Insurance_fraud_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07c611f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sbn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59e06ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"fraud_oracle.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2b58bd03",
   "metadata": {
    "scrolled": true
   },
   "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>Month</th>\n",
       "      <th>WeekOfMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Make</th>\n",
       "      <th>AccidentArea</th>\n",
       "      <th>DayOfWeekClaimed</th>\n",
       "      <th>MonthClaimed</th>\n",
       "      <th>WeekOfMonthClaimed</th>\n",
       "      <th>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>AddressChange_Claim</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>Year</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dec</td>\n",
       "      <td>5</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Jan</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>3 years</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>1 year</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>1994</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Jan</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>6 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>no change</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>1994</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oct</td>\n",
       "      <td>5</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Nov</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>7 years</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>no change</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>1994</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jun</td>\n",
       "      <td>2</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Jul</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>...</td>\n",
       "      <td>more than 7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>no change</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>1994</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>5 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>no change</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>1994</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month  WeekOfMonth  DayOfWeek    Make AccidentArea DayOfWeekClaimed  \\\n",
       "0   Dec            5  Wednesday   Honda        Urban          Tuesday   \n",
       "1   Jan            3  Wednesday   Honda        Urban           Monday   \n",
       "2   Oct            5     Friday   Honda        Urban         Thursday   \n",
       "3   Jun            2   Saturday  Toyota        Rural           Friday   \n",
       "4   Jan            5     Monday   Honda        Urban          Tuesday   \n",
       "\n",
       "  MonthClaimed  WeekOfMonthClaimed     Sex MaritalStatus  ...  AgeOfVehicle  \\\n",
       "0          Jan                   1  Female        Single  ...       3 years   \n",
       "1          Jan                   4    Male        Single  ...       6 years   \n",
       "2          Nov                   2    Male       Married  ...       7 years   \n",
       "3          Jul                   1    Male       Married  ...   more than 7   \n",
       "4          Feb                   2  Female        Single  ...       5 years   \n",
       "\n",
       "  AgeOfPolicyHolder PoliceReportFiled WitnessPresent AgentType  \\\n",
       "0          26 to 30                No             No  External   \n",
       "1          31 to 35               Yes             No  External   \n",
       "2          41 to 50                No             No  External   \n",
       "3          51 to 65               Yes             No  External   \n",
       "4          31 to 35                No             No  External   \n",
       "\n",
       "   NumberOfSuppliments  AddressChange_Claim  NumberOfCars  Year  BasePolicy  \n",
       "0                 none               1 year        3 to 4  1994   Liability  \n",
       "1                 none            no change     1 vehicle  1994   Collision  \n",
       "2                 none            no change     1 vehicle  1994   Collision  \n",
       "3          more than 5            no change     1 vehicle  1994   Liability  \n",
       "4                 none            no change     1 vehicle  1994   Collision  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef5488f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'WeekOfMonth', 'DayOfWeek', 'Make', 'AccidentArea',\n",
       "       'DayOfWeekClaimed', 'MonthClaimed', 'WeekOfMonthClaimed', 'Sex',\n",
       "       'MaritalStatus', 'Age', 'Fault', 'PolicyType', 'VehicleCategory',\n",
       "       'VehiclePrice', 'FraudFound_P', 'PolicyNumber', 'RepNumber',\n",
       "       'Deductible', 'DriverRating', 'Days_Policy_Accident',\n",
       "       'Days_Policy_Claim', 'PastNumberOfClaims', 'AgeOfVehicle',\n",
       "       'AgeOfPolicyHolder', 'PoliceReportFiled', 'WitnessPresent', 'AgentType',\n",
       "       'NumberOfSuppliments', 'AddressChange_Claim', 'NumberOfCars', 'Year',\n",
       "       'BasePolicy'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17e02325",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15420 entries, 0 to 15419\n",
      "Data columns (total 33 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Month                 15420 non-null  object\n",
      " 1   WeekOfMonth           15420 non-null  int64 \n",
      " 2   DayOfWeek             15420 non-null  object\n",
      " 3   Make                  15420 non-null  object\n",
      " 4   AccidentArea          15420 non-null  object\n",
      " 5   DayOfWeekClaimed      15420 non-null  object\n",
      " 6   MonthClaimed          15420 non-null  object\n",
      " 7   WeekOfMonthClaimed    15420 non-null  int64 \n",
      " 8   Sex                   15420 non-null  object\n",
      " 9   MaritalStatus         15420 non-null  object\n",
      " 10  Age                   15420 non-null  int64 \n",
      " 11  Fault                 15420 non-null  object\n",
      " 12  PolicyType            15420 non-null  object\n",
      " 13  VehicleCategory       15420 non-null  object\n",
      " 14  VehiclePrice          15420 non-null  object\n",
      " 15  FraudFound_P          15420 non-null  int64 \n",
      " 16  PolicyNumber          15420 non-null  int64 \n",
      " 17  RepNumber             15420 non-null  int64 \n",
      " 18  Deductible            15420 non-null  int64 \n",
      " 19  DriverRating          15420 non-null  int64 \n",
      " 20  Days_Policy_Accident  15420 non-null  object\n",
      " 21  Days_Policy_Claim     15420 non-null  object\n",
      " 22  PastNumberOfClaims    15420 non-null  object\n",
      " 23  AgeOfVehicle          15420 non-null  object\n",
      " 24  AgeOfPolicyHolder     15420 non-null  object\n",
      " 25  PoliceReportFiled     15420 non-null  object\n",
      " 26  WitnessPresent        15420 non-null  object\n",
      " 27  AgentType             15420 non-null  object\n",
      " 28  NumberOfSuppliments   15420 non-null  object\n",
      " 29  AddressChange_Claim   15420 non-null  object\n",
      " 30  NumberOfCars          15420 non-null  object\n",
      " 31  Year                  15420 non-null  int64 \n",
      " 32  BasePolicy            15420 non-null  object\n",
      "dtypes: int64(9), object(24)\n",
      "memory usage: 3.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c135afa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "l=list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66368b6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan    1411\n",
      "May    1367\n",
      "Mar    1360\n",
      "Jun    1321\n",
      "Oct    1305\n",
      "Dec    1285\n",
      "Apr    1280\n",
      "Feb    1266\n",
      "Jul    1257\n",
      "Sep    1240\n",
      "Nov    1201\n",
      "Aug    1127\n",
      "Name: Month, dtype: int64\n",
      "3    3640\n",
      "2    3558\n",
      "4    3398\n",
      "1    3187\n",
      "5    1637\n",
      "Name: WeekOfMonth, dtype: int64\n",
      "Monday       2616\n",
      "Friday       2445\n",
      "Tuesday      2300\n",
      "Thursday     2173\n",
      "Wednesday    2159\n",
      "Saturday     1982\n",
      "Sunday       1745\n",
      "Name: DayOfWeek, dtype: int64\n",
      "Pontiac      3837\n",
      "Toyota       3121\n",
      "Honda        2801\n",
      "Mazda        2354\n",
      "Chevrolet    1681\n",
      "Accura        472\n",
      "Ford          450\n",
      "VW            283\n",
      "Dodge         109\n",
      "Saab          108\n",
      "Mercury        83\n",
      "Saturn         58\n",
      "Nisson         30\n",
      "BMW            15\n",
      "Jaguar          6\n",
      "Porche          5\n",
      "Mecedes         4\n",
      "Ferrari         2\n",
      "Lexus           1\n",
      "Name: Make, dtype: int64\n",
      "Urban    13822\n",
      "Rural     1598\n",
      "Name: AccidentArea, dtype: int64\n",
      "Monday       3757\n",
      "Tuesday      3375\n",
      "Wednesday    2951\n",
      "Thursday     2660\n",
      "Friday       2497\n",
      "Saturday      127\n",
      "Sunday         52\n",
      "0               1\n",
      "Name: DayOfWeekClaimed, dtype: int64\n",
      "Jan    1446\n",
      "May    1411\n",
      "Mar    1348\n",
      "Oct    1339\n",
      "Jun    1293\n",
      "Feb    1287\n",
      "Nov    1285\n",
      "Apr    1271\n",
      "Sep    1242\n",
      "Jul    1225\n",
      "Dec    1146\n",
      "Aug    1126\n",
      "0         1\n",
      "Name: MonthClaimed, dtype: int64\n",
      "2    3720\n",
      "3    3583\n",
      "1    3450\n",
      "4    3433\n",
      "5    1234\n",
      "Name: WeekOfMonthClaimed, dtype: int64\n",
      "Male      13000\n",
      "Female     2420\n",
      "Name: Sex, dtype: int64\n",
      "Married     10625\n",
      "Single       4684\n",
      "Divorced       76\n",
      "Widow          35\n",
      "Name: MaritalStatus, dtype: int64\n",
      "30    596\n",
      "33    574\n",
      "34    573\n",
      "35    569\n",
      "28    560\n",
      "     ... \n",
      "20     28\n",
      "70     27\n",
      "79     20\n",
      "16      9\n",
      "17      6\n",
      "Name: Age, Length: 66, dtype: int64\n",
      "Policy Holder    11230\n",
      "Third Party       4190\n",
      "Name: Fault, dtype: int64\n",
      "Sedan - Collision       5584\n",
      "Sedan - Liability       4987\n",
      "Sedan - All Perils      4087\n",
      "Sport - Collision        348\n",
      "Utility - All Perils     340\n",
      "Utility - Collision       30\n",
