Insurance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5b78a564-e14d-4fa5-b613-df3caab6db73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings \n",
    "from scipy.stats import chi2_contingency\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,mean_squared_error,mean_absolute_error,r2_score\n",
    "import mlflow \n",
    "from mlflow.models import infer_signature\n",
    "import os\n",
    "\n",
    "\n",
    "sns.set_theme()\n",
    "sns.set_context(\"paper\")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "execution_count": 207,
   "id": "5111c15d-1c40-41cc-9458-789b961910b9",
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     "output_type": "stream",
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      "Installing collected packages: aniso8601, waitress, sqlparse, rsa, querystring-parser, Mako, graphql-core, deprecated, cachetools, opentelemetry-api, graphql-relay, google-auth, docker, alembic, opentelemetry-semantic-conventions, graphene, databricks-sdk, opentelemetry-sdk, mlflow-skinny, mlflow\n",
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      "    Found existing installation: cachetools 4.2.2\n",
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      "Successfully installed Mako-1.3.5 alembic-1.13.2 aniso8601-9.0.1 cachetools-5.4.0 databricks-sdk-0.29.0 deprecated-1.2.14 docker-7.1.0 google-auth-2.32.0 graphene-3.3 graphql-core-3.2.3 graphql-relay-3.2.0 mlflow-2.15.1 mlflow-skinny-2.15.1 opentelemetry-api-1.26.0 opentelemetry-sdk-1.26.0 opentelemetry-semantic-conventions-0.47b0 querystring-parser-1.2.4 rsa-4.9 sqlparse-0.5.1 waitress-3.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1296decd-4ba6-4f0b-9c25-8bbf9a5ed08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv(\"Insurance claims data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf2e2d2c-82b5-4577-89d0-df8a2a4be2ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_id</th>\n",
       "      <th>subscription_length</th>\n",
       "      <th>vehicle_age</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>region_code</th>\n",
       "      <th>region_density</th>\n",
       "      <th>segment</th>\n",
       "      <th>model</th>\n",
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       "      <th>max_torque</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POL045360</td>\n",
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       "      <td>C2</td>\n",
       "      <td>M4</td>\n",
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       "      <td>250Nm@2750rpm</td>\n",
       "      <td>...</td>\n",
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       "      <td>Yes</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>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POL016745</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>35</td>\n",
       "      <td>C2</td>\n",
       "      <td>27003</td>\n",
       "      <td>C1</td>\n",
       "      <td>M9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>200Nm@1750rpm</td>\n",
       "      <td>...</td>\n",
       "      <td>No</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>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POL007194</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>C2</td>\n",
       "      <td>M4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>250Nm@2750rpm</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>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POL018146</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>44</td>\n",
       "      <td>C10</td>\n",
       "      <td>73430</td>\n",
       "      <td>A</td>\n",
       "      <td>M1</td>\n",
       "      <td>CNG</td>\n",
       "      <td>60Nm@3500rpm</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>4</th>\n",
       "      <td>POL049011</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>C13</td>\n",
       "      <td>5410</td>\n",
       "      <td>B2</td>\n",
       "      <td>M5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>200Nm@3000rpm</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 41 columns</p>\n",
       "</div>"
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       "   policy_id  subscription_length  vehicle_age  customer_age region_code  \\\n",
       "0  POL045360                  9.3          1.2            41          C8   \n",
       "1  POL016745                  8.2          1.8            35          C2   \n",
       "2  POL007194                  9.5          0.2            44          C8   \n",
       "3  POL018146                  5.2          0.4            44         C10   \n",
       "4  POL049011                 10.1          1.0            56         C13   \n",
       "\n",
       "   region_density segment model fuel_type     max_torque  ... is_brake_assist  \\\n",
       "0            8794      C2    M4    Diesel  250Nm@2750rpm  ...             Yes   \n",
       "1           27003      C1    M9    Diesel  200Nm@1750rpm  ...              No   \n",
       "2            8794      C2    M4    Diesel  250Nm@2750rpm  ...             Yes   \n",
       "3           73430       A    M1       CNG   60Nm@3500rpm  ...              No   \n",
       "4            5410      B2    M5    Diesel  200Nm@3000rpm  ...              No   \n",
       "\n",
       "  is_power_door_locks  is_central_locking is_power_steering  \\\n",
       "0                 Yes                 Yes               Yes   \n",
       "1                 Yes                 Yes               Yes   \n",
       "2                 Yes                 Yes               Yes   \n",
       "3                  No                  No               Yes   \n",
       "4                 Yes                 Yes               Yes   \n",
       "\n",
       "  is_driver_seat_height_adjustable is_day_night_rear_view_mirror is_ecw  \\\n",
       "0                              Yes                            No    Yes   \n",
       "1                              Yes                           Yes    Yes   \n",
       "2                              Yes                            No    Yes   \n",
       "3                               No                            No     No   \n",
       "4                               No                            No    Yes   \n",
       "\n",
       "  is_speed_alert ncap_rating  claim_status  \n",
       "0            Yes           3             0  \n",
       "1            Yes           4             0  \n",
       "2            Yes           3             0  \n",
       "3            Yes           0             0  \n",
