UmojaHack 2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UmojaHack Hackathon Nigeria 2020:AXA CAR INSURANCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HOST: ZINDI AFRICA"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Team:Mobility(UNIVERSITY OF NIGERIA)\n",
    "No_Of_Team_Memebers:2\n",
    "Final_Ranking:11th Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary Libaries\n",
    "import numpy as np;import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix,f1_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import datset(Train and test)\n",
    "train_data=pd.read_csv('Train.csv',na_values='NULL')\n",
    "test_data=pd.read_csv('Test.csv',na_values='NULL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>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>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>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>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>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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12074</th>\n",
       "      <td>ZZA1SES</td>\n",
       "      <td>2010-05-25</td>\n",
       "      <td>2011-05-24</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>2010-05-25</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black</td>\n",
       "      <td>Range Rover</td>\n",
       "      <td>Ibeju/Lekki</td>\n",
       "      <td>Ibeju-Lekki</td>\n",
       "      <td>Car Classic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12075</th>\n",
       "      <td>ZZDAC3K</td>\n",
       "      <td>2010-10-03</td>\n",
       "      <td>2011-10-02</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>2010-10-03</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>Car Classic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12076</th>\n",
       "      <td>ZZIU2XC</td>\n",
       "      <td>2010-10-10</td>\n",
       "      <td>2011-10-08</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>2010-10-10</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>12077</th>\n",
       "      <td>ZZRQ1NF</td>\n",
       "      <td>2010-02-27</td>\n",
       "      <td>2011-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>2010-02-27</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Victoria Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>CVTP</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12078</th>\n",
       "      <td>ZZWRIIE</td>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>2</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>Black</td>\n",
       "      <td>Land Rover</td>\n",
       "      <td>Lagos Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Car Classic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12079 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID Policy Start Date Policy End Date  Gender  Age  \\\n",
       "0      0040R73        2010-05-14      2011-05-13    Male   30   \n",
       "1      0046BNK        2010-11-29      2011-11-28  Female   79   \n",
       "2      005QMC3        2010-03-21      2011-03-20    Male   43   \n",
       "3      0079OHW        2010-08-21      2011-08-20    Male    2   \n",
       "4      00BRP63        2010-08-29      2010-12-31  Entity   20   \n",
       "...        ...               ...             ...     ...  ...   \n",
       "12074  ZZA1SES        2010-05-25      2011-05-24  Female   30   \n",
       "12075  ZZDAC3K        2010-10-03      2011-10-02  Female   59   \n",
       "12076  ZZIU2XC        2010-10-10      2011-10-08    Male   34   \n",
       "12077  ZZRQ1NF        2010-02-27      2011-02-26     NaN  120   \n",
       "12078  ZZWRIIE        2010-07-01      2011-06-30  Female   46   \n",
       "\n",
       "      First Transaction Date  No_Pol Car_Category Subject_Car_Colour  \\\n",
       "0                 2010-05-14       1       Saloon              Black   \n",
       "1                 2010-11-29       1         JEEP               Grey   \n",
       "2                 2010-03-21       1       Saloon                Red   \n",
       "3                 2010-08-21       1          NaN                NaN   \n",
       "4                 2010-08-29       3          NaN                NaN   \n",
       "...                      ...     ...          ...                ...   \n",
       "12074             2010-05-25       1          NaN              Black   \n",
       "12075             2010-10-03       1          NaN                NaN   \n",
       "12076             2010-10-10       1          NaN                NaN   \n",
       "12077             2010-02-27       2          NaN              White   \n",
       "12078             2010-07-01       2         JEEP              Black   \n",
       "\n",
       "      Subject_Car_Make         LGA_Name        State  ProductName  target  \n",
       "0               TOYOTA              NaN          NaN  Car Classic       0  \n",
       "1               TOYOTA              NaN          NaN  Car Classic       1  \n",
       "2               TOYOTA              NaN          NaN  Car Classic       0  \n",
       "3                  NaN              NaN          NaN      CarSafe       0  \n",
       "4                  NaN            Lagos        Lagos        Muuve       1  \n",
       "...                ...              ...          ...          ...     ...  \n",
       "12074      Range Rover      Ibeju/Lekki  Ibeju-Lekki  Car Classic       1  \n",
       "12075              NaN              NaN          NaN  Car Classic       0  \n",
       "12076              NaN              NaN          NaN      CarSafe       0  \n",
       "12077           TOYOTA  Victoria Island        Lagos         CVTP       0  \n",
       "12078       Land Rover     Lagos Island        Lagos  Car Classic       0  \n",
       "\n",
       "[12079 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>009D84L</td>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>2011-03-27</td>\n",
       "      <td>NO GENDER</td>\n",
       "      <td>25</td>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As Attached</td>\n",
       "      <td>Iveco</td>\n",
       "      <td>Victoria Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>CVTP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01DO2EQ</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As Attached</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Victoria Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Muuve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>3</th>\n",
       "      <td>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>4</th>\n",
       "      <td>02BYET3</td>\n",
       "      <td>2010-09-16</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>2010-09-16</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Victoria Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Muuve</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>ZYXX5AF</td>\n",
