      "source": [
        "### Descriptive statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvYyjWUsyy_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "6367c1d2-802f-4219-fe1e-b8548a4c4d71"
      },
      "source": [
        "#Statistical Summary of Numeric Variables\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</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",
              "      <td>381109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>190555.000000</td>\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>110016.836208</td>\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>1.000000</td>\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>95278.000000</td>\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>190555.000000</td>\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>285832.000000</td>\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>381109.000000</td>\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": [
              "                  id            Age  ...        Vintage       Response\n",
              "count  381109.000000  381109.000000  ...  381109.000000  381109.000000\n",
              "mean   190555.000000      38.822584  ...     154.347397       0.122563\n",
              "std    110016.836208      15.511611  ...      83.671304       0.327936\n",
              "min         1.000000      20.000000  ...      10.000000       0.000000\n",
              "25%     95278.000000      25.000000  ...      82.000000       0.000000\n",
              "50%    190555.000000      36.000000  ...     154.000000       0.000000\n",
              "75%    285832.000000      49.000000  ...     227.000000       0.000000\n",
              "max    381109.000000      85.000000  ...     299.000000       1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmvn-Md9zRqi"
      },
      "source": [
        "### Data visualizations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eH2Wcqk1zu27"
      },
      "source": [
        "##### Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWNmk8TBSvja",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "b545a349-8e98-4af3-c448-e100fec8c1c4"
      },
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "sns.countplot(data['Response'],data=data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0576ad20f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L03HgoO-z3jx"
      },
      "source": [
        "* The data is highly imbalanced."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lr7gDrXc5N25"
      },
      "source": [
        "##### Gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcwrR26ez2wM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "867f7b2e-7ec1-418f-f02f-41df40759759"
      },
      "source": [
        "plt.figure(figsize = (13,5))\n",
        "plt.subplot(1,2,1)\n",
        "sns.countplot(data['Gender'],palette='husl')\n",
        "plt.title(\"count of male and female\")\n",
        "plt.subplot(1,2,2)\n",
        "sns.countplot(data['Gender'], hue = data['Response'],palette=\"husl\")\n",
        "plt.title(\"Response in Male and female category\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7rwMgtZ-Wfc"
      },
      "source": [
        "* The gender variable in the dataset is almost equally distributed.\n",
        "* Male category is slightly greater than that of female and chances of buying the insurance is also little high."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYyGY2np6sM0"
      },
      "source": [
        "##### Age Vs Response"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cniTlZtO6scs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "d70c9f57-ec35-445f-a0bc-fc82179866c8"
      },
      "source": [
        "#Age VS Response\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(x='Age',hue='Response',data=data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0576c516a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrVmn9KvAEfL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "ccb0e871-91b1-4965-bf9c-4c4895b3ad65"
      },
      "source": [
        "sns.boxplot(data['Age'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05768a3b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YY8aM0ejRo/XKK6/we76IOlxPFzFrzZo1qq6uVmFhodejAFeMZ7qIGX6/X999952cczp48KA++ugjTZw40euxgL+EH6QhZjQ1NenJJ5/UqVOnlJmZqblz52r8+PFejwX8JZxeAABDnF4AAENEFwAMEV0AMER0AcAQ0QUAQ0QXAAz9P9AnaY9lG2r/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMYUuYtd0-x_"
      },
      "source": [
