28. Car Insurance Rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6356dad7",
   "metadata": {},
   "source": [
    "# Car Insurance Claim Prediction\n",
    "\n",
    "## Importing required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c53499fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128685f3",
   "metadata": {},
   "source": [
    "## Getting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9b4bad1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_id</th>\n",
       "      <th>policy_tenure</th>\n",
       "      <th>age_of_car</th>\n",
       "      <th>age_of_policyholder</th>\n",
       "      <th>area_cluster</th>\n",
       "      <th>population_density</th>\n",
       "      <th>make</th>\n",
       "      <th>segment</th>\n",
       "      <th>model</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>...</th>\n",
       "      <th>is_brake_assist</th>\n",
       "      <th>is_power_door_locks</th>\n",
       "      <th>is_central_locking</th>\n",
       "      <th>is_power_steering</th>\n",
       "      <th>is_driver_seat_height_adjustable</th>\n",
       "      <th>is_day_night_rear_view_mirror</th>\n",
       "      <th>is_ecw</th>\n",
       "      <th>is_speed_alert</th>\n",
       "      <th>ncap_rating</th>\n",
       "      <th>is_claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID00001</td>\n",
       "      <td>0.515874</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.644231</td>\n",
       "      <td>C1</td>\n",
       "      <td>4990</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>M1</td>\n",
       "      <td>CNG</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID00002</td>\n",
       "      <td>0.672619</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>C2</td>\n",
       "      <td>27003</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>M1</td>\n",
       "      <td>CNG</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID00003</td>\n",
       "      <td>0.841110</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>C3</td>\n",
       "      <td>4076</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>M1</td>\n",
       "      <td>CNG</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID00004</td>\n",
       "      <td>0.900277</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>C4</td>\n",
       "      <td>21622</td>\n",
       "      <td>1</td>\n",
       "      <td>C1</td>\n",
       "      <td>M2</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID00005</td>\n",
       "      <td>0.596403</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>C5</td>\n",
       "      <td>34738</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>M3</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  policy_id  policy_tenure  age_of_car  age_of_policyholder area_cluster  \\\n",
       "0   ID00001       0.515874        0.05             0.644231           C1   \n",
       "1   ID00002       0.672619        0.02             0.375000           C2   \n",
       "2   ID00003       0.841110        0.02             0.384615           C3   \n",
       "3   ID00004       0.900277        0.11             0.432692           C4   \n",
       "4   ID00005       0.596403        0.11             0.634615           C5   \n",
       "\n",
       "   population_density  make segment model fuel_type  ... is_brake_assist  \\\n",
       "0                4990     1       A    M1       CNG  ...              No   \n",
       "1               27003     1       A    M1       CNG  ...              No   \n",
       "2                4076     1       A    M1       CNG  ...              No   \n",
       "3               21622     1      C1    M2    Petrol  ...             Yes   \n",
       "4               34738     2       A    M3    Petrol  ...              No   \n",
       "\n",
       "  is_power_door_locks is_central_locking  is_power_steering  \\\n",
       "0                  No                 No                Yes   \n",
       "1                  No                 No                Yes   \n",
       "2                  No                 No                Yes   \n",
       "3                 Yes                Yes                Yes   \n",
       "4                 Yes                Yes                Yes   \n",
       "\n",
       "  is_driver_seat_height_adjustable is_day_night_rear_view_mirror is_ecw  \\\n",
       "0                               No                            No     No   \n",
       "1                               No                            No     No   \n",
       "2                               No                            No     No   \n",
       "3                              Yes                           Yes    Yes   \n",
       "4                               No                           Yes    Yes   \n",
       "\n",
       "  is_speed_alert ncap_rating is_claim  \n",
       "0            Yes           0        0  \n",
       "1            Yes           0        0  \n",
       "2            Yes           0        0  \n",
       "3            Yes           2        0  \n",
       "4            Yes           2        0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"data/proj28/train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "66c7193e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>policy_id</th>\n",
       "      <td>ID00001</td>\n",
       "      <td>ID00002</td>\n",
       "      <td>ID00003</td>\n",
       "      <td>ID00004</td>\n",
       "      <td>ID00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_tenure</th>\n",
       "      <td>0.515874</td>\n",
       "      <td>0.672619</td>\n",
       "      <td>0.84111</td>\n",
       "      <td>0.900277</td>\n",
       "      <td>0.596403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_of_car</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_of_policyholder</th>\n",
       "      <td>0.644231</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>0.634615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_cluster</th>\n",
       "      <td>C1</td>\n",
       "      <td>C2</td>\n",
       "      <td>C3</td>\n",
       "      <td>C4</td>\n",
       "      <td>C5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>4990</td>\n",
       "      <td>27003</td>\n",
       "      <td>4076</td>\n",
       "      <td>21622</td>\n",