       "      <td>2010-07-18</td>\n",
       "      <td>2011-07-17</td>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>2010-07-18</td>\n",
       "      <td>1</td>\n",
       "      <td>Saloon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Honda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CarSafe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5173</th>\n",
       "      <td>ZYYOZ5L</td>\n",
       "      <td>2010-12-04</td>\n",
       "      <td>2011-12-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>2010-12-04</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Victoria Island</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>Car Classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5174</th>\n",
       "      <td>ZZ1GTKD</td>\n",
       "      <td>2010-09-24</td>\n",
       "      <td>2011-09-23</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-09-24</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>ZZDXQSI</td>\n",
       "      <td>2010-07-17</td>\n",
       "      <td>2011-07-16</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>2010-07-17</td>\n",
       "      <td>1</td>\n",
       "      <td>Saloon</td>\n",
       "      <td>Grey</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Car Classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5176</th>\n",
       "      <td>ZZYTLV1</td>\n",
       "      <td>2010-07-17</td>\n",
       "      <td>2011-07-16</td>\n",
       "      <td>Male</td>\n",
       "      <td>68</td>\n",
       "      <td>2010-07-17</td>\n",
       "      <td>1</td>\n",
       "      <td>Saloon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peugeot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CarSafe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5177 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID Policy Start Date Policy End Date     Gender  Age  \\\n",
       "0     009D84L        2010-04-24      2011-03-27  NO GENDER   25   \n",
       "1     01DO2EQ        2010-01-01      2010-12-31        NaN  120   \n",
       "2     01QM0NU        2010-10-23      2011-10-22     Female   46   \n",
       "3     024NJLZ        2010-10-14      2011-10-13       Male   32   \n",
       "4     02BYET3        2010-09-16      2010-12-31        NaN  120   \n",
       "...       ...               ...             ...        ...  ...   \n",
       "5172  ZYXX5AF        2010-07-18      2011-07-17       Male   48   \n",
       "5173  ZYYOZ5L        2010-12-04      2011-12-03        NaN   50   \n",
       "5174  ZZ1GTKD        2010-09-24      2011-09-23       Male    1   \n",
       "5175  ZZDXQSI        2010-07-17      2011-07-16     Female   52   \n",
       "5176  ZZYTLV1        2010-07-17      2011-07-16       Male   68   \n",
       "\n",
       "     First Transaction Date  No_Pol Car_Category Subject_Car_Colour  \\\n",
       "0                2010-04-24       1          NaN        As Attached   \n",
       "1                2010-01-01       4          NaN        As Attached   \n",
       "2                2010-10-23       1          NaN                NaN   \n",
       "3                2010-10-14       1          NaN                NaN   \n",
       "4                2010-09-16       4          NaN                NaN   \n",
       "...                     ...     ...          ...                ...   \n",
       "5172             2010-07-18       1       Saloon                NaN   \n",
       "5173             2010-12-04       1          NaN                NaN   \n",
       "5174             2010-09-24       1          NaN                NaN   \n",
       "5175             2010-07-17       1       Saloon               Grey   \n",
       "5176             2010-07-17       1       Saloon                NaN   \n",
       "\n",
       "     Subject_Car_Make         LGA_Name            State  ProductName  \n",
       "0               Iveco  Victoria Island            Lagos         CVTP  \n",
       "1              TOYOTA  Victoria Island            Lagos        Muuve  \n",
       "2                Ford  Abuja Municipal  Abuja-Municipal  Car Classic  \n",
       "3                 NaN           Kosofe            Benue  Car Classic  \n",
       "4              TOYOTA  Victoria Island            Lagos        Muuve  \n",
       "...               ...              ...              ...          ...  \n",
       "5172            Honda              NaN              NaN      CarSafe  \n",
       "5173              NaN  Victoria Island            Lagos  Car Classic  \n",
       "5174              NaN              NaN              NaN      CarSafe  \n",
       "5175           TOYOTA              NaN              NaN  Car Classic  \n",
       "5176          Peugeot              NaN              NaN      CarSafe  \n",
       "\n",
       "[5177 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12079, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5177, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign Target values in train to y_train\n",
    "y_train=train_data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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: 802.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine Test and Train data for Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=train_data.append(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17256, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17256 entries, 0 to 5176\n",
      "Data columns (total 14 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ID                      17256 non-null  object \n",
      " 1   Policy Start Date       17256 non-null  object \n",
      " 2   Policy End Date         17256 non-null  object \n",
      " 3   Gender                  16741 non-null  object \n",
      " 4   Age                     17256 non-null  int64  \n",
      " 5   First Transaction Date  17256 non-null  object \n",
      " 6   No_Pol                  17256 non-null  int64  \n",
      " 7   Car_Category            11880 non-null  object \n",
      " 8   Subject_Car_Colour      7289 non-null   object \n",
      " 9   Subject_Car_Make        13719 non-null  object \n",
      " 10  LGA_Name                7998 non-null   object \n",
      " 11  State                   7980 non-null   object \n",
      " 12  ProductName             17256 non-null  object \n",
      " 13  target                  12079 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(11)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Target values and ID column\n",
    "data.drop('target',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17256 entries, 0 to 5176\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   Policy Start Date       17256 non-null  object\n",
      " 1   Policy End Date         17256 non-null  object\n",
      " 2   Gender                  16741 non-null  object\n",