        "* Young people below 30 are not interested in vehicle insurance. Reasons could be lack of experience, less maturity level and they don't have expensive vehicles yet.\n",
        "* People aged between 30-60 are more likely to be interested.\n",
        "* From the boxplot we can see that there no outlier in the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Rz58OX2_icV"
      },
      "source": [
        "#### Driving License"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHfDWHnE_FWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "5b80d750-30eb-4ff2-e17a-4e08c7069c91"
      },
      "source": [
        "sns.countplot(data['Driving_License'],hue=data['Response'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05769fd400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tO6gGu2_enH"
      },
      "source": [
        "* Customers who are interested in Vehicle Insurance almost all have driving license"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-59jw_kZSgZ0"
      },
      "source": [
        "##### Previously_Insured Vs Response"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7CdTC4t7MVR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "4c5a1140-2232-44ed-e7e7-83e3f5c8f8a3"
      },
      "source": [
        "sns.countplot(x='Previously_Insured',hue='Response',data=data,palette='husl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0576cf1cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpNv8rSfCotD"
      },
      "source": [
        "* Customer who are not perviosly insured are likely to be inetrested"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oKYS906Spq3"
      },
      "source": [
        "##### Vehicle_Age Vs Response"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz21SxeN7dN2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "4ed703cd-ad86-443b-a9a1-49b3d67e0249"
      },
      "source": [
        "sns.countplot(x='Vehicle_Age',hue='Response',data=data,palette='husl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f057b288048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6UhiN9nuJHnvQUD8MCgquS91CvcDtV7/I5bJav7xLPhy3atUqZGRkIDk5GY6O/7nuWLdu3VBWVobjx48DALZs2YIRI0ZYrUZERLZnsz2hhIQE7NmzB4WFhZg6dSqUSiVWr16N9evXo3379ggNDQUAtG3bFsnJyZDL5VixYgXi4uJQXl4OHx8fJCYmAoBVakREZHsyYc+Xfn5AHI5r/Dw9nZG7JEnqNupEFRvZaIdz6orbr36p18NxRERkvxhCREQkGYYQERFJhiFERESSYQgREZFkGEJERCQZhhAREUmGIURERJJhCBERkWQYQkREJBmGEBERSYYhREREkmEIERGRZBhCREQkGYYQERFJhiFERESSYQgREZFkbBJCGo0GAQEB8PX1xblz50zTs7OzMXHiRAQGBmLixIm4cOGCZDUiIrI9m4TQkCFDsGnTJvj4+NSYHhcXh7CwMGi1WoSFhSE2NlayGhER2Z5NQkitVkOlUtWYptPpkJmZieDgYABAcHAwMjMzodfrbV4jIiJpOEj1wrm5ufDy8oJCoQAAKBQKtG7dGrm5uRBC2LTm5uZWp97d3VtaajUQWZSnp7PULdBDsMftJ1kINWQ6XSmMRiF1G2RFDfXDoKDgutQt1AvcfvWLXC6r9cu72cNxH3300V2nb9y48YGaUqlUyMvLg8FgAAAYDAbk5+dDpVLZvEZERNIwO4SSk5PvOn3t2rUP9MLu7u7w8/NDWloaACAtLQ1+fn5wc3OzeY2IiKQhE0Lcc1zp8OHDAICXX34Z69atwx9nv3z5Mt5//33s37//ni+SkJCAPXv2oLCwEK6urlAqlUhPT0dWVhaio6NRUlICFxcXaDQadOjQAQBsXqsLDsc1fp6ezshdkiR1G3Wiio1stMM5dcXtV7/cazjuviEUEBAAoPpEgj8OXclkMnh6emLGjBkYMmSIBdut/xhCjR8/xBo2br/65V4hdN8TE/bt2wcAmD9/PlasWGHZzoiIyK6ZfXbcHwPIaDTWqMnlvPoPERHVndkhdObMGSxZsgRnz55FeXk5AEAIAZlMhl9++cVqDRIRUeNldghFR0fj6aefxrJly9C0aVNr9kRERHbC7BC6cuUK5syZA5lMZs1+iIjIjph9MGfYsGH47rvvrNkLERHZGbP3hMrLyxEZGYnevXvDw8OjRo1nzRER0YMwO4Q6duyIjh07WrMXIiKyM2aHUGRkpDX7ICIiO2R2CN2+fM/dPPnkkxZphoiI7IvZIfTmm2/WeFxUVITKykp4eXnh66+/tnhjRETU+JkdQrcv33ObwWDA2rVr0aJFC4s3RURE9uGBr7ejUCgQERGBDz/80JL9EBGRHXmoi74dOnSIP14lIqIHZvZw3ODBg2sEzq1bt1BRUYG4uDirNEZERI2f2SGUmJhY43GzZs3w6KOPomXLu98jgoiI6H7MDqG+ffsCqL6NQ2FhITw8PCx2C4f9+/djzZo1EEJACIHIyEgMHz4c2dnZiI6ORnFxMZRKJTQaDdq3bw8AVqkREZFtmZ0ipaWlmD9/Ph5//HEMGjQIjz/+OBYsWIDr1x/uToBCCNMN83bs2IEVK1ZgwYIFMBqNiIuLQ1hYGLRaLcLCwhAbG2tazho1IiKyLbNDKCEhAbdu3UJqaipOnz6N1NRU3Lp1CwkJCQ/fhFxuCrPr16+jdevWKCoqQmZmJoKDgwEAwcHByMzMhF6vh06ns3iNiIhsz+zhuG+//RZ79+5Fs2bNAACPPvooli9fjmHDhj1UAzKZDKtXr8Yrr7yC5s2b48aNG9iwYQNyc3Ph5eUFhUIBoPqU8NatWyM3NxdCCIvX3NzczO65tnulE0nN09NZ6hboIdjj9jM7hJycnKDX6+Hj42OaVlRUBEdHx4dqoKqqCuvXr8f777+P3r1748SJE5g9e3a9vjK3TlcKo1FI3QZZUUP9MCgoeLjh8caC269+kctltX55NzuExo8fj/DwcEyZMgXe3t7IycnB3/72N0yYMOGhmvvll1+Qn5+P3r17AwB69+6NZs2awcnJCXl5eTAYDFAoFDAYDMjPz4dKpYIQwuI1IiKyPbOPCb388suYOXMmtFotNBoNtFotpk+fjlmzZj1UA23atMHVq1dx/vx5AEBWVhZ0Oh0eeeQR+Pn5IS0tDQCQlpYGPz8/uLm5wd3d3eI1IiKyPZkQwqxxpYSEBIwcORK9evUyTfvxxx+xa9euOy5uWldffvklPvjgA9OPYV977TUMHToUWVlZiI6ORklJCVxcXKDRaNChQwcAsErNXByOa/w8PZ2RuyRJ6jbqRBUb2WiHc+qK269+uddwnNkh1K9fPxw8eLDGMaCKigoMHjz4nrd5aIwYQo0fP8QaNm6/+uVeIWT2cJxMJoPRaKwxzWAw3DGNiIjIXGaHkFqtxpo1a0yhYzQa8d5770GtVlutOSIiatzqdFO7l156CQMGDIC3tzdyc3Ph6emJdevWWbM/IiJqxMwOoTZt2mD79u04ffo0cnNzoVKp8Pjjj1vs+nFERGR/zA4hoPryOj179kTPnj2t1Q8REdkR7sYQEZFkGEJERCQZhhAREUmGIURERJJhCBERkWQYQkREJBmGEBERSYYhREREkmEIERGRZBhCREQkGYYQERFJpl6EUHl5OeLi4jB8+HCEhITgrbfeAgBkZ2dj4sSJCAwMxMSJE3HhwgXTMtaoERGRbdWLEEpMTISTkxO0Wi1SU1MRFRUFAIiLi0NYWBi0Wi3CwsIQGxtrWsYaNSIisi3JQ+jGjRtISUlBVFQUZDIZAMDDwwM6nQ6ZmZkIDg4GAAQHByMzMxN6vd4qNSIisr063crBGi5dugSlUomkpCQcPXoULVq0QFRUFJo2bQovLy8oFAoAgEKhQOvWrZGbmwshhMVrbm5uZvdc273SiaTm6eksdQv0EOxx+0keQgaDAZcuXUKXLl2wYMECnDp1ChEREVizZo3UrdVKpyuF0SikboOsqKF+GBQUXJe6hXqB269+kctltX55lzyEVCoVHBwcTENkPXr0gKurK5o2bYq8vDwYDAYoFAoYDAbk5+dDpVJBCGHxGhER2Z7kx4Tc3Nzg7++PQ4cOAag+e02n06F9+/bw8/NDWloaACAtLQ1+fn5wc3ODu7u7xWtERGR7MiGE5ONKly5dQkxMDIqLi+Hg4IDZs2dj8ODByMrKQnR0NEpKSuDi4gKNRoMOHToAgFVq5uJwXOPn6emM3CVJUrdRJ6rYyEY7nFNX3H71y72G4+pFCDU0DKHGjx9iDRu3X/1yrxCSfDiOiIjsF0OIiIgkwxAiIiLJMISIiEgyDCEiIpIMQ4iIiCTDECIiIskwhIiISDIMISIikgxDiIiIJMMQIiIiyTCEiIhIMgwhIiKSDEOIiIgkwxAiIiLJMISIiEgy9SqEkpKS4Ovri3PnzgEATp48iVGjRiEwMBDh4eHQ6XSmea1RIyIi26o3IXTmzBmcPHkSPj4+AACj0Yh58+YhNjYWWq0WarUaK1eutFqNiIhsr16EUEVFBZYsWYJFixaZpmVkZMDJyQlqtRoAEBoait27d1utRkREtucgdQMAsGbNGowaNQpt27Y1TcvNzYW3t7fpsZubG4xGI4qLi61SUyqVZvdb273SiaTm6eksdQv0EOxx+0keQj/99BMyMjLw+uuvS92K2XS6UhiNQuo2yIoa6odBQcF1qVuoF7j96he5XFbrl3fJQ+jYsWPIysrCkCFDAABXr17FtGnT8MILLyAnJ8c0n16vh1wuh1KphEqlsniNiIhsT/JjQjNnzsR3332Hffv2Yd++fWjTpg0++ugjTJ8+HWVlZTh+/DgAYMuWLRgxYgQAoFu3bhavERGR7Um+J1QbuVyOFStWIC4uDuXl5fDx8UFiYqLVakREZHsyIQQPbtQRjwk1fp6ezshdkiR1G3Wiio1stMcU6orbr3651zEhyYfjiIjIftXb4TgiqpuKqqoGd1ZYWUUlrl8rk7oNkhBDiKiRcHRwwPCN66Ruo072TI3AdTCE7BmH44iISDIMISIikgxDiIiIJMMQIiIiyTCEiIhIMgwhIiKSDEOIiIgkwxAiIiLJMISIiEgyDCEiIpIMQ4iIiCTDECIiIskwhIiISDKSh1BRURFmzJiBwMBAhISEIDIyEnq9HgBw8uRJjBo1CoGBgQgPD4dOpzMtZ40aERHZluQhJJPJMH36dGi1WqSmpqJdu3ZYuXIljEYj5s2bh9jYWGi1WqjVaqxcuRIArFIjIiLbkzyElEol/P39TY979uyJnJwcZGRkwMnJCWq1GgAQGhqK3bt3A4BVakREZHv16qZ2RqMRmzdvRkBAAHJzc+Ht7W2qubm5wWg0ori42Co1pVJpdp+13SudiOquod0N1prscV3UqxCKj49H8+bNMXnyZHz11VdSt1Mrna4URqOQug2yInv8MJBKQcF1iz9nQ91+1lgX9YFcLqv1y3u9CSGNRoOLFy9i3bp1kMvlUKlUyMnJMdX1ej3kcjmUSqVVakREZHuSHxMCgFWrViEjIwPJyclwdHQEAHTr1g1lZWU4fvw4AGDLli0YMWKE1WpERGR7ku8J/fvf/8b69evRvn17hIaGAgDatm2L5ORkrFixAnFxcSgvL4ePjw8SExMBAHK53OI1IiKyPclD6LHHHsPZs2fvWuvVqxdSU1NtViMiItuqF8NxRERknxhCREQkGYYQERFJhiFERESSYQgREZFkJD87rjFza9UUCscmUrdRJ4aKSuivlUndBhHZCYaQFSkcmyB3SZLUbdSJKjYSAEOIiGyDw3FERCQZhhAREUmGIURERJLhMSEionqgoqqqQd2CoqyiEtctcBITQ4iIqB5wdHDA8I3rpG7DbHumRuC6BU5i4nAcERFJhntCVIO9DgkQkTQYQlSDvQ4JEJE0OBxHRESSscsQys7OxsSJExEYGIiJEyfiwoULUrdERGSX7DKE4uLiEBYWBq1Wi7CwMMTGxkrdEhGRXbK7Y0I6nQ6ZmZnYuHEjACA4OBjx8fHQ6/Vwc3Mz6znkcpnZr6do1XAO8t/m1bJh9VyX7VEX3Ha2we33Hw1t+5m77e41n0wIISzVUEOQkZGBBQsWID093TRt5MiRSExMRNeuXSXsjIjI/tjlcBwREdUPdhdCKpUKeXl5MBgMAACDwYD8/HyoVCqJOyMisj92F0Lu7u7w8/NDWloaACAtLQ1+fn5mHw8iIiLLsbtjQgCQlZWF6OholJSUwMXFBRqNBh06dJC6LSIiu2OXIURERPWD3Q3HERFR/cEQIiIiyTCEiIhIMgwhIiKSDEOokdq7dy/Gjh2L4OBgBAUF4eOPP77rfG+88QYSExNrTJsyZQo+//xzW7RptzQaDQICAuDr64tz587VOl9RURFmzJiBwMBAhISEIDIyEnq9/o75fvjhBwwbNgw3b940TUtJSUFoaKjpN3Fke3l5eXjhhRfQu3dvjB07ttb5kpKSMHv27BrTYmJi7vi32SgJahD0en2d5j958qS4evWqEEKIkpISMXToUHHs2LE75ispKRFPP/20OHnypBBCiM2bN4spU6YIo9H40D1XVlY+9HM0VseOHRM5OTni6aefFmfPnq11vqKiInHkyBHT47ffflu88cYbd503Pj5eLF68WAghRH5+vhg4cKA4f/78Q/fK7Xh/tf37LCkpEceOHRP79+8XY8aMqXX5yspKMWbMGLFr1y4hhBAHDx4UQUFBory8/KF7q+/bz+4uYNpQvfbaa5DJZAgJCUFgYCBcXFzuOX+PHj1Mf3d2dsaf//xnXLlyBWq1usZ8zs7OiI+PxxtvvIHk5GSsXbsWmzdvRkFBARISEpCTk4Py8nIEBQUhIiICQPW3+B9++AGVlZVwdXXFsmXL4OPjg8uXL2PcuHEYO3Ysjhw5gueeew6enp5Ys2YN5HI5DAYD3nrrLfj7+1t+BTUw/70daqNUKmusr549e2Lz5s13nXfu3LkYPXo0jhw5gk8//RTTpk3Do48+ig0bNmDPnj0wGAzw8vJCfHw8PD09cfjwYaxevRrl5eUwGAyIiIhAUFAQAOCFF15A586dcerUKbRq1QoffPDBw7/pRubWrVvYt28fUlNT8dtvv2Hv3r13zOPs7FQ+iGkAAAt2SURBVAy1Wo2jR4/e87kcHByg0Wgwffp0dOnSBYsWLcLq1atRUVGBxYsX4+zZsygvL4e/vz/eeOMNKBQKfPzxx0hPT4fBYICTkxMWLVoEPz8/AICvry8iIyNx4MABDBw48I69rHpF6hQk82VkZIi3335bDBs2TMyaNUvs3r1blJWV3Xe53377Tfj7+5v2jO4mNjZW9OrVS/zjH/8QQggxZcoU8cMPPwghhCgvLxeTJk0S3333nRBCCJ1OZ1ruiy++ELNnzxZCCHHp0iXRqVMnkZ6ebqqHhISIH3/8UQghRFVVlbh+/Xod33Xjdr89oT8yGAzixRdfFJ988kmt85w4cUL06dNHTJ48WRiNRpGSkiIWLlwoDAaDEEKITZs2ib/+9a9CCCGKi4tFVVWVEEKIgoICMXDgQFFcXCyEEGLy5MnipZdeqvffom2tsrJSfPPNN+L1118XAQEBIi4uThw7duy+IwdHjhy5557QbevXrxe9evUSq1evFkIIERMTI7Zv3y6EqN7+c+bMEVu3bhVC1Px3eOjQITFhwgTT406dOon169fX+f1JgXtCDUjXrl3RtWtXzJ8/H0eOHMGiRYsQExODEydO1LpMfn4+XnnlFcTFxcHLy6vW+aZNm4Zdu3Zh/PjxuHnzJn744Ycaxx5u3LiBrKws9O/fHwcPHsTnn3+OmzdvoqqqqsbzODk54ZlnnjE97tevH5YvX47hw4dj0KBB6NSp00OsAfsWHx+P5s2bY/LkybXO06tXL3Ts2BEvvvgiZDIZ9u3bh4yMDIwZMwZA9bUSW7ZsCQDQ6/WIiYnBxYsXoVAocO3aNWRnZ6Nnz54AgJCQEDg48CPij8aOHQu9Xo+33noLy5cvt/j6mTZtGt555x2Eh4cDAPbt24fTp0+bbj1TVlZm+neckZGB9evX49q1a5DJZHfcnPP2Nq/v+