      "Sport - All Perils        22\n",
      "Utility - Liability       21\n",
      "Sport - Liability          1\n",
      "Name: PolicyType, dtype: int64\n",
      "Sedan      9671\n",
      "Sport      5358\n",
      "Utility     391\n",
      "Name: VehicleCategory, dtype: int64\n",
      "20000 to 29000     8079\n",
      "30000 to 39000     3533\n",
      "more than 69000    2164\n",
      "less than 20000    1096\n",
      "40000 to 59000      461\n",
      "60000 to 69000       87\n",
      "Name: VehiclePrice, dtype: int64\n",
      "0    14497\n",
      "1      923\n",
      "Name: FraudFound_P, dtype: int64\n",
      "1        1\n",
      "10361    1\n",
      "10273    1\n",
      "10274    1\n",
      "10275    1\n",
      "        ..\n",
      "5145     1\n",
      "5146     1\n",
      "5147     1\n",
      "5148     1\n",
      "15420    1\n",
      "Name: PolicyNumber, Length: 15420, dtype: int64\n",
      "7     1069\n",
      "9      999\n",
      "1      987\n",
      "5      987\n",
      "10     986\n",
      "12     977\n",
      "15     977\n",
      "16     967\n",
      "2      956\n",
      "3      949\n",
      "11     948\n",
      "6      942\n",
      "14     941\n",
      "8      931\n",
      "4      912\n",
      "13     892\n",
      "Name: RepNumber, dtype: int64\n",
      "400    14838\n",
      "700      311\n",
      "500      263\n",
      "300        8\n",
      "Name: Deductible, dtype: int64\n",
      "1    3944\n",
      "3    3884\n",
      "2    3801\n",
      "4    3791\n",
      "Name: DriverRating, dtype: int64\n",
      "more than 30    15247\n",
      "none               55\n",
      "8 to 15            55\n",
      "15 to 30           49\n",
      "1 to 7             14\n",
      "Name: Days_Policy_Accident, dtype: int64\n",
      "more than 30    15342\n",
      "15 to 30           56\n",
      "8 to 15            21\n",
      "none                1\n",
      "Name: Days_Policy_Claim, dtype: int64\n",
      "2 to 4         5485\n",
      "none           4352\n",
      "1              3573\n",
      "more than 4    2010\n",
      "Name: PastNumberOfClaims, dtype: int64\n",
      "7 years        5807\n",
      "more than 7    3981\n",
      "6 years        3448\n",
      "5 years        1357\n",
      "new             373\n",
      "4 years         229\n",
      "3 years         152\n",
      "2 years          73\n",
      "Name: AgeOfVehicle, dtype: int64\n",
      "31 to 35    5593\n",
      "36 to 40    4043\n",
      "41 to 50    2828\n",
      "51 to 65    1392\n",
      "26 to 30     613\n",
      "over 65      508\n",
      "16 to 17     320\n",
      "21 to 25     108\n",
      "18 to 20      15\n",
      "Name: AgeOfPolicyHolder, dtype: int64\n",
      "No     14992\n",
      "Yes      428\n",
      "Name: PoliceReportFiled, dtype: int64\n",
      "No     15333\n",
      "Yes       87\n",
      "Name: WitnessPresent, dtype: int64\n",
      "External    15179\n",
      "Internal      241\n",
      "Name: AgentType, dtype: int64\n",
      "none           7047\n",
      "more than 5    3867\n",
      "1 to 2         2489\n",
      "3 to 5         2017\n",
      "Name: NumberOfSuppliments, dtype: int64\n",
      "no change         14324\n",
      "4 to 8 years        631\n",
      "2 to 3 years        291\n",
      "1 year              170\n",
      "under 6 months        4\n",
      "Name: AddressChange_Claim, dtype: int64\n",
      "1 vehicle      14316\n",
      "2 vehicles       709\n",
      "3 to 4           372\n",
      "5 to 8            21\n",
      "more than 8        2\n",
      "Name: NumberOfCars, dtype: int64\n",
      "1994    6142\n",
      "1995    5195\n",
      "1996    4083\n",
      "Name: Year, dtype: int64\n",
      "Collision     5962\n",
      "Liability     5009\n",
      "All Perils    4449\n",
      "Name: BasePolicy, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in l:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb9cfe1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eaffeec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15420 entries, 0 to 15419\n",
      "Data columns (total 33 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Month                 15420 non-null  object\n",
      " 1   WeekOfMonth           15420 non-null  int64 \n",
      " 2   DayOfWeek             15420 non-null  object\n",
      " 3   Make                  15420 non-null  object\n",
      " 4   AccidentArea          15420 non-null  object\n",
      " 5   DayOfWeekClaimed      15420 non-null  object\n",
      " 6   MonthClaimed          15420 non-null  object\n",
      " 7   WeekOfMonthClaimed    15420 non-null  int64 \n",
      " 8   Sex                   15420 non-null  object\n",
      " 9   MaritalStatus         15420 non-null  object\n",
      " 10  Age                   15420 non-null  int64 \n",
      " 11  Fault                 15420 non-null  object\n",
      " 12  PolicyType            15420 non-null  object\n",
      " 13  VehicleCategory       15420 non-null  object\n",
      " 14  VehiclePrice          15420 non-null  object\n",
      " 15  FraudFound_P          15420 non-null  int64 \n",
      " 16  PolicyNumber          15420 non-null  int64 \n",
      " 17  RepNumber             15420 non-null  int64 \n",
      " 18  Deductible            15420 non-null  int64 \n",
      " 19  DriverRating          15420 non-null  int64 \n",
      " 20  Days_Policy_Accident  15420 non-null  object\n",
      " 21  Days_Policy_Claim     15420 non-null  object\n",
      " 22  PastNumberOfClaims    15420 non-null  object\n",
      " 23  AgeOfVehicle          15420 non-null  object\n",
      " 24  AgeOfPolicyHolder     15420 non-null  object\n",
      " 25  PoliceReportFiled     15420 non-null  object\n",
      " 26  WitnessPresent        15420 non-null  object\n",
      " 27  AgentType             15420 non-null  object\n",
      " 28  NumberOfSuppliments   15420 non-null  object\n",
      " 29  AddressChange_Claim   15420 non-null  object\n",
      " 30  NumberOfCars          15420 non-null  object\n",
      " 31  Year                  15420 non-null  int64 \n",
      " 32  BasePolicy            15420 non-null  object\n",
      "dtypes: int64(9), object(24)\n",
      "memory usage: 3.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b778a13d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month                   0\n",
       "WeekOfMonth             0\n",
       "DayOfWeek               0\n",
       "Make                    0\n",
       "AccidentArea            0\n",
       "DayOfWeekClaimed        0\n",
       "MonthClaimed            0\n",
       "WeekOfMonthClaimed      0\n",
       "Sex                     0\n",
       "MaritalStatus           0\n",
       "Age                     0\n",
       "Fault                   0\n",
       "PolicyType              0\n",
       "VehicleCategory         0\n",
       "VehiclePrice            0\n",
       "FraudFound_P            0\n",
       "PolicyNumber            0\n",
       "RepNumber               0\n",
       "Deductible              0\n",
       "DriverRating            0\n",
       "Days_Policy_Accident    0\n",
       "Days_Policy_Claim       0\n",
       "PastNumberOfClaims      0\n",
       "AgeOfVehicle            0\n",
       "AgeOfPolicyHolder       0\n",
       "PoliceReportFiled       0\n",
       "WitnessPresent          0\n",
       "AgentType               0\n",
       "NumberOfSuppliments     0\n",
       "AddressChange_Claim     0\n",
       "NumberOfCars            0\n",
       "Year                    0\n",
       "BasePolicy              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ea42700",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_drop=[\"Month\",\n",
    "             \"WeekOfMonth\",\n",
    "             \"DayOfWeek\",\n",
    "             \"Make\",\n",
    "             \"AccidentArea\",\n",
    "             \"DayOfWeekClaimed\",\n",
    "             \"MonthClaimed\",\n",
    "             \"WeekOfMonthClaimed\",\n",
    "             \"Age\",\n",
    "             \"VehicleCategory\",\n",
    "             \"PolicyNumber\",\n",
    "             \"RepNumber\",\n",
    "             \"AddressChange_Claim\",\n",
    "             \"Year\"]\n",
    "# these columns does not have effect on the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86cb20c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=col_to_drop,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "225abaf5",
   "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>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Fault</th>\n",
       "      <th>PolicyType</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>FraudFound_P</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Liability</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>3 years</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>6 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>7 years</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>Third Party</td>\n",
       "      <td>Sedan - Liability</td>\n",
       "      <td>20000 to 29000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>Third Party</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>5 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>External</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex MaritalStatus          Fault         PolicyType     VehiclePrice  \\\n",
       "0  Female        Single  Policy Holder  Sport - Liability  more than 69000   \n",
       "1    Male        Single  Policy Holder  Sport - Collision  more than 69000   \n",
       "2    Male       Married  Policy Holder  Sport - Collision  more than 69000   \n",
       "3    Male       Married    Third Party  Sedan - Liability   20000 to 29000   \n",
       "4  Female        Single    Third Party  Sport - Collision  more than 69000   \n",
       "\n",
       "   FraudFound_P  Deductible  DriverRating Days_Policy_Accident  \\\n",
       "0             0         300             1         more than 30   \n",
       "1             0         400             4         more than 30   \n",
       "2             0         400             3         more than 30   \n",
       "3             0         400             2         more than 30   \n",
       "4             0         400             1         more than 30   \n",
       "\n",
       "  Days_Policy_Claim PastNumberOfClaims AgeOfVehicle AgeOfPolicyHolder  \\\n",
       "0      more than 30               none      3 years          26 to 30   \n",
       "1      more than 30               none      6 years          31 to 35   \n",
       "2      more than 30                  1      7 years          41 to 50   \n",
       "3      more than 30                  1  more than 7          51 to 65   \n",