       "4            Yes           5             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0efb4cc2-0a2d-41aa-aac7-144b0f9c71ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policy_id                           0\n",
       "subscription_length                 0\n",
       "vehicle_age                         0\n",
       "customer_age                        0\n",
       "region_code                         0\n",
       "region_density                      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",
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       "width                               0\n",
       "gross_weight                        0\n",
       "is_front_fog_lights                 0\n",
       "is_rear_window_wiper                0\n",
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       "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",
       "claim_status                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "093fc32c-cad5-43c0-85cf-b0010b6e886e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policy_id                            object\n",
       "subscription_length                 float64\n",
       "vehicle_age                         float64\n",
       "customer_age                          int64\n",
       "region_code                          object\n",
       "region_density                        int64\n",
       "segment                              object\n",
       "model                                object\n",
       "fuel_type                            object\n",
       "max_torque                           object\n",
       "max_power                            object\n",
       "engine_type                          object\n",
       "airbags                               int64\n",
       "is_esc                               object\n",
       "is_adjustable_steering               object\n",
       "is_tpms                              object\n",
       "is_parking_sensors                   object\n",
       "is_parking_camera                    object\n",
       "rear_brakes_type                     object\n",
       "displacement                          int64\n",
       "cylinder                              int64\n",
       "transmission_type                    object\n",
       "steering_type                        object\n",
       "turning_radius                      float64\n",
       "length                                int64\n",
       "width                                 int64\n",
       "gross_weight                          int64\n",
       "is_front_fog_lights                  object\n",
       "is_rear_window_wiper                 object\n",
       "is_rear_window_washer                object\n",
       "is_rear_window_defogger              object\n",
       "is_brake_assist                      object\n",
       "is_power_door_locks                  object\n",
       "is_central_locking                   object\n",
       "is_power_steering                    object\n",
       "is_driver_seat_height_adjustable     object\n",
       "is_day_night_rear_view_mirror        object\n",
       "is_ecw                               object\n",
       "is_speed_alert                       object\n",
       "ncap_rating                           int64\n",
       "claim_status                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e6fe62b-70f4-49d2-aecb-8c9437427412",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "      <td>58592.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.111688</td>\n",
       "      <td>1.388473</td>\n",
       "      <td>44.823935</td>\n",
       "      <td>18826.858667</td>\n",
       "      <td>3.137066</td>\n",
       "      <td>1162.355851</td>\n",
       "      <td>3.626963</td>\n",
       "      <td>4.852893</td>\n",
       "      <td>3850.476891</td>\n",
       "      <td>1672.233667</td>\n",
       "      <td>1385.276813</td>\n",
       "      <td>1.759950</td>\n",
       "      <td>0.063968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.142790</td>\n",
       "      <td>1.134413</td>\n",
       "      <td>6.935604</td>\n",
       "      <td>17660.174792</td>\n",
       "      <td>1.832641</td>\n",
       "      <td>266.304786</td>\n",
       "      <td>0.483616</td>\n",
       "      <td>0.228061</td>\n",
       "      <td>311.457119</td>\n",
       "      <td>112.089135</td>\n",
       "      <td>212.423085</td>\n",
       "      <td>1.389576</td>\n",
       "      <td>0.244698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3445.000000</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>1051.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>6112.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>3445.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.700000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>8794.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>3845.000000</td>\n",
       "      <td>1735.000000</td>\n",
       "      <td>1335.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.400000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>27003.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3995.000000</td>\n",
       "      <td>1755.000000</td>\n",
       "      <td>1510.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>73430.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1498.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4300.000000</td>\n",
       "      <td>1811.000000</td>\n",
       "      <td>1720.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       subscription_length   vehicle_age  customer_age  region_density  \\\n",
       "count         58592.000000  58592.000000  58592.000000    58592.000000   \n",
       "mean              6.111688      1.388473     44.823935    18826.858667   \n",
       "std               4.142790      1.134413      6.935604    17660.174792   \n",
       "min               0.000000      0.000000     35.000000      290.000000   \n",
       "25%               2.100000      0.400000     39.000000     6112.000000   \n",
       "50%               5.700000      1.200000     44.000000     8794.000000   \n",
       "75%              10.400000      2.200000     49.000000    27003.000000   \n",
       "max              14.000000     20.000000     75.000000    73430.000000   \n",
       "\n",
       "            airbags  displacement      cylinder  turning_radius        length  \\\n",
       "count  58592.000000  58592.000000  58592.000000    58592.000000  58592.000000   \n",
       "mean       3.137066   1162.355851      3.626963        4.852893   3850.476891   \n",
       "std        1.832641    266.304786      0.483616        0.228061    311.457119   \n",