       "      <td>34738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</th>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C1</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>M1</td>\n",
       "      <td>M1</td>\n",
       "      <td>M1</td>\n",
       "      <td>M2</td>\n",
       "      <td>M3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>CNG</td>\n",
       "      <td>CNG</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_torque</th>\n",
       "      <td>60Nm@3500rpm</td>\n",
       "      <td>60Nm@3500rpm</td>\n",
       "      <td>60Nm@3500rpm</td>\n",
       "      <td>113Nm@4400rpm</td>\n",
       "      <td>91Nm@4250rpm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_power</th>\n",
       "      <td>40.36bhp@6000rpm</td>\n",
       "      <td>40.36bhp@6000rpm</td>\n",
       "      <td>40.36bhp@6000rpm</td>\n",
       "      <td>88.50bhp@6000rpm</td>\n",
       "      <td>67.06bhp@5500rpm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_type</th>\n",
       "      <td>F8D Petrol Engine</td>\n",
       "      <td>F8D Petrol Engine</td>\n",
       "      <td>F8D Petrol Engine</td>\n",
       "      <td>1.2 L K12N Dualjet</td>\n",
       "      <td>1.0 SCe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airbags</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_esc</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_adjustable_steering</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_tpms</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_parking_sensors</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_parking_camera</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rear_brakes_type</th>\n",
       "      <td>Drum</td>\n",
       "      <td>Drum</td>\n",
       "      <td>Drum</td>\n",
       "      <td>Drum</td>\n",
       "      <td>Drum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>796</td>\n",
       "      <td>796</td>\n",
       "      <td>796</td>\n",
       "      <td>1197</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinder</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_type</th>\n",
       "      <td>Manual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gear_box</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steering_type</th>\n",
       "      <td>Power</td>\n",
       "      <td>Power</td>\n",
       "      <td>Power</td>\n",
       "      <td>Electric</td>\n",
       "      <td>Electric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turning_radius</th>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>3445</td>\n",
       "      <td>3445</td>\n",
       "      <td>3445</td>\n",
       "      <td>3995</td>\n",
       "      <td>3731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1735</td>\n",
       "      <td>1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>1475</td>\n",
       "      <td>1475</td>\n",
       "      <td>1475</td>\n",
       "      <td>1515</td>\n",
       "      <td>1490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross_weight</th>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1335</td>\n",
       "      <td>1155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_front_fog_lights</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_rear_window_wiper</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_rear_window_washer</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_rear_window_defogger</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_brake_assist</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_power_door_locks</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_central_locking</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_power_steering</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_driver_seat_height_adjustable</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_day_night_rear_view_mirror</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_ecw</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_speed_alert</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncap_rating</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_claim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  0                  1  \\\n",
       "policy_id                                   ID00001            ID00002   \n",
       "policy_tenure                              0.515874           0.672619   \n",
       "age_of_car                                     0.05               0.02   \n",
       "age_of_policyholder                        0.644231              0.375   \n",
       "area_cluster                                     C1                 C2   \n",
       "population_density                             4990              27003   \n",
       "make                                              1                  1   \n",
       "segment                                           A                  A   \n",
       "model                                            M1                 M1   \n",
       "fuel_type                                       CNG                CNG   \n",
       "max_torque                             60Nm@3500rpm       60Nm@3500rpm   \n",
       "max_power                          40.36bhp@6000rpm   40.36bhp@6000rpm   \n",
       "engine_type                       F8D Petrol Engine  F8D Petrol Engine   \n",
       "airbags                                           2                  2   \n",
       "is_esc                                           No                 No   \n",
       "is_adjustable_steering                           No                 No   \n",
       "is_tpms                                          No                 No   \n",
       "is_parking_sensors                              Yes                Yes   \n",
       "is_parking_camera                                No                 No   \n",
       "rear_brakes_type                               Drum               Drum   \n",
       "displacement                                    796                796   \n",
       "cylinder                                          3                  3   \n",
       "transmission_type                            Manual             Manual   \n",
       "gear_box                                          5                  5   \n",
       "steering_type                                 Power              Power   \n",
       "turning_radius                                  4.6                4.6   \n",
       "length                                         3445               3445   \n",