       "4      more than 30               none      5 years          31 to 35   \n",
       "\n",
       "  PoliceReportFiled WitnessPresent AgentType NumberOfSuppliments NumberOfCars  \\\n",
       "0                No             No  External                none       3 to 4   \n",
       "1               Yes             No  External                none    1 vehicle   \n",
       "2                No             No  External                none    1 vehicle   \n",
       "3               Yes             No  External         more than 5    1 vehicle   \n",
       "4                No             No  External                none    1 vehicle   \n",
       "\n",
       "  BasePolicy  \n",
       "0  Liability  \n",
       "1  Collision  \n",
       "2  Collision  \n",
       "3  Liability  \n",
       "4  Collision  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4ff2189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15420 entries, 0 to 15419\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Sex                   15420 non-null  object\n",
      " 1   MaritalStatus         15420 non-null  object\n",
      " 2   Fault                 15420 non-null  object\n",
      " 3   PolicyType            15420 non-null  object\n",
      " 4   VehiclePrice          15420 non-null  object\n",
      " 5   FraudFound_P          15420 non-null  int64 \n",
      " 6   Deductible            15420 non-null  int64 \n",
      " 7   DriverRating          15420 non-null  int64 \n",
      " 8   Days_Policy_Accident  15420 non-null  object\n",
      " 9   Days_Policy_Claim     15420 non-null  object\n",
      " 10  PastNumberOfClaims    15420 non-null  object\n",
      " 11  AgeOfVehicle          15420 non-null  object\n",
      " 12  AgeOfPolicyHolder     15420 non-null  object\n",
      " 13  PoliceReportFiled     15420 non-null  object\n",
      " 14  WitnessPresent        15420 non-null  object\n",
      " 15  AgentType             15420 non-null  object\n",
      " 16  NumberOfSuppliments   15420 non-null  object\n",
      " 17  NumberOfCars          15420 non-null  object\n",
      " 18  BasePolicy            15420 non-null  object\n",
      "dtypes: int64(3), object(16)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "860dfb5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      13000\n",
      "Female     2420\n",
      "Name: Sex, dtype: int64\n",
      "Married     10625\n",
      "Single       4684\n",
      "Divorced       76\n",
      "Widow          35\n",
      "Name: MaritalStatus, dtype: int64\n",
      "Policy Holder    11230\n",
      "Third Party       4190\n",
      "Name: Fault, dtype: int64\n",
      "Sedan - Collision       5584\n",
      "Sedan - Liability       4987\n",
      "Sedan - All Perils      4087\n",
      "Sport - Collision        348\n",
      "Utility - All Perils     340\n",
      "Utility - Collision       30\n",
      "Sport - All Perils        22\n",
      "Utility - Liability       21\n",
      "Sport - Liability          1\n",
      "Name: PolicyType, dtype: int64\n",
      "20000 to 29000     8079\n",
      "30000 to 39000     3533\n",
      "more than 69000    2164\n",
      "less than 20000    1096\n",
      "40000 to 59000      461\n",
      "60000 to 69000       87\n",
      "Name: VehiclePrice, dtype: int64\n",
      "0    14497\n",
      "1      923\n",
      "Name: FraudFound_P, dtype: int64\n",
      "400    14838\n",
      "700      311\n",
      "500      263\n",
      "300        8\n",
      "Name: Deductible, dtype: int64\n",
      "1    3944\n",
      "3    3884\n",
      "2    3801\n",
      "4    3791\n",
      "Name: DriverRating, dtype: int64\n",
      "more than 30    15247\n",
      "none               55\n",
      "8 to 15            55\n",
      "15 to 30           49\n",
      "1 to 7             14\n",
      "Name: Days_Policy_Accident, dtype: int64\n",
      "more than 30    15342\n",
      "15 to 30           56\n",
      "8 to 15            21\n",
      "none                1\n",
      "Name: Days_Policy_Claim, dtype: int64\n",
      "2 to 4         5485\n",
      "none           4352\n",
      "1              3573\n",
      "more than 4    2010\n",
      "Name: PastNumberOfClaims, dtype: int64\n",
      "7 years        5807\n",
      "more than 7    3981\n",
      "6 years        3448\n",
      "5 years        1357\n",
      "new             373\n",
      "4 years         229\n",
      "3 years         152\n",
      "2 years          73\n",
      "Name: AgeOfVehicle, dtype: int64\n",
      "31 to 35    5593\n",
      "36 to 40    4043\n",
      "41 to 50    2828\n",
      "51 to 65    1392\n",
      "26 to 30     613\n",
      "over 65      508\n",
      "16 to 17     320\n",
      "21 to 25     108\n",
      "18 to 20      15\n",
      "Name: AgeOfPolicyHolder, dtype: int64\n",
      "No     14992\n",
      "Yes      428\n",
      "Name: PoliceReportFiled, dtype: int64\n",
      "No     15333\n",
      "Yes       87\n",
      "Name: WitnessPresent, dtype: int64\n",
      "External    15179\n",
      "Internal      241\n",
      "Name: AgentType, dtype: int64\n",
      "none           7047\n",
      "more than 5    3867\n",
      "1 to 2         2489\n",
      "3 to 5         2017\n",
      "Name: NumberOfSuppliments, dtype: int64\n",
      "1 vehicle      14316\n",
      "2 vehicles       709\n",
      "3 to 4           372\n",
      "5 to 8            21\n",
      "more than 8        2\n",
      "Name: NumberOfCars, dtype: int64\n",
      "Collision     5962\n",
      "Liability     5009\n",
      "All Perils    4449\n",
      "Name: BasePolicy, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in df:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "77456c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label Encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "#Use different encoder for different columns\n",
    "le1=LabelEncoder()\n",
    "le2=LabelEncoder()\n",
    "df[\"Sex\"]=le1.fit_transform(df[\"Sex\"])\n",
    "df[\"AgentType\"]=le2.fit_transform(df[\"AgentType\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f86c6ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b0f384ea",
   "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>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Fault</th>\n",
       "      <th>PolicyType</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>FraudFound_P</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Liability</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>3 years</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>6 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>Policy Holder</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>7 years</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>Third Party</td>\n",
       "      <td>Sedan - Liability</td>\n",
       "      <td>20000 to 29000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Third Party</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>5 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex MaritalStatus          Fault         PolicyType     VehiclePrice  \\\n",
       "0    0        Single  Policy Holder  Sport - Liability  more than 69000   \n",
       "1    1        Single  Policy Holder  Sport - Collision  more than 69000   \n",
       "2    1       Married  Policy Holder  Sport - Collision  more than 69000   \n",
       "3    1       Married    Third Party  Sedan - Liability   20000 to 29000   \n",
       "4    0        Single    Third Party  Sport - Collision  more than 69000   \n",
       "\n",
       "   FraudFound_P  Deductible  DriverRating Days_Policy_Accident  \\\n",
       "0             0         300             1         more than 30   \n",
       "1             0         400             4         more than 30   \n",
       "2             0         400             3         more than 30   \n",
       "3             0         400             2         more than 30   \n",
       "4             0         400             1         more than 30   \n",
       "\n",
       "  Days_Policy_Claim PastNumberOfClaims AgeOfVehicle AgeOfPolicyHolder  \\\n",
       "0      more than 30               none      3 years          26 to 30   \n",
       "1      more than 30               none      6 years          31 to 35   \n",
       "2      more than 30                  1      7 years          41 to 50   \n",
       "3      more than 30                  1  more than 7          51 to 65   \n",
       "4      more than 30               none      5 years          31 to 35   \n",
       "\n",
       "  PoliceReportFiled WitnessPresent  AgentType NumberOfSuppliments  \\\n",
       "0                No             No          0                none   \n",
       "1               Yes             No          0                none   \n",
       "2                No             No          0                none   \n",
       "3               Yes             No          0         more than 5   \n",
       "4                No             No          0                none   \n",
       "\n",
       "  NumberOfCars BasePolicy  \n",
       "0       3 to 4  Liability  \n",
       "1    1 vehicle  Collision  \n",
       "2    1 vehicle  Collision  \n",
       "3    1 vehicle  Liability  \n",
       "4    1 vehicle  Collision  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1da89bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#custom encoding\n",
    "df[\"Fault\"]=df[\"Fault\"].map({\"Policy Holder\":0,\"Third Party\":1})\n",
    "df[\"PoliceReportFiled\"]=df[\"PoliceReportFiled\"].map({\"No\":0,\"Yes\":1})\n",
    "df[\"WitnessPresent\"]=df[\"WitnessPresent\"].map({\"No\":0,\"Yes\":1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd0a3b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a998834b",
   "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>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Fault</th>\n",
       "      <th>PolicyType</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>FraudFound_P</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Liability</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>3 years</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>6 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>7 years</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>Sedan - Liability</td>\n",
       "      <td>20000 to 29000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>5 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex MaritalStatus  Fault         PolicyType     VehiclePrice  FraudFound_P  \\\n",
       "0    0        Single      0  Sport - Liability  more than 69000             0   \n",
       "1    1        Single      0  Sport - Collision  more than 69000             0   \n",