H9YA2I0GnH06FGkp6fj8OHD6NevH0JCQmqdX6fTYerUqZg+fXqNYLgbuVwOmUxmeh2ZTIZt27ahSZMmNea7cuUKli9fjm3btqFdu3b48ccf8frrr5vqzZo1Mz0PUH1w9ezZszhy5AiioqIwdepUPPfccw/y9u3CP//5T3z66acAqj+QRo0aBaB6CPTixYtYt24d5PJ7n0+kUCigUCgAAEIIvPzyyxg/fvwd8y1atAgBAQFISkqCTCZDYGAgysvLTfXmzZtb6m01GkuXLsWOHTuQmJiIPXv2ICgoCAMGDICjo6NFnv/2dru9jYUQeP/999GuXbsa81VUVCAqKgqfffYZunbtiry8PAwaNKjGPA1l+/HsuAZi9erVGDp0KDZv3oxBgwZh165dWLp0Kfr163fX+YuKijB16lQ8//zzmDBhQp1eq2XLlujduzc2bNhgmpabm4uCggKUlpaiSZMm8PT0hNFoxJYtW+75XOfPn4evry9efPFFjBo1Cj///HOderE348aNw44dO7Bjxw5TAK1atQoZGRlITk6u84ddQEAAPv/8c1y7dg1A9YfXr7/+CgC4fv06fHx8IJPJcOjQIVy8eNGyb6YR6t69OxYuXAitVovRo0dDq9UiMDAQb775plVeLyAgABs2bDCd4ajX63Hp0iVUVFSgqqrKdPX/hnw2K/eEGohu3bph2rRpcHY2786LGzZswIULF7B161Zs3boVAPA///M/GDdunFnLr1y5EsuXLzftabVo0QJLly6Fr68vRowYgZEjR8LV1RWDBw/G8ePHa32ed955xzTc4+LigqVLl5r1+o1dQkIC9uzZg8LCQkydOhVKpbLGjRZv+/e//43169ejffv2CA0NBQC0bdsWycnJZr3O6NGjUVxcbBrCE0Jg0qRJ6Ny5M+bOnYvFixfjvffeQ/fu3eHr62u5N9jIKRQKDBw4EAMHDkRZWRn27dt31/kMBgOefvppVFRUoLS0FIMGDcKECRPw6quvmvU6t0/TfvbZZyGTydCkSRPExMSgXbt2eO211zB+/HgolUoEBgZa8u3ZFC9gSkREkuFwHBERSYYhREREkmEIERGRZBhCREQkGYYQERFJhiFEZGOXL1+Gr6/vHVebuG3dunVm/e4kOjoa7777rqXbI7IphhDRA5o2bRrWrFlzx/S9e/eif//+tYbM/URERNj891QvvPAC+vTpg4qKCpu+LhFDiOgBjRkzBl9++SX++6d2X375ZYO67trly5dx/PhxyGQyfP3111K3Q3aGIUT0gIYOHYri4uIaV4y4du0a9u/fj9GjR2PDhg0YOnQo/P39ERUVheLi4hrLp6am4qmnnoK/vz/Wrl1rmv7ee+/VuB7f8ePHERoaCrVajcGDB+Nf//rXXfvZv38/nn32WajVaoSGhpouz3M/KSkp6NGjB8aMGYOUlJQataKiIkRERKBXr14YN24c3n33XUyaNMlUz8rKwtSpU9G3b18EBgZi586dZr0m0W0MIaIH1LRpUzzzzDM1Prh37dqFDh064OjRo9i7dy8+++wzfPvtt2jVqhWWLFlSY/kTJ05g9+7d+OSTT5CcnIysrKw7XuPKlSuYMWMGJk+ejMOHDyMlJcV0z5g/yszMRExMDJYsWYKjR49i4sSJeOWVV8waXtuxYwdCQkIQEhKC7777DoWFhabakiVL0KxZMxw6dAgajabGe7158ybCw8MRHByM77//Hu+++y4WL16M3377zaz1RwQwhIgeyu2LWN6++nRKSgrGjBmDLVu2YM6cOWjTpg0cHR0RGRkJrVZb4zhRZGQkmjZtis6dO6Nz58533XNJS0vDX/7yFwQHB6NJkyZwdXW9awht3boVEydORI8ePaBQKDBmzBg0adIEJ0+evGf/x48fR05ODp555hl069YN7dq1Q1paGoDq657t2bMHr776Kpo1a4aOHTti9OjRpmUPHDgAHx8fjBs3Dg4ODujSpQsCAwOxe/fuB1qXZJ8axqA1UT2lVqvh6uqKvXv3onv37vj555+RlJSEd999F7Nmzapx2wW5XA6dTmd67OHhYfp7s2bNcPPmzTuePzc3F3/605/u20dOTg5SUlLw2WefmaZVVlYiPz//nsulpKSgf//+cHNzAwAEBwdj+/btmDJlCvR6fY0rNQOo8fcrV67g9OnTNe4SazAYTFf/JjIHQ4joIT377LNISUlBdnY2BgwYAA8PD7Rp0wbLli1D796975j/8uXLZj+3SqXC6dOnzZovIiICL7/8stnPXVZWhl27dsFoNKJ///4Aqm/1UFJSgl9//RWPPfYYHBwccPXqVTz66KMAqkPxj6/Zp08f0w3XiB4Eh+OIHtLo0aNx+PBhfPHFF6bhqkmTJmH16tW4cuUKgOr7wOzdu7fOzx0SEoLvv/8eO3fuRFVVFYqKivDLL7/cMd+ECROwZcsWnDp1CkII3Lx5EwcOHEBpaWmtz713714oFAqkp6cjJSUFKSkp2LlzJ9RqNVJSUqBQKDBs2DAkJSXh1q1byMrKwo4dO0zLP/XUU7hw4QJSUlJQWVmJyspKnD59+q7HtohqwxAiekht27bFE088gVu3bmHIkCEAqu/dFBAQgPDwcDzxxBN47rnnzNqj+W/e3t744IMPsHHjRvTt2xejR4++67Gj7t27Iz4+HkuWLEGfPn0wfPjwWs+iu2379u0YO3YsvL294enpafrv+eefR2pqKqqqqhAbG4vr16+jf//+mD9/PoKCgkw31mvZsiU++ugj7Ny5EwMHDsSAAQOwcuVK/taI6oT3EyIisyUmJqKwsBAajUbqVqiR4J4QEdUqKysLv/76K4QQOH36NLZt24Zhw4ZJ3RY1IjwxgagRy8nJQVBQ0F1r6enp8Pb2vufyN27cwNy5c5Gfnw93d3eEh4ebhhyJLIHDcUREJBkOxxERkWQYQkREJBmGEBERSYYhREREkmEIERGRZBhCREQkmf8DgxQQMP3ilhkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGdyt5pU_7Q6"
      },
      "source": [
        "* Customers with vechicle age 1-2 years are more likely to interested as compared to the other two\n",
        "\n",
        "* Customers with with Vehicle_Age <1 years have very less chance of buying Insurance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NnDIcxPrBcWV"
      },
      "source": [
        "##### Annual_Premium\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjKHCL-bBben",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "017669fc-65c6-4e03-b87b-72ca670839e9"
      },
      "source": [
        "plt.figure(figsize=(13,7))\n",
        "plt.subplot(2,1,1)\n",
        "sns.distplot(data['Annual_Premium'], color='green')\n",
        "plt.title(\"Distribution of Annual premium\")\n",
        "plt.show()\n",
        "plt.figure(figsize=(13,7))\n",
        "plt.subplot(2,1,2)\n",
        "sns.boxplot(data['Annual_Premium'])\n",
        "plt.title(\"boxplot of Annual premium\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhO_L4_UCKUM"
      },
      "source": [
        "\n",
        "* From the distribution plot we can infer that the annual premimum variable is right skewed\n",
        "* From the boxplot we can observe lot of outliers in the variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlaYpu4YC6VZ"
      },
      "source": [
        "##### Correlation Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJWdHzibC6jL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "outputId": "0ce247c3-3cae-4659-acca-1146cbc74233"