       "width                                          1515               1515   \n",
       "height                                         1475               1475   \n",
       "gross_weight                                   1185               1185   \n",
       "is_front_fog_lights                              No                 No   \n",
       "is_rear_window_wiper                             No                 No   \n",
       "is_rear_window_washer                            No                 No   \n",
       "is_rear_window_defogger                          No                 No   \n",
       "is_brake_assist                                  No                 No   \n",
       "is_power_door_locks                              No                 No   \n",
       "is_central_locking                               No                 No   \n",
       "is_power_steering                               Yes                Yes   \n",
       "is_driver_seat_height_adjustable                 No                 No   \n",
       "is_day_night_rear_view_mirror                    No                 No   \n",
       "is_ecw                                           No                 No   \n",
       "is_speed_alert                                  Yes                Yes   \n",
       "ncap_rating                                       0                  0   \n",
       "is_claim                                          0                  0   \n",
       "\n",
       "                                                  2                   3  \\\n",
       "policy_id                                   ID00003             ID00004   \n",
       "policy_tenure                               0.84111            0.900277   \n",
       "age_of_car                                     0.02                0.11   \n",
       "age_of_policyholder                        0.384615            0.432692   \n",
       "area_cluster                                     C3                  C4   \n",
       "population_density                             4076               21622   \n",
       "make                                              1                   1   \n",
       "segment                                           A                  C1   \n",
       "model                                            M1                  M2   \n",
       "fuel_type                                       CNG              Petrol   \n",
       "max_torque                             60Nm@3500rpm       113Nm@4400rpm   \n",
       "max_power                          40.36bhp@6000rpm    88.50bhp@6000rpm   \n",
       "engine_type                       F8D Petrol Engine  1.2 L K12N Dualjet   \n",
       "airbags                                           2                   2   \n",
       "is_esc                                           No                 Yes   \n",
       "is_adjustable_steering                           No                 Yes   \n",
       "is_tpms                                          No                  No   \n",
       "is_parking_sensors                              Yes                 Yes   \n",
       "is_parking_camera                                No                 Yes   \n",
       "rear_brakes_type                               Drum                Drum   \n",
       "displacement                                    796                1197   \n",
       "cylinder                                          3                   4   \n",
       "transmission_type                            Manual           Automatic   \n",
       "gear_box                                          5                   5   \n",
       "steering_type                                 Power            Electric   \n",
       "turning_radius                                  4.6                 4.8   \n",
       "length                                         3445                3995   \n",
       "width                                          1515                1735   \n",
       "height                                         1475                1515   \n",
       "gross_weight                                   1185                1335   \n",
       "is_front_fog_lights                              No                 Yes   \n",
       "is_rear_window_wiper                             No                  No   \n",
       "is_rear_window_washer                            No                  No   \n",
       "is_rear_window_defogger                          No                 Yes   \n",
       "is_brake_assist                                  No                 Yes   \n",
       "is_power_door_locks                              No                 Yes   \n",
       "is_central_locking                               No                 Yes   \n",
       "is_power_steering                               Yes                 Yes   \n",
       "is_driver_seat_height_adjustable                 No                 Yes   \n",
       "is_day_night_rear_view_mirror                    No                 Yes   \n",
       "is_ecw                                           No                 Yes   \n",
       "is_speed_alert                                  Yes                 Yes   \n",
       "ncap_rating                                       0                   2   \n",
       "is_claim                                          0                   0   \n",
       "\n",
       "                                                 4  \n",
       "policy_id                                  ID00005  \n",
       "policy_tenure                             0.596403  \n",
       "age_of_car                                    0.11  \n",
       "age_of_policyholder                       0.634615  \n",
       "area_cluster                                    C5  \n",
       "population_density                           34738  \n",
       "make                                             2  \n",
       "segment                                          A  \n",
       "model                                           M3  \n",
       "fuel_type                                   Petrol  \n",
       "max_torque                            91Nm@4250rpm  \n",
       "max_power                         67.06bhp@5500rpm  \n",
       "engine_type                                1.0 SCe  \n",
       "airbags                                          2  \n",
       "is_esc                                          No  \n",