      " 3   Age                     17256 non-null  int64 \n",
      " 4   First Transaction Date  17256 non-null  object\n",
      " 5   No_Pol                  17256 non-null  int64 \n",
      " 6   Car_Category            11880 non-null  object\n",
      " 7   Subject_Car_Colour      7289 non-null   object\n",
      " 8   Subject_Car_Make        13719 non-null  object\n",
      " 9   LGA_Name                7998 non-null   object\n",
      " 10  State                   7980 non-null   object\n",
      " 11  ProductName             17256 non-null  object\n",
      "dtypes: int64(2), object(10)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting The Hist of Target Values To see the Frequency Distribution\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.hist(y_train);plt.xlabel('Target')\n",
    "plt.ylabel('Frequency');plt.grid('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From The Histogram of the Target Values It \n",
    "Can be seen that there is an imbalance in \n",
    "the target with a ratio of 90:10 %"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three columns in Date and Time formats\n",
    "Namely Policy Start Date,Policy End Date and \n",
    "First Transaction Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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>Policy Start Date</th>\n",
       "      <th>Policy End Date</th>\n",
       "      <th>First Transaction Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-05-14</td>\n",
       "      <td>2011-05-13</td>\n",
       "      <td>2010-05-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-11-29</td>\n",
       "      <td>2011-11-28</td>\n",
       "      <td>2010-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-03-21</td>\n",
       "      <td>2011-03-20</td>\n",
       "      <td>2010-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-08-21</td>\n",
       "      <td>2011-08-20</td>\n",
       "      <td>2010-08-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-08-29</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>2010-08-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Policy Start Date Policy End Date First Transaction Date\n",
       "0        2010-05-14      2011-05-13             2010-05-14\n",
       "1        2010-11-29      2011-11-28             2010-11-29\n",
       "2        2010-03-21      2011-03-20             2010-03-21\n",
       "3        2010-08-21      2011-08-20             2010-08-21\n",
       "4        2010-08-29      2010-12-31             2010-08-29"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Policy Start Date','Policy End Date','First Transaction Date']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These Date Time Feature format cannot be used for analysis\n",
    "in its raw format. To extract useful features from it we split\n",
    "the Year,Month and Day and then add them up this will make it \n",
    "available for use in Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that splits date time objects into year,month and day and then adds them up\n",
    "def date_time_func(col):\n",
    "    # Input(col) into this function must be a list type\n",
    "    count=0;result=[]\n",
    "    for x in range(len(col)):\n",
    "        u=col[x].split('-')\n",
    "        for y in u:\n",
    "            v=int(y);count+=v\n",
    "        result.append(count);count=0\n",
    "    return result\n",
    "#Output from this function will be a list type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert each of the datetime column to list then apply date_time_func\n",
    "# for Policy Start Date\n",
    "dates=list(data['Policy Start Date'])\n",
    "#Applying function date_time_func\n",
    "start=date_time_func(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2029, 2050, 2034, 2039, 2047, 2041, 2020, 2031, 2023, 2040]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace policy start date with start\n",
    "data['Policy Start Date']=start\n",
    "# doing same for other date time columns\n",
    "dates_end=list(data['Policy End Date'])\n",
    "end=date_time_func(dates_end)\n",
    "data['Policy End Date']=end\n",
    "dates_trans=list(data['First Transaction Date'])\n",
    "tran=date_time_func(dates_trans)\n",
    "data['First Transaction Date']=tran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['First Transaction Date']=tran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2029\n",
       "1       2050\n",
       "2       2034\n",
       "3       2039\n",
       "4       2047\n",
       "        ... \n",
       "5172    2035\n",
       "5173    2026\n",
       "5174    2043\n",
       "5175    2034\n",
       "5176    2034\n",
       "Name: First Transaction Date, Length: 17256, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['First Transaction Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17256 entries, 0 to 5176\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   ID                      17256 non-null  object\n",
      " 1   Policy Start Date       17256 non-null  int64 \n",
      " 2   Policy End Date         17256 non-null  int64 \n",
      " 3   Gender                  16741 non-null  object\n",
      " 4   Age                     17256 non-null  int64 \n",
      " 5   First Transaction Date  17256 non-null  int64 \n",
      " 6   No_Pol                  17256 non-null  int64 \n",
      " 7   Car_Category            11880 non-null  object\n",
      " 8   Subject_Car_Colour      7289 non-null   object\n",
      " 9   Subject_Car_Make        13719 non-null  object\n",
      " 10  LGA_Name                7998 non-null   object\n",
      " 11  State                   7980 non-null   object\n",
      " 12  ProductName             17256 non-null  object\n",
      "dtypes: int64(5), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male            10888\n",
       "Female           4735\n",
       "Entity            413\n",
       "Joint Gender      306\n",
       "NOT STATED        244\n",
       "NO GENDER         101\n",
       "SEX                54\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gender Column have some missing values in addition to other \n",
    "gender attributes that are not clearly specified\n",
    "We convert every other gender value that is not Male or Female\n",
    "into missing values for ease of grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Gender'].replace(['NOT STATED','NO GENDER','SEX','Joint Gender','Entity'],[np.nan,np.nan,np.nan,np.nan,np.nan],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      10888\n",
       "Female     4735\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1633"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17256 entries, 0 to 5176\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   ID                      17256 non-null  object\n",
      " 1   Policy Start Date       17256 non-null  int64 \n",