      },
      "source": [
        "corr = data.corr()\n",
        "\n",
        "f, ax = plt.subplots(figsize=(10,10))\n",
        "\n",
        "sns.heatmap(corr, ax=ax, annot=True,linewidths=3,cmap='YlGn')\n",
        "\n",
        "plt.title(\"Pearson correlation of Features\", y=1.05, size=15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Pearson correlation of Features')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuime_De5rqX"
      },
      "source": [
        "*  Target variable is not much affected by Vintage variable. we can drop least correlated variable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMGE-jsqDdMD"
      },
      "source": [
        "# 3. Prepare Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0e6_PYaD2Q-"
      },
      "source": [
        "##### Label encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33bVs5voDcVi"
      },
      "source": [
        "#changing categorical value to numerical values\n",
        "labelEncoder= LabelEncoder()\n",
        "data['Gender'] = labelEncoder.fit_transform(data['Gender'])\n",
        "data['Vehicle_Age'] = labelEncoder.fit_transform(data['Vehicle_Age'])\n",
        "data['Vehicle_Damage'] = labelEncoder.fit_transform(data['Vehicle_Damage'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srTqpmDyDTBX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55df3b6c-8c3a-4e3f-be29-45b4e19a590b"
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                        int64\n",
              "Gender                    int64\n",
              "Age                       int64\n",
              "Driving_License           int64\n",
              "Region_Code             float64\n",
              "Previously_Insured        int64\n",
              "Vehicle_Age               int64\n",
              "Vehicle_Damage            int64\n",
              "Annual_Premium          float64\n",
              "Policy_Sales_Channel    float64\n",
              "Vintage                   int64\n",
              "Response                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp3G0NkrK9KD"
      },
      "source": [
        "##### Checking duplicate rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmkpVFJZLEsj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dc06c21-5378-44bd-bb5c-d02e2d1ed59b"
      },
      "source": [
        "duplicate=data[data.duplicated()]\n",
        "print(duplicate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [id, Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage, Response]\n",
            "Index: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVugvmgPLaDy"
      },
      "source": [
        "* there is no duplicated rows in the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kmwat7jyNRJR"
      },
      "source": [
        "###### Seprating dependent and independent variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9TZkvWpMqe7"
      },
      "source": [
        "x=data.drop(['Response'],axis=1) #contain all  independent variable\n",
        "y=data['Response']           #dependent variable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnQskJFZvYG9"
      },
      "source": [
        "#### Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozylvoiOfXWA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "a3dd9970-a4e5-4a86-9e9e-00d1b3889caa"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(x,y)\n",
        "print(model.feature_importances_) #use inbuilt class feature_importances of tree based clas\n",
        "#plot graph of feature importances for better visualization\n",
        "feat_importances = pd.Series(model.feature_importances_, index=x.columns)\n",
        "feat_importances.nlargest(11).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.19378262 0.0056787  0.13825795 0.00047622 0.09227821 0.05869156\n",
            " 0.01554295 0.07346389 0.1641505  0.06996283 0.18771457]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7U-x6cKavchK"
      },
      "source": [
        "* We can remove less important features from the data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFC9-0xnvy-s"
      },
      "source": [
        "x=x.drop(['Driving_License','Gender'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FH2OdIc69xN"
      },
      "source": [
        "#### Handling Imbalanced data\n",
        "*  When observation in one class is higher than the observation in other classes then there exists a class imbalance. We can clearly see that there is a huge difference between the data set. Solving this issue we use resampling technique."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6PU302txx7G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "9b9aed5d-976d-471e-fd6f-eee03cd240fe"