       "is_adjustable_steering                          No  \n",
       "is_tpms                                         No  \n",
       "is_parking_sensors                              No  \n",
       "is_parking_camera                              Yes  \n",
       "rear_brakes_type                              Drum  \n",
       "displacement                                   999  \n",
       "cylinder                                         3  \n",
       "transmission_type                        Automatic  \n",
       "gear_box                                         5  \n",
       "steering_type                             Electric  \n",
       "turning_radius                                 5.0  \n",
       "length                                        3731  \n",
       "width                                         1579  \n",
       "height                                        1490  \n",
       "gross_weight                                  1155  \n",
       "is_front_fog_lights                             No  \n",
       "is_rear_window_wiper                            No  \n",
       "is_rear_window_washer                           No  \n",
       "is_rear_window_defogger                         No  \n",
       "is_brake_assist                                 No  \n",
       "is_power_door_locks                            Yes  \n",
       "is_central_locking                             Yes  \n",
       "is_power_steering                              Yes  \n",
       "is_driver_seat_height_adjustable                No  \n",
       "is_day_night_rear_view_mirror                  Yes  \n",
       "is_ecw                                         Yes  \n",
       "is_speed_alert                                 Yes  \n",
       "ncap_rating                                      2  \n",
       "is_claim                                         0  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4742015a",
   "metadata": {},
   "source": [
    "## Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1bf2d195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policy_id                           0\n",
       "policy_tenure                       0\n",
       "age_of_car                          0\n",
       "age_of_policyholder                 0\n",
       "area_cluster                        0\n",
       "population_density                  0\n",
       "make                                0\n",
       "segment                             0\n",
       "model                               0\n",
       "fuel_type                           0\n",
       "max_torque                          0\n",
       "max_power                           0\n",
       "engine_type                         0\n",
       "airbags                             0\n",
       "is_esc                              0\n",
       "is_adjustable_steering              0\n",
       "is_tpms                             0\n",
       "is_parking_sensors                  0\n",
       "is_parking_camera                   0\n",
       "rear_brakes_type                    0\n",
       "displacement                        0\n",
       "cylinder                            0\n",
       "transmission_type                   0\n",
       "gear_box                            0\n",
       "steering_type                       0\n",
       "turning_radius                      0\n",
       "length                              0\n",
       "width                               0\n",
       "height                              0\n",
       "gross_weight                        0\n",
       "is_front_fog_lights                 0\n",
       "is_rear_window_wiper                0\n",
       "is_rear_window_washer               0\n",
       "is_rear_window_defogger             0\n",
       "is_brake_assist                     0\n",
       "is_power_door_locks                 0\n",
       "is_central_locking                  0\n",
       "is_power_steering                   0\n",
       "is_driver_seat_height_adjustable    0\n",
       "is_day_night_rear_view_mirror       0\n",
       "is_ecw                              0\n",
       "is_speed_alert                      0\n",
       "ncap_rating                         0\n",
       "is_claim                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ff387de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 58592 entries, 0 to 58591\n",
      "Data columns (total 44 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   policy_id                         58592 non-null  object \n",
      " 1   policy_tenure                     58592 non-null  float64\n",
      " 2   age_of_car                        58592 non-null  float64\n",
      " 3   age_of_policyholder               58592 non-null  float64\n",
      " 4   area_cluster                      58592 non-null  object \n",
      " 5   population_density                58592 non-null  int64  \n",
      " 6   make                              58592 non-null  int64  \n",
      " 7   segment                           58592 non-null  object \n",
      " 8   model                             58592 non-null  object \n",
      " 9   fuel_type                         58592 non-null  object \n",
      " 10  max_torque                        58592 non-null  object \n",
      " 11  max_power                         58592 non-null  object \n",
      " 12  engine_type                       58592 non-null  object \n",
      " 13  airbags                           58592 non-null  int64  \n",
      " 14  is_esc                            58592 non-null  object \n",
      " 15  is_adjustable_steering            58592 non-null  object \n",
      " 16  is_tpms                           58592 non-null  object \n",
      " 17  is_parking_sensors                58592 non-null  object \n",
      " 18  is_parking_camera                 58592 non-null  object \n",
      " 19  rear_brakes_type                  58592 non-null  object \n",
      " 20  displacement                      58592 non-null  int64  \n",
      " 21  cylinder                          58592 non-null  int64  \n",
      " 22  transmission_type                 58592 non-null  object \n",
      " 23  gear_box                          58592 non-null  int64  \n",
      " 24  steering_type                     58592 non-null  object \n",
      " 25  turning_radius                    58592 non-null  float64\n",
