Indabax_Ghana_AutoInland_5th_Place_Solution.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "colab": {
      "name": "Indabax_Ghana_AutoInland 5th Place Solution.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Vpki3KJKzTl"
      },
      "source": [
        "# IndabaX Ghana AutoInland Vehicle Insurance Claim by Data Science Network\n",
        "\n",
        "The objective of this hackathon is to develop a predictive model that determines if a customer will submit a vehicle insurance claim in the next three months.\n",
        "\n",
        "This solution will help streamline financial planning at AutoInland and allow them to better serve their customers by understanding which customers are likely to submit a claim."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqIYtm8gKzUE"
      },
      "source": [
        "## Loading required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eu_TpOklKzUN"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import matplotlib.ticker as ticker\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmZdMpEwLWYn",
        "outputId": "4d137cb6-1771-4c40-88db-acc1da86cdad"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDMSax2LLmHe",
        "outputId": "5ad75fdf-35ec-4784-8a0b-6bd2d425d760"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/My Drive/IndabaX Ghana AutoInland')\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Baseline.csv\n",
            " catboost_info\n",
            " cb_new.csv\n",
            " cb_prediction2.csv\n",
            " cb_prediction.csv\n",
            " Ghana_Indabax_AutoInland_Challenge.ipynb\n",
            " Ghana_Indabax_Challenge2.ipynb\n",
            " Indabax_AutoInland.ipynb\n",
            "'Indabax_AutoInland_LGBM_feature_13 - Copy.ipynb'\n",
            "'Indabax_Ghana_AutoInland 5th Place Solution.ipynb'\n",
            " indabax_NigerianStateNames.csv\n",
            " Indabax_Python_StarterNotebook.ipynb\n",
            " indabax_SampleSubmission.csv\n",
            " indabax_Test.csv\n",
            " indabax_Train.csv\n",
            " indabax_VariableDefinitions.csv\n",
            " lgbm_featselect10.csv\n",
            " lgbm_featselect12.csv\n",
            " lgbm_featselect15.csv\n",
            " lgbm_featselect20.csv\n",
            " lgbm_featselect25.csv\n",
            " lgbm_featselect8.csv\n",
            " lgbm_featselect.csv\n",
            " lgbm_fresh11.csv\n",
            " lgbm_fresh15.csv\n",
            " lgbm_fresh20.csv\n",
            " lgbm_fresh30.csv\n",
            " lgbm_fresh50.csv\n",
            " lgbm_fresh.csv\n",
            " lgbm_fresh_cv2.csv\n",
            " lgbm_fresh_cv3.csv\n",
            " lgbm_fresh_cv.csv\n",
            " lgbm_fresh_featselect.csv\n",
            " lgbm_last.csv\n",
            " lgbm_mappercv10.csv\n",
            " lgbm_mappercv10_feat13.csv\n",
            " lgbm_new1.csv\n",
            " lgbm_new2.csv\n",
            " lgbm_new3.csv\n",
            " lgbm_new4.csv\n",
            " lgbm_new5.csv\n",
            " lgbm_new6.csv\n",
            " lgbm_new7.csv\n",
            " lgbm_new8.csv\n",
            " lgbm_new.csv\n",
            " lgbm_no_mapper.csv\n",
            " lgbm_no_mappercv10_feat13.csv\n",
            " lgbm_no_mappercv.csv\n",
            " lgbm_no_mappercv_feat13.csv\n",
            " lgbm_prediction2.csv\n",
            " lgbm_prediction2_featselect10.csv\n",
            " lgbm_prediction2_featselect11.csv\n",
            " lgbm_prediction2_featselect12.csv\n",
            " lgbm_prediction2_featselect13.csv\n",
            " lgbm_prediction2_featselect15.csv\n",
            " lgbm_prediction2_featselect8.csv\n",
            " lgbm_prediction3.csv\n",
            " lgbm_prediction4.csv\n",
            " lgbm_prediction.csv\n",
            " lgbm_prediction_fresh.csv\n",
            "'Mo give up.csv'\n",
            " nn_prediction2.csv\n",
            " nn_prediction.csv\n",
            " reproducibility_prediction.csv\n",
            " rffeat_lgbmodel.csv\n",
            " rf_fresh.csv\n",
            " rf_new.csv\n",
            " rf_new_featselect1.csv\n",
            " rf_new_featselect.csv\n",
            " rf_prediction2.csv\n",
            " rf_prediction.csv\n",
            " xgb_new1.csv\n",
            " xgb_new.csv\n",
            " xgboost_fresh.csv\n",
            " xgboost_fresh_cv.csv\n",
            " xgboost_prediction2.csv\n",
            " xgboost_prediction3.csv\n",
            " xgboost_prediction4.csv\n",
            " xgboost_prediction.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OLIGwF8KzUa"
      },
      "source": [
        "### Loading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SULbZhmLKzUb"
      },
      "source": [
        "# Importing the data\n",
        "\n",
        "try:\n",
        "    train = pd.read_csv(\"indabax_Train.csv\" , low_memory=False)\n",
        "    test = pd.read_csv(\"indabax_Test.csv\" , low_memory=False)\n",
        "    ss = pd.read_csv(\"indabax_SampleSubmission.csv\" , low_memory=False)\n",
        "    ns = pd.read_csv('indabax_NigerianStateNames.csv')\n",
        "    #var_def = pd.read_csv(\"../VariableDefinitions.csv\" , low_memory=False)\n",
        "except:\n",
        "    !colab_download\n",
        "    train = pd.read_csv(\"indabax_Train.csv\" , low_memory=False)\n",
        "    test = pd.read_csv(\"indabax_Test.csv\" , low_memory=False)\n",
        "    ss = pd.read_csv(\"indabax_SampleSubmission.csv\" , low_memory=False)\n",
        "    ns = pd.read_csv('indabax_NigerianStateNames.csv')\n",
        "    #var_def = pd.read_csv(\"../autoinland_VariableDefinitions.csv\" , low_memory=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjTSZBl_KzUc"
      },
      "source": [
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIK-B0uRKzUf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "3a23466f-5ae6-47a3-8652-b188736200bd"
      },
      "source": [
        "train.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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>2011-05-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>2011-11-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>79</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>2011-03-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>2011-08-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>Entity</td>\n",
              "      <td>20</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID Policy Start Date Policy End Date  ...  State  ProductName target\n",
              "0  ID_0040R73        2010-05-14      2011-05-13  ...    NaN  Car Classic      0\n",
              "1  ID_0046BNK        2010-11-29      2011-11-28  ...    NaN  Car Classic      1\n",
              "2  ID_005QMC3        2010-03-21      2011-03-20  ...    NaN  Car Classic      0\n",
              "3  ID_0079OHW        2010-08-21      2011-08-20  ...    NaN      CarSafe      0\n",
              "4  ID_00BRP63        2010-08-29      2010-12-31  ...  Lagos        Muuve      1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SGzCWbvKzU5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "e41a85ef-5e98-4ac2-aa2e-c7403d13e45a"
      },
      "source": [
        "test.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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_01QM0NU</td>\n",
              "      <td>2010-10-23</td>\n",
              "      <td>2011-10-22</td>\n",
              "      <td>Female</td>\n",
              "      <td>46</td>\n",
              "      <td>2010-10-23</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ford</td>\n",
              "      <td>Abuja Municipal</td>\n",
              "      <td>Abuja-Municipal</td>\n",
              "      <td>Car Classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_024NJLZ</td>\n",
              "      <td>2010-10-14</td>\n",
              "      <td>2011-10-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>2010-10-14</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kosofe</td>\n",
              "      <td>Benue</td>\n",
              "      <td>Car Classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_02NOVWQ</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2011-08-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Wuse 11</td>\n",
              "      <td>Abuja</td>\n",
              "      <td>Car Classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_02VSP68</td>\n",
              "      <td>2010-06-13</td>\n",
              "      <td>2011-06-12</td>\n",
              "      <td>Female</td>\n",
              "      <td>58</td>\n",
              "      <td>2010-06-13</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CarSafe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_02YB37K</td>\n",
              "      <td>2010-07-01</td>\n",
              "      <td>2011-06-30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120</td>\n",
              "      <td>2010-07-01</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Victoria Island</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Car Classic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID Policy Start Date  ...            State  ProductName\n",
              "0  ID_01QM0NU        2010-10-23  ...  Abuja-Municipal  Car Classic\n",
              "1  ID_024NJLZ        2010-10-14  ...            Benue  Car Classic\n",
              "2  ID_02NOVWQ        2010-08-29  ...            Abuja  Car Classic\n",
              "3  ID_02VSP68        2010-06-13  ...              NaN      CarSafe\n",
              "4  ID_02YB37K        2010-07-01  ...            Lagos  Car Classic\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOa7p2ilKzU9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e370b888-8c8c-421f-82e3-5c65664935dc"
      },
      "source": [
        "ss.head()"
      ],
      "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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_01QM0NU</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_024NJLZ</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_02NOVWQ</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_02VSP68</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_02YB37K</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  target\n",
              "0  ID_01QM0NU       0\n",
              "1  ID_024NJLZ       0\n",
              "2  ID_02NOVWQ       0\n",
              "3  ID_02VSP68       0\n",
              "4  ID_02YB37K       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "37BVvADIsUE_",
        "outputId": "94133069-0437-4b15-8c89-5f8399cd16cf"
      },
      "source": [
        "ns.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>LGA</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abadam</td>\n",
              "      <td>Borno State</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abaji</td>\n",
              "      <td>Federal Capital Territory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abak</td>\n",
              "      <td>Akwa Ibom State</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abakaliki</td>\n",
              "      <td>Ebonyi State</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aba-North</td>\n",
              "      <td>Abia State</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         LGA                      State\n",
              "0     Abadam                Borno State\n",
              "1      Abaji  Federal Capital Territory\n",
              "2       Abak            Akwa Ibom State\n",
              "3  Abakaliki               Ebonyi State\n",