       "2    1       Married      0  Sport - Collision  more than 69000             0   \n",
       "3    1       Married      1  Sedan - Liability   20000 to 29000             0   \n",
       "4    0        Single      1  Sport - Collision  more than 69000             0   \n",
       "\n",
       "   Deductible  DriverRating Days_Policy_Accident Days_Policy_Claim  \\\n",
       "0         300             1         more than 30      more than 30   \n",
       "1         400             4         more than 30      more than 30   \n",
       "2         400             3         more than 30      more than 30   \n",
       "3         400             2         more than 30      more than 30   \n",
       "4         400             1         more than 30      more than 30   \n",
       "\n",
       "  PastNumberOfClaims AgeOfVehicle AgeOfPolicyHolder  PoliceReportFiled  \\\n",
       "0               none      3 years          26 to 30                  0   \n",
       "1               none      6 years          31 to 35                  1   \n",
       "2                  1      7 years          41 to 50                  0   \n",
       "3                  1  more than 7          51 to 65                  1   \n",
       "4               none      5 years          31 to 35                  0   \n",
       "\n",
       "   WitnessPresent  AgentType NumberOfSuppliments NumberOfCars BasePolicy  \n",
       "0               0          0                none       3 to 4  Liability  \n",
       "1               0          0                none    1 vehicle  Collision  \n",
       "2               0          0                none    1 vehicle  Collision  \n",
       "3               0          0         more than 5    1 vehicle  Liability  \n",
       "4               0          0                none    1 vehicle  Collision  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3c72087a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    13000\n",
      "0     2420\n",
      "Name: Sex, dtype: int64\n",
      "Married     10625\n",
      "Single       4684\n",
      "Divorced       76\n",
      "Widow          35\n",
      "Name: MaritalStatus, dtype: int64\n",
      "0    11230\n",
      "1     4190\n",
      "Name: Fault, dtype: int64\n",
      "Sedan - Collision       5584\n",
      "Sedan - Liability       4987\n",
      "Sedan - All Perils      4087\n",
      "Sport - Collision        348\n",
      "Utility - All Perils     340\n",
      "Utility - Collision       30\n",
      "Sport - All Perils        22\n",
      "Utility - Liability       21\n",
      "Sport - Liability          1\n",
      "Name: PolicyType, dtype: int64\n",
      "20000 to 29000     8079\n",
      "30000 to 39000     3533\n",
      "more than 69000    2164\n",
      "less than 20000    1096\n",
      "40000 to 59000      461\n",
      "60000 to 69000       87\n",
      "Name: VehiclePrice, dtype: int64\n",
      "0    14497\n",
      "1      923\n",
      "Name: FraudFound_P, dtype: int64\n",
      "400    14838\n",
      "700      311\n",
      "500      263\n",
      "300        8\n",
      "Name: Deductible, dtype: int64\n",
      "1    3944\n",
      "3    3884\n",
      "2    3801\n",
      "4    3791\n",
      "Name: DriverRating, dtype: int64\n",
      "more than 30    15247\n",
      "none               55\n",
      "8 to 15            55\n",
      "15 to 30           49\n",
      "1 to 7             14\n",
      "Name: Days_Policy_Accident, dtype: int64\n",
      "more than 30    15342\n",
      "15 to 30           56\n",
      "8 to 15            21\n",
      "none                1\n",
      "Name: Days_Policy_Claim, dtype: int64\n",
      "2 to 4         5485\n",
      "none           4352\n",
      "1              3573\n",
      "more than 4    2010\n",
      "Name: PastNumberOfClaims, dtype: int64\n",
      "7 years        5807\n",
      "more than 7    3981\n",
      "6 years        3448\n",
      "5 years        1357\n",
      "new             373\n",
      "4 years         229\n",
      "3 years         152\n",
      "2 years          73\n",
      "Name: AgeOfVehicle, dtype: int64\n",
      "31 to 35    5593\n",
      "36 to 40    4043\n",
      "41 to 50    2828\n",
      "51 to 65    1392\n",
      "26 to 30     613\n",
      "over 65      508\n",
      "16 to 17     320\n",
      "21 to 25     108\n",
      "18 to 20      15\n",
      "Name: AgeOfPolicyHolder, dtype: int64\n",
      "0    14992\n",
      "1      428\n",
      "Name: PoliceReportFiled, dtype: int64\n",
      "0    15333\n",
      "1       87\n",
      "Name: WitnessPresent, dtype: int64\n",
      "0    15179\n",
      "1      241\n",
      "Name: AgentType, dtype: int64\n",
      "none           7047\n",
      "more than 5    3867\n",
      "1 to 2         2489\n",
      "3 to 5         2017\n",
      "Name: NumberOfSuppliments, dtype: int64\n",
      "1 vehicle      14316\n",
      "2 vehicles       709\n",
      "3 to 4           372\n",
      "5 to 8            21\n",
      "more than 8        2\n",
      "Name: NumberOfCars, dtype: int64\n",
      "Collision     5962\n",
      "Liability     5009\n",
      "All Perils    4449\n",
      "Name: BasePolicy, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in df:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b8048111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Fault</th>\n",
       "      <th>PolicyType</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>FraudFound_P</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Liability</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>3 years</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>6 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>7 years</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>Sedan - Liability</td>\n",
       "      <td>20000 to 29000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>more than 30</td>\n",
       "      <td>none</td>\n",
       "      <td>5 years</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex MaritalStatus  Fault         PolicyType     VehiclePrice  FraudFound_P  \\\n",
       "0    0        Single      0  Sport - Liability  more than 69000             0   \n",
       "1    1        Single      0  Sport - Collision  more than 69000             0   \n",
       "2    1       Married      0  Sport - Collision  more than 69000             0   \n",
       "3    1       Married      1  Sedan - Liability   20000 to 29000             0   \n",
       "4    0        Single      1  Sport - Collision  more than 69000             0   \n",
       "\n",
       "   Deductible  DriverRating Days_Policy_Accident Days_Policy_Claim  \\\n",
       "0         300             1         more than 30      more than 30   \n",
       "1         400             4         more than 30      more than 30   \n",
       "2         400             3         more than 30      more than 30   \n",
       "3         400             2         more than 30      more than 30   \n",
       "4         400             1         more than 30      more than 30   \n",
       "\n",
       "  PastNumberOfClaims AgeOfVehicle AgeOfPolicyHolder  PoliceReportFiled  \\\n",
       "0               none      3 years          26 to 30                  0   \n",
       "1               none      6 years          31 to 35                  1   \n",
       "2                  1      7 years          41 to 50                  0   \n",
       "3                  1  more than 7          51 to 65                  1   \n",
       "4               none      5 years          31 to 35                  0   \n",
       "\n",
       "   WitnessPresent  AgentType NumberOfSuppliments NumberOfCars BasePolicy  \n",
       "0               0          0                none       3 to 4  Liability  \n",
       "1               0          0                none    1 vehicle  Collision  \n",
       "2               0          0                none    1 vehicle  Collision  \n",
       "3               0          0         more than 5    1 vehicle  Liability  \n",
       "4               0          0                none    1 vehicle  Collision  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f53d0805",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Categorical encoding \n",
    "#OneHotEncoder\n",
    "#custom categorical encoding\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "afe2dd24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Days_Policy_Accident\"]=df[\"Days_Policy_Accident\"].map({\"more than 30\":4,\"none\":0,\"8 to 15\":2,\"15 to 30\":3,\"1 to 7\":1})\n",
    "df[\"Days_Policy_Claim\"]=df[\"Days_Policy_Claim\"].map({\"more than 30\":4,\"15 to 30\":3,\"8 to 15\":2,\"none\":1})\n",
    "df[\"PastNumberOfClaims\"]=df[\"PastNumberOfClaims\"].map({\"2 to 4\":2,\"1\":1,\"none\":0,\"more than 4\":3})\n",
    "df[\"AgeOfVehicle\"]=df[\"AgeOfVehicle\"].map({\"new\":0,\"2 years\":1,\"3 years\":2,\"4 years\":3,\"5 years\":4,\"6 years\":5,\"7 years\":6,\"more than 7\":7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "68049906",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Fault</th>\n",
       "      <th>PolicyType</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>FraudFound_P</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>PoliceReportFiled</th>\n",
       "      <th>WitnessPresent</th>\n",
       "      <th>AgentType</th>\n",
       "      <th>NumberOfSuppliments</th>\n",
       "      <th>NumberOfCars</th>\n",
       "      <th>BasePolicy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
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       "      <td>Sport - Liability</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>26 to 30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>3 to 4</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>41 to 50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>Sedan - Liability</td>\n",
       "      <td>20000 to 29000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>51 to 65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>more than 5</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Liability</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "      <td>Sport - Collision</td>\n",
       "      <td>more than 69000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>31 to 35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "      <td>1 vehicle</td>\n",
       "      <td>Collision</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex MaritalStatus  Fault         PolicyType     VehiclePrice  FraudFound_P  \\\n",
       "0    0        Single      0  Sport - Liability  more than 69000             0   \n",
       "1    1        Single      0  Sport - Collision  more than 69000             0   \n",