      " 26  length                            58592 non-null  int64  \n",
      " 27  width                             58592 non-null  int64  \n",
      " 28  height                            58592 non-null  int64  \n",
      " 29  gross_weight                      58592 non-null  int64  \n",
      " 30  is_front_fog_lights               58592 non-null  object \n",
      " 31  is_rear_window_wiper              58592 non-null  object \n",
      " 32  is_rear_window_washer             58592 non-null  object \n",
      " 33  is_rear_window_defogger           58592 non-null  object \n",
      " 34  is_brake_assist                   58592 non-null  object \n",
      " 35  is_power_door_locks               58592 non-null  object \n",
      " 36  is_central_locking                58592 non-null  object \n",
      " 37  is_power_steering                 58592 non-null  object \n",
      " 38  is_driver_seat_height_adjustable  58592 non-null  object \n",
      " 39  is_day_night_rear_view_mirror     58592 non-null  object \n",
      " 40  is_ecw                            58592 non-null  object \n",
      " 41  is_speed_alert                    58592 non-null  object \n",
      " 42  ncap_rating                       58592 non-null  int64  \n",
      " 43  is_claim                          58592 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(28)\n",
      "memory usage: 19.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e73c24e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop([\"policy_id\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f0b02d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area_cluster\n",
      "--------------------\n",
      "['C1' 'C10' 'C11' 'C12' 'C13' 'C14' 'C15' 'C16' 'C17' 'C18' 'C19' 'C2'\n",
      " 'C20' 'C21' 'C22' 'C3' 'C4' 'C5' 'C6' 'C7' 'C8' 'C9']\n",
      "--------------------\n",
      "segment\n",
      "--------------------\n",
      "['A' 'B1' 'B2' 'C1' 'C2' 'Utility']\n",
      "--------------------\n",
      "model\n",
      "--------------------\n",
      "['M1' 'M10' 'M11' 'M2' 'M3' 'M4' 'M5' 'M6' 'M7' 'M8' 'M9']\n",
      "--------------------\n",
      "fuel_type\n",
      "--------------------\n",
      "['CNG' 'Diesel' 'Petrol']\n",
      "--------------------\n",
      "max_torque\n",
      "--------------------\n",
      "['113Nm@4400rpm' '170Nm@4000rpm' '200Nm@1750rpm' '200Nm@3000rpm'\n",
      " '250Nm@2750rpm' '60Nm@3500rpm' '82.1Nm@3400rpm' '85Nm@3000rpm'\n",
      " '91Nm@4250rpm']\n",
      "--------------------\n",
      "max_power\n",
      "--------------------\n",
      "['113.45bhp@4000rpm' '118.36bhp@5500rpm' '40.36bhp@6000rpm'\n",
      " '55.92bhp@5300rpm' '61.68bhp@6000rpm' '67.06bhp@5500rpm'\n",
      " '88.50bhp@6000rpm' '88.77bhp@4000rpm' '97.89bhp@3600rpm']\n",
      "--------------------\n",
      "engine_type\n",
      "--------------------\n",
      "['1.0 SCe' '1.2 L K Series Engine' '1.2 L K12N Dualjet' '1.5 L U2 CRDi'\n",
      " '1.5 Turbocharged Revotorq' '1.5 Turbocharged Revotron'\n",
      " 'F8D Petrol Engine' 'G12B' 'K Series Dual jet' 'K10C' 'i-DTEC']\n",
      "--------------------\n",
      "is_esc\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_adjustable_steering\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_tpms\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_parking_sensors\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_parking_camera\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "rear_brakes_type\n",
      "--------------------\n",
      "['Disc' 'Drum']\n",
      "--------------------\n",
      "transmission_type\n",
      "--------------------\n",
      "['Automatic' 'Manual']\n",
      "--------------------\n",
      "steering_type\n",
      "--------------------\n",
      "['Electric' 'Manual' 'Power']\n",
      "--------------------\n",
      "is_front_fog_lights\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_rear_window_wiper\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_rear_window_washer\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_rear_window_defogger\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_brake_assist\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_power_door_locks\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_central_locking\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_power_steering\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_driver_seat_height_adjustable\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_day_night_rear_view_mirror\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_ecw\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n",
      "is_speed_alert\n",
      "--------------------\n",
      "['No' 'Yes']\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "for column in df_train.keys():\n",
    "    if pd.api.types.is_object_dtype(df_train[column]) == True:\n",
    "        le = LabelEncoder()\n",
    "        df_train[column] = le.fit_transform(df_train[column])\n",
    "        print(column)\n",
    "        print(\"-\"*20)\n",
    "        print(le.classes_)\n",
    "        print(\"-\"*20)\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4cf00d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_tenure</th>\n",
       "      <th>age_of_car</th>\n",
       "      <th>age_of_policyholder</th>\n",
       "      <th>area_cluster</th>\n",
       "      <th>population_density</th>\n",
       "      <th>make</th>\n",
       "      <th>segment</th>\n",
       "      <th>model</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>max_torque</th>\n",
       "      <th>...</th>\n",
       "      <th>is_brake_assist</th>\n",
       "      <th>is_power_door_locks</th>\n",
       "      <th>is_central_locking</th>\n",
       "      <th>is_power_steering</th>\n",
       "      <th>is_driver_seat_height_adjustable</th>\n",
       "      <th>is_day_night_rear_view_mirror</th>\n",
       "      <th>is_ecw</th>\n",
       "      <th>is_speed_alert</th>\n",
       "      <th>ncap_rating</th>\n",
       "      <th>is_claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.515874</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.644231</td>\n",
       "      <td>0</td>\n",
       "      <td>4990</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.672619</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>11</td>\n",
       "      <td>27003</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.841110</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>15</td>\n",