              "4  Aba-North                 Abia State"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w45W756CKzVE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecf365a4-798b-4be3-90cf-04bf09afca44"
      },
      "source": [
        "print('Train shape:',train.shape,'\\nTest shape:', test.shape, '\\nsamplesubmission shape:',ss.shape,'\\nNigerianStateNames shape:',ns.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape: (12079, 14) \n",
            "Test shape: (1202, 13) \n",
            "samplesubmission shape: (1202, 2) \n",
            "NigerianStateNames shape: (875, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssA4l4ddKzVG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bc8346f-29cc-436c-aa63-5f366a5d9fee"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12079 entries, 0 to 12078\n",
            "Data columns (total 14 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   ID                      12079 non-null  object\n",
            " 1   Policy Start Date       12079 non-null  object\n",
            " 2   Policy End Date         12079 non-null  object\n",
            " 3   Gender                  11720 non-null  object\n",
            " 4   Age                     12079 non-null  int64 \n",
            " 5   First Transaction Date  12079 non-null  object\n",
            " 6   No_Pol                  12079 non-null  int64 \n",
            " 7   Car_Category            8341 non-null   object\n",
            " 8   Subject_Car_Colour      5117 non-null   object\n",
            " 9   Subject_Car_Make        9603 non-null   object\n",
            " 10  LGA_Name                5603 non-null   object\n",
            " 11  State                   5591 non-null   object\n",
            " 12  ProductName             12079 non-null  object\n",
            " 13  target                  12079 non-null  int64 \n",
            "dtypes: int64(3), object(11)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6m1OVJMKKzVH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ffb7557-d38f-43a2-a547-c0a790e17804"
      },
      "source": [
        "test.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1202 entries, 0 to 1201\n",
            "Data columns (total 13 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   ID                      1202 non-null   object\n",
            " 1   Policy Start Date       1202 non-null   object\n",
            " 2   Policy End Date         1202 non-null   object\n",
            " 3   Gender                  1161 non-null   object\n",
            " 4   Age                     1202 non-null   int64 \n",
            " 5   First Transaction Date  1202 non-null   object\n",
            " 6   No_Pol                  1202 non-null   int64 \n",
            " 7   Car_Category            830 non-null    object\n",
            " 8   Subject_Car_Colour      505 non-null    object\n",
            " 9   Subject_Car_Make        954 non-null    object\n",
            " 10  LGA_Name                546 non-null    object\n",
            " 11  State                   546 non-null    object\n",
            " 12  ProductName             1202 non-null   object\n",
            "dtypes: int64(2), object(11)\n",
            "memory usage: 122.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5NKnrf2KzVg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "698e5e50-02b9-4f70-8047-c02d07ccf4a1"
      },
      "source": [
        "# join train and test together\n",
        "ntrain = train.shape[0]\n",
        "ntest = test.shape[0]\n",
        "\n",
        "all_data = pd.concat((train, test)).reset_index(drop=True)\n",
        "print(\"all_data size is : {}\".format(all_data.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "all_data size is : (13281, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iag-HAlQKzVl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "05296f28-b27a-4400-af71-6f04ab4269d3"
      },
      "source": [
        "all_data.tail()"
      ],
      "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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13276</th>\n",
              "      <td>ID_ZTTHC5X</td>\n",
              "      <td>2010-12-05</td>\n",
              "      <td>2011-12-04</td>\n",
              "      <td>Male</td>\n",
              "      <td>67</td>\n",
              "      <td>2010-12-05</td>\n",
              "      <td>2</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>Victoria Island</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13277</th>\n",
              "      <td>ID_ZUJAFUP</td>\n",
              "      <td>2010-01-14</td>\n",
              "      <td>2011-01-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>2010-01-14</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Silver</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Surulere</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13278</th>\n",
              "      <td>ID_ZWHCTUM</td>\n",
              "      <td>2010-07-26</td>\n",
              "      <td>2011-07-25</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-07-26</td>\n",
              "      <td>1</td>\n",
              "      <td>Truck</td>\n",
              "      <td>White</td>\n",
              "      <td>Iveco</td>\n",
              "      <td>Victoria Island</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>CVTP</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13279</th>\n",
              "      <td>ID_ZWQRL8L</td>\n",
              "      <td>2010-02-16</td>\n",
              "      <td>2011-02-15</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>2010-02-16</td>\n",
              "      <td>2</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nissan</td>\n",
              "      <td>Aba North</td>\n",
              "      <td>Aba-North</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13280</th>\n",
              "      <td>ID_ZWZ92GU</td>\n",
              "      <td>2010-03-18</td>\n",
              "      <td>2011-03-17</td>\n",
              "      <td>Male</td>\n",
              "      <td>28</td>\n",
              "      <td>2010-03-18</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Silver</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ID Policy Start Date  ...  ProductName target\n",
              "13276  ID_ZTTHC5X        2010-12-05  ...  Car Classic    NaN\n",
              "13277  ID_ZUJAFUP        2010-01-14  ...  Car Classic    NaN\n",
              "13278  ID_ZWHCTUM        2010-07-26  ...         CVTP    NaN\n",
              "13279  ID_ZWQRL8L        2010-02-16  ...  Car Classic    NaN\n",
              "13280  ID_ZWZ92GU        2010-03-18  ...  Car Classic    NaN\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xe_zwu44KzVp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f041f41-b967-4f71-a240-f749c1570e0f"
      },
      "source": [
        "date_col = ['Policy Start Date','Policy End Date','First Transaction Date']\n",
        "num_col = ['Age']\n",
        "cat_col = [col for col in test.columns if col not in date_col+num_col]\n",
        "cat_col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ID',\n",
              " 'Gender',\n",
              " 'No_Pol',\n",
              " 'Car_Category',\n",
              " 'Subject_Car_Colour',\n",
              " 'Subject_Car_Make',\n",
              " 'LGA_Name',\n",
              " 'State',\n",
              " 'ProductName']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7u4cXpeKzVw"
      },
      "source": [
        "cat_col.remove('ID')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POvT-OqZKzVx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e388ed8f-97f0-4363-cc7d-377747360391"
      },
      "source": [
        "train.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>No_Pol</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12079.000000</td>\n",
              "      <td>12079.000000</td>\n",
              "      <td>12079.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>42.234539</td>\n",
              "      <td>1.307227</td>\n",
              "      <td>0.120457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>97.492565</td>\n",
              "      <td>0.733085</td>\n",
              "      <td>0.325509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-6099.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>320.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Age        No_Pol        target\n",
              "count  12079.000000  12079.000000  12079.000000\n",
              "mean      42.234539      1.307227      0.120457\n",
              "std       97.492565      0.733085      0.325509\n",
              "min    -6099.000000      1.000000      0.000000\n",
              "25%       35.000000      1.000000      0.000000\n",
              "50%       41.000000      1.000000      0.000000\n",
              "75%       50.000000      1.000000      0.000000\n",
              "max      320.000000     10.000000      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkOMlJiaKzVy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "9171e13a-73be-4175-9a7e-51ae09f45b88"
      },
      "source": [
        "test.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>No_Pol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1202.000000</td>\n",
              "      <td>1202.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.792845</td>\n",
              "      <td>1.257903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>19.986245</td>\n",
              "      <td>0.613510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-26.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age       No_Pol\n",
              "count  1202.000000  1202.000000\n",
              "mean     43.792845     1.257903\n",
              "std      19.986245     0.613510\n",
              "min     -26.000000     1.000000\n",
              "25%      35.000000     1.000000\n",
              "50%      41.000000     1.000000\n",
              "75%      50.000000     1.000000\n",
              "max     120.000000     7.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gy1_u2FzKzV-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "c99c47d0-4894-41aa-dee1-efe193130dcd"
      },
      "source": [
        "sns.countplot(train.target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bb356ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juvPaeTQ9aLV"
      },
      "source": [
        "The above plot reveals that we have an imbalanced dataset. This has to be catered for in the course of training the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LImz-CNS9oS8"
      },
      "source": [
        "#### Checking for missing values in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQt4zEqCKzWA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff3245f6-b7bc-44c1-e1ba-e0d94a48fe44"
      },
      "source": [
        "# check for missing values in the train set\n",
        "print(\"Are There Missing value in train? :\",train.isnull().any().any())\n",
        "print((train.isnull().sum()/train.shape[0])*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are There Missing value in train? : True\n",
            "ID                         0.000000\n",
            "Policy Start Date          0.000000\n",
            "Policy End Date            0.000000\n",
            "Gender                     2.972100\n",
            "Age                        0.000000\n",
            "First Transaction Date     0.000000\n",
            "No_Pol                     0.000000\n",
            "Car_Category              30.946270\n",