      " 2   Policy End Date         17256 non-null  int64 \n",
      " 3   Gender                  15623 non-null  object\n",
      " 4   Age                     17256 non-null  int64 \n",
      " 5   First Transaction Date  17256 non-null  int64 \n",
      " 6   No_Pol                  17256 non-null  int64 \n",
      " 7   Car_Category            11880 non-null  object\n",
      " 8   Subject_Car_Colour      7289 non-null   object\n",
      " 9   Subject_Car_Make        13719 non-null  object\n",
      " 10  LGA_Name                7998 non-null   object\n",
      " 11  State                   7980 non-null   object\n",
      " 12  ProductName             17256 non-null  object\n",
      "dtypes: int64(5), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 40      1171\n",
       " 50       889\n",
       " 41       707\n",
       " 36       705\n",
       " 38       664\n",
       "         ... \n",
       " 17         1\n",
       " 88         1\n",
       " 102        1\n",
       " 133        1\n",
       "-6099       1\n",
       "Name: Age, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17256.000000\n",
       "mean        42.677272\n",
       "std         82.489880\n",
       "min      -6099.000000\n",
       "25%         34.000000\n",
       "50%         40.000000\n",
       "75%         50.000000\n",
       "max        320.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Age Feature using Boxplot\n",
    "plt.close();plt.figure(figsize=(3,3))\n",
    "sns.boxplot(data=data,x='Age')\n",
    "plt.xlabel('Values');\n",
    "plt.grid('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Boxplot we can see that There are some unusual\n",
    "values in the Age such as Ages less than 0 These values are unusual and indicates an errors in the Age Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17256.000000\n",
       "mean        42.677272\n",
       "std         82.489880\n",
       "min      -6099.000000\n",
       "25%         34.000000\n",
       "50%         40.000000\n",
       "75%         50.000000\n",
       "max        320.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min values in Age is -6099 while the maximum value is 320\n",
    "These values are impractical and we have little or no information about them.\n",
    "Our Preferred solution to this is to replace all Ages less than 0 and greater than 100 with NaN\n",
    "And thereafter fill them with the mean of the Ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17256.000000\n",
       "mean        41.057600\n",
       "std         13.862711\n",
       "min          0.000000\n",
       "25%         35.000000\n",
       "50%         40.000000\n",
       "75%         48.000000\n",
       "max        100.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handling the Age column\n",
    "#For values greater than 100\n",
    "age=np.where(data.Age>100,np.nan,data.Age)\n",
    "data.Age=age\n",
    "#For values less than 0\n",
    "age=np.where(data.Age<0,np.nan,data.Age)\n",
    "data.Age=age\n",
    "data.Age.fillna(data.Age.mean(),inplace=True)\n",
    "data.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17256 entries, 0 to 5176\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ID                      17256 non-null  object \n",
      " 1   Policy Start Date       17256 non-null  int64  \n",
      " 2   Policy End Date         17256 non-null  int64  \n",
      " 3   Gender                  15623 non-null  object \n",
      " 4   Age                     17256 non-null  float64\n",
      " 5   First Transaction Date  17256 non-null  int64  \n",
      " 6   No_Pol                  17256 non-null  int64  \n",
      " 7   Car_Category            11880 non-null  object \n",
      " 8   Subject_Car_Colour      7289 non-null   object \n",
      " 9   Subject_Car_Make        13719 non-null  object \n",
      " 10  LGA_Name                7998 non-null   object \n",
      " 11  State                   7980 non-null   object \n",
      " 12  ProductName             17256 non-null  object \n",
      "dtypes: float64(1), int64(4), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of missing values Cat Codes encoder from the pandas package is a suitable encoder\n",
    "for NaN putting them in a seperate group of -1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>165</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>17242</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5173</th>\n",
       "      <td>17244</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>258</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5174</th>\n",
       "      <td>17246</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>17251</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5176</th>\n",
       "      <td>17255</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>55</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17256 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Gender  Car_Category  Subject_Car_Colour  Subject_Car_Make  \\\n",
       "0         0       1             8                   4                68   \n",
       "1         1       0             2                  26                68   \n",
       "2         2       1             8                  32                68   \n",
       "3         3       1            -1                  -1                -1   \n",
       "4         5      -1            -1                  -1                -1   \n",
       "...     ...     ...           ...                 ...               ...   \n",
       "5172  17242       1             8                  -1                27   \n",
       "5173  17244      -1            -1                  -1                -1   \n",
       "5174  17246       1            -1                  -1                -1   \n",
       "5175  17251       0             8                  26                68   \n",
       "5176  17255       1             8                  -1                55   \n",
       "\n",
       "      LGA_Name  State  ProductName  \n",
       "0           -1     -1            1  \n",
       "1           -1     -1            1  \n",
       "2           -1     -1            1  \n",
       "3           -1     -1            5  \n",
       "4          165     74            8  \n",
       "...        ...    ...          ...  \n",
       "5172        -1     -1            5  \n",
       "5173       258     74            1  \n",
       "5174        -1     -1            5  \n",
       "5175        -1     -1            1  \n",
       "5176        -1     -1            5  \n",
       "\n",
       "[17256 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For cat codes first seperate the features with object dtypes/\n",
    "object_data=data.select_dtypes('object')\n",
    "# Convert object_data dtypes from object to category\n",
    "object_data=object_data.astype('category')\n",