       "      <td>4076</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.900277</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>16</td>\n",
       "      <td>21622</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.596403</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>17</td>\n",
       "      <td>34738</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   policy_tenure  age_of_car  age_of_policyholder  area_cluster  \\\n",
       "0       0.515874        0.05             0.644231             0   \n",
       "1       0.672619        0.02             0.375000            11   \n",
       "2       0.841110        0.02             0.384615            15   \n",
       "3       0.900277        0.11             0.432692            16   \n",
       "4       0.596403        0.11             0.634615            17   \n",
       "\n",
       "   population_density  make  segment  model  fuel_type  max_torque  ...  \\\n",
       "0                4990     1        0      0          0           5  ...   \n",
       "1               27003     1        0      0          0           5  ...   \n",
       "2                4076     1        0      0          0           5  ...   \n",
       "3               21622     1        3      3          2           0  ...   \n",
       "4               34738     2        0      4          2           8  ...   \n",
       "\n",
       "   is_brake_assist  is_power_door_locks  is_central_locking  \\\n",
       "0                0                    0                   0   \n",
       "1                0                    0                   0   \n",
       "2                0                    0                   0   \n",
       "3                1                    1                   1   \n",
       "4                0                    1                   1   \n",
       "\n",
       "   is_power_steering  is_driver_seat_height_adjustable  \\\n",
       "0                  1                                 0   \n",
       "1                  1                                 0   \n",
       "2                  1                                 0   \n",
       "3                  1                                 1   \n",
       "4                  1                                 0   \n",
       "\n",
       "   is_day_night_rear_view_mirror  is_ecw  is_speed_alert  ncap_rating  \\\n",
       "0                              0       0               1            0   \n",
       "1                              0       0               1            0   \n",
       "2                              0       0               1            0   \n",
       "3                              1       1               1            2   \n",
       "4                              1       1               1            2   \n",
       "\n",
       "   is_claim  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bdb36eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_train.drop(\"is_claim\", axis=1), df_train[\"is_claim\"], test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8496f28",
   "metadata": {},
   "source": [
    "## Training the Model SKLEARN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c3c175fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9324174417612424"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rclf = RandomForestClassifier()\n",
    "rclf.fit(X_train, y_train)\n",
    "rclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "af7d1e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9338680774810137"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knclf = KNeighborsClassifier()\n",
    "knclf.fit(X_train, y_train)\n",
    "knclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "88dadca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93625735984299"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svclf = SVC()\n",
    "svclf.fit(X_train, y_train)\n",
    "svclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "418adfde",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/djoe/HardDisk/LearningStuff/AnaCondaEnvs/deepLearning/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/media/djoe/HardDisk/LearningStuff/AnaCondaEnvs/deepLearning/lib/python3.9/site-packages/xgboost/data.py:250: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:00:38] WARNING: /tmp/abs_40obctay9q/croots/recipe/xgboost-split_1659548945886/work/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9361720283300623"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgclf = XGBClassifier()\n",
    "xgclf.fit(X_train, y_train)\n",
    "xgclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95b0843",
   "metadata": {},
   "source": [
    "## Training Artificial Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8383dc8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "44516d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = tf.keras.models.Sequential()\n",
    "ann.add(tf.keras.layers.Dense(units=42, activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=24, activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=12, activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=6,  activation='relu'))\n",
    "ann.add(tf.keras.layers.Dense(units=1,  activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "09fc0397",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9c117c6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.7364 - accuracy: 0.9294 - val_loss: 0.3326 - val_accuracy: 0.9363\n",
      "Epoch 2/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2846 - accuracy: 0.9360 - val_loss: 0.2548 - val_accuracy: 0.9363\n",
      "Epoch 3/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2455 - accuracy: 0.9360 - val_loss: 0.2393 - val_accuracy: 0.9363\n",
      "Epoch 4/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2386 - accuracy: 0.9360 - val_loss: 0.2373 - val_accuracy: 0.9363\n",
      "Epoch 5/25\n",
      "1465/1465 [==============================] - 2s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 6/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 7/25\n",
      "1465/1465 [==============================] - 2s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 8/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 9/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 10/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 11/25\n",