      },
      "source": [
        "randomsample=  RandomOverSampler()\n",
        "x_new,y_new=randomsample.fit_sample(x,y)\n",
        "\n",
        "from collections import Counter\n",
        "print('Original dataset shape {}'.format(Counter(y)))\n",
        "print('Resampled dataset shape {}'.format(Counter(y_new)))\n",
        "sns.countplot(y_new,palette='husl')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Original dataset shape Counter({0: 334399, 1: 46710})\n",
            "Resampled dataset shape Counter({1: 334399, 0: 334399})\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05764f1400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5E91XeazrSO"
      },
      "source": [
        "#### spliting of data into train and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OO-iSJ5z1G7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26036788-827b-4ff3-e391-437d4e2cd659"
      },
      "source": [
        "#dividing the dataset into training and testing\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x_new,y_new,test_size=.30,random_state=0)\n",
        "print(xtrain.shape,xtest.shape,ytrain.shape,ytest.shape)\n",
        "\n",
        "#feature scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "xtrain=scaler.fit_transform(xtrain)\n",
        "xtest=scaler.transform(xtest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(468158, 9) (200640, 9) (468158,) (200640,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUVfQnLMYmIL"
      },
      "source": [
        "#4. Model Selection\n",
        "* Problem can be identified as Binary Classification (wheather customer opts for vehicle insurance or not)\n",
        "\n",
        "* Dataset has more than 300k records\n",
        "\n",
        "* cannot go with SVM Classifier as it takes more time to train as dataset increase\n",
        "* Idea is to start selection of models as:\n",
        "\n",
        "       1.Logistic Regression\n",
        "\n",
        "       2.Random Forest\n",
        "\n",
        "       3.XGBClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi4NxeryCeig"
      },
      "source": [
        "###  1.Logistic Regression\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KehTebV_0Fa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "489f54e9-a150-4d36-c559-8772598ccd87"
      },
      "source": [
        "\n",
        "model=LogisticRegression()\n",
        "model=model.fit(xtrain,ytrain)\n",
        "pred=model.predict(xtest)\n",
        "lr_probability =model.predict_proba(xtest)[:,1]\n",
        "\n",
        "\n",
        "acc_lr=accuracy_score(ytest,pred)\n",
        "recall_lr=recall_score(ytest,pred)\n",
        "precision_lr=precision_score(ytest,pred)\n",
        "f1score_lr=f1_score(ytest,pred)\n",
        "AUC_LR=roc_auc_score(pred,ytest)\n",
        "#print accuracy and Auc values of model\n",
        "print(\"Accuracy : \", accuracy_score(ytest,pred))\n",
        "print(\"ROC_AUC Score:\",AUC_LR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy :  0.7836822169059011\n",
            "ROC_AUC Score: 0.8337747833543817\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evPcOfNH7VuP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f99ec89-7875-4743-c455-cf72fc3fd92f"
      },
      "source": [
        "print(classification_report(pred,ytest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.96      0.73     61462\n",
            "           1       0.98      0.70      0.82    139178\n",
            "\n",
            "    accuracy                           0.78    200640\n",
            "   macro avg       0.78      0.83      0.78    200640\n",
            "weighted avg       0.86      0.78      0.79    200640\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KCaxThKbGzQ"
      },
      "source": [
        "### ROC Curve\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1F_LYGVGMESk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "052fa6c1-d74a-497c-e165-f9890b7c420b"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, _ = roc_curve(ytest, lr_probability)\n",
        "\n",
        "plt.title('Linear Regression ROC curve')\n",
        "plt.xlabel('FPR (Precision)')\n",
        "plt.ylabel('TPR (Recall)')\n",
        "\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), ls='dashed',color='black')\n",
        "plt.show()"
      ],