       "2    1       Married      0  Sport - Collision  more than 69000             0   \n",
       "3    1       Married      1  Sedan - Liability   20000 to 29000             0   \n",
       "4    0        Single      1  Sport - Collision  more than 69000             0   \n",
       "\n",
       "   Deductible  DriverRating  Days_Policy_Accident  Days_Policy_Claim  \\\n",
       "0         300             1                     4                  4   \n",
       "1         400             4                     4                  4   \n",
       "2         400             3                     4                  4   \n",
       "3         400             2                     4                  4   \n",
       "4         400             1                     4                  4   \n",
       "\n",
       "   PastNumberOfClaims  AgeOfVehicle AgeOfPolicyHolder  PoliceReportFiled  \\\n",
       "0                   0             2          26 to 30                  0   \n",
       "1                   0             5          31 to 35                  1   \n",
       "2                   1             6          41 to 50                  0   \n",
       "3                   1             7          51 to 65                  1   \n",
       "4                   0             4          31 to 35                  0   \n",
       "\n",
       "   WitnessPresent  AgentType NumberOfSuppliments NumberOfCars BasePolicy  \n",
       "0               0          0                none       3 to 4  Liability  \n",
       "1               0          0                none    1 vehicle  Collision  \n",
       "2               0          0                none    1 vehicle  Collision  \n",
       "3               0          0         more than 5    1 vehicle  Liability  \n",
       "4               0          0                none    1 vehicle  Collision  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2704d7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"AgeOfPolicyHolder\"]=df[\"AgeOfPolicyHolder\"].map({\"16 to 17\":0,\"18 to 20\":1,\"21 to 25\":2,\"26 to 30\":3,\"31 to 35\":4,\"36 to 40\":5,\"41 to 50\":6,\"51 to 65\":7,\"over 65\":8})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "39ea693f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"NumberOfSuppliments\"]=df[\"NumberOfSuppliments\"].map({\"none\":0,\"1 to 2\":1,\"3 to 5\":2,\"more than 5\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "fdd84b2c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"NumberOfCars\"]=df[\"NumberOfCars\"].map({\"1 vehicle\":1,\"2 vehicles\":2,\"3 to 4\":3.5,\"5 to 8\":6.5,\"more than 8\":9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "faf7686d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"VehiclePrice\"]=df[\"VehiclePrice\"].map({\"less than 20000\":20000,\"20000 to 29000\":25000,\"30000 to 39000\":35000,\"40000 to 59000\":52000,\"60000 to 69000\":65000,\"more than 69000\":70000})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c338f196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15420 entries, 0 to 15419\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Sex                   15420 non-null  int32  \n",
      " 1   MaritalStatus         15420 non-null  object \n",
      " 2   Fault                 15420 non-null  int64  \n",
      " 3   PolicyType            15420 non-null  object \n",
      " 4   VehiclePrice          15420 non-null  int64  \n",
      " 5   FraudFound_P          15420 non-null  int64  \n",
      " 6   Deductible            15420 non-null  int64  \n",
      " 7   DriverRating          15420 non-null  int64  \n",
      " 8   Days_Policy_Accident  15420 non-null  int64  \n",
      " 9   Days_Policy_Claim     15420 non-null  int64  \n",
      " 10  PastNumberOfClaims    15420 non-null  int64  \n",
      " 11  AgeOfVehicle          15420 non-null  int64  \n",
      " 12  AgeOfPolicyHolder     15420 non-null  int64  \n",
      " 13  PoliceReportFiled     15420 non-null  int64  \n",
      " 14  WitnessPresent        15420 non-null  int64  \n",
      " 15  AgentType             15420 non-null  int32  \n",
      " 16  NumberOfSuppliments   15420 non-null  int64  \n",
      " 17  NumberOfCars          15420 non-null  float64\n",
      " 18  BasePolicy            15420 non-null  object \n",
      "dtypes: float64(1), int32(2), int64(13), object(3)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "81f60e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MaritalStatus\n",
    "#PolicyType\n",
    "#BasePolicy\n",
    "\n",
    "df_temp1=df[\"MaritalStatus\"]\n",
    "df_temp2=df[\"PolicyType\"]\n",
    "df_temp3=df[\"BasePolicy\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "408ee65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp1=pd.get_dummies(df_temp1,columns=[\"MaritalStatus\"],prefix=\"MaritalStatus\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f17a3f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   MaritalStatus_Divorced  MaritalStatus_Married  MaritalStatus_Single  \\\n",
       "0                       0                      0                     1   \n",
       "1                       0                      0                     1   \n",
       "2                       0                      1                     0   \n",
       "3                       0                      1                     0   \n",
       "4                       0                      0                     1   \n",
       "\n",
       "   MaritalStatus_Widow  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1caf430a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   PolicyType_Sedan - All Perils  PolicyType_Sedan - Collision  \\\n",
       "0                              0                             0   \n",
       "1                              0                             0   \n",
       "2                              0                             0   \n",
       "3                              0                             0   \n",
       "4                              0                             0   \n",
       "\n",
       "   PolicyType_Sedan - Liability  PolicyType_Sport - All Perils  \\\n",
       "0                             0                              0   \n",
       "1                             0                              0   \n",
       "2                             0                              0   \n",
       "3                             1                              0   \n",
       "4                             0                              0   \n",
       "\n",
       "   PolicyType_Sport - Collision  PolicyType_Sport - Liability  \\\n",
       "0                             0                             1   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             0                             0   \n",
       "4                             1                             0   \n",
       "\n",
       "   PolicyType_Utility - All Perils  PolicyType_Utility - Collision  \\\n",
       "0                                0                               0   \n",
       "1                                0                               0   \n",
       "2                                0                               0   \n",
       "3                                0                               0   \n",
       "4                                0                               0   \n",
       "\n",
       "   PolicyType_Utility - Liability  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp2=pd.get_dummies(df_temp2,columns=\"PolicyType\",prefix=\"PolicyType\")\n",
    "df_temp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4a32ca9d",
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   "outputs": [
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       "   BasePolicy_All Perils  BasePolicy_Collision  BasePolicy_Liability\n",
       "0                      0                     0                     1\n",
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       "2                      0                     1                     0\n",
       "3                      0                     0                     1\n",
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    "df_temp3=pd.get_dummies(df_temp3,columns=\"BasePolicy\",prefix=\"BasePolicy\")\n",
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       "      <td>Sedan - Liability</td>\n",
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       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "   Sex MaritalStatus  Fault         PolicyType  VehiclePrice  FraudFound_P  \\\n",
       "0    0        Single      0  Sport - Liability         70000             0   \n",
       "1    1        Single      0  Sport - Collision         70000             0   \n",
       "2    1       Married      0  Sport - Collision         70000             0   \n",
       "3    1       Married      1  Sedan - Liability         25000             0   \n",
       "4    0        Single      1  Sport - Collision         70000             0   \n",
       "\n",
       "   Deductible  DriverRating  Days_Policy_Accident  Days_Policy_Claim  \\\n",
       "0         300             1                     4                  4   \n",
       "1         400             4                     4                  4   \n",
       "2         400             3                     4                  4   \n",
       "3         400             2                     4                  4   \n",
       "4         400             1                     4                  4   \n",
       "\n",
       "   PastNumberOfClaims  AgeOfVehicle  AgeOfPolicyHolder  PoliceReportFiled  \\\n",
       "0                   0             2                  3                  0   \n",
       "1                   0             5                  4                  1   \n",
       "2                   1             6                  6                  0   \n",
       "3                   1             7                  7                  1   \n",
       "4                   0             4                  4                  0   \n",
       "\n",
       "   WitnessPresent  AgentType  NumberOfSuppliments  NumberOfCars BasePolicy  \n",
       "0               0          0                    0           3.5  Liability  \n",
       "1               0          0                    0           1.0  Collision  \n",
       "2               0          0                    0           1.0  Collision  \n",
       "3               0          0                    3           1.0  Liability  \n",
       "4               0          0                    0           1.0  Collision  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "85df8f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_drop=[\"MaritalStatus\",\"PolicyType\",\"BasePolicy\"]\n",