      "1465/1465 [==============================] - 2s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 12/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 13/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 14/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 15/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2372 - val_accuracy: 0.9363\n",
      "Epoch 16/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 17/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 18/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 19/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2372 - val_accuracy: 0.9363\n",
      "Epoch 20/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 21/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 22/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 23/25\n",
      "1465/1465 [==============================] - 2s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 24/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n",
      "Epoch 25/25\n",
      "1465/1465 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9360 - val_loss: 0.2371 - val_accuracy: 0.9363\n"
     ]
    }
   ],
   "source": [
    "model_info = ann.fit(X_train.values, y_train.values, batch_size=32, epochs=25, validation_data=[X_test.values, y_test.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a386795f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c6b23a5b0>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Overfitting\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "model_train_loss = model_info.history[\"loss\"]\n",
    "model_train_acc  = model_info.history['accuracy']\n",
    "model_test_loss  = model_info.history[\"val_loss\"]\n",
    "model_test_acc  = model_info.history[\"val_accuracy\"]\n",
    "\n",
    "ax.plot(model_train_loss, label=\"Train Loss\")\n",
    "ax.plot(model_test_loss, label=\"Test Loss\")\n",
    "plt.title(\"Model Loss\")         \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "15da8331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "ax.plot(model_train_acc, label=\"Train Accuracy\")\n",
    "ax.plot(model_test_acc, label=\"Test Accuracy\")\n",
    "plt.title(\"Model Accuracy\")         \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "907d119c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_tenure</th>\n",
       "      <th>age_of_car</th>\n",
       "      <th>age_of_policyholder</th>\n",
       "      <th>area_cluster</th>\n",
       "      <th>population_density</th>\n",
       "      <th>make</th>\n",
       "      <th>segment</th>\n",
       "      <th>model</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>max_torque</th>\n",
       "      <th>...</th>\n",
       "      <th>is_rear_window_defogger</th>\n",
       "      <th>is_brake_assist</th>\n",
       "      <th>is_power_door_locks</th>\n",
       "      <th>is_central_locking</th>\n",
       "      <th>is_power_steering</th>\n",
       "      <th>is_driver_seat_height_adjustable</th>\n",
       "      <th>is_day_night_rear_view_mirror</th>\n",
       "      <th>is_ecw</th>\n",
       "      <th>is_speed_alert</th>\n",
       "      <th>ncap_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.341732</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.586538</td>\n",
       "      <td>15</td>\n",
       "      <td>4076</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.307241</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.442308</td>\n",
       "      <td>20</td>\n",
       "      <td>8794</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.327924</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.451923</td>\n",
       "      <td>20</td>\n",
       "      <td>8794</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.782654</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>17</td>\n",
       "      <td>34738</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.233404</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>17</td>\n",
       "      <td>34738</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39058</th>\n",
       "      <td>0.047982</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.740385</td>\n",
       "      <td>11</td>\n",
       "      <td>27003</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>1.224838</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.509615</td>\n",
       "      <td>20</td>\n",
       "      <td>8794</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39060</th>\n",
       "      <td>0.520287</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.355769</td>\n",
       "      <td>18</td>\n",
       "      <td>13051</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39061</th>\n",
       "      <td>1.172391</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.644231</td>\n",
       "      <td>17</td>\n",
       "      <td>34738</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39062</th>\n",
       "      <td>0.094030</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>20</td>\n",
       "      <td>8794</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39063 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       policy_tenure  age_of_car  age_of_policyholder  area_cluster  \\\n",
       "0           0.341732        0.00             0.586538            15   \n",
       "1           0.307241        0.13             0.442308            20   \n",
       "2           0.327924        0.12             0.451923            20   \n",
       "3           0.782654        0.01             0.461538            17   \n",
       "4           1.233404        0.02             0.634615            17   \n",
       "...              ...         ...                  ...           ...   \n",
       "39058       0.047982        0.15             0.740385            11   \n",
       "39059       1.224838        0.05             0.509615            20   \n",
       "39060       0.520287        0.12             0.355769            18   \n",
       "39061       1.172391        0.08             0.644231            17   \n",
       "39062       0.094030        0.12             0.307692            20   \n",
       "\n",
       "       population_density  make  segment  model  fuel_type  max_torque  ...  \\\n",
       "0                    4076     1        0      0          0           5  ...   \n",
       "1                    8794     1        2      7          2           0  ...   \n",