    "# iterate over each column in object_data and convert each to cat codes\n",
    "for x in object_data.columns:\n",
    "    object_data[x]=object_data[x].cat.codes\n",
    "object_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace columns with object dtype in data with the encodings from object_data to complete encoding\n",
    "for x in object_data.columns:\n",
    "    data[x]=object_data[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Policy Start Date</th>\n",
       "      <th>Policy End Date</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Car_Category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>2029</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2034</td>\n",
       "      <td>2034</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2034</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2039</td>\n",
       "      <td>2039</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2047</td>\n",
       "      <td>2053</td>\n",
       "      <td>-1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2047</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>165</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2041</td>\n",
       "      <td>2041</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2041</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>123</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2031</td>\n",
       "      <td>2031</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2031</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2040</td>\n",
       "      <td>2040</td>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2040</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>89</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Policy Start Date  Policy End Date  Gender   Age  \\\n",
       "0   0               2029             2029       1  30.0   \n",
       "1   1               2050             2050       0  79.0   \n",
       "2   2               2034             2034       1  43.0   \n",
       "3   3               2039             2039       1   2.0   \n",
       "4   5               2047             2053      -1  20.0   \n",
       "5   6               2041             2041       1  37.0   \n",
       "6   7               2020             2020       0  40.0   \n",
       "7   8               2031             2031       1  43.0   \n",
       "8   9               2023             2023       0  71.0   \n",
       "9  10               2040             2040       0  58.0   \n",
       "\n",
       "   First Transaction Date  No_Pol  Car_Category  Subject_Car_Colour  \\\n",
       "0                    2029       1             8                   4   \n",
       "1                    2050       1             2                  26   \n",
       "2                    2034       1             8                  32   \n",
       "3                    2039       1            -1                  -1   \n",
       "4                    2047       3            -1                  -1   \n",
       "5                    2041       2            -1                  -1   \n",
       "6                    2020       3            -1                  -1   \n",
       "7                    2031       1             8                  -1   \n",
       "8                    2023       2             2                   4   \n",
       "9                    2040       1            -1                  -1   \n",
       "\n",
       "   Subject_Car_Make  LGA_Name  State  ProductName  \n",
       "0                68        -1     -1            1  \n",
       "1                68        -1     -1            1  \n",
       "2                68        -1     -1            1  \n",
       "3                -1        -1     -1            5  \n",
       "4                -1       165     74            8  \n",
       "5                -1        -1     -1            1  \n",
       "6                -1       123     74            0  \n",
       "7                68        -1     -1            5  \n",
       "8                58        52     23            1  \n",
       "9                -1        89     39            1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entries with value -1 were the Null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE_EXTRACTION(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The Policy Range or Duration can be gotten from the absolute value Difference between \n",
    "the Policy End Date and Policy Start Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Policy Range']=np.abs(data['Policy End Date'])-np.abs(data['Policy Start Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The Difference between The Policy Start Date \n",
    "and a Fixed point in Time in this case 2020 is gotten "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Policy_Status']=2020-data['Policy Start Date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Combine the car category feature with subject car make by summing them up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['cat_make']=data['Car_Category']+data['Subject_Car_Make']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Combine Subject Car Colour Feature and Car Category feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Cat_Col']=data['Car_Category']-data['Subject_Car_Colour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   ID  Policy Start Date  Policy End Date  Gender   Age  \\\n",
       "0   0               2029             2029       1  30.0   \n",
       "1   1               2050             2050       0  79.0   \n",
       "2   2               2034             2034       1  43.0   \n",
       "3   3               2039             2039       1   2.0   \n",
       "4   5               2047             2053      -1  20.0   \n",
       "5   6               2041             2041       1  37.0   \n",
       "6   7               2020             2020       0  40.0   \n",
       "7   8               2031             2031       1  43.0   \n",
       "8   9               2023             2023       0  71.0   \n",
       "9  10               2040             2040       0  58.0   \n",
       "\n",
       "   First Transaction Date  No_Pol  Car_Category  Subject_Car_Colour  \\\n",
       "0                    2029       1             8                   4   \n",
       "1                    2050       1             2                  26   \n",
       "2                    2034       1             8                  32   \n",
       "3                    2039       1            -1                  -1   \n",
       "4                    2047       3            -1                  -1   \n",
       "5                    2041       2            -1                  -1   \n",
       "6                    2020       3            -1                  -1   \n",
       "7                    2031       1             8                  -1   \n",