            "Subject_Car_Colour        57.637222\n",
            "Subject_Car_Make          20.498386\n",
            "LGA_Name                  53.613710\n",
            "State                     53.713056\n",
            "ProductName                0.000000\n",
            "target                     0.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLDQgjlbKzWB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a933d558-6b06-4cfe-a4bc-255ff74c31fd"
      },
      "source": [
        "# check for missing values in the test set\n",
        "print(\"Are There Missing value in test? :\",test.isnull().any().any())\n",
        "print((test.isnull().sum()/test.shape[0])*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are There Missing value in test? : True\n",
            "ID                         0.000000\n",
            "Policy Start Date          0.000000\n",
            "Policy End Date            0.000000\n",
            "Gender                     3.410982\n",
            "Age                        0.000000\n",
            "First Transaction Date     0.000000\n",
            "No_Pol                     0.000000\n",
            "Car_Category              30.948419\n",
            "Subject_Car_Colour        57.986689\n",
            "Subject_Car_Make          20.632280\n",
            "LGA_Name                  54.575707\n",
            "State                     54.575707\n",
            "ProductName                0.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2qRAIhe-JUS"
      },
      "source": [
        "#### Some visual plot showing relationship between features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJjzb_uSKzWD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "139c951e-2106-49d0-9ae5-e49404ec6256"
      },
      "source": [
        "f,ax=plt.subplots(figsize=(8,8))\n",
        "sns.heatmap(train.corr(),annot=True,linewidth=.5,fmt='.1f',ax=ax)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbsqzIFVKzWF"
      },
      "source": [
        "# checking relationship of  categorical features and numerical features\n",
        "def check_categorical_relationship(cat_col,y_col,df):\n",
        "    for feat in cat_col:\n",
        "        plt.figure(figsize=(20,5))\n",
        "        sns.barplot(df[feat],df[y_col])\n",
        "        plt.show()\n",
        "        print(\"\\n \\n \\n \")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "XjfupIDEKzW0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "be1680a2-d17e-4d0b-e300-9d9a8044d8d5"
      },
      "source": [
        "check_categorical_relationship(cat_col,'Age',all_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "mhLH02LSKzW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c2774584-9c0c-416c-a126-482664c794a2"
      },
      "source": [
        "check_categorical_relationship(cat_col,'No_Pol',all_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x1X1LAJjXt6Q1/dLD3p/KnX/60OXy9EsPf08ud8awy7v08NmjFPnGO/C8b7fGV/UtA2Be37LW9EuOOLorcTXFWNa7Q867ctjXLzpir44ufzTW9Yjzbxz29fMO332j5veW8/847OvfP/w5j5k20vUYD+eU4dbhWxcsGPa97zhsWidC6piJ0Pe47fSHh339+e956mOmjYd6PFKjsa4Pn7T+Nuqp/7Rx7ZMev/lfPX/Y13d63+FjFMno2JgvU/8mcDGwM/A04Ed52gaFELYFzgc+GGNcurFBAsQYz4gxzowxzpw2bdM6CUiauLbZNjBluzSUJEmSJA1vYz76Ny3GWE9MnRVC+OCG3hRC2JyUpPpOjPGCIYr0ALvW/t8lT5OkTd6r9p3c7RAkSZIkaZOxMU9UPRpCODqEMDn/HQ08Otwb8i/6nQncHmP84nqKXQy8MySvAJb4/VSSJEmSJEkTz8Y8UfV3pO+oOon0HVW/Ad61gffsAbwDuCWEUD68+nFgN4AY4+nAZcD+wF3AihHMU5IkSZIkSePQiBNVMcb7gVkbM/MY49XAsF/Mkn/t7/0bM19JkiRJkiSNPxtMVIUQThzm5Rhj/MwoxqMJwp9ClSRJo8E+hSRJ48tInqhaPsS0KcCxwFMAE1XaaBPpZ18lSVLn2KeQJGl82WCiKsb4hTIeQpgKfID0PVLnAl9Y3/skaTz66rf3AWDJsv487GlNe9/Rl3ctLkmSJEkaD0b0HVUhhCcDHwLeDpwN/E2McVEnA5MkSZIkSdLEMpLvqPpP4DDgDOCFMca+jkclSZIkSZKkCWckT1R9GFgN/AvwiRBaP+IXSF+mvl2HYpNGlV+2KjWDx6KaLEzddtBQkiRJY2sk31E1aSwCmeimbbP1oKFGn1+2KjWDx6KabMtZb+h2CJIkSRPaiL6jSp13wmtf0u0Q1GU+ZSJJkiRJmuhMVEkN4VMmkqQNCVOnDhpKkiSNNyaqJGmC+dT392mNL+zrz8Oe1vRPveXyrsQ1Xvm0pEbTlgft3+0QJEmSOspEldRh4+EidTysgzTaRnpc+LSkJE1c9qEkaeOZqJI6bDxcpI6HdZBGm8eFJGlDPFdI0sbzF/0kSZIkSZLUCCaqJEmSJEmS1Ah+9E+Sxol//97QX5Jepn/irePzS9L9/o/uOuCCL7XGV/ctBmBe3+LW9EsPO74rcUmSJGnTZKLqCfDiSJK6z+//6DzPd4/lNpEkSeoME1VPgBdHkqSJwPPdY7lNJGn8+OOpD7fG1y4eaA3/eOrDPOe4p3YrLGnCGheJKu9qSpIkSZIkbfrGRaKq6Xc1TaRJkiRJkiRt2LhIVDVd0xNpI9GtZJtJvsfPbSc9Ph47kiRJUvdssomqBad9uzU+sGRZa1imT3vv0Y973uPhImW016FbybbxkOQbzk+/fkBrfMXS1Xk4rzV973df+rjnPd633Xjwxf+pfqVv8bL+POxpTf/Q28bXr/S994J9AZjftzYPe1rTTjvsJ12Lq10Tj539L/wMAGv6FgIwr29ha9plh/5r1+KSuuWQ865ojff1rQBgXt+K1vSLjnhjV+KSJElP3CabqOqkJl6kbKzxsA6SmmM8JPAlSZIkNZ+JKknSBpn83jj7X/TR1via5Y8AMG/5I63plx3y+a7EJUmSJDVdRxNVIYRvAAcC82OMLxji9T2BHwL35kkXxBg/vbHLmbbNtoOGUnHdfx/UGl+9ZGUezmtNn/kPP+pKXOoOnwqSJEmSpGbr9BNVZwGnAucMU+ZXMcYDn8hCPvHafTZcSNKE51NBkiRJktRsHU1UxRh/GUKY0clljLUFp3+1NT6wZElrWKZPe8/7uhKXJD0eW04NQMxDSZIkSequJnxH1StDCDcB84CPxBhv7XZAkjRRvPCAyd0OQZIkSZJaup2ougF4RoyxL4SwP3AR8OyhCoYQZgOzAXbbbbdRD2TB6V9vjQ8sWdoalunT3vPuUV/m47Epf8fOnace3Bpfu3h5Hs5rTX/ucT/sSlwAvz2j+vTpqiWr8nBea/orZl/SlbgkSRoPZp13aWt8RV/qA8zrW96afvERB3Qlrk457PzfArCsL/UpHupb1Zp2weGv6FpcG+PIC+5rjS/s6wegt6+/Nf3cw2aMfVAb6dQLH26NL+4baA3L9OMOfSoAX79gfqvc0lxuad9Aa/q7D9tpTOIdCz/7nwWt8ZXLBlrDMv0Nb5vWkeVec1a1jVctHWgNy/SXHzN+tnHT9X7hjtb4wKK1rWGZPv3Dz+tKXFJdVxNVMcaltfHLQghfDSHsGGN8ZIiyZwBnAMycOTOOYZiNMtx37Dz01epXpgaWPNIaluk7v89fmZIkSRoNk6buMGgoSZJGR1cTVSGE6cDDMcYYQngZMAl4tFPLG8unkeaffnJrfGDJ4tZw/ukns9N7PtjRZWt825SfqpMkabyYMuud3Q5BkqRxqaOJqhDCd4E9gR1DCA8CnwQ2B4gxng4cAbw3hNAPrASOjDF27Gkpf/Fr4zzw5aNb4/2L5+dhb2v6rv/47a7E1RTdShhZjyVJkiRJ41Wnf/XvqA28fipwaidjkDrFhFFz+JTZxLP/xfu2xtcsT9+vMG95T2v6ZbN+0pW4JEmSJD0x3f4ydUl6wkwaSpIkSdL4MO4TVQtOO6s1PujX/PL0ae89ZuyD0rBu/eqs1viaJSvycF5r+l+97+KuxNVp208Jg4aSJEmSJE004z5R9XhMmzJl0HD95bYZNNwU+ZGp5jjq9Vt0OwRJkiRJkrrKRNUQPvHa14+w3Gs6HEnn+ZGpzrjqawe0xlcuXZ2H81rT9/z7S7sSl9QEm28XgJiH6gRvQkiSJGlTZaJqE9B72r+1xgeWLGwNy/Tp7/1kV+KSJrJttk3JljTUxthllqeeTvMmhCRJkjZVXi2MUztus+WgoaTR9Zp9Jnc7BEmSJEkadyZUomraNtsOGo5nH3vNc7odgkbR5Wfu3xpfsXRNHs5rTd/n2MsAuPTM/Vrlludyy5f2tKYfcOyPxyTeDRnuY0nfPmuf1viypf152NOafvQxl49doNI4dMCF/9kaX923CIB5fYta0y899J9TuQtOqpVbnMstbk2/9LB/GpN4JTXbey98oDU+v6+/NSzTTzt0167EJUnadE2oRNUnXrt3V5Y7bcrWg4bSRDeWH0s685w3tcaXLhvIw57W9GPf+b9jEocmsKlbEvJQkiRJ0vAmVKKqWz7+2ld2O4QnbMdtJg8aTlRXf+3A1viqpavycF5r+qv//pKuxCXpidnvh//QGl+zfD4APcvnt6b/+OD/ftzz3uKQifOEa9hum0FDSZIkaWOZqNKI/PMeT+l2CE/Yk6aEQUNpPNsqf9n7Vk/gy95P+MG+rfFH+tbmYU9r+mff/JMnFKPGny1mvarbIWgCmjR1e9bloSRJ2vSZqNKE8e7XbdXtEDSKzjq79pG+pfkjfUt7WtOP+duJ/ZG+Fx8wsZ9+lDRxbHPQkd0OQZIkjSITVZKGdME3q6dp+pauzcOe1vTD3uXTNJp49rvog63xNcsXANCzfEFr+o8PObkrcUmSmuN75z/SGu/rW9calulvPXzHrsQlSZuKSd0OQJIkSZIkSQKfqJqQer5yXGu8f8mC1rBMf/r7T+1KXEN58jZh0FCw/RSAkIeSpLFy4PnntMZX9S0DYF7fstb0Sw5/Z1fikiRJGk9MVG1idtxmy0HD8e79r9m62yE0zlv22qLbIUjNsl0gEGE7E9qSJEnSps5E1SbmhNe+qNshSI/Ld8/apzW+bGl/Hva0ph91zOVdiaspts6/0rf1E/iVvolqi0Mn0Klsu60IeShJkiSNRxOody9JzfXy/fyVPm3YFgf/dbdDkCRJkjrKL1OXJEmSJElSI/hElfQ47ZC/4H0Hv+hdUgPsf+FnW+Nr+hYBMK9vUWv6ZYee0JW4JEmSpI1hokpq86QpYdBwfd75+onxhfaSJEmSJI0VE1VSm3ftaQJKkiRJkqRuMFEldZgfEZS0qQvbbT1oKEmSJHWKiaoJbto2mw8ajpUnT5kErMvD8e3tr9+i2yGMe9tuO3goaXRtMesV3Q5BkiRJE0RHE1UhhG8ABwLzY4wvGOL1AJwC7A+sAI6JMd7QyZg02JxX79aV5R6/h3flJ7Kp2wYg5uETt/cbJ4+o3JQpg4eSJEmSpGbp9BNVZwGnAues5/X9gGfnv5cDp+WhpCdo+ykpGbT9Br4Uvhv223tkiaXRtteburNcSZIkSdLIdDRRFWP8ZQhhxjBFDgbOiTFG4LchhB1CCDvHGB/qZFzSRHD4XmP7cU5JkiRJkp6obn9B0NOBB2r/P5inSZIkSZIkaYLZZL5MPYQwG5gNsNtu3fleJQ3t3i8fMuzrz/zHi0Z9mTeedtCwr+/+3h+N+jLHi4u/sd+wr8/6ux+PUSQaqbnf3WfY1+ccdfkYRTK0D52/77Cvf/Hwn4x4XkdfNPy8vn3IyOcFsN8P3z7s6z8++DsbNb/Rtv9FJw77+mWHfHqMIhm/Djj/zPW+dunhx45hJI914Hn/M+zrlxzxto2c3w82ML83A3DQeecPW+5HRxyey63//P2jI9K5f9Z5Fw87r4uPmDXs6+0OPm/4Y/yHRwzfRmxKDj//umFfP//wmRs1vzef/4dhX//B4Y/5utgx9dELe4Z9/fOHjvxe9X9d2Dvs6x85dPqI59VNF573yLCvH3rEjgBc8v3hyx34llTuJ+euv9y+R+64UbH94tsLhn39dUdPA+DX5wxfbo93Ttuo5V5/5vxhX3/JsTsBcPPXhi/3or/faaOWOxL3njJ8vXvmB1K9e/ALw5fb5cOp3ENz5w1bbuc5TwOg9z/vH7bc9H9+Rir3X3cNX+4jzxr29Xa9Xxy+TZn+oZG3KQ+fPHx799QPpvbu4VOuGb7cB16ey/16A+X2SOW+9Mvhyx3/WgDmf/nKYcvt9I975XJXbKDcG1O5U4c/l+103L653CXDlDkwlfnKD4ef1/sPHvb1dvO/eu7w83vfkRs1v07pdqKqB9i19v8uedpjxBjPAM4AmDlzZux8aJKK7fKXn2+3gS8/H+0vSd82z2/bUZrfSE3Jy50yxsttsi2npm2ShuNbmLoZMQ+HL7dFLtfAX/bcbitCHg4nbLf1oOFYCdttM2i43nJTpwwaNslIYwtTtx00FISp2w8ajmeTpj5p0HBTNWm7pwwars/m2+04aNgkU7abNmj4RMuN1LZTpw0aNsn2204bNBwrT8rLe9IYL3eknjJl2qDheLbjNk8eNJSaotuJqouB40II55K+RH2J308lNc+sN4ysqThwhOVGap8ufen6nn7p+mM878Buny7GzuYjvKu/+SHP7HAkj98WB//NCMu9tMORrGe5s149wnKv7XAkj9+Ws940snIHDf9E5ES09UGHdzuEMTNl1ru6HcKo2GHWh0dUbpeD/7nDkTx+exx8wojKvX7Wx0d1uQfM+sSozm80HbVfd2L7u71HdxuPtuP3GFldGQ9OeNk/djsEaUgdvfIIIXwX2BPYMYTwIPBJYHOAGOPpwGXA/sBdwApgfJzNJUmSJEmStNE6/at/R23g9Qi8v5MxSJIkSZIkadPQ7V/9kyRJkiRJkgATVZIkSZIkSWoIE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqBBNVkiRJkiRJagQTVZIkSZIkSWoEE1WSJEmSJElqhI4nqkII+4YQ7gwh3BVC+NgQrx8TQlgQQrgx/7270zFJkiRJkiSpeTbr5MxDCJOBrwBvBB4Erg0hXBxjvK2t6PdijMd1MhZJkiRJkiQ1W6efqHoZcFeM8Z4Y4xrgXODgDi9TkiRJkiRJm6BOJ6qeDjxQ+//BPK3d4SGEm0MI54UQdh1qRiGE2SGE60II1y1YsKATsUqSJEmSJHdXtoMAACAASURBVKmLmvBl6j8CZsQYXwRcAZw9VKEY4xkxxpkxxpnTpk0b0wAlSZIkSZLUeZ1OVPUA9SekdsnTWmKMj8YYV+d/vw68pMMxSZIkSZIkqYE6nai6Fnh2COGZIYQtgCOBi+sFQgg71/6dBdze4ZgkSZIkSZLUQB391b8YY38I4TjgcmAy8I0Y460hhE8D18UYLwaODyHMAvqBhcAxnYxJkiRJkiRJzdTRRBVAjPEy4LK2aSfWxk8ATuh0HJIkSZIkSWq2JnyZuiRJkiRJkmSiSpIkSZIkSc1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY1gokqSJEmSJEmNYKJKkiRJkiRJjWCiSpIkSZIkSY3Q8URVCGHfEMKdIYS7QggfG+L1LUMI38uvXxNCmNHpmCRJkiRJktQ8HU1UhRAmA18B9gOeDxwVQnh+W7FjgUUxxmcBJwGf72RMkiRJkiRJaqZOP1H1MuCuGOM9McY1wLnAwW1lDgbOzuPnAW8IIYQOxyVJkiRJkqSG6XSi6unAA7X/H8zThiwTY+wHlgBP6XBckiRJkiRJapgQY+zczEM4Atg3xvju/P87gJfHGI+rlflDLvNg/v/uXOaRtnnNBmbnf58L3Nm2uB2BR9iwJpdrcmxNL9fk2JpersmxNb1ck2Nrerkmx9b0ck2Orenlmhxb08s1Obaml2tybOOlXJNja3q5JsfW9HJNjq3p5ZocW9PLjcUynxFjnDaC93ZOjLFjf8Argctr/58AnNBW5nLglXl8M9JGCo9jWddt6uWaHFvTyzU5tqaXa3JsTS/X5NiaXq7JsTW9XJNja3q5JsfW9HJNjq3p5Zoc23gp1+TYml6uybE1vVyTY2t6uSbH1vRy3YptrP86/dG/a4FnhxCeGULYAjgSuLitzMXA3+bxI4ArY95ikiRJkiRJmjg26+TMY4z9IYTjSE9NTQa+EWO8NYTwaVLm7mLgTOBbIYS7gIWkZJYkSZIkSZImmI4mqgBijJcBl7VNO7E2vgp48ygs6oxxUK7JsTW9XJNja3q5JsfW9HJNjq3p5ZocW9PLNTm2ppdrcmxNL9fk2JpersmxjZdyTY6t6eWaHFvTyzU5tqaXa3JsTS/XrdjGVEe/TF2SJEmSJEkaqU5/R5UkSZIkSZI0Mt3+NvchvnW+bwRlBoAbgZuAG4BXref18vexPP2DwDZ5/BAgAi/N/++Z/78RuBW4A/gTcGce/iu1XyMEdsnLfzQvbxHwIPBDYIv1xD0DWAMM1JZ5SV7mH8q6l+nref9C4PnATOBLefrH8nb4E3A3sAC4GjgmL+8B4NdlnkAf8Js8vj/wR+AZwFnA94GP1LbP7cB9wC15m0RgTX7vC4GlwJ+BHUe4f3cATgKW1+Z/I7B3W7lXANfk124HPpXLfyFv+5i3+wBwT475ncDTgPNq87kWWFD7f9C2ra3n3cDPgXXAaqq6sw54KC9nHTAPeHne/ncB2wCfA+5ti/8qYGYenwN8LS/joVxP/j6X+YdcB0ocz8v7YCBvo7vzclcBvbnMvFp876wt8//m1/fN7/1hXl4/sDbvwxtz/Bfm8Xvzazfm+T5aqyMn5HVcQzom/gQsaVvPs0g/gvAZ4Je5/J/ysnch1dm3baBet6YDs6iO1762cquAU/N8r87r+hBwSn5fH/DWtvnGsl3ztEvy/jo1lz8GeFp+7etAD211Oa9TfR5rgf+ovX4bcFbt/08BH6ltnx5gy9p2vQ+YDfypNq3s47Lf7wBuzvvlDcD7ctl/zLGsyWVmA18EvpOXe25tff4ll72BVF9/QjqWvgk8nPf3AlK9/6v1HK+rcrw7DPHal/L8ZwFL8vyW5n1+OalNXJrrw4q8Hbaovf++vI/+G3hmjv3uvA0uIx0fOwDvYXA9P4nUlu+S1ynm7fcw8GPgG8CH1rM+lwFvz+85GVhJqtv9efxOUt16TV6/Pmr1kMHt5S6kev4o6ddqTwGOLdu/tv1urP0/M6/Xp4GVten/meP4b1L7EIG9ge/mdbsqT/tznuc9ebsuynE/CPw/YL+29W21h1Rt/gzgD0Mcg98g1acIfCVPP4pU3/+U98ULgZtr7/sc1TF2K6k9+Wj+fxGpzXoIuKhW11vLJ9XZ2/J2XJ635Smkun8b8NU8r6W5bpwCbJG3397r2cfL6+tHOsZ/RzpH7EI6HtaR6uMp5DoJHJxfOzX//1sGH/fH5vctIJ37f5NfvyUvc22uByXOX5B+KOYaUl3fsRbTLOAHeRstz/twFfAV4Ll5f5dz3/V5u8RcbiWpTboeeFkue3fetquAv6mVWZdfW5P3w2rgftJxM0Cq90tyuY/lffwo6WeqP5DX9Vc55nXAT/P++6+8z75E7otQncOW5/fem5e5Kv/1k9rdY0jt4l3AYUPsv6vyfH4AvC3PP5Lq0r35fb25bKtPN0RfqZx3nkbqB5w6xLnr1jz/+/L2uDcv62ek4/ytQH9be/o+cn3O08t5cndS+3AQ6UeCPgX8e37PamA6qY1bkpd3GXAB6fzZirc230h1Pn0IuA74TG0bHcTgev6nvA535P3/EVL/opwrn5fX+b2k/uZV5F95Ai4itZ+RVG/fBgRSHSvH97I87xtJdeU+Ups/O8+j9JEWkOrAx0j1807SMbKIdIwszdvgIao+VunPrSWdK0q7NonU5pT+zjKqfuNPSe1ef35tfp7HMtJx+BWqY7uPtranNn174Py8j+