    "df.drop(columns=col_drop,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f601721",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   Sex  Fault  VehiclePrice  FraudFound_P  Deductible  DriverRating  \\\n",
       "0    0      0         70000             0         300             1   \n",
       "1    1      0         70000             0         400             4   \n",
       "2    1      0         70000             0         400             3   \n",
       "3    1      1         25000             0         400             2   \n",
       "4    0      1         70000             0         400             1   \n",
       "\n",
       "   Days_Policy_Accident  Days_Policy_Claim  PastNumberOfClaims  AgeOfVehicle  \\\n",
       "0                     4                  4                   0             2   \n",
       "1                     4                  4                   0             5   \n",
       "2                     4                  4                   1             6   \n",
       "3                     4                  4                   1             7   \n",
       "4                     4                  4                   0             4   \n",
       "\n",
       "   AgeOfPolicyHolder  PoliceReportFiled  WitnessPresent  AgentType  \\\n",
       "0                  3                  0               0          0   \n",
       "1                  4                  1               0          0   \n",
       "2                  6                  0               0          0   \n",
       "3                  7                  1               0          0   \n",
       "4                  4                  0               0          0   \n",
       "\n",
       "   NumberOfSuppliments  NumberOfCars  \n",
       "0                    0           3.5  \n",
       "1                    0           1.0  \n",
       "2                    0           1.0  \n",
       "3                    3           1.0  \n",
       "4                    0           1.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "97a69109",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df,df_temp1,df_temp2,df_temp3],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "824ef93a",
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Fault  VehiclePrice  FraudFound_P  Deductible  DriverRating  \\\n",
       "0    0      0         70000             0         300             1   \n",
       "1    1      0         70000             0         400             4   \n",
       "2    1      0         70000             0         400             3   \n",
       "3    1      1         25000             0         400             2   \n",
       "4    0      1         70000             0         400             1   \n",
       "\n",
       "   Days_Policy_Accident  Days_Policy_Claim  PastNumberOfClaims  AgeOfVehicle  \\\n",
       "0                     4                  4                   0             2   \n",
       "1                     4                  4                   0             5   \n",
       "2                     4                  4                   1             6   \n",
       "3                     4                  4                   1             7   \n",
       "4                     4                  4                   0             4   \n",
       "\n",
       "   ...  PolicyType_Sedan - Liability  PolicyType_Sport - All Perils  \\\n",
       "0  ...                             0                              0   \n",
       "1  ...                             0                              0   \n",
       "2  ...                             0                              0   \n",
       "3  ...                             1                              0   \n",
       "4  ...                             0                              0   \n",
       "\n",
       "   PolicyType_Sport - Collision  PolicyType_Sport - Liability  \\\n",
       "0                             0                             1   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             0                             0   \n",
       "4                             1                             0   \n",
       "\n",
       "   PolicyType_Utility - All Perils  PolicyType_Utility - Collision  \\\n",
       "0                                0                               0   \n",
       "1                                0                               0   \n",
       "2                                0                               0   \n",
       "3                                0                               0   \n",
       "4                                0                               0   \n",
       "\n",
       "   PolicyType_Utility - Liability  BasePolicy_All Perils  \\\n",
       "0                               0                      0   \n",
       "1                               0                      0   \n",
       "2                               0                      0   \n",
       "3                               0                      0   \n",
       "4                               0                      0   \n",
       "\n",
       "   BasePolicy_Collision  BasePolicy_Liability  \n",
       "0                     0                     1  \n",
       "1                     1                     0  \n",
       "2                     1                     0  \n",
       "3                     0                     1  \n",
       "4                     1                     0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0553a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    13000\n",
      "0     2420\n",
      "Name: Sex, dtype: int64\n",
      "0    11230\n",
      "1     4190\n",
      "Name: Fault, dtype: int64\n",
      "25000    8079\n",
      "35000    3533\n",
      "70000    2164\n",
      "20000    1096\n",
      "52000     461\n",
      "65000      87\n",
      "Name: VehiclePrice, dtype: int64\n",
      "0    14497\n",
      "1      923\n",
      "Name: FraudFound_P, dtype: int64\n",
      "400    14838\n",
      "700      311\n",
      "500      263\n",
      "300        8\n",
      "Name: Deductible, dtype: int64\n",
      "1    3944\n",
      "3    3884\n",
      "2    3801\n",
      "4    3791\n",
      "Name: DriverRating, dtype: int64\n",
      "4    15247\n",
      "0       55\n",
      "2       55\n",
      "3       49\n",
      "1       14\n",
      "Name: Days_Policy_Accident, dtype: int64\n",
      "4    15342\n",
      "3       56\n",
      "2       21\n",
      "1        1\n",
      "Name: Days_Policy_Claim, dtype: int64\n",
      "2    5485\n",
      "0    4352\n",
      "1    3573\n",
      "3    2010\n",
      "Name: PastNumberOfClaims, dtype: int64\n",
      "6    5807\n",
      "7    3981\n",
      "5    3448\n",
      "4    1357\n",
      "0     373\n",
      "3     229\n",
      "2     152\n",
      "1      73\n",
      "Name: AgeOfVehicle, dtype: int64\n",
      "4    5593\n",
      "5    4043\n",
      "6    2828\n",
      "7    1392\n",
      "3     613\n",
      "8     508\n",
      "0     320\n",
      "2     108\n",
      "1      15\n",
      "Name: AgeOfPolicyHolder, dtype: int64\n",
      "0    14992\n",
      "1      428\n",
      "Name: PoliceReportFiled, dtype: int64\n",
      "0    15333\n",
      "1       87\n",
      "Name: WitnessPresent, dtype: int64\n",
      "0    15179\n",
      "1      241\n",
      "Name: AgentType, dtype: int64\n",
      "0    7047\n",
      "3    3867\n",
      "1    2489\n",
      "2    2017\n",
      "Name: NumberOfSuppliments, dtype: int64\n",
      "1.0    14316\n",
      "2.0      709\n",
      "3.5      372\n",
      "6.5       21\n",
      "9.0        2\n",
      "Name: NumberOfCars, dtype: int64\n",
      "0    15344\n",
      "1       76\n",
      "Name: MaritalStatus_Divorced, dtype: int64\n",
      "1    10625\n",
      "0     4795\n",
      "Name: MaritalStatus_Married, dtype: int64\n",
      "0    10736\n",
      "1     4684\n",
      "Name: MaritalStatus_Single, dtype: int64\n",
      "0    15385\n",
      "1       35\n",
      "Name: MaritalStatus_Widow, dtype: int64\n",
      "0    11333\n",
      "1     4087\n",
      "Name: PolicyType_Sedan - All Perils, dtype: int64\n",
      "0    9836\n",
      "1    5584\n",
      "Name: PolicyType_Sedan - Collision, dtype: int64\n",
      "0    10433\n",
      "1     4987\n",
      "Name: PolicyType_Sedan - Liability, dtype: int64\n",
      "0    15398\n",
      "1       22\n",
      "Name: PolicyType_Sport - All Perils, dtype: int64\n",
      "0    15072\n",
      "1      348\n",
      "Name: PolicyType_Sport - Collision, dtype: int64\n",
      "0    15419\n",
      "1        1\n",
      "Name: PolicyType_Sport - Liability, dtype: int64\n",
      "0    15080\n",
      "1      340\n",
      "Name: PolicyType_Utility - All Perils, dtype: int64\n",
      "0    15390\n",
      "1       30\n",
      "Name: PolicyType_Utility - Collision, dtype: int64\n",
      "0    15399\n",
      "1       21\n",
      "Name: PolicyType_Utility - Liability, dtype: int64\n",
      "0    10971\n",
      "1     4449\n",
      "Name: BasePolicy_All Perils, dtype: int64\n",
      "0    9458\n",
      "1    5962\n",
      "Name: BasePolicy_Collision, dtype: int64\n",
      "0    10411\n",
      "1     5009\n",
      "Name: BasePolicy_Liability, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in df:\n",
    "    print(df[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c7eabcf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"FraudFound_P\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa158024",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=\"FraudFound_P\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d7e7d824",
   "metadata": {
    "scrolled": true
   },
   "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>Sex</th>\n",
       "      <th>Fault</th>\n",
       "      <th>VehiclePrice</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>DriverRating</th>\n",
       "      <th>Days_Policy_Accident</th>\n",
       "      <th>Days_Policy_Claim</th>\n",
       "      <th>PastNumberOfClaims</th>\n",
       "      <th>AgeOfVehicle</th>\n",
       "      <th>AgeOfPolicyHolder</th>\n",
       "      <th>...</th>\n",
       "      <th>PolicyType_Sedan - Liability</th>\n",
       "      <th>PolicyType_Sport - All Perils</th>\n",
       "      <th>PolicyType_Sport - Collision</th>\n",
       "      <th>PolicyType_Sport - Liability</th>\n",
       "      <th>PolicyType_Utility - All Perils</th>\n",
       "      <th>PolicyType_Utility - Collision</th>\n",
       "      <th>PolicyType_Utility - Liability</th>\n",
       "      <th>BasePolicy_All Perils</th>\n",