       "8                    2023       2             2                   4   \n",
       "9                    2040       1            -1                  -1   \n",
       "\n",
       "   Subject_Car_Make  LGA_Name  State  ProductName  Policy Range  \\\n",
       "0                68        -1     -1            1             0   \n",
       "1                68        -1     -1            1             0   \n",
       "2                68        -1     -1            1             0   \n",
       "3                -1        -1     -1            5             0   \n",
       "4                -1       165     74            8             6   \n",
       "5                -1        -1     -1            1             0   \n",
       "6                -1       123     74            0             0   \n",
       "7                68        -1     -1            5             0   \n",
       "8                58        52     23            1             0   \n",
       "9                -1        89     39            1             0   \n",
       "\n",
       "   Policy_Status  cat_make  Cat_Col  \n",
       "0             -9        76        4  \n",
       "1            -30        70      -24  \n",
       "2            -14        76      -24  \n",
       "3            -19        -2        0  \n",
       "4            -27        -2        0  \n",
       "5            -21        -2        0  \n",
       "6              0        -2        0  \n",
       "7            -11        76        9  \n",
       "8             -3        60       -2  \n",
       "9            -20        -2        0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get Dummy Variable for Gender and ProductName Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dummy=pd.get_dummies(data,columns=['Gender','ProductName'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>52.0</td>\n",
       "      <td>2034</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>5176</th>\n",
       "      <td>17255</td>\n",
       "      <td>2034</td>\n",
       "      <td>2034</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2034</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>55</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17256 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Policy Start Date  Policy End Date   Age  First Transaction Date  \\\n",
       "0         0               2029             2029  30.0                    2029   \n",
       "1         1               2050             2050  79.0                    2050   \n",
       "2         2               2034             2034  43.0                    2034   \n",
       "3         3               2039             2039   2.0                    2039   \n",
       "4         5               2047             2053  20.0                    2047   \n",
       "...     ...                ...              ...   ...                     ...   \n",
       "5172  17242               2035             2035  48.0                    2035   \n",
       "5173  17244               2026             2026  50.0                    2026   \n",
       "5174  17246               2043             2043   1.0                    2043   \n",
       "5175  17251               2034             2034  52.0                    2034   \n",
       "5176  17255               2034             2034  68.0                    2034   \n",
       "\n",
       "      No_Pol  Car_Category  Subject_Car_Colour  Subject_Car_Make  LGA_Name  \\\n",
       "0          1             8                   4                68        -1   \n",
       "1          1             2                  26                68        -1   \n",
       "2          1             8                  32                68        -1   \n",
       "3          1            -1                  -1                -1        -1   \n",
       "4          3            -1                  -1                -1       165   \n",
       "...      ...           ...                 ...               ...       ...   \n",
       "5172       1             8                  -1                27        -1   \n",
       "5173       1            -1                  -1                -1       258   \n",
       "5174       1            -1                  -1                -1        -1   \n",
       "5175       1             8                  26                68        -1   \n",
       "5176       1             8                  -1                55        -1   \n",
       "\n",
       "      ...  Gender_0  Gender_1  ProductName_1  ProductName_2  ProductName_3  \\\n",
       "0     ...         0         1              1              0              0   \n",
       "1     ...         1         0              1              0              0   \n",
       "2     ...         0         1              1              0              0   \n",
       "3     ...         0         1              0              0              0   \n",
       "4     ...         0         0              0              0              0   \n",
       "...   ...       ...       ...            ...            ...            ...   \n",
       "5172  ...         0         1              0              0              0   \n",
       "5173  ...         0         0              1              0              0   \n",
       "5174  ...         0         1              0              0              0   \n",
       "5175  ...         1         0              1              0              0   \n",
       "5176  ...         0         1              0              0              0   \n",
       "\n",
       "      ProductName_4  ProductName_5  ProductName_6  ProductName_7  \\\n",
       "0                 0              0              0              0   \n",
       "1                 0              0              0              0   \n",
       "2                 0              0              0              0   \n",
       "3                 0              1              0              0   \n",
       "4                 0              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "5172              0              1              0              0   \n",
       "5173              0              0              0              0   \n",
       "5174              0              1              0              0   \n",
       "5175              0              0              0              0   \n",
       "5176              0              1              0              0   \n",
       "\n",
       "      ProductName_8  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 1  \n",
       "...             ...  \n",
       "5172              0  \n",
       "5173              0  \n",
       "5174              0  \n",
       "5175              0  \n",
       "5176              0  \n",
       "\n",
       "[17256 rows x 25 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17256, 17)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dummy.drop('ID',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We first split our Dataset back into train and test data\n",