4m9b/Oz+Ml7hW1GH7Ttp8ec81Cak9X5O18Y57PmaRjpvTTT8nb/ZDa+9YBV+Z9tz/puJnTNu+TSW3XpPX0OQ4Bnl873+3A4P5lH1W7fX9t363Mw6V5fq1z3RDrV7bnoOOP6hpmHanuDnUN1kdq31flGFYAf6zNo1xb9ZLOc6dS9Z9OAj5YP48Bv6699wu0nftJbdla4D21930hL7uHdE5dkPfJO2vHV+m/35fLLsjDP1D110qb+o28/erXnWVeK0ntyQrgTXl/nAAsBs4mtxu1enMt6djpp2p778vrsCjP+0vrWdd63A/n7XxTXvZnastZRzoP9Obxcp27Ni/3Nqr+5yG17fU8Uvt9IYOvoz7e1vY+kvfbq/K0flJdmpO31V15u6zOZa/L8/9CbT4fIZ23Yl7+nsCr8vRPkfqYa0htVanvkXS9W/pZ5bxxY96/K6muuRfXzsVfBq6q9RdKH+BBUt/hqjzP0u8+m1SPl+R5/X3tnLKSfD1POp76SPWkbL9P5XWYlPfjorwvrgWeWVuPW/P+WE3ql3yWdK4o23Lv2rb6LuncVa71+qn6C/cBv6qtQ1n+jBznFqQ2ZdC12/r6isOda9u3X1u5+xhhnqD9b1N9ompljHH3GONfkw74z67n9fL3uTz9g6TEAqQO+NXAgbX3DcQYdyftzK1IJ7xzgb8mHSDvAwghBFIH42TSQfFQ/v9m4NmkzsmwQgi75tFtR7zWybwY420xxutijMfnWA4DvhxjfDbwHNIBMKO+XsDc+kxijK8KIbyBdKDsF2O8v205ZfvMIHVYAKaROiCb5eW+CvgfUkM3UuWkuSTP//t5H/20rdzZpA7Q7sALSJ2H1XldL87j95EavQHg4BjjOTHGeTHGI2rz2QzYbph4jiI1FL+MMb4+z/cKUmO/O9WF+tWkRuufSPsc0gl1m/YZDuGTpMbq2aQL0a8BTx4ijqvz8PWkbfpz0kV6AM4hXRwsjDE+rVa3z6nNY/PaPLYidYLuJzWs3yZdnOwNPBBjPJRUz98FXJ7X9b3AlNr8XknqtGwOvIjUYM9bzzq+AdgSeG6uhxeRjokZpBPJiMQYL64dr+tzAVXn+37Sdj07x/dwW9mVpOP4qPXM6xjShQwxxneTGvmWEMJk0gXGfOCoEMJTSPtmZq3YrjmO9RkA/q5t2ptI2wvS9u2PMW4FvJpUB8+OMb4I2CfH/74QwnSqi6V+0gn2H4CX5G0A8NKyPqQfpugnddSK5aQT2jbAs2KM00jt58UhhK3WE/+lMcbFQ0x/eY71P/JwKqntCaRjYztgaa4P80gn+fa28YWkffN90on0uaR9dgLp5kBfjPF0UjtT/JrU9lwAPCtvj2tIHZCHSRdHvxlqRWKM+5O227XA0aTOy7fz/xH4Fikh8bsY4/HlfSGEybX28kBS5/YCUj3/MvB5Ulv+SartX+wYQtivLY4TSfWimE06xsq57BbSsflS0rZ9DuniDFLH4h7SxdSPSMf3XaR2dWot5s3q7WGM8VVDbZNsT1Ln9Ov5/4NCCDuStvPDpE7c4vx/+7YtF4+75W1wVP7/CuB0Uhv9rPUs9z7gKaQ2/U/ApXkel5OO53fk6SflbbAt8O8xxhOHOGc8Rj5PhdqkC4Dfk87Zh5b55dd+A/xlreyupOPl2Pz/S/K6fi/3Pc7O00t7fXuMccdanM8ktcML2uOK6YdkLs3/virGuDWp7f0KqY6dlNv3/0Oq2w+Sjp/Tctm/IiWtvw98nNT+riWdV2fXNwHVhf0q0nH4LtLNrnV53qtJ7cR/kc7PpQ79msHnt0i6qP8LUod7Bemi/bp8rJRz2Ob5vTuS+gxTSB3itaQE8MHAG0nnwCGP02w/ql+EjqS69AvSRcsjeXqrTxdC2Kw2nEE+78QY5+XtuiErSXXuFuDFefn188lepBuFzyTdSCvKefI1pIv79mNkPmmbXERq475Fulg4gVQ/oO08mestpO39auB/8/heQwWe25dlpH28L6l9WAbsTKqLpV8w6G3ATiGEI0h1ewvSdv4bUh16P6k9Xpy3zeF5PV5RO0dPztPJZdaQjo8pucxUUlJ/TV7fbUlt2otIN3t2J9Wj5aSLqVeS+nhbky6wXxdjvKX0d0j7/Q5Sv3HvvNzFpP11HynxcCNVAmqDfXFSAukBUjvzrBzDjBjjX5Lq1+oSa/4brh2tWwkck+M+iLT9L8rnw+flbbEDqb6Q+xaRdO58FqkdCHk7kMtMIrVbDwCva1teqTOHAM8PIUyKMe6f2+16/7JYnZdfYr0bWBVj3I60L9Z3bVBXv6aqWxFjfNZ6rsEmkforbyft96mk9n3QtVWMcXoutzXV9Us590M6Dncj7fuideyV9oD0Hci/ra37jDy+jpQgOZFUn/6yrT9dt5pUp9aSEomQkgf3k+rNFFJfpX7dWZ9XyO9/EWmbv51UX6FKcBcP5+21jtT3LG4F7sjzPp6hz8Xkdd+cdF59O+nYfhJpW++ft8uKGOMzSG3qWuDuXC8fIe2fn+T+596kY3yo+kNIJpHOQXUrSOfFV+XXQ17ve0jXc9uSbqxNISVGrs3b+LDc76iLpDahtK3FDqTz1/tI57NiR9KxX/w0H4NvJSXkii1DCPvlc/HZtMnbabMcb72O70xqZ7aLMW5PdS4r7iXV02+SrrluzPNZx+Dt91ZSX/GHwIdJx3W9n/00Uj9lMmkfzaq99se2/s9q4J9jjJuR2rxzSe3/VNK5prQhk9vXk9TnnErbtVvtHLQhM9iIa7zH5fFktzr5x9B3Jw4iVcjfk+6iLM/Tp5F2wFJSB/t+0oXCAKlDNp/UQfo0KatansApWcdHSQ30TFJHfYB0gPWSMpZn5LIDpAvEpaQLghWkDsFC0gn4Nqq7yOWvPE0wkMvekN+3Jk9bTOrA/jmPD1BdgC4kNUqLSJW7PA2zpja/Vfm1N+T1voLUSb2V6k7Fn9tiWkv1VFT5K3fB+vOwLK+f6i5umRZzrOWu6YrafH9IdWd2QZ62qvb+NaSO9f+2xbOKdHdnz/z/QO31D+f9/ElSA1ruTK6hugu3iirrfnWef5nHQC4f8zbtry1jgMF38tbl/VXiXUDKUpd9Ekkn76vzdvld7bWlpI7DvLxNyp3BW0gngpjHf05qNM4gNdLLSBcgS3P5w/N85ucYyrzK3Zsra7HE/L5leVrZF/X1KnVkXY63Xm9W52WeR5X1vz6/dkHtfWVZ95Dq46N5P5dt+2hep3KRVOrS6rzNF9bmsTov/7ekZEJP3nfL8msLcvlFtfUs61P26SqqJzzq9bgcG+X4qL9nbf4rx8O62vzX1f4Wk4791Xl7DlXu9No8y2v9eTv25fcuIbVL2+btU46tsn3KXZI1eV+Xdbg/T1tAqkv3tO2D8jRF+b9+XK5tm1av2yXG8lRDef8fSB21u/M+KHdVL8txlfmsI7UrvaQ6tyJvy6Wk9rgssxzP+9TWeXXez2Xf1Y/9gbxN6uszry3uNTm+1aQbAYvy/NaSjp1ltfktrM3neqpjot72LcjzXlN77Y9t27k/r1+9DpXttozqeOxve89/tMVe/sq5Z13etqXtHCB1Zsp8VpEujJdRPXFab6eX8tjjol5Xl+b31dfl4Vz2EQbHWm+Xy/FSrzfr2pZdnkAqZVblZffWtuWtDG43lpEudtYwuF0q9WR+bf+X95VzcL2OlDvZ/aRjrJ/UgavvwzKP1W2x19e3HDtl2z7M4HPUSqpjs7ynnO/X8thjseznco5Zk8vW31+vf/9Z28f3kTrn60gXb9eQ6v7y/Pcz0vF5c20fP1qbd+kfzCfVu/rxX9rf+rT6ObEe109r0+rr11cbL099RdL5aSGDj4vf5n20htSerCN1zNvrT+nP1JezhKqfVM7Pi9piLMta2vbeMr4m74+etvfV62yZtphUL8vTPUtI7Utpm+v1/1bSU8Jl2fV9X69T7fVtHelpmcva5lnuxrfXi3o7NdA27SEeW5fmUtW5+nFc3ns71fFcP2+WWEpbP5DXcWVtO5Tz+pq2+Q7UtkW9H1GOp3p7tZrq6bR6W3MH6Ty5lFSHziQl6upxrSL1jUp7V55Y6W9b3kLSDYJ6XzAy+Nir78t1VE9UrSEdg3+ovW9Ffv3OWvzliYj+/J7LGdxelGuIcrysJtWvvameii1tZTk3lTbnHtLxXo7XK0j9u1IHlpOSsuVplxJLWe5xpJs3q6navHIOLvN8JA+X5eU/h1TvS5+7vO8eqrpTlr+MdF6s17NSD25mcH0syyvta2mX1+Z5/IF0I+f42mvLSdcuA8D3SNdQS0jtSFmXlaSbQnswuA0rx0X9nFJv55fW4l6a/27P//+KwW1bqe9l/5dzf3l68DdUn5op59BS1xfV1qfe/663eeU9C9uWWa+39XVYm18rT8OW83q5Hvgt6QbknbXypW93D+kYnUnVzpQEZDme1lI9RTUv/z1IOh/9D4P78JF0XV3WrcRZ6k17u1ee/Cl9jrKu5RMDj1Kd82LexseSrqnqffXSx6+32+VYLcduibO3Nr+yX1eT+t/1PvBbSee6cn1d9vkA6RyxKsfzQH7fQqpPs5T1rfdfyzzWktrQeTnGhbX5/Jl0fq6332sYXP/qbcRS0jViqVOl7am3Z6trr9X3wQLgQ/m9y6nasCvb3v8I6XptGdX5srSN38rX2y+m+qTSr4E78/TzSG36S0k3+PtJN17K0+A3kh7kuIF0bCzL+2pubHuiipQMuz5vu9kbygttKk9UXU26i/NiUqZw6xDCjaRG8LmkzO15pOz6X+T3PIl0EtwaeDcpy7qE9BGZ60g7oGTIn5ffM4lU0b5LehpgKtXj9O8m3XH6N1IlWE71WOU60gXHA1QJgSmkO5SRlIT5l/z+zakOtn7SXdtr8vLXkRrtbUkZ4lfn5S8i7dDNc7yvJu3oQMrm9pIyzT+lOmEuz9sEUqP+AKnzWM+olg7Dz3KcP8sxXJnLbUf1cbOY31P/v5zAJ5Gy2/21v/mkJ0bKSfVR0mOLH8vziaQL8y1JT4XskeM7NS+3H5gbQrg0/39TXqcleb1L0m8dKUm2lnQA3ZrnP0C6c7lF/v9DeXg/1cm5NP6lkfxO7b2TSY0bVE/Z7EH1WOsL8vLvJ9XDAdKdizNJd28DaR+9KS/j9TE9sbUNqSOzN+lAXUraZ5eT6tZaqidttgY+kee1OelO3+Qc612k+nk/6YmQVXnb/F+q5GrZ7mtJd+A3I3WkljD4gnY1Vd3vIR1PkVQvL86x9JPq9Pakxun1OYZVpCexFuZ1+2Zexi/zem9P2ncXUX207Tn5vdfmWL6SY1tFSjROzeu5Oi97EdWJN+R5lCcfSgd2ElUH4FxSPQ15nvfm+U+h2pflUeN1pI+LzaO6w1i2y8eoOvurScfQsXnanaQ7xKUzuFdexrPzvurJcawmHevfompLenJ8K/O2KTFNJzXuT87jT8rb8q/zem1FdUxDqjv/L48voerc9eX16cuvle1YP/5XkNrO/rxtS8c25nW5I2+Hsg2/Sdq/348xbkP6SNZmpKeq/lzbTluQ7gxNzv/PJ9XjkghYVYt/MXBaHu8hJQHLHd5r8/tLm/d70l2vC0lt43LSHaff53L3UR0XD1DdZf1M3jZ3kjoek0l15Ygcy3JS3SS///ekNugfqI6L0oGCVK9/k7dLeULkrlzu2DzP0j49I79nO9KxVMpfm+OZRNqnV1O1mf9CqqeBVGd68jzKo/LLqZ7EGiAdX+W4ODnPs3T07yfdVa2315D27arafC/M759P6mRAdXd+NemCO+T3BVJi5T7Suep+0rEPqa0JpIuOEt9bSPswUt0RXJuX+zBV3XgRVbu8bS4/Occcauv09Tx9KtWFXlm/QDpOSuxLcvkHa+s0QNrH6/JySv1alsfLxUp5/46kfVQ622U7lqdPyOOlrt5OqnulPSrn1Emk9mA5KWF1Gun4g9RPeH5e1r+SjunXA08lHXercgxb5WVNItXZ7fNrx+RtsZj0xG45X5QnCubVtkkgPfZfYn811XeVnlebfnmOm7ydypN2O1ElL/pIbdNL8/i3SE/hBKo2C1Jd78nxP0rqe5WPgM3M63A06eK5j7Rvy0UxpGO1xFFuRED1RMJq0nmq1PWvku6+X0l1E7KHtD+3IdXP5Xn7bJvjKXf9oeoT3AX8nzztq/k9UCUzIT0JcgPp2CxPV5Rj94W5TLmwnUQ6Rlfl4eIcwyWk8165YHg+VR9gd6o2+sJcpuy/X1Ipd+w3y7HfSWr7Sn/t46SL3KWk/fAw1dNhK0nn4J1yjKUNWEeqTyvy9L/K0zcnPSUZ8naYTNXPivn1SaRk3WSqC7GdSH2YX5H2169I9aW8b1Je71dQPck9ldS3XUHql5dzfCDV+9VUx3U5Lhfn95ek5A9ymcl5fTcjtV0lIXoJ6bjbJq/vXbn8Z0hPp5Xz4KupzhlXkJ6CKJ+IOCvPp5d0bJ9FVYfupOq3fYH00fC/oHpatvStds/zKn3HaaTj5UxSvdguj68ifc3F3nl9r86xlj7+T/P2uo10Tn8glyuJwFLPtyDVgavzti/twCRSu/weqgvaB0j93ElUT4+UduAhqv4eVE9BbgY8Pa/fAaR+Qmmz/5i316S8Hcp6rKW6efYz0jn/tDyPBTn+PlIdLhfla0jHxp9Jbf1UUt07O2/7rak+Pvw3pH1cPhVB3gZb5n30L6R6tzWpTTqF1A9bSXqCuWwfatsoUt04LW1WSRCWNql8UuG6HGN5IrQk08r5sOzDX5H2UyA9MbQ6L+85pET1tqR+yIl5+B95u5Qnl3ehurHz+zyf6/K6X0v1pO2TSH3N35HandJml37135ISwuU4u4Oqb72OtM/I4x8l1dlyQ7g/r+sDufyDOf4+0o37rUg3supJuRtIdWE56QGTcj4qdfYOqnNH+XhwJB0nl+QyC0ht75L8/yTSNdIr8vhv8vy3JrWJN5H2+f15+0Dad2/P4+XTGw9QJYhKP3sy6bz5k7y+l5D29U6kuj8tx1FuVpaHOKC66R3y8m4i1d3y6YuHc7zlY4OryU8e5vf8nNRfgpSL+Gguf22OLZD6GJfVttE6Up2dQtX3WUe6Fj0zz6sksU8mtUM7hxBKnuXUGOO1te16OOkJ6TXAu2OMJ+X5LCLVxUdIn0TZlcH+Lsb4EtJxeXx+mnT9uv0E1QifqHoh6UR+C6nhKd8ZcCPpkcBb805ZSXUnvJd0ABxLOhjmkw6UP+cddhmpo3MPqVOxJ9UFzxdzuUVUHaZyYV+eeujP478nZVL3YvBdnwGqz8Ten2Mvne3y9Eq5mLyIKiv+v1QZ0PKeR/Iy1pAq0B1U3ydwFqmS9pIOxgfz+/qonvIqdyrXMPjOYGn8h8rsR6rP+ZdMcCQ1HuXuRvv6ljvSC6jutK8jXfjdQToh1GO6nepk/uX83pOoPlO9htQo39G2nB4GZ5N/TfW9BiWDvJz0sZES99dIHZfyBEv9zk5/3hfX1LZDuasRSQmkeXn/rMjzKEmHG0gH9P2kRq+eeT8hb4sVVJnkm6juXpXPLq8kJRz/TEq6lf1Vvq9lHamRfJAqU35LXsbivM5l+5ensSJVh3sV6bipPw1Rngj5Ui53DalO3kRVt0qdiXl/LsxxrK7t64EcQ7mQKyfH06ieKvwZVZJxIM/nYKrvqujN8/ox6aNMPVRPBZT5lXpaErztd0vLtJUMTj6uITXc5e5MSRT9gOrY/ibp4rA8bbOSdFJ/OVV9m09K0PXn9b08x13ahfJU2I212P+c/1+T31NiLU9erCKdlOpPyZRjryQkyvJL/D+p7Zvb8/aKVHf3ykf7yl2dkhCuPzlT7sz0k+pR2WalXbs5b+drqJ4Y/FHeD4/mdSrJznmkxMUA1V3L8gTKQ3lfP5jf+0BtPUqSuLRH5eMlpSPwOgY/IbCQ1Ln9QD6Oyh22crerXDg/TLqIKE9a9deGpT0uT42V+liOmXVUd+pKQrvUoUVUd69Lx6H9Lm/ZpyVpXNq6laRHvkvdrkEHUgAAGthJREFUuYrqo4afyP+vIHVOj6Sqp/fU5v8rBt9di1TfFVjOebdR1Y0BBj+p0Vd775LafEs7U+rZdbX3rMv7eE5tH5VtWepQefoi5jjq58rLSHfvynmvXEyvy2VLW7M677O1wI/zOpVy5WmEEnu58C7ffVZ/ymAd1XcMRarvbSjrWmI9k+q8sybP/y7S8V8u7kp9voXqCZfy5E77fi/n0YG8bY+nqg9n1Nblc3l9H6ptq0dJF9zlfLmCdIwvIHV6f0h1rPwox1fu4pa74uUYrZ/X6ufz46meSivrXcosq23TLajOj+Xp1/L02vep2qUyfRXV0xVlvuU7h/6LdLxG0vm7XnfKOayf6lwz1NMR5Vi8om19ShtS1nUe6Tz8e6q6fjnpgr18XK/er7mc6phbQXqC+OHaMkq7+2eqJ6dLzCVRUc539Xa1vowjeezd77UMPj+XvzsZ/ETSH2pltmfwkz317fOWvOxykVwu2v6Z6txT5vNILZ7yVEEv1Xdf/qy2Pb9RW6d3UD31WG5o3Erq09X32QNUbd8NedoMqicvz8n7aQnVEz4lSV7aqqvy+xbmsueS6s0vGLzN1pLaqvKE3uocUzkm6k9Ur6Nqr/tJT42URHl5cuPCHGsk1YNSv+bncv05voeonmBZQ9UPjVTf4fodUrt9L9V3Ft1CdRPoGVQfxSnHYelPlXj782tLScfQlTy2zZlHlUy+ipR4+3L+/6y8P76c9+180rEwi5RQ6qe6GL2bVP/a62Q/KUm3kKrulqf3/5uqTStPXiyhupAvn5pYR/qqEkjXYV8kXV+V9mw2VQLkEdLHh0oSuOznVXm+JTFRnhYpN2Dvy8v8MVWbUbZpuWFbnqYu63UL1fcPXpHfuyBv64+QEgDL8j4o8dZvhi2s/V9ivZvB+6gkncv5ovRJL8/bpyRJy1Odi6luyK0iHV/ltVOpPlZ7cm1d608dlb5BSf6XG69zSX2MSGqrT83bek1e15/keexA1c9eTmpTSntzF1W//TyqvtUjpLpWtvfJ+b33MPiTG+XceAXpmBkgJX/Kjbdybij993Iz/Waqeln6g+WJx/I9f6VOLiMlZUo7tZAqKVSuG26iekK/tPF7UZ3vy1P5a/L8n5HnVxK/Zb1L/6xcA5W+VelLDJDau4NIfYjS7yhJ9vp2vZvqvFrqfv28Ua6vSv/2c7Xl30tq69eREv/lRvBVVNchfyC1eaUtupLU/iwCXkt1Lr+T9DAQVJ+aOZ6UbFpHdaPkiPj/2zv3YLur6o5/9rk3r0tMgBAeCVJExIr1UUEJOlZQEa1TqrbTqeO0YGd0aK3U55QOTgex/cM60lasdqyt6HSqYFEctaWiBkUIKkoIptAQIAkxCRDIOzf3ufvHWl/XPichJiSXe8D1mblzz2P/fr/9WHvttddee584h2rMX5+D9R2dHXe93+NOv8+tmJN/DTEPvty/V5olT4eIqqswT94LsJXudu/kHcRZCGCdZQz4TLU90vISqiO/GRv8X4dFMy3CJs2fxTyEA5gxOIRN1LYSk4hHsU6/FGv0zZgTbSZhyMmhMYw13gxsT+sqz0f1/z/GvK5apRMTXr5xbPVdhvWNxKrtn2IrfGCD0GmY536Jf3Yq5jF9FaFEpNTaiCpFXkF3hIlYgB36TXPdBz1/HayjaGVxEyZwD2OrZKrvScy7fxqmDP/c0096vXSwNngNNpG5CFt1W4l1+t1Y9IyMwgki6kID/CARvSU6RAcH6ygySsexUGTlQ2U9k24H2Hxii9F8v+dKzBOvFbpl2OBwNKYof0aspCoKZg4mM/j12s5xO7afeCYmf4uw1UOtvq3DIvkKJt+TWPvv8b4gA0/nkhVshURl/gCxTUsGgbZLrcbabIHX3fP9/xZixeyt2Mod2ErgRqzNBgmlPo4pnSGsf6zB5OJtXvYJz996f+ZWz+sXMRm9AXMUa1VUESGTzXOWY040lU96Swq7Np/hz13trwsWrdF+P8He9OrCUzHHbwfrp0dijqsBTOZe4GXWYa4zMQNIkyU5uRdjbXghobdUBzM9b2rvFVhbXU9Ece3CVlLXeppjiMFyNTbYqJzSLcdiK26L/f0zMbnRtqRRwuhaiMnDbsyIKpiszyUMDDGDkPtnYPJyPLayDCbn1f8rMnC232sUkx0Z45v8PoocWE93pI/0koy5NV6OK0spG4mJnSYRX8IG8mMJ4/BhTJ9sJQxTRaac7/WqSBdt0dxBOEFaB/8w1l4DmFO5el0sIaK6lnqaQaxvDHsZOsQKqSIPpE9PxPSdVn11tsK9mBElZ/8uutt4DNhTSplHrLxqq7KcwoqQ2U1EPYFNSrXteAOxIHIU3Wc7TGDt055bJ2NMcqAD0CeJcxykh1+JOTELdvCvDOlJTH5OIRwAH/L7nV9K+VJTpllElCOYYSMjDsKw04LATU1e30yMexB6Qte20cAn+vO2e3k7mKH6XGJ8uZFov/uIiMV7/PuKjfNz/PNZhPO2YLpUW+K2NnX4t1jfkpH7fM/TeV5HBZPZ12AT6g7mQChY1JVWUbdiE5HJ5g8sukl5Gsf0keRoDjYGgK2YimEiKnIOEVk0SZyZ8SNsXBWL/VkFs1H+zT9fSETOyZnRwc6AaiekN9N9JtT1fo103LDX0UPYD6TMJKJdKrZgtdH/vxozoP/Tn/0wEd02B4uskO55HhF9JifrBCYHGssVJTQLk3OdLTXpn8ne2eGf/52nlw4ZwfrbQv/sY0QEyjzM5pTd8cXmu/cSk9GvN88s2KRF8ggxho1itsSlxJhzVJPu5/75XM/7cZitOMO/n9/Ux2l0o355UvNZxfqzZKodM1SmX8Ps5Z9g9f8/fo9zmzyK2di48juex7MIO7Zi8ncsMZYMenmP8+vXE9GMWwj7sUNEdSqyaACTYa34f7wp02NYX9H2ncv9nhuw9rqfkL2rsL76eqy9LsPaZjH2wwL/7Pd9sb/H8zuBTTI1iZVT/Fl+/YlYxNEEpku1BXgeFsE/jsn7acSB6UKvF2C6+DpsV8BAT5qriagOLbboTLK7sX4+TPTh84mJvRwoNxD6dCXhWJ1RSnkXppfejdkvcnwtwSa7rdwMEAtKn/X/x/mzl2H6VZGQg4Rz9VX++iq/15Vert1eNum/AUy/vtDfv8z/JB/F60jnhyliaLjWerLnW+PDwqYuj/Jrv0c4ueUEwuu2Q3fdq/7HsHkJWER+B9vNMUBExWjcWIu15VasfTrYj5sMYnJxBDaWrsLGsoLN4SYxGVni5Wr13SA2bm5p6klzgElMrjuYzL2GsPNaWdPunjlYX78fc36M+vMqZt9oHrMc0z0bsAgfyct7MNtlLt3n4V7rz1BeZN8e4fU0G1sUAtMdjxLzRDyNIrXluC611u/6dwoCmUXohjd5XhcT8qEyq/6ku6R75aBcQTjkFd3coXtXw2/RvTBUsT7/gH+/DGvnuYR9Jd2q5y7216+mW4/2znGU79/EdNN8rB9Lhx1F9B3ZXSuba9dhfbfTpOmdM9VSyjlYZN6Xq53jqWg+1Ree5rXA2U2axzsfF/bxoH5lPrEF4sLm81swp8gAZnjMxibIE8CbSinPLqWcjDXIbkzp/TZm1