       "      <th>BasePolicy_Collision</th>\n",
       "      <th>BasePolicy_Liability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70000</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70000</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70000</td>\n",
       "      <td>400</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "      <td>400</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70000</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Fault  VehiclePrice  Deductible  DriverRating  Days_Policy_Accident  \\\n",
       "0    0      0         70000         300             1                     4   \n",
       "1    1      0         70000         400             4                     4   \n",
       "2    1      0         70000         400             3                     4   \n",
       "3    1      1         25000         400             2                     4   \n",
       "4    0      1         70000         400             1                     4   \n",
       "\n",
       "   Days_Policy_Claim  PastNumberOfClaims  AgeOfVehicle  AgeOfPolicyHolder  \\\n",
       "0                  4                   0             2                  3   \n",
       "1                  4                   0             5                  4   \n",
       "2                  4                   1             6                  6   \n",
       "3                  4                   1             7                  7   \n",
       "4                  4                   0             4                  4   \n",
       "\n",
       "   ...  PolicyType_Sedan - Liability  PolicyType_Sport - All Perils  \\\n",
       "0  ...                             0                              0   \n",
       "1  ...                             0                              0   \n",
       "2  ...                             0                              0   \n",
       "3  ...                             1                              0   \n",
       "4  ...                             0                              0   \n",
       "\n",
       "   PolicyType_Sport - Collision  PolicyType_Sport - Liability  \\\n",
       "0                             0                             1   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             0                             0   \n",
       "4                             1                             0   \n",
       "\n",
       "   PolicyType_Utility - All Perils  PolicyType_Utility - Collision  \\\n",
       "0                                0                               0   \n",
       "1                                0                               0   \n",
       "2                                0                               0   \n",
       "3                                0                               0   \n",
       "4                                0                               0   \n",
       "\n",
       "   PolicyType_Utility - Liability  BasePolicy_All Perils  \\\n",
       "0                               0                      0   \n",
       "1                               0                      0   \n",
       "2                               0                      0   \n",
       "3                               0                      0   \n",
       "4                               0                      0   \n",
       "\n",
       "   BasePolicy_Collision  BasePolicy_Liability  \n",
       "0                     0                     1  \n",
       "1                     1                     0  \n",
       "2                     1                     0  \n",
       "3                     0                     1  \n",
       "4                     1                     0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5a5f1353",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: FraudFound_P, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "065ee53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# understanding the frequency(occurrence) and distribution of values in the modified dataframe df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d6f2142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1096., 8079.,    0., 3533.,    0.,    0.,  461.,    0.,    0.,\n",
       "        2251.]),\n",
       " array([20000., 25000., 30000., 35000., 40000., 45000., 50000., 55000.,\n",
       "        60000., 65000., 70000.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"VehiclePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "590a7137",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CCE\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Density'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.distplot(df[\"Sex\"])\n",
    "#data is uniformly distributed for all categories of gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2f41898d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CCE\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='VehiclePrice', ylabel='Density'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.distplot(df[\"VehiclePrice\"])\n",
    "#from here we can see that most of the claims are for vehicle have price in between 20000 to 30000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "11bfbb9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CCE\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AgeOfPolicyHolder', ylabel='Density'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.distplot(df[\"AgeOfPolicyHolder\"])\n",
    "# people in the age group of 31 to 35 claimed the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d757502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "sbn.heatmap(X.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f4a16f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i,init=\"k-means++\",random_state=42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c9716c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,10),wcss,linestyle=\"solid\",marker=\"o\")\n",
    "plt.xlabel(\"number of clusters\")\n",
    "plt.ylabel(\"wcss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2d711216",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the number of clusters =3\n",
    "\"\"\"kmeans=KMeans(n_clusters=3,init=\"k-means++\",random_state=0)\n",
    "cluster_number=kmeans.fit_predict(X)\"\"\"\n",
    "#cluster_number is an array storing the cluster number of each row in X\n",
    "kmeans=KMeans(n_clusters=3,init=\"k-means++\",random_state=0)\n",
    "kmeans.fit(X)\n",
    "cluster_number=kmeans.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cf38d88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(cluster_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5662d82e",
   "metadata": {},
   "outputs": [],
   "source": [
    "l=list(cluster_number)\n",
    "count_0=l.count(0)\n",
    "count_1=l.count(1)\n",
    "count_2=l.count(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb700b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3994 2251 9175\n"
     ]
    }
   ],
   "source": [
    "print(count_0,count_1,count_2)\n",
    "#we can see we have enough number of points in each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "adf159a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#l_0 stores the index of those rows in df which belong to cluster 0\n",
    "#l_1 stores the index of those rows in df which belong to cluster 1\n",
    "#l_2 stores the index of those rows in df which belong to cluster 2\n",
    "\n",
    "l_0=[]\n",
    "l_1=[]\n",
    "l_2=[]\n",
    "for i in range(15420):\n",
    "    if l[i]==0:\n",
    "        l_0.append(i)\n",
    "    elif l[i]==1:\n",
    "        l_1.append(i)\n",
    "    else:\n",
    "        l_2.append(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7dc4cd3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_0=df.iloc[l_0,:]\n",
    "df_1=df.iloc[l_1,:]\n",
    "df_2=df.iloc[l_2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7102793f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting the indexs of each data frame df_0,df_1,df_2\n",
    "df_0.set_index(pd.Index(range(3994)),inplace=True)\n",
    "df_1.set_index(pd.Index(range(2251)),inplace=True)\n",
    "df_2.set_index(pd.Index(range(9175)),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "412faf0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating X and y for each clusters (independent and dependent varialbles)\n",
    "y_0=df_0[\"FraudFound_P\"]\n",
    "X_0=df_0.drop(columns=\"FraudFound_P\")\n",
    "y_1=df_1[\"FraudFound_P\"]\n",
    "X_1=df_1.drop(columns=\"FraudFound_P\")\n",
    "y_2=df_2[\"FraudFound_P\"]\n",
    "X_2=df_2.drop(columns=\"FraudFound_P\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fd125b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we will define a function train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_0_train,X_0_test,y_0_train,y_0_test=train_test_split(X_0,y_0,test_size=0.2,random_state=42)\n",
    "X_1_train,X_1_test,y_1_train,y_1_test=train_test_split(X_1,y_1,test_size=0.2,random_state=42)\n",
    "X_2_train,X_2_test,y_2_train,y_2_test=train_test_split(X_2,y_2,test_size=0.2,random_state=42)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e9546257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_0_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7b2e6503",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating numpy arrays(ndarrays) of train and test data\n",
    "\n",
    "X_0_train=X_0_train.values\n",
    "X_0_test=X_0_test.values\n",
    "y_0_train=y_0_train.values\n",
    "y_0_test=y_0_test.values\n",
    "X_1_train=X_1_train.values\n",
    "X_1_test=X_1_test.values\n",
    "y_1_train=y_1_train.values\n",
    "y_1_test=y_1_test.values\n",
    "X_2_train=X_2_train.values\n",
    "X_2_test=X_2_test.values\n",
    "y_2_train=y_2_train.values\n",
    "y_2_test=y_2_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c988fd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling the train and test data of each cluster\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc0=StandardScaler()\n",
    "sc1=StandardScaler()\n",
    "sc2=StandardScaler()\n",
    "X_0_train[:,2:4]=sc0.fit_transform(X_0_train[:,2:4])\n",
    "X_0_test[:,2:4]=sc0.transform(X_0_test[:,2:4])\n",
    "X_1_train[:,2:4]=sc1.fit_transform(X_1_train[:,2:4])\n",
    "X_1_test[:,2:4]=sc1.transform(X_1_test[:,2:4])\n",
    "X_2_train[:,2:4]=sc2.fit_transform(X_2_train[:,2:4])\n",
    "X_2_test[:,2:4]=sc2.transform(X_2_test[:,2:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6d689e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we will define a function to find a best model(classifier) for each cluster \n",
    "def model_finder(xtrain,xtest,ytrain,ytest):\n",