    "train_data=data_dummy.iloc[:12079]\n",
    "test_data=data_dummy.iloc[12079:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12079, 24)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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>Policy Start Date</th>\n",
       "      <th>Policy End Date</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>...</th>\n",
       "      <th>Gender_0</th>\n",
       "      <th>Gender_1</th>\n",
       "      <th>ProductName_1</th>\n",
       "      <th>ProductName_2</th>\n",
       "      <th>ProductName_3</th>\n",
       "      <th>ProductName_4</th>\n",
       "      <th>ProductName_5</th>\n",
       "      <th>ProductName_6</th>\n",
       "      <th>ProductName_7</th>\n",
       "      <th>ProductName_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2038</td>\n",
       "      <td>2041</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>2038</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>258</td>\n",
       "      <td>74</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>2053</td>\n",
       "      <td>41.0576</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>258</td>\n",
       "      <td>74</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2043</td>\n",
       "      <td>2043</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>2043</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2034</td>\n",
       "      <td>2034</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>2034</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>161</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>2035</td>\n",
       "      <td>2053</td>\n",
       "      <td>41.0576</td>\n",
       "      <td>2035</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>68</td>\n",
       "      <td>258</td>\n",
       "      <td>74</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2047</td>\n",
       "      <td>2047</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>2047</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>263</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>6</th>\n",
       "      <td>2029</td>\n",
       "      <td>2029</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>2029</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>2047</td>\n",
       "      <td>41.0576</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>258</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>8</th>\n",
       "      <td>2038</td>\n",
       "      <td>2034</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>2038</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>68</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2035</td>\n",
       "      <td>2034</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>2035</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Policy Start Date  Policy End Date      Age  First Transaction Date  \\\n",
       "0               2038             2041  25.0000                    2038   \n",
       "1               2012             2053  41.0576                    2012   \n",
       "2               2043             2043  46.0000                    2043   \n",
       "3               2034             2034  32.0000                    2034   \n",
       "4               2035             2053  41.0576                    2035   \n",
       "5               2047             2047  45.0000                    2047   \n",
       "6               2029             2029  58.0000                    2029   \n",
       "7               2018             2047  41.0576                    2018   \n",
       "8               2038             2034  37.0000                    2038   \n",
       "9               2035             2034  42.0000                    2035   \n",
       "\n",
       "   No_Pol  Car_Category  Subject_Car_Colour  Subject_Car_Make  LGA_Name  \\\n",
       "0       1            -1                   0                34       258   \n",
       "1       4            -1                   0                68       258   \n",
       "2       1            -1                  -1                22        18   \n",
       "3       1            -1                  -1                -1       161   \n",
       "4       4            -1                  -1                68       258   \n",
       "5       2             8                   4                27       263   \n",
       "6       1             8                  -1                68        -1   \n",
       "7       1             8                  32                30       258   \n",
       "8       1             2                  -1                68        -1   \n",
       "9       1             2                  -1                 2        -1   \n",
       "\n",
       "   State  ...  Gender_0  Gender_1  ProductName_1  ProductName_2  \\\n",
       "0     74  ...         0         0              0              0   \n",
       "1     74  ...         0         0              0              0   \n",
       "2      7  ...         1         0              1              0   \n",
       "3     23  ...         0         1              1              0   \n",
       "4     74  ...         0         0              0              0   \n",
       "5      6  ...         1         0              1              0   \n",
       "6     -1  ...         1         0              0              0   \n",
       "7     74  ...         0         0              1              0   \n",
       "8     -1  ...         1         0              0              0   \n",
       "9     -1  ...         0         1              0              0   \n",
       "\n",
       "   ProductName_3  ProductName_4  ProductName_5  ProductName_6  ProductName_7  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "5              0              0              0              0              0   \n",
       "6              0              0              1              0              0   \n",
       "7              0              0              0              0              0   \n",
       "8              0              1              0              0              0   \n",
       "9              0              0              1              0              0   \n",
       "\n",
       "   ProductName_8  \n",
       "0              0  \n",
       "1              1  \n",
       "2              0  \n",
       "3              0  \n",
       "4              1  \n",
       "5              0  \n",
       "6              0  \n",
       "7              0  \n",
       "8              0  \n",
       "9              0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL BUILDING "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Fit a DecisionTreeClassifier on the Data\n",
    "Note: Scaling the data is not necessary Because we are fitting a DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting a Decision Tree Classifier\n",