Olw7i9jk6Yv+D0L5oGchw1gnyKcQZuwCK6dWEMq0uHoJo8yWI8gDiW9xt/LCQU2aXvMr5XBpcOw1bDn+We/iylvKaudxORhCzFxuBMbpDuel28R4dlb6RZ0MEXwTP9siBC+jf69BgD8823EAbAyElReOXbmYgYomNIawLaj7fa8HUdMyovX3wZMwVfMeVMw43GBv99BbG3aTZxrIOeU9trOwmRChv1JhDE65K/f4nXxLkwhXYdNcrRyWjAldxvWfgVT4hu8LCdhq1Iq23JsG+Acr+ebsAgdMKW82vP+fT+Yez0x2QBzBg1jofl3E4d0D2JbUmXU7CTaeXMp5ZWEwtMk9N4mDZiRW7CBYzE22V9EOAMGMW/3CKYsFL2k+74aM7zAvOsP+Xc3E1sMd2MyNIy1/0c93yrfXExW7yK2MWz379ZgdX+Sp5ehfCQhpwOYoanJlPIux5tW2Wjej2OGrCapG4jV5pmYUj6O2Cp0Ima8yBk6w19/3fOxHdMXW5o8HUkY+dWvWYxNOrdg+uQ6T3cMMegq8lOrH88jVn0W+vN+g+jXHWyypUH3VEI/LCcU/AyvhxnEAfLrmnoex2RUxsEiv/cw1t8XYCuXHaw9Fa6tAeYmr4f7sQHtC17WXdhkY8zvWYhIrnnEjxnM9nrTqtQCItwar7uRpjwXEI6ojtf/ZdhE4YeYnGmiMYPY+jPu/2di8qhIj5nEL+fch8mH2kRbsB4hjEltM1JfaA8tbSfnl2HyPoC122asT3y/1voJT9Mhzm2YTfdEbz5mkOzBxhk5OnWgqK6bg8mEVowV/v4JYoKmKK1nYPpuITGRUnQVWH+cRTg9jyQi3+TchHAUv9vfzyO2WUgfjzTpb8V063p/rqKEtgCUUs6je7vIHL9nxdpAEQ4aDyuxFUHG5bmE026IkOnj/TONi3IWzyN0iSb8cnxo285OIipN/RpsrFMkZMHkpeN1dTyxRXCc0F1jWDQRhOzJYTQbm7x8Hxtv5BTeihnoMmq3eZ6WYttXKtZvPkfIrA6wlmwt8WtfSjj9NQ7oO7A2fSYhFyuJydQVhJ79AVbPckprIj/qf7MwZ9EvIiKrHfIunXI75oAFq2ediyQnJcR2ywFM7keJiRL+DDnXIZxkRwELSyl/4c8bwiZmCz0vbyPsiCHCOfkCQr/9GJO3WdiEVg7aS7GxX5MSyYLKrnxLL7T/tzdl+2tii8wmTKaHiLFiBuHYnYG1oZxANxPttYQYM/XDPx2szXT8wynEuAXmwABzZClyY9zLrf4FcVbKywkHzjjWbo9isvYBYiz/P3+enGjKhxwBev4pTZ3Jpprt9z7drx/0Ml3RpFXbfxWTPUW+aIGk3XK9mphkdbBxQVumj/d7n4uN4fP8vhov1mAOVR25sQ3bOgWmT9UfPoa19RFejos93+uweYPGDbCxbwMWBTlBbHG7A3OKqE7egPWtlxDtcBdhT44R8jcDk+uP+3MvwBbq78Hs5fP985WYXjnB76cImRf6M4YxGXw+sS3rNOIokhv9Oi3AaivzGzF9UAi7sIONN4pWmk04auWQaG2igjnpbsG28D5AyODpmCNL85VX+HeLMZ14BnF+5j2YXXEsNr6o3x3j16hPHeufn4X1by0myo6e9DrQ9qV/wMb9bZheqUQk5+uJPkwp5VQvv+ZA2loK1u/HvM73YDa3HCOFGOfOJn4ABiKS7gTCNuxgc8ZRf/6LmvS7CR240dPuKaWcicnpMGabqy981MtXPL8ribPzIBZer8QWjDWm3kwsoMouBpt/6/V8zKEqvfxhIup0Eutfc5t7fA3TR7IDxzCd/XZivF9FtPFZBHKWajvcTsx2XIvZTR3CqbiJ2HkhOalE/9D50MWdJj8k7NOHsfadxCLJR7H5R8XGY42tA4RN9e/EWPppTLbPwvrFywgHdMVsA9kNr/f6k05TPi/x12cSAReDXnd/5Gmkh15OLNhvIux1OcaO9PxIL/4rZrdv9XK2cygtRknGvos563Zhi34nYdthwY83KKUcTSyM3ILJxC5gvJTy63T/6IiYD2ypte7eT5pupnur3z62+U1iQqq/92GOmvux1ZiPecMsx6JXdmADz79gHfd9hCGvFbwfeNp/IlYufo4NJkuIw9TUoeTY0XaPdsL7KBH+rdVchSzqehlkOuR2JyagCv+dILbDrSUOzJwkwh1ltLfhtRL4tf5+BOtYG4kDSRVauIt9Hy7a+37PPr4fI/aDt9eMEw4Kvdf12han1dE2xFFRHO8nDF3dc8LzuZ44aE7h4iPElgxdo7DwNs+PNG14L6EUdjV1rsm7JkpqI+1zvpnurX/tM9uQ653Y4KjJuEKTVzRtqPT3YQPXB4mtTRu8vdb6vUaJye0qbABVZER7L0VP1Z4/TRY1EWiV4mTPf62UqY73YJNQRQwuxwwtTYY3032w7TWYMlQYsw6V/RaxpbFtl//G+mUrQwqnXofJ7CPYADlCHHyprZU6FLOthxsIWWvrQeHDbbi1/kYwJ6AM83GvZ8nEZPOMEb/354j++00iTF9RenqGDpKdxFabfkxsaXkLEW2ynZCnPcQW1Yea+lH0zKjnSxFNyp8OHm7L3R4CPN7cS85d3VdtrnTt9dJdep7OxdOBw9sx41OrpgoDX4pNluTk1H2uwibS7XNU78qfopDeSbeMt68Vknwf1m/u9DyuJkLkh3ueUb1Ob6d7m4109GPEJEjRPQq7btM+SLcMtQfCa1tn73O3YAsbqmNF/+zxsU1nBUjOJjA50zaic4mDLLcSv9Q2hsmLDkPtfa7u2Y4ZCqHXdxubtI826dQ/tMrdjmOV7m2O0jeSSelI5emTXp5WDzyITU5aHaDnjhLb2NsfstD9tDAywt71PNLzHL2+n5D5Vn+rTfY0aRXt8o6mHF9pyjiB6cBVPc9o5bl9re9V1luJ7e3tODjZ/Fd5dY3sip2EntVZmhN016O2+f605/PR5h77kpXesaF9rbp/EJtYKL3G+WsJp944MRZvx+T9f/3Zl9BtW+wgDoxv87CZ0KWqQ/WPEfbO677aYAKTo1U96bb489r2HvGyqU7vJX52vK0P/bV1qIWyzXTnq1c+K6YH/ovuiZuiLHv77Rjd23plQ1Vi21d7zS7iUOxWjlVO5XnSyz/W/FXPw2ZiO2hpbO8xzJb5CbHtqzb31T3UTmOen+GedBWbtMkukU042Vz3HWLRse1fKs8ezBm0o/le+vpu9t4S2I7jbX2NNN/dhtmaPyOOT5CNr365jbBf2+3yrc0xgUWF6f3fYHbN573OH6BbH2gs0gR91OtfOy/aOlWUxU8w3ait820fvRVzSksPP0QsVDzoeZBtsQ3T498i9Ju2hu0iDuZu/3YS25J6+8Vnm3q+v/lcMrCuuUbydpvL2LuJfiX5HfM868iS8Z7nabtZOx/SdQ/3tLd+UGS4J317EPpDxMH124jtXuN+P22TvcfbVe2iLdLtGPl447HaehthG7V6TGO+vm91925sLNXWam2RmyQOelc/kO4ZxnTvTkyfbSbGdvXzizEZWdnUn+aQ9xJjZzu26Qww9d/Rnu/Ud9pt8Y/458uaMo1hCwJrvS4VES+9tLN531snk0R0stpaCxh3E/IrPXEPNldZ39xjHaFjv0GcLb0H6xuau+7y9x9t8lOx/qyxS3qwtee3eh1KHltbt62zNYRd1jsOtGN+W3b9YMNDxFZ/tZ3y8AA21xwl5H83NieR7TqGRdtdjsn8eUQEpepcWwole+1h6s/xPD6A2WXj2BxAsnMntrCxwu9/PbbAvQyLbl6DzZ1nYXPDu5s05+zXLzTdjqlDdGrNAgb99dnA8if5+VcBb+/5bACY7a+f7Y068zA+8y7gWYdw/UXYNsqprpuTgZ9Nk1zMbV5fCvzjdORjGsq9lzxOR71jXvpPAe99Avd4P7Yy8yLgR/tJdyZw83TX+WGuvzOA703xM34P+HwflHURfjjrId5nv3LSL/U+jfX8hMZIGRXTnf/95E9ReguI7dmHbZydpjJJfw5hjtaX7CftAclsc88F2MTz+J7vp9Reyb8Dbvur8fM/DiDtIbUZtvB77XSX+XHyttcZtftJ28EW155zAGkXYZOs1053Gfvl71DnEn6PA9ZZT/D+L8ecG3vdF5vgnjnNdXjItkM/z1cOxU5zmbgPmD/d5ejHdj+EZw8Riwt/CHxtGvJwEU+CH0F/v9g3+BTlJOBaDx0dJc5TmnJKKR/Bwvsu7/lqCFhaSlHY65/VWkc5DJRSbgTuqrU+cDju9zTmjaWUv8LCH9dineppzX7k8cnkHaWUC7FQ0zuIX9A4IEopF2NtdS0WVfaex0l3KXYGytsOJbP9hIdu/wfxq5hT8YwLsLDfP5mqZxxgPv7Y8/G+WuvkIdznYixyY59ycoD3mPJ6n2ambYycYr6JGYs6V+T9h2ucnUY+U0o5HXPAfb7W+tN9JTpImf1GKUXh/x+ptW7q+X7K7JVkynjCbVZKuQJzVF00ddmberyffAP4aq313l+S9mJsEe/2Wuu3n4z89TuHcS5xQDrriVJrvZX49dy+4jDaDn05XzkUO62U8lpsm9nf11q3/bL0TyX6wGY8A/hkKUXbOafVnn8ykFcuSZIkSZIkSZIkSZIkSaaVp8ph6kmSJEmSJEmSJEmSJMnTnHRUJUmSJEmSJEmSJEmSJH1BOqqSJEmSJEmSJEmSJEmSviAdVUmSJEmSJAdJKeWyUsrKUsqKUsryUspZpZT3lFKGDuDaA0qXJEmSJEnyq0gepp4kSZIkSXIQlFLOBq4Ezqm1jpRSjsF+Xe9W7GfTN/+S69ccSLokSZIkSZJfRTKiKkmSJEmS5OA4Adhcax0BcIfT7wOLgKWllKUApZRPl1Ju98irD/tnl+wj3etKKctKKT8tpXy5lDJ3OgqVJEmSJEnSD2REVZIkSZIkyUHgjqQfAEPAt4Fraq3f642UKqUcXWt9rJQyAHwHuKTWuqJN59FYXwHeUGvdVUr5S2BWrfWKaShakiRJkiTJtDM43RlIkiRJkiR5KlFr3VlKOQN4JXAucE0p5dJ9JP2DUso7MXvrBOB0YEVPmiX++S2lFLAthMumKu9JkiRJkiT9TjqqkiRJkiRJDpJa6wRwE3BTKeUu4ML2+1LKs4APAC+ttW4ppVwNzN7HrQpwY631rVOb4yRJkiRJkqcGeUZVkiRJkiTJQVBKeW4p5TnNRy8G1gI7gGf4Z/OAXcC2UspxwBua9G2624BXlFJO9XsfUUo5bSrznyRJkiRJ0s9kRFWSJEmSJMnBMRe4qpRyJDAOrAbeCbwVuKGUsqHWem4p5Q7gHuBB4Jbm+s/0pLsI+GIpZZZ//yFg1ZNUliRJkiRJkr4iD1NPkiRJkiRJkiRJkiRJ+oLc+pckSZIkSZIkSZIkSZL0BemoSpIkSZIkSZIkSZIkSfqCdFQlSZIkSZIkSZIkSZIkfUE6qpIkSZIkSZIkSZIkSZK+IB1VSZIkSZIkSZIkSZIkSV+QjqokSZIkSZIkSZIkSZKkL0hHVZIkSZIkSZIkSZIkSdIXpKMqSZIkSZIkSZIkSZIk6Qv+HzHrPtVemUWZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTy7lSuOKzW3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "614cb8b7-b307-499a-a097-3ddffefc7247"
      },
      "source": [
        "# Gender distribution \n",
        "sns.countplot(all_data.Gender)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4b17e5d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0SGzmIVKzW4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74566db6-366d-4cb3-f8e5-d705d8030c4c"
      },
      "source": [
        "all_data.Gender.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', 'Female', 'Entity', 'Joint Gender', nan, 'NO GENDER',\n",
              "       'NOT STATED', 'SEX'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGE9nU-RKzW5"
      },
      "source": [
        "### Data Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5sPxlmKzW6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "505e9691-e8e8-4c31-bfe6-5b778a7f4a48"
      },
      "source": [
        "train.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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>2011-05-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>2011-11-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>79</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>2011-03-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>2011-08-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>Entity</td>\n",
              "      <td>20</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID Policy Start Date Policy End Date  ...  State  ProductName target\n",
              "0  ID_0040R73        2010-05-14      2011-05-13  ...    NaN  Car Classic      0\n",
              "1  ID_0046BNK        2010-11-29      2011-11-28  ...    NaN  Car Classic      1\n",
              "2  ID_005QMC3        2010-03-21      2011-03-20  ...    NaN  Car Classic      0\n",
              "3  ID_0079OHW        2010-08-21      2011-08-20  ...    NaN      CarSafe      0\n",
              "4  ID_00BRP63        2010-08-29      2010-12-31  ...  Lagos        Muuve      1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sV1kxPuyKzW7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "12da06b8-8cd8-4790-fbd6-8f189907380f"
      },
      "source": [
        "# replacing all missing values with a string constant\n",
        "all_data = all_data.fillna(\"no data\")\n",
        "all_data.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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>2011-05-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>2011-11-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>79</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>2011-03-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>2011-08-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>1</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>Entity</td>\n",
              "      <td>20</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>3</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID Policy Start Date Policy End Date  ...    State  ProductName target\n",
              "0  ID_0040R73        2010-05-14      2011-05-13  ...  no data  Car Classic      0\n",
              "1  ID_0046BNK        2010-11-29      2011-11-28  ...  no data  Car Classic      1\n",
              "2  ID_005QMC3        2010-03-21      2011-03-20  ...  no data  Car Classic      0\n",
              "3  ID_0079OHW        2010-08-21      2011-08-20  ...  no data      CarSafe      0\n",
              "4  ID_00BRP63        2010-08-29      2010-12-31  ...    Lagos        Muuve      1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjTrA4V_KzW7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "838e5e09-d085-444e-e3fd-80ed1c7234a2"
      },
      "source": [
        "#checking if missing values still exist\n",
        "print(\"Are There still Missing value in data? :\",all_data.isnull().any().any())\n",
        "print((all_data.isnull().sum()/all_data.shape[0])*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are There still Missing value in data? : False\n",
            "ID                        0.0\n",
            "Policy Start Date         0.0\n",
            "Policy End Date           0.0\n",
            "Gender                    0.0\n",
            "Age                       0.0\n",
            "First Transaction Date    0.0\n",
            "No_Pol                    0.0\n",
            "Car_Category              0.0\n",
            "Subject_Car_Colour        0.0\n",
            "Subject_Car_Make          0.0\n",
            "LGA_Name                  0.0\n",
            "State                     0.0\n",
            "ProductName               0.0\n",
            "target                    0.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bi-g_Yc9KzW8"
      },
      "source": [
        "### Dealing with date columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meb68QfPKzXI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ef213ab-aa30-495b-862e-cad565a9fb53"
      },
      "source": [
        "date_col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Policy Start Date', 'Policy End Date', 'First Transaction Date']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Vo_yG6tKzXL"
      },
      "source": [
        "# assigning appropriate data type to the date features\n",
        "for feat in date_col:\n",
        "    all_data[feat] = pd.to_datetime(all_data[feat])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCDgFX9wKzXL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e063df22-a61e-45a7-944a-07263e86b6c2"
      },
      "source": [
        "all_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13281 entries, 0 to 13280\n",
            "Data columns (total 14 columns):\n",
            " #   Column                  Non-Null Count  Dtype         \n",
            "---  ------                  --------------  -----         \n",
            " 0   ID                      13281 non-null  object        \n",
            " 1   Policy Start Date       13281 non-null  datetime64[ns]\n",