    "    from sklearn.metrics import f1_score\n",
    "    #1 Logistic regression\n",
    "    #2 K-Nearest Neighbors\n",
    "    #3 Support Vector Machine(SVM)\n",
    "    #4 Kernel SVM\n",
    "    #5 Naive Bayes\n",
    "    #6 Decision Tree Classifier\n",
    "    #7 Random Forest Classifier\n",
    "    #8 XGBoost Classifier\n",
    "    cls_name=[\"Logistic Regression\",\"K-Nearest Neighbors\",\"Support Vector Machine\",\"Kernel SVM\",\"Naive Bayes\",\"Decision Tree Classifier\",\"Random Forest Classifier\",\"XGBoost Classifier\"]\n",
    "    score=[]\n",
    "    #1 Logistic regression\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    classifier_LR=LogisticRegression(random_state=42,solver=\"sag\",max_iter=1000)\n",
    "    classifier_LR.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_LR.predict(xtest)\n",
    "    score_LR=f1_score(ytest,y_pred)#we are keeping the default values of other parameters like average=\"binary\",pos_label=1 are default values\n",
    "    score.append(score_LR)\n",
    "    \n",
    "    #2 K-Nearest Neighbors\n",
    "    from sklearn.neighbors import KNeighborsClassifier\n",
    "    classifier_KNN=KNeighborsClassifier(n_neighbors=5,metric=\"minkowski\",p=2)\n",
    "    classifier_KNN.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_KNN.predict(xtest)\n",
    "    score_KNN=f1_score(ytest,y_pred)\n",
    "    score.append(score_KNN)\n",
    "    \n",
    "    #3 Support Vector Machine(SVM)\n",
    "    from sklearn.svm import SVC\n",
    "    classifier_SVM=SVC(kernel=\"linear\",random_state=42)\n",
    "    classifier_SVM.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_SVM.predict(xtest)\n",
    "    score_SVM=f1_score(ytest,y_pred)\n",
    "    score.append(score_SVM)\n",
    "    \n",
    "    #4 Kernel SVM\n",
    "    from sklearn.svm import SVC\n",
    "    classifier_KSVM=SVC(kernel=\"rbf\",random_state=42)\n",
    "    classifier_KSVM.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_KSVM.predict(xtest)\n",
    "    score_KSVM=f1_score(ytest,y_pred)\n",
    "    score.append(score_KSVM)\n",
    "    \n",
    "    #5 Naive Bayes\n",
    "    from sklearn.naive_bayes import GaussianNB\n",
    "    classifier_NB=GaussianNB()\n",
    "    classifier_NB.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_NB.predict(xtest)\n",
    "    score_NB=f1_score(ytest,y_pred)\n",
    "    score.append(score_NB)\n",
    "    \n",
    "    #6 Decision Tree Classifier\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    classifier_DT=DecisionTreeClassifier(criterion=\"entropy\", random_state=42)\n",
    "    classifier_DT.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_DT.predict(xtest)\n",
    "    score_DT=f1_score(ytest,y_pred)\n",
    "    score.append(score_DT)\n",
    "    \n",
    "    #7 Random Forest Classifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    classifier_RF=RandomForestClassifier(n_estimators=100,criterion=\"entropy\",random_state=42)\n",
    "    classifier_RF.fit(xtrain,ytrain)\n",
    "    y_pred=classifier_RF.predict(xtest)\n",
    "    score_RF=f1_score(ytest,y_pred)\n",
    "    score.append(score_RF)\n",
    "    \n",
    "    #8 XGBoost Classifier\n",
    "    from xgboost import XGBClassifier\n",
    "    classifier_xgb=XGBClassifier(use_label_encoder=False)#making it equal to false will just remove the warnings\n",
    "    classifier_xgb.fit(xtrain,ytrain,eval_metric='rmse')\n",
    "    y_pred=classifier_xgb.predict(xtest)\n",
    "    score_xgb=f1_score(ytest,y_pred)\n",
    "    score.append(score_xgb)\n",
    "    \n",
    "    classifier_list=[classifier_LR,classifier_KNN,classifier_SVM,classifier_KSVM,classifier_NB,classifier_DT,classifier_RF,classifier_xgb]\n",
    "    max_score=max(score)\n",
    "    classifier_name=cls_name[score.index(max_score)]\n",
    "    classifier=classifier_list[(score.index(max_score))]\n",
    "    return classifier_name,classifier,max_score\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d4e419ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_name_0,classifier_0,max_score_0=model_finder(X_0_train,X_0_test,y_0_train,y_0_test)\n",
    "classifier_name_1,classifier_1,max_score_1=model_finder(X_1_train,X_1_test,y_1_train,y_1_test)\n",
    "classifier_name_2,classifier_2,max_score_2=model_finder(X_2_train,X_2_test,y_2_train,y_2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a9e0c53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_list=[classifier_0,classifier_1,classifier_2]\n",
    "classifier_name_list=[classifier_name_0,classifier_name_1,classifier_name_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "21259ea2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Hyperparameters tuning\n",
    "\n",
    "def hyperparameter_tuning(classifier,classifier_name,xtrain,ytrain):\n",
    "    \n",
    "    from sklearn.model_selection import GridSearchCV #This time we'll use GridSearchCV instead of RandomizedSearchCV\n",
    "    \n",
    "    if classifier_name==\"Logistic Regression\":\n",
    "        \n",
    "        param_grid = [    \n",
    "                {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
    "                 'C' : np.logspace(-4, 4, 20),\n",
    "                 'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
    "                 'max_iter' : [1000,2500, 5000]\n",
    "                }\n",
    "                    ]\n",
    "        \n",
    "        clf = GridSearchCV(classifier, param_grid = param_grid, cv = 3, verbose=True, n_jobs=-1)\n",
    "        clf.fit(xtrain,ytrain)\n",
    "        \n",
    "    elif classifier_name==\"K-Nearest Neighbors\":\n",
    "\n",
    "        leaf_size = list(range(1,50))\n",
    "        n_neighbors = list(range(1,30))\n",
    "        p=[1,2]\n",
    "        \n",
    "        #Convert to dictionary\n",
    "        hyperparameters = dict(leaf_size=leaf_size, n_neighbors=n_neighbors, p=p)\n",
    "        \n",
    "        clf = GridSearchCV(classifier, hyperparameters, cv=10)\n",
    "        clf.fit(xtrain,ytrain)\n",
    "           \n",
    "    elif classifier_name==\"Support Vector Machine\":\n",
    "        param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
    "                      'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "                      }\n",
    " \n",
    "        clf = GridSearchCV(classifier, param_grid, refit = True, verbose = 3)\n",
    "        clf.fit(xtrain,ytrain)\n",
    "        \n",
    "    elif classifier_name==\"Kernel SVM\":\n",
    "        param_grid = {'C': [0.1, 1, 10, 100, 1000],\n",
    "                      'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "                      }\n",
    " \n",
    "        clf = GridSearchCV(classifier, param_grid, refit = True, verbose = 3)\n",
    "    \n",
    "        clf.fit(xtrain,ytrain)\n",
    "        \n",
    "    elif classifier_name==\"Naive Bayes\":\n",
    "        param_grid_nb = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "        \n",
    "        clf = GridSearchCV(estimator=classifier, param_grid=param_grid_nb, verbose=1, cv=10, n_jobs=-1)\n",
    "        \n",
    "        clf.fit(xtrain, ytrain)\n",
    "        \n",
    "    elif classifier_name==\"Decision Tree Classifier\":\n",
    "        params = { 'max_depth': [2, 3, 5, 10, 20],\n",
    "                   'min_samples_leaf': [5, 10, 20, 50, 100],\n",
    "                   'criterion': [\"gini\", \"entropy\"]\n",
    "                  }\n",
    "        clf = GridSearchCV(estimator=classifier,param_grid=params,cv=4,n_jobs=-1,verbose=1)\n",
    "        \n",
    "        clf.fit(xtrain, ytrain)\n",
    "        \n",
    "    elif classifier_name==\"Random Forest Classifier\":\n",
    "        \n",
    "        \n",
    "        param_grid = {'bootstrap': [True],\n",
    "                      'max_depth': [80, 90, 100, 110],\n",
    "                      'max_features': [2, 3],\n",
    "                      'min_samples_leaf': [3, 4, 5],\n",
    "                      'min_samples_split': [8, 10, 12],\n",
    "                      'n_estimators': [100, 200, 300, 1000]\n",
    "                     }\n",
    "\n",
    "\n",
    "        clf = GridSearchCV(estimator = classifier, param_grid = param_grid,cv = 3, n_jobs = -1, verbose = 2)\n",
    "        \n",
    "        clf.fit(xtrain,ytrain)\n",
    "         \n",
    "    else:#it will be XGBoost Classifier\n",
    "        params = { 'max_depth': [3,6,10],\n",
    "                   'learning_rate': [0.01, 0.05, 0.1],\n",
    "                   'n_estimators': [100, 500, 1000],\n",
    "                   'colsample_bytree': [0.3, 0.7]}\n",
    "        clf = GridSearchCV(estimator=classifier,param_grid=params,verbose=1)\n",
    "        clf.fit(xtrain, ytrain, eval_metric='rmse')\n",
    "    \n",
    "\n",
    "    return clf\n",
    "        \n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6071f4d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 50 candidates, totalling 200 fits\n",
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n",
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    }
   ],
   "source": [
    "\n",
    "classifier_0=hyperparameter_tuning(classifier_0,classifier_name_0,X_0_train,y_0_train)\n",
    "classifier_1=hyperparameter_tuning(classifier_1,classifier_name_1,X_1_train,y_1_train)\n",
    "classifier_2=hyperparameter_tuning(classifier_2,classifier_name_2,X_2_train,y_2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e5fd8dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now all is done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "94086ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we'll have the best model for each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "15accbbe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'if cluster_belong==0:\\n    ypredicted=classifier_0.predict(xnew)\\nelif cluster_belong==1:\\n    ypredicted=classifier_1.predict(xnew)\\nelse:\\n    ypredicted=classifier_2.predict(xnew)'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"if cluster_belong==0:\n",
    "    ypredicted=classifier_0.predict(xnew)\n",
    "elif cluster_belong==1:\n",
    "    ypredicted=classifier_1.predict(xnew)\n",
    "else:\n",
    "    ypredicted=classifier_2.predict(xnew)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d777448",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1558f2cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f553da25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe6defd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e271944",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dd9dfdc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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