    "tree_clf=DecisionTreeClassifier(random_state=42)\n",
    "seed=42\n",
    "tree_clf.fit(train_data,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9449 1175]\n",
      " [1147  308]]\n",
      "f1_score: 0.20966643975493532\n"
     ]
    }
   ],
   "source": [
    "#Evaluate using k-fold Validation\n",
    "tree_pre=cross_val_predict(tree_clf,train_data,y_train,cv=5)\n",
    "# Confusion Matrix\n",
    "print(confusion_matrix(y_train,tree_pre))\n",
    "#F1_score\n",
    "print('f1_score:',f1_score(y_train,tree_pre))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From The confusion matrix of the DecisionTreeClassifier\n",
    "it can be seen that the classifier performs very well on\n",
    "one class which is the negative class but fails woefully on predicting the \n",
    "other class which is the positive class. This may be attributed to the skewness in the dataset\n",
    "i.e The imbalance between the Positive class target and the Negative Class target, they are skewed\n",
    "in a ratio of 10:90 therefore the classifier tend to predict  Positive classes as a negative classes because it has little information about the positive classes of the target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possible Solutions to this is:\n",
    "1. Use of Weights to compel the classifier to pay more attention to the classes with lesser occurence\n",
    "2. OverSampling the Fewer classes instances or Under Sampling the Larger Class Instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Our Solution We Used the RandomOverSampler from the over_sampling class in imblearn module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19123, 24)\n",
      "(19123,)\n"
     ]
    }
   ],
   "source": [
    "# Over_sampling using RandomOverSampler from imblearn module\n",
    "over=RandomOverSampler(sampling_strategy=0.8)\n",
    "#We are making the fewer instances to be 0.8% of the mucher instances\n",
    "x_over,y_over=over.fit_resample(train_data,y_train)\n",
    "print(x_over.shape)\n",
    "print(y_over.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Train dataset now contains a total of 19123 of duplicating instances up from the original 12079  skewd instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refitting the tree_clf on this new data \n",
    "tree_clf.fit(x_over,y_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8981 1643]\n",
      " [  81 8418]]\n",
      "F1_score: 0.9071120689655172\n"
     ]
    }
   ],
   "source": [
    "#Evaluating using k-fold cross validation\n",
    "tree_pre=cross_val_predict(tree_clf,x_over,y_over,cv=5)\n",
    "print(confusion_matrix(y_over,tree_pre))\n",
    "print('F1_score:',f1_score(y_over,tree_pre))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The F1_score of the over_sampled dataset from above is 0.907\n",
    "this is a stark increase from the first F1_score of 0.20 gotren from the original skewd datset\n",
    ".This score may not represent the true F1_score due to the fact that over-sampling of data normally leads to overfitting\n",
    "in some instances and in this case it is overfitting the data. To reduce the risk of overfitting we use an ensemble classifiers that fits\n",
    "ny trees on diffrent subset of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we try to fit the the RandomForestClassifier with different \n",
    "number of estimators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators=[2,5,10,15,20,40,50] #List of different estimators\n",
    "scores=[] # A list to record the possible scores\n",
    "for x in estimators:\n",
    "    forest_clf=RandomForestClassifier(random_state=42,n_estimators=x)\n",
    "    # K-Fold validation\n",
    "    forest_pre=cross_val_predict(forest_clf,x_over,y_over,cv=5)\n",
    "    scores.append(f1_score(y_over,forest_pre))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "print(len(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot The Number of estimators against Scores\n",
    "plt.close()\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.plot(estimators,scores,'r*--')\n",
    "plt.xlabel('N_of_Estimators');plt.ylabel('Scores')\n",
    "plt.title('Random_Forest_N_Estimators_Scores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the plot above we pick the n_estimators with the highest score in this case 50\n",
    "and retrain our classifier on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=50, random_state=42)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RandomForestClassifier Model\n",
    "forest_clf=RandomForestClassifier(n_estimators=50,random_state=42)\n",
    "#fitting the model\n",
    "forest_clf.fit(x_over,y_over)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9547 1077]\n",
      " [  60 8439]]\n",
      "F1_score: 0.9368859283930059\n"
     ]
    }
   ],
   "source": [
    "#K-Fold Validation\n",
    "forest_pre=cross_val_predict(forest_clf,x_over,y_over,cv=5)\n",
    "print(confusion_matrix(y_over,forest_pre))\n",
    "print('F1_score:',f1_score(y_over,forest_pre))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RandomForestClassifier performs genrally better but still suffers greatly from overfitting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>009D84L</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01DO2EQ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01QM0NU</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>024NJLZ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02BYET3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>ZYXX5AF</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5173</th>\n",
       "      <td>ZYYOZ5L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5174</th>\n",
       "      <td>ZZ1GTKD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>ZZDXQSI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5176</th>\n",
       "      <td>ZZYTLV1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5177 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  target\n",
       "0     009D84L       1\n",
       "1     01DO2EQ       1\n",
       "2     01QM0NU       0\n",
       "3     024NJLZ       0\n",
       "4     02BYET3       1\n",
       "...       ...     ...\n",
       "5172  ZYXX5AF       0\n",
       "5173  ZYYOZ5L       0\n",
       "5174  ZZ1GTKD       0\n",
       "5175  ZZDXQSI       1\n",
       "5176  ZZYTLV1       0\n",
       "\n",
       "[5177 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict Test Instances\n",
    "test_pre=forest_clf.predict(test_data)\n",
    "#Import SampleSubmission\n",
    "sub=pd.read_csv('SampleSubmission.csv')\n",
    "# Replace 'target' column in SampleSubmission to test prediction\n",
    "sub['target']=test_pre\n",
    "sub"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
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