      "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": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-2zkeps9EqP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "247e4338-4096-41ef-b5fe-d0346a489ae7"
      },
      "source": [
        "cm=confusion_matrix(ytest,pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True,cmap='BuPu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[59194 41134]\n",
            " [ 2268 98044]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f057642eef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QoW703KK-jIB"
      },
      "source": [
        "## 2.RandomForest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MGbU9Kj_CE-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ad9e59-5aeb-4947-faa8-c1b8bfcb942a"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "randomforest = RandomForestClassifier()\n",
        "randomforest=randomforest.fit(xtrain, ytrain)\n",
        "y_pred = randomforest.predict(xtest)\n",
        "RF_probability = randomforest.predict_proba(xtest)[:,1]\n",
        "\n",
        "\n",
        "\n",
        "AUC_RF=roc_auc_score(y_pred,ytest)\n",
        "acc_rf=accuracy_score(ytest,y_pred)\n",
        "recall_rf=recall_score(ytest,y_pred)\n",
        "precision_rf=precision_score(ytest,y_pred)\n",
        "f1score_rf=f1_score(ytest,y_pred)\n",
        "\n",
        "print(accuracy_score(y_pred,ytest))\n",
        "print(\"ROC_AUC Score:\",AUC_RF)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9448265550239234\n",
            "ROC_AUC Score: 0.9498072137131631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xYikSt5_lYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fb3148d-5a48-46aa-da12-2f842ebe1fc4"
      },
      "source": [
        "print(classification_report(y_pred,ytest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      1.00      0.94     89799\n",
            "           1       1.00      0.90      0.95    110841\n",
            "\n",
            "    accuracy                           0.94    200640\n",
            "   macro avg       0.94      0.95      0.94    200640\n",
            "weighted avg       0.95      0.94      0.95    200640\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHl8VljhMnYg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "f37fac56-386c-4438-fcc6-7cd35b24de1d"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, _ = roc_curve(ytest, RF_probability)\n",
        "\n",
        "plt.title('Linear Regression ROC curve')\n",
        "plt.xlabel('FPR (Precision)')\n",
        "plt.ylabel('TPR (Recall)')\n",
        "\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), ls='dashed',color='black')\n",
        "plt.show()"
      ],
      "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": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0-CHgSgAhg7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "7c7f1673-74d9-406f-8104-32d5555d0b13"
      },
      "source": [
        "cm=confusion_matrix(y_pred,ytest)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True,cmap='RdPu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 89542    257]\n",
            " [ 10786 100055]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05763b8f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSj7gm5YApSq"
      },
      "source": [
        "## 3. XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQzof4KREpf4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b8da7e1-fa86-4943-a606-c8d56dfe6edf"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "xgb=XGBClassifier()\n",
        "XGB_fit=xgb.fit(xtrain, ytrain)\n",
        "y_predict = XGB_fit.predict(xtest)\n",
        "XGB_probability = XGB_fit.predict_proba(xtest)[:,1]\n",
        "\n",
        "acc_xgb=accuracy_score(ytest,y_predict)\n",
        "recall_xgb=recall_score(ytest,y_predict)\n",
        "precision_xgb=precision_score(ytest,y_predict)\n",
        "f1score_xgb=f1_score(ytest,y_predict)\n",
        "\n",
        "AUC_xgb=roc_auc_score(y_predict,ytest)\n",
        "\n",
        "\n",
        "print(accuracy_score(y_predict,ytest))\n",
        "print(\"ROC_AUC Score:\",AUC_xgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.797692384370016\n",
            "ROC_AUC Score: 0.8205726541130131\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6A1WgYDA8Lx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2f7627b-1811-4c45-8cc9-a49503b2549a"
      },
      "source": [
        "print(classification_report(y_predict,ytest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.91      0.77     73525\n",