            " 2   Policy End Date         13281 non-null  datetime64[ns]\n",
            " 3   Gender                  13281 non-null  object        \n",
            " 4   Age                     13281 non-null  int64         \n",
            " 5   First Transaction Date  13281 non-null  datetime64[ns]\n",
            " 6   No_Pol                  13281 non-null  int64         \n",
            " 7   Car_Category            13281 non-null  object        \n",
            " 8   Subject_Car_Colour      13281 non-null  object        \n",
            " 9   Subject_Car_Make        13281 non-null  object        \n",
            " 10  LGA_Name                13281 non-null  object        \n",
            " 11  State                   13281 non-null  object        \n",
            " 12  ProductName             13281 non-null  object        \n",
            " 13  target                  13281 non-null  object        \n",
            "dtypes: datetime64[ns](3), int64(2), object(9)\n",
            "memory usage: 1.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6B6FVlJKzXM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "ae3fa8e3-ec4c-4d3e-a27b-dc448df35793"
      },
      "source": [
        "all_data.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>Policy Start Date</th>\n",
              "      <th>Policy End Date</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>First Transaction Date</th>\n",
              "      <th>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>2011-05-13</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-05-14</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>2011-11-28</td>\n",
              "      <td>Female</td>\n",
              "      <td>79</td>\n",
              "      <td>2010-11-29</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>2011-03-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>2010-03-21</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>2011-08-20</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>1</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>Entity</td>\n",
              "      <td>20</td>\n",
              "      <td>2010-08-29</td>\n",
              "      <td>3</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID Policy Start Date Policy End Date  ...    State  ProductName target\n",
              "0  ID_0040R73        2010-05-14      2011-05-13  ...  no data  Car Classic      0\n",
              "1  ID_0046BNK        2010-11-29      2011-11-28  ...  no data  Car Classic      1\n",
              "2  ID_005QMC3        2010-03-21      2011-03-20  ...  no data  Car Classic      0\n",
              "3  ID_0079OHW        2010-08-21      2011-08-20  ...  no data      CarSafe      0\n",
              "4  ID_00BRP63        2010-08-29      2010-12-31  ...    Lagos        Muuve      1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-toi7GL2KzXO"
      },
      "source": [
        "# extracting information in the date columns\n",
        "def extract_date_info(df,cols,):\n",
        "    for feat in cols:\n",
        "        df[feat +'_year'] = df[feat].dt.quarter\n",
        "        df[feat +'_day'] = df[feat].dt.day\n",
        "        df[feat +'_month'] = df[feat].dt.month\n",
        "        df[feat +'_quarter'] = df[feat].dt.quarter\n",
        "    df.drop(columns=date_col,axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_SEZafaKzXP"
      },
      "source": [
        "extract_date_info(all_data,date_col)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gX2bVUibKzXQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "2568a0a7-d726-48d1-b24f-b3c1a9a0c57c"
      },
      "source": [
        "all_data.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>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "      <th>Policy Start Date_year</th>\n",
              "      <th>Policy Start Date_day</th>\n",
              "      <th>Policy Start Date_month</th>\n",
              "      <th>Policy Start Date_quarter</th>\n",
              "      <th>Policy End Date_year</th>\n",
              "      <th>Policy End Date_day</th>\n",
              "      <th>Policy End Date_month</th>\n",
              "      <th>Policy End Date_quarter</th>\n",
              "      <th>First Transaction Date_year</th>\n",
              "      <th>First Transaction Date_day</th>\n",
              "      <th>First Transaction Date_month</th>\n",
              "      <th>First Transaction Date_quarter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>Female</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>28</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>Male</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>Male</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>Entity</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ... First Transaction Date_quarter\n",
              "0  ID_0040R73  ...                              2\n",
              "1  ID_0046BNK  ...                              4\n",
              "2  ID_005QMC3  ...                              1\n",
              "3  ID_0079OHW  ...                              3\n",
              "4  ID_00BRP63  ...                              3\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJbo0P3QKzXS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7cc592b-9be8-4395-8f37-8ff2cb4d202e"
      },
      "source": [
        "all_data.Gender.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Male', 'Female', 'Entity', 'Joint Gender', 'no data', 'NO GENDER',\n",
              "       'NOT STATED', 'SEX'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzgzmbEKAJzJ"
      },
      "source": [
        "##### Dealing with the Gender column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apsouvB8KzXT"
      },
      "source": [
        "mapper = {\"Male\":\"M\",\"Female\":'F','Entity':'O','Joint Gender':'O','no data':'O','NO GENDER':'O','NOT STATED':'O','SEX':'O' }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQCZmQc9KzXU"
      },
      "source": [
        "all_data.Gender = all_data.Gender.map(mapper)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1zeL4uHKzXU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66ae6b17-ac0d-4b16-af0d-2bbcf1376446"
      },
      "source": [
        "all_data.Gender.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'F', 'O'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5Lxo6MNKzXW"
      },
      "source": [
        "# replacing  missing values in the with a numeric constant just as a placeholder\n",
        "all_data.target.replace(to_replace=\"no data\", value=9999, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrOT4TS9KzXY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f630839-c7a4-4cb2-cf3c-0ab72cd21dd8"
      },
      "source": [
        "all_data.target.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqOxfbbJKzXZ"
      },
      "source": [
        "all_data.target = all_data.target.astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZaYifcRKzXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "adbd8e72-4acd-4561-dc47-f623ed459123"
      },
      "source": [
        "all_data.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>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "      <th>Policy Start Date_year</th>\n",
              "      <th>Policy Start Date_day</th>\n",
              "      <th>Policy Start Date_month</th>\n",
              "      <th>Policy Start Date_quarter</th>\n",
              "      <th>Policy End Date_year</th>\n",
              "      <th>Policy End Date_day</th>\n",
              "      <th>Policy End Date_month</th>\n",
              "      <th>Policy End Date_quarter</th>\n",
              "      <th>First Transaction Date_year</th>\n",
              "      <th>First Transaction Date_day</th>\n",
              "      <th>First Transaction Date_month</th>\n",
              "      <th>First Transaction Date_quarter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>M</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Black</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>F</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>JEEP</td>\n",
              "      <td>Grey</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>28</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>M</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>Saloon</td>\n",
              "      <td>Red</td>\n",
              "      <td>TOYOTA</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Car Classic</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>CarSafe</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>O</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>no data</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>Muuve</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ... First Transaction Date_quarter\n",
              "0  ID_0040R73  ...                              2\n",
              "1  ID_0046BNK  ...                              4\n",
              "2  ID_005QMC3  ...                              1\n",
              "3  ID_0079OHW  ...                              3\n",
              "4  ID_00BRP63  ...                              3\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvOZblZoKzXb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2ed79db-f5a8-459c-a621-ef4c0d98d818"
      },
      "source": [
        "all_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13281 entries, 0 to 13280\n",
            "Data columns (total 23 columns):\n",
            " #   Column                          Non-Null Count  Dtype \n",
            "---  ------                          --------------  ----- \n",
            " 0   ID                              13281 non-null  object\n",
            " 1   Gender                          13281 non-null  object\n",
            " 2   Age                             13281 non-null  int64 \n",
            " 3   No_Pol                          13281 non-null  int64 \n",
            " 4   Car_Category                    13281 non-null  object\n",
            " 5   Subject_Car_Colour              13281 non-null  object\n",
            " 6   Subject_Car_Make                13281 non-null  object\n",
            " 7   LGA_Name                        13281 non-null  object\n",
            " 8   State                           13281 non-null  object\n",
            " 9   ProductName                     13281 non-null  object\n",
            " 10  target                          13281 non-null  int64 \n",
            " 11  Policy Start Date_year          13281 non-null  int64 \n",
            " 12  Policy Start Date_day           13281 non-null  int64 \n",
            " 13  Policy Start Date_month         13281 non-null  int64 \n",
            " 14  Policy Start Date_quarter       13281 non-null  int64 \n",
            " 15  Policy End Date_year            13281 non-null  int64 \n",
            " 16  Policy End Date_day             13281 non-null  int64 \n",
            " 17  Policy End Date_month           13281 non-null  int64 \n",
            " 18  Policy End Date_quarter         13281 non-null  int64 \n",
            " 19  First Transaction Date_year     13281 non-null  int64 \n",
            " 20  First Transaction Date_day      13281 non-null  int64 \n",
            " 21  First Transaction Date_month    13281 non-null  int64 \n",
            " 22  First Transaction Date_quarter  13281 non-null  int64 \n",
            "dtypes: int64(15), object(8)\n",
            "memory usage: 2.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WgLbmZlyKzXb"