       "2                    8794     2        0      4          2           8  ...   \n",
       "3                   34738     1        0      0          0           5  ...   \n",
       "4                   34738     1        0      0          0           5  ...   \n",
       "...                   ...   ...      ...    ...        ...         ...  ...   \n",
       "39058               27003     1        2      7          2           0  ...   \n",
       "39059                8794     3        4      5          1           4  ...   \n",
       "39060               13051     1        2      8          2           0  ...   \n",
       "39061               34738     1        3      3          2           0  ...   \n",
       "39062                8794     3        4      5          1           4  ...   \n",
       "\n",
       "       is_rear_window_defogger  is_brake_assist  is_power_door_locks  \\\n",
       "0                            0                0                    0   \n",
       "1                            0                1                    1   \n",
       "2                            0                0                    1   \n",
       "3                            0                0                    0   \n",
       "4                            0                0                    0   \n",
       "...                        ...              ...                  ...   \n",
       "39058                        0                1                    1   \n",
       "39059                        1                1                    1   \n",
       "39060                        1                1                    1   \n",
       "39061                        1                1                    1   \n",
       "39062                        1                1                    1   \n",
       "\n",
       "       is_central_locking  is_power_steering  \\\n",
       "0                       0                  1   \n",
       "1                       1                  1   \n",
       "2                       1                  1   \n",
       "3                       0                  1   \n",
       "4                       0                  1   \n",
       "...                   ...                ...   \n",
       "39058                   1                  1   \n",
       "39059                   1                  1   \n",
       "39060                   1                  1   \n",
       "39061                   1                  1   \n",
       "39062                   1                  1   \n",
       "\n",
       "       is_driver_seat_height_adjustable  is_day_night_rear_view_mirror  \\\n",
       "0                                     0                              0   \n",
       "1                                     1                              1   \n",
       "2                                     0                              1   \n",
       "3                                     0                              0   \n",
       "4                                     0                              0   \n",
       "...                                 ...                            ...   \n",
       "39058                                 1                              1   \n",
       "39059                                 1                              0   \n",
       "39060                                 1                              1   \n",
       "39061                                 1                              1   \n",
       "39062                                 1                              0   \n",
       "\n",
       "       is_ecw  is_speed_alert  ncap_rating  \n",
       "0           0               1            0  \n",
       "1           1               1            2  \n",
       "2           1               1            2  \n",
       "3           0               1            0  \n",
       "4           0               1            0  \n",
       "...       ...             ...          ...  \n",
       "39058       1               1            2  \n",
       "39059       1               1            3  \n",
       "39060       1               1            0  \n",
       "39061       1               1            2  \n",
       "39062       1               1            3  \n",
       "\n",
       "[39063 rows x 42 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"data/proj28/test.csv\")\n",
    "df_test.drop([\"policy_id\"], axis=1, inplace=True)\n",
    "for column in df_test.keys():\n",
    "    if pd.api.types.is_object_dtype(df_test[column]) == True:\n",
    "        le = LabelEncoder()\n",
    "        df_test[column] = le.fit_transform(df_test[column])\n",
    "    else:\n",
    "        pass\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "41f182a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.41731679e-01, 0.00000000e+00, 5.86538462e-01, 1.50000000e+01,\n",
       "       4.07600000e+03, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 5.00000000e+00, 2.00000000e+00, 6.00000000e+00,\n",
       "       2.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.00000000e+00, 0.00000000e+00, 1.00000000e+00, 7.96000000e+02,\n",
       "       3.00000000e+00, 1.00000000e+00, 5.00000000e+00, 2.00000000e+00,\n",
       "       4.60000000e+00, 3.44500000e+03, 1.51500000e+03, 1.47500000e+03,\n",
       "       1.18500000e+03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "1c96a59b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1221/1221 [==============================] - 1s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "neural_preds = ann.predict(df_test.values) > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "cedc2dc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/djoe/HardDisk/LearningStuff/AnaCondaEnvs/deepLearning/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "skl_preds = rclf.predict(df_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "afce0b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), array([38873,   190]))"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(skl_preds, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3c3f11c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Poor Ann for my ML *_*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a20186",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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