            "           1       0.93      0.73      0.82    127115\n",
            "\n",
            "    accuracy                           0.80    200640\n",
            "   macro avg       0.80      0.82      0.79    200640\n",
            "weighted avg       0.83      0.80      0.80    200640\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ht7hDDp_M0CV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "2893ee19-4d56-4a5a-b866-176b16d034d3"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr, tpr, _ = roc_curve(ytest, XGB_probability)\n",
        "\n",
        "plt.title('XGBoost ROC curve')\n",
        "plt.xlabel('FPR (Precision)')\n",
        "plt.ylabel('TPR (Recall)')\n",
        "\n",
        "plt.plot(fpr,tpr)\n",
        "plt.plot((0,1), ls='dashed',color='black')\n",
        "plt.show()"
      ],
      "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": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMhcqz0LA_Wx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "ffa432eb-4993-409d-a830-48e4ee3d6c88"
      },
      "source": [
        "#it helps to identify how many values are classified correctly\n",
        "cm=confusion_matrix(ytest,y_predict)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True,cmap='GnBu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[66631 33697]\n",
            " [ 6894 93418]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05762f1c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmJgsmr3BjOo"
      },
      "source": [
        "## Comparing the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52Gs25CDBI3B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6653f11c-2739-4b8a-c101-fbd86eef6792"
      },
      "source": [
        "ind=['Logistic regression','Randomforest','XGBClassifier']\n",
        "data={\"Accuracy\":[acc_lr,acc_rf,acc_xgb],\"Recall\":[recall_lr,recall_rf,recall_xgb],\"Precision\":[precision_lr,precision_rf,precision_xgb],\n",
        "    'f1_score':[f1score_lr,f1score_rf,f1score_xgb],\"ROC_AUC\":[AUC_LR,AUC_RF,AUC_xgb]}\n",
        "result=pd.DataFrame(data=data,index=ind)\n",
        "result\n"
      ],
      "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>Accuracy</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>ROC_AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Logistic regression</th>\n",
              "      <td>0.783682</td>\n",
              "      <td>0.977391</td>\n",
              "      <td>0.704450</td>\n",
              "      <td>0.818773</td>\n",
              "      <td>0.833775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Randomforest</th>\n",
              "      <td>0.944961</td>\n",
              "      <td>0.997438</td>\n",
              "      <td>0.902689</td>\n",
              "      <td>0.947701</td>\n",
              "      <td>0.949914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.797692</td>\n",
              "      <td>0.931274</td>\n",
              "      <td>0.734909</td>\n",
              "      <td>0.821521</td>\n",
              "      <td>0.820573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Accuracy    Recall  Precision  f1_score   ROC_AUC\n",
              "Logistic regression  0.783682  0.977391   0.704450  0.818773  0.833775\n",
              "Randomforest         0.944961  0.997438   0.902689  0.947701  0.949914\n",
              "XGBClassifier        0.797692  0.931274   0.734909  0.821521  0.820573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITkevz6VFknV"
      },
      "source": [
        "The ML model for the problem statement was created using python with the help of the dataset, and the ML model created with Random Forest and XGBClassifier models performed better than Logistics Regression model. Thus, for the given problem, the models created by Random Forest and XGBClassifier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCXyDT03FnhG"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pj9Jsm10VpEM"
      },
      "source": [
        "* Customers of age between 30 to 60 are more likely to buy insurance.\n",
        "* Customers with Driving License have higher chance of buying Insurance.\n",
        "* Customers with Vehicle_Damage are likely to buy insurance.\n",
        "*  The variable such as Age, Previously_insured,Annual_premium  are more afecting the target variable.\n",
        "* comparing ROC curve we can see that Random Forest model preform better. Because curves closer to the top-left corner, it indicate a better performance.\n"
      ]
    }
  ]
}