      },
      "source": [
        "### Encoding Categorical Variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGVuhljPA4W2"
      },
      "source": [
        "##### Checking for unique entries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04TqWUxLKzXc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92098fb5-b254-44af-87b2-0c9091793c66"
      },
      "source": [
        "all_data.Car_Category.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Saloon', 'JEEP', 'no data', 'Motorcycle', 'Truck', 'Bus',\n",
              "       'Mini Bus', 'Pick Up', 'Mini Van', 'Van', 'Pick Up > 3 Tons',\n",
              "       'CAMRY CAR HIRE', 'Wagon', 'Shape Of Vehicle Chasis', 'Sedan',\n",
              "       'Station 4 Wheel', 'Tipper Truck'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sA55g9t_KzXd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d750da5e-7bd0-455f-ced6-1c85ca120778"
      },
      "source": [
        "all_data.Subject_Car_Colour.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Black', 'Grey', 'Red', 'no data', 'As Attached', 'Blue', 'Silver',\n",
              "       'Green', 'Ash', 'White', 'Gray & Gray', 'Wine', 'Brown',\n",
              "       'Red & Blue', 'Gold', 'Gray & Silver', 'Orange', 'Red & Black',\n",
              "       'Dark Grey', 'White & Red', 'Light Green', 'Dark Gray', 'B.Silver',\n",
              "       'Purple', 'Blue & Red', 'Red & Yellow', 'Yellow', 'Red & White',\n",
              "       'D. Red', 'Dark Red', 'Black & White', 'White & Blue', 'Cream',\n",
              "       'Dark Green', 'D. Gold', 'Champagne', 'Light Blue',\n",
              "       'Blue&White&Red', 'White & Yellow', 'Dark Blue', 'Black & Orange',\n",
              "       'Burgundy', 'Yellow & White', 'Beige Mitalic', 'Light Gray',\n",
              "       'Blue Sky', 'Red Maroon'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOOAz4NYKzXe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ead60555-e791-43ed-e3fc-facce7e05dde"
      },
      "source": [
        "all_data.Subject_Car_Make.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['TOYOTA', 'no data', 'REXTON', 'Lexus', 'Hyundai', 'Iveco', 'DAF',\n",
              "       'Honda', 'Mercedes', 'Jincheng', 'ACURA', 'Ford', 'Volkswagen',\n",
              "       'Nissan', '.', 'Pontiac', 'Range Rover', 'Kia', 'Mitsubishi',\n",
              "       'Scania', 'BMW', 'Infiniti', 'Renault', 'Volvo', 'Hummer', 'Mack',\n",
              "       'Grand Cherokee', 'Porsche', 'Peugeot', 'Land Rover', 'Chevrolet',\n",
              "       'Mazda', 'Man', 'Jeep', 'Audi', 'Suzuki', 'MINI COOPER', 'KA',\n",
              "       'As Attached', 'Innson', 'Isuzu', 'Skoda', 'Jaguar', 'Chrysler',\n",
              "       'Dodge', 'GMC', 'Land Rover.', 'Subaru', 'GAC', 'Fiat', 'Opel',\n",
              "       'Astra', 'Motorcycle', 'ZOYTE', 'Seat', 'FOTON', 'Howo', 'Lincoln',\n",
              "       'Rols Royce', 'CHANGAN', 'LIBERTY', 'ABG', 'Yamaha', 'Black',\n",
              "       'Tata', 'Ashok Leyland', 'Geely', 'Caddillac', 'Wrangler Jeep',\n",
              "       'Raston', 'MG', 'BRILLIANCE', 'Buik', 'COMMANDER', 'Bajaj',\n",
              "       'Datsun'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "lcogugFrKzXf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80935361-1e20-47e4-8e41-915864160bbb"
      },
      "source": [
        "all_data.LGA_Name.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no data', 'Lagos', 'Ikeja', 'Badagry', 'Eti-Osa',\n",
              "       'Victoria Island', 'Ikoyi', 'Lagos Mainland', 'EFFURUN',\n",
              "       'Abeokuta', 'Abuja Municipal', 'Yaba', 'Aguda', 'Surulere',\n",
              "       'Oshodi-Isolo', 'Alimosho', 'Okpe, Delta State', 'Asaba',\n",
              "       'IbadanCentral', 'Lekki', 'Ibeju/Lekki', 'OBANIKORO', 'Agege',\n",
              "       'ISHERI', 'Kosofe', 'OGUN', 'Apapa', 'Ibadan South West',\n",
              "       'Obia/Akpor', 'Ibadan South East', 'Sapele', 'Kaduna South',\n",
              "       'Ikorodu', 'Amuwo-Odofin', 'Awka', 'GBAGADA', 'Port Harcourt',\n",
              "       'Minna', 'Shomolu', 'Benin', 'Lagos Island', 'Central',\n",
              "       'Ebute-Metta', 'Ketu', 'Uyo', 'Mushin', 'Katagum', 'ENUGU EAST',\n",
              "       'Oredo', 'Abuja', 'Festac', 'Ipaja', 'Zaria ', 'Esan West',\n",
              "       'Oluyole', 'Bwari', 'Obafemi-Owode', 'LGA', 'Orile-Iganmu',\n",
              "       'Egbeda', 'Ido', 'Ifako-Ijaye ', 'Ibadan North', 'Oguta',\n",
              "       'Jos North', 'Ajah', 'Ondo West', 'Kaduna North', 'Warri',\n",
              "       'Aniocha South', 'Owerri', 'Awka South', 'Asokoro District',\n",
              "       'Ile-Oluji', 'Ijebu Ode', 'Port-Harcourt', 'Oshodi', 'EMENE',\n",
              "       'Alagbado', 'Wuse 11', 'Egor', 'Biase', 'Shagamu', 'Nnewi North',\n",
              "       'Aboh-Mbaise', 'IBADAN NORTH EAST', 'Akinyele', 'Oyo',\n",
              "       'Akoko North West', 'OGBA', 'Warri Central', 'Ilupeju', 'Calabar',\n",
              "       'Jos South', 'Onitsha North', 'AKUTE', 'Ifo', 'Ojodu', 'Bekwara',\n",
              "       'Ibadan North West', 'Epe', 'Maryland', 'Osogbo', 'Kuje', 'Idanre',\n",
              "       'Ajeromi-Ifelodun', 'Olorunsogo', 'Udu', 'OGUDU', 'Kano Municipal',\n",
              "       'Ife Central', 'Ilorin West', 'Lagelu Ogbomosho North', 'Katcha',\n",
              "       'Isolo', 'Akwa Ibom', 'Anthony Village', 'Abeokuta South',\n",
              "       'MAGODO', 'Kaduna', 'Owerri-Municipal', 'OJOTA', 'Somolu',\n",
              "       'Nsit Ubium', 'Bonny', 'Udi Agwu', 'Essien Udim', 'Owerri West ',\n",
              "       'Akure', 'IJAIYE', 'Ogbmosho South', 'Marina', 'Orolu',\n",
              "       'Umuahia South', 'Ile-Ife', 'ABULE EGBA', 'Enugu North',\n",
              "       'Ovia SouthWest', 'OKOTA', 'Argungu', 'Okpe',\n",
              "       'Ajegunle,Lagos  State', 'Nnewi', 'AREPO', 'Irepodun',\n",
              "       'Ekiti South-West', 'Ogun Waterside', 'Ife North', 'RIVERS',\n",
              "       'Dopemu', 'Akure South', 'ONDO', 'Oyi ', 'Anambra East', 'Keffi',\n",
              "       'Etsako West', 'Uvwie', 'Ilorin', 'Akoka', 'Aba North',\n",
              "       'Awka North', 'Ibarapa Central', 'Ojo', 'AWOYAYA', 'Idemili North',\n",
              "       'Onitsha', 'IBA', 'Ikot Ekpene', 'Ijora', ' IFAKO', 'Niger State',\n",
              "       'Ogba/Egbema/Ndoni', 'Eleme', 'Ejigbo', 'Calabar Municipality',\n",
              "       'Abeokuta North', 'Yorro', 'Ilesha', 'Ikeja G.R.A', 'Goronyo',\n",
              "       'Chanchaga', 'AJAO ESTATE', 'Ekiti-East', 'Gombe', 'Ikotun', 'Iwo',\n",
              "       'AKOKO-EDO', 'Jibia', 'Obalende', 'Yenagoa', 'Ado-Odo/Ota',\n",
              "       'Oyo East', 'Rogo', 'Ilorin East', 'Ilesha West', 'Garki',\n",
              "       'kumbotso', 'Asa', 'Central Area, Abuja', 'Ukpoba', 'BARIGA',\n",
              "       'Ikenne', 'Kano', 'Ilasamaja', 'Ekeremor', 'ONIRU', 'SANGO OTTA',\n",
              "       'Owerri North', 'Umuahia', 'Eket', 'Garko', 'SANGOTEDO', 'Lokoja',\n",
              "       'Ilesha East', 'Esan Central', 'Isoko south', 'Ethiope East',\n",
              "       'QuaAn Pan', 'Warri North', 'Njaba', 'Akuku Toru', 'Ado-Ekiti',\n",
              "       'Agbor', 'Karu', 'Bauchi', 'Ijebu East', 'Orsu', 'MAGBORO',\n",
              "       'Bosso', 'Onitsha South', 'Ughelli North', 'Warri South',\n",
              "       'Nwangele', 'Abakaliki', 'Oyo West', 'Aba South', 'Ekiti',\n",
              "       'ALAPERE', 'ISHAGA', 'Isoko North', 'Oshimili North',\n",
              "       'Ndokwa East', 'Okrika', 'Tai ', 'OWODE', 'Oturkpo', 'Bida',\n",
              "       'Oshimili', 'Nnewi South', 'ENUGU SOUTH', 'Olamabolo', 'Ikwerre',\n",
              "       'Kajola', 'Palm Groove', 'Ijebu North', 'Akure North', 'Ohaukwu',\n",
              "       'Asari-Toru', 'Ogbomoso', 'AKOWONJO', 'VGC', 'Ankpa', 'Zaria',\n",
              "       'AGBARA', 'Idemili south', 'Ekwusigo', 'Hong', 'Ifako-Agege',\n",
              "       'Benue', 'Okpokwu', 'Ngor-Okpala'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "T7Gai7zHKzXg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c4fbb7f-ccf5-49d6-cb71-0f1a53818224"
      },
      "source": [
        "all_data.State.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no data', 'Lagos', 'Benue', 'Eti-Osa', 'Delta', 'Ogun',\n",
              "       'Abuja-Municipal', 'Oshodi-Isolo', 'Ibeju-Lekki', 'Ibadan-West',\n",
              "       'Obia-Akpor', 'Ibadan-East', 'Kaduna-South', 'Amuwo-Odofin',\n",
              "       'Anambra', 'Rivers', 'Niger-State', 'Edo', 'Akwa-Ibom',\n",
              "       'ENUGU-EAST', 'Abuja', 'Kaduna', 'Esan-West', 'Obafemi-Owode',\n",
              "       'Orile-Iganmu', 'Ifako-Ijaye', 'Ibadan-North', 'Imo', 'Jos-North',\n",
              "       'Ondo-West', 'Kaduna-North', 'Aniocha-South', 'Awka-South',\n",
              "       'Ile-Oluji', 'Ijebu-Ode', 'Port-Harcourt', 'Nnewi-North',\n",
              "       'Aboh-Mbaise', 'Oyo', 'Akoko-West', 'Warri-Central', 'Cross-River',\n",
              "       'Jos-South', 'Onitsha-North', 'Osun', 'Ajeromi-Ifelodun',\n",
              "       'Kano-Municipal', 'Ife-Central', 'Ilorin-West', 'Lagelu-North',\n",
              "       'Owerri-Municipal', 'Nsit-Ubium', 'Udi-Agwu', 'Essien-Udim',\n",
              "       'Owerri-West', 'Ondo', 'Ogbmosho-South', 'Umuahia-South',\n",
              "       'ABULE-EGBA', 'Enugu-North', 'Ovia-SouthWest', 'Kebbi',\n",
              "       'Ajegunle-State', 'Ekiti-West', 'Ogun-Waterside', 'Ife-North',\n",
              "       'Anambra-East', 'Nasarawa', 'Etsako-West', 'Kwara', 'Aba-North',\n",
              "       'Awka-North', 'Ibarapa-Central', 'Idemili-North', 'Ogba-Ndoni',\n",
              "       'Calabar-Municipality', 'AJAO-ESTATE', 'Ekiti-East', 'Gombe',\n",
              "       'Bayelsa', 'Ado-Ota', 'Oyo-East', 'Ilorin-East', 'Ilesha-West',\n",
              "       'Central-Abuja', 'Kano', 'Owerri-North', 'Abia', 'Kogi',\n",
              "       'Ilesha-East', 'Esan-Central', 'Isoko-south', 'Ethiope-East',\n",
              "       'QuaAn-Pan', 'Warri-North', 'Ado-Ekiti', 'Bauchi', 'Ijebu-East',\n",
              "       'Onitsha-South', 'Ughelli-North', 'Warri-South', 'Ebonyi',\n",
              "       'Oyo-West', 'Aba-South', 'N-A', 'Isoko-North', 'Oshimili-North',\n",
              "       'Ndokwa-East', 'Nnewi-South', 'ENUGU-SOUTH', 'Ijebu-North',\n",
              "       'Asari-Toru', 'Idemili-south', 'Ngor-Okpala'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koumWzqiKzXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8bcacff-2670-4507-e5d5-758dbe00763f"
      },
      "source": [
        "all_data.ProductName.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Car Classic', 'CarSafe', 'Muuve', 'CVTP', 'Car Plus',\n",
              "       'Motor Cycle', 'Customized Motor', 'CarFlex', 'Car Vintage'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn-TRoQUBC9I"
      },
      "source": [
        "#### Implementing Label Encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "eR7d25yuKzXh"
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "all_data.Gender = le.fit_transform(all_data.Gender)\n",
        "all_data.Car_Category = le.fit_transform(all_data.Car_Category)\n",
        "all_data.Subject_Car_Colour = le.fit_transform(all_data.Subject_Car_Colour)\n",
        "all_data.Subject_Car_Make = le.fit_transform(all_data.Subject_Car_Make)\n",
        "all_data.LGA_Name = le.fit_transform(all_data.LGA_Name)\n",
        "all_data.State = le.fit_transform(all_data.State)\n",
        "all_data.ProductName = le.fit_transform(all_data.ProductName)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnCqd2T-KzXi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "24063aad-1570-41cf-e1ef-5f28a4ba7a6a"
      },
      "source": [
        "all_data.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>No_Pol</th>\n",
              "      <th>Car_Category</th>\n",
              "      <th>Subject_Car_Colour</th>\n",
              "      <th>Subject_Car_Make</th>\n",
              "      <th>LGA_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>target</th>\n",
              "      <th>Policy Start Date_year</th>\n",
              "      <th>Policy Start Date_day</th>\n",
              "      <th>Policy Start Date_month</th>\n",
              "      <th>Policy Start Date_quarter</th>\n",
              "      <th>Policy End Date_year</th>\n",
              "      <th>Policy End Date_day</th>\n",
              "      <th>Policy End Date_month</th>\n",
              "      <th>Policy End Date_quarter</th>\n",
              "      <th>First Transaction Date_year</th>\n",
              "      <th>First Transaction Date_day</th>\n",
              "      <th>First Transaction Date_month</th>\n",
              "      <th>First Transaction Date_quarter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ID_0040R73</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>68</td>\n",
              "      <td>270</td>\n",
              "      <td>113</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_0046BNK</td>\n",
              "      <td>0</td>\n",
              "      <td>79</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>68</td>\n",
              "      <td>270</td>\n",
              "      <td>113</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>28</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_005QMC3</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>68</td>\n",
              "      <td>270</td>\n",
              "      <td>113</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ID_0079OHW</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>46</td>\n",
              "      <td>75</td>\n",
              "      <td>270</td>\n",
              "      <td>113</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ID_00BRP63</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>46</td>\n",
              "      <td>75</td>\n",
              "      <td>165</td>\n",
              "      <td>74</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ...  First Transaction Date_quarter\n",
              "0  ID_0040R73  ...                               2\n",
              "1  ID_0046BNK  ...                               4\n",
              "2  ID_005QMC3  ...                               1\n",
              "3  ID_0079OHW  ...                               3\n",
              "4  ID_00BRP63  ...                               3\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDp3iOzlKzXj"
      },
      "source": [
        "all_data.drop(columns=['ID'],inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6kbm2TmKzXk"
      },
      "source": [
        "### Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjqzzvB3KzXk"
      },
      "source": [
        "#Get the new dataset\n",
        "train_n = all_data[:ntrain]\n",
        "test_n = all_data[ntrain:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lc0y77l0KzXl"
      },
      "source": [
        "test_n.drop(\"target\",axis = 1,inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VemlmA10KzXm"
      },
      "source": [
        "X= train_n.drop(columns=['target'])\n",
        "y= train_n.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6M_Ngj6OKzXm"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=45, stratify=y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnV8dDqHFN5W"
      },
      "source": [
        "### Create Model - LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxlhMimzFN5X"
      },
      "source": [
        "from lightgbm import LGBMClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUMyLIBodvKS"
      },
      "source": [
        "#from sklearn.utils import class_weight\n",
        "#class_weight = dict(zip(np.unique(y), class_weight.compute_class_weight('balanced',np.unique(y),y))) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sidQvRtryb2M"
      },
      "source": [
        "In training this model, 8-fold cross validation on the training data was performed, thereby predicting on the test set after each iteration. The modal value of all the 8 predictions was then used to make the final submission.\n",
        "\n",
        "Since an imbalanced dataset was involved, the `scale_pos_weight` hyperperameter was considered, which was manually tuned so as to balance the ratio of both positive and negative classes in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBmsN-rWFN5Z"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "predictions = []\n",
        "fold=StratifiedKFold(n_splits=8,shuffle=True, random_state=45)\n",
        "for train_index, test_index in fold.split(X,y):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    Y_train, Y_test = y[train_index], y[test_index]\n",
        "    classifier2=LGBMClassifier(learning_rate=0.1, n_estimators=800, scale_pos_weight = 5.0)\n",
        "    classifier2.fit(X_train, Y_train)\n",
        "    preds=classifier2.predict(X_test)\n",
        "    predictions.append(classifier2.predict(test_n))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Iunop7lIdR8"
      },
      "source": [
        "##### Feature Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wa-iMsP430lS"
      },
      "source": [
        "Here, the key features that are relevant to creating the model was extracted, and then the model is retrained using these important features. This is done so as to improve the model performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMhLY9A6GXg0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "8229bdc2-0fb7-43f1-97a4-5fb96adc7480"
      },
      "source": [
        "feature_importance_df = pd.DataFrame(classifier2.feature_importances_, columns=['importance'])\n",
        "feature_importance_df['feature'] = X_train.columns\n",
        "\n",
        "plt.figure(figsize=(20, 12));\n",
        "sns.barplot(x=\"importance\", y=\"feature\", data=feature_importance_df.sort_values(by = ['importance'], ascending = False))\n",
        "plt.title('LGBM features importance:');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRpQwFVd4i2z"
      },
      "source": [
        "From the above plot, it is obvious that some of the features are absolutely dummies in the model, after much training, the top 13 features are selected as the optimal features for retraining the model to yield a better performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLFwdRZCIqPo",
        "outputId": "3cfa9115-6f43-478b-ad9f-f41c60760434"
      },
      "source": [
        "feature_importance_df = pd.DataFrame(classifier2.feature_importances_, columns=['importance'])\n",
        "feature_importance_df['feature'] = X_train.columns\n",
        "feature_importance = feature_importance_df.sort_values(by='importance',ascending=False).head(13)\n",
        "feat_imp = feature_importance['feature'].to_list()\n",
        "feat_imp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'Policy Start Date_day',\n",
              " 'Policy End Date_day',\n",
              " 'LGA_Name',\n",
              " 'Subject_Car_Make',\n",
              " 'Subject_Car_Colour',\n",
              " 'Policy Start Date_month',\n",
              " 'Policy End Date_month',\n",
              " 'State',\n",
              " 'Car_Category',\n",
              " 'Gender',\n",
              " 'ProductName',\n",
              " 'Policy Start Date_year']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-gUWMuOKfTd"
      },
      "source": [
        "##### Training model with selected features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFZ7nRQ3Ipzm"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "predictions = []\n",
        "fold=StratifiedKFold(n_splits=8,shuffle=True, random_state=45)\n",
        "for train_index, test_index in fold.split(X[feat_imp],y):\n",
        "    X_train, X_test = X[feat_imp].iloc[train_index], X[feat_imp].iloc[test_index]\n",
        "    Y_train, Y_test = y[train_index], y[test_index]\n",
        "    classifier2=LGBMClassifier(learning_rate=0.1, n_estimators=800, scale_pos_weight = 5.0, objective='binary')\n",
        "    classifier2.fit(X_train, Y_train)\n",
        "    preds=classifier2.predict(X_test)\n",
        "    predictions.append(classifier2.predict(test_n[feat_imp]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2oh3RuF5SGY"
      },
      "source": [
        "### Framing the predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RmW12K7Ipl9"
      },
      "source": [
        "predictions = pd.DataFrame(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "hCv2iMyHIpSe",
        "outputId": "ed41ce0e-f123-4f2b-ff22-150e6c1f6c25"
      },
      "source": [
        "predictions.T.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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0  1  2  3  4  5  6  7\n",
              "0  0  0  0  0  0  0  0  0\n",
              "1  0  0  0  0  0  0  0  0\n",
              "2  0  0  0  0  0  0  0  0\n",
              "3  0  0  0  0  0  0  0  0\n",
              "4  0  0  0  0  0  0  0  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XO8nHIaIpJH"
      },
      "source": [
        "predictions = predictions.mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "ozM4HImOIoxh",
        "outputId": "21f422e0-3475-49e0-f2ab-be2da38252e3"
      },
      "source": [
        "predictions = predictions.T\n",
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1197</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1198</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1199</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1200</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1201</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1202 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0   1\n",
              "0     0.0 NaN\n",
              "1     0.0 NaN\n",
              "2     0.0 NaN\n",
              "3     0.0 NaN\n",
              "4     0.0 NaN\n",
              "...   ...  ..\n",
              "1197  0.0 NaN\n",
              "1198  0.0 NaN\n",
              "1199  0.0 NaN\n",
              "1200  0.0 NaN\n",
              "1201  1.0 NaN\n",
              "\n",
              "[1202 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzvAmyFsIoeO",
        "outputId": "1c67ec7e-5760-4b8d-ab9b-d2ea47995777"
      },
      "source": [
        "sub = predictions[0]\n",
        "sub.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: 0, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SyvYIZx5IoLu"
      },
      "source": [
        "sub_file = ss.copy()\n",
        "sub_file.target = sub"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5GescX5In-d"
      },
      "source": [
        "sub_file.to_csv('reproducibility_prediction1.csv',index=False)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_hWTGh2HZNo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}