       "min        1.000000    796.000000      3.000000        4.500000   3445.000000   \n",
       "25%        2.000000    796.000000      3.000000        4.600000   3445.000000   \n",
       "50%        2.000000   1197.000000      4.000000        4.800000   3845.000000   \n",
       "75%        6.000000   1493.000000      4.000000        5.000000   3995.000000   \n",
       "max        6.000000   1498.000000      4.000000        5.200000   4300.000000   \n",
       "\n",
       "              width  gross_weight   ncap_rating  claim_status  \n",
       "count  58592.000000  58592.000000  58592.000000  58592.000000  \n",
       "mean    1672.233667   1385.276813      1.759950      0.063968  \n",
       "std      112.089135    212.423085      1.389576      0.244698  \n",
       "min     1475.000000   1051.000000      0.000000      0.000000  \n",
       "25%     1515.000000   1185.000000      0.000000      0.000000  \n",
       "50%     1735.000000   1335.000000      2.000000      0.000000  \n",
       "75%     1755.000000   1510.000000      3.000000      0.000000  \n",
       "max     1811.000000   1720.000000      5.000000      1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde87921-96c9-42f4-870c-5d55cf3ff04b",
   "metadata": {},
   "source": [
    " ### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a71495e0-9ace-4690-872f-126c6db2dbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a claim status \n",
    "data['claim_status_text']= data[\"claim_status\"].map({1:'Yes', 0:'No'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1312863f-1158-42e6-84f6-397c4c7faf13",
   "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_id</th>\n",
       "      <th>subscription_length</th>\n",
       "      <th>vehicle_age</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>region_code</th>\n",
       "      <th>region_density</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_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>claim_status</th>\n",
       "      <th>claim_status_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POL045360</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>41</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>C2</td>\n",
       "      <td>M4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>250Nm@2750rpm</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POL016745</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>35</td>\n",
       "      <td>C2</td>\n",
       "      <td>27003</td>\n",
       "      <td>C1</td>\n",
       "      <td>M9</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>200Nm@1750rpm</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POL007194</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>C2</td>\n",
       "      <td>M4</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>250Nm@2750rpm</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POL018146</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>44</td>\n",
       "      <td>C10</td>\n",
       "      <td>73430</td>\n",
       "      <td>A</td>\n",
       "      <td>M1</td>\n",
       "      <td>CNG</td>\n",
       "      <td>60Nm@3500rpm</td>\n",
       "      <td>...</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POL049011</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>C13</td>\n",
       "      <td>5410</td>\n",
       "      <td>B2</td>\n",
       "      <td>M5</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>200Nm@3000rpm</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   policy_id  subscription_length  vehicle_age  customer_age region_code  \\\n",
       "0  POL045360                  9.3          1.2            41          C8   \n",
       "1  POL016745                  8.2          1.8            35          C2   \n",
       "2  POL007194                  9.5          0.2            44          C8   \n",
       "3  POL018146                  5.2          0.4            44         C10   \n",
       "4  POL049011                 10.1          1.0            56         C13   \n",
       "\n",
       "   region_density segment model fuel_type     max_torque  ...  \\\n",
       "0            8794      C2    M4    Diesel  250Nm@2750rpm  ...   \n",
       "1           27003      C1    M9    Diesel  200Nm@1750rpm  ...   \n",
       "2            8794      C2    M4    Diesel  250Nm@2750rpm  ...   \n",
       "3           73430       A    M1       CNG   60Nm@3500rpm  ...   \n",
       "4            5410      B2    M5    Diesel  200Nm@3000rpm  ...   \n",
       "\n",
       "  is_power_door_locks is_central_locking  is_power_steering  \\\n",
       "0                 Yes                Yes                Yes   \n",
       "1                 Yes                Yes                Yes   \n",
       "2                 Yes                Yes                Yes   \n",
       "3                  No                 No                Yes   \n",
       "4                 Yes                Yes                Yes   \n",
       "\n",
       "  is_driver_seat_height_adjustable is_day_night_rear_view_mirror is_ecw  \\\n",
       "0                              Yes                            No    Yes   \n",
       "1                              Yes                           Yes    Yes   \n",
       "2                              Yes                            No    Yes   \n",
       "3                               No                            No     No   \n",
       "4                               No                            No    Yes   \n",
       "\n",
       "  is_speed_alert ncap_rating claim_status  claim_status_text  \n",
       "0            Yes           3            0                 No  \n",
       "1            Yes           4            0                 No  \n",
       "2            Yes           3            0                 No  \n",
       "3            Yes           0            0                 No  \n",
       "4            Yes           5            0                 No  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "308c32d5-44a5-4abf-9d3c-44852e502830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin customer subscription_length\n",
    "bins= [0,3,8,10,14]\n",
    "group=['New members','Recent members','Regular members','Golden members']\n",
    "data['Members_binned']= pd.cut(data['subscription_length'],bins,labels=group,include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec9cb155-9e53-4d4a-9b9f-4c6e2aece15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin vehicle_age\n",
    "bins= [0,3,8,13,20]\n",
    "group=['New Cars','Recent Cars','Regular Cars','Golden Cars']\n",
    "data['vehicle_age_binned']= pd.cut(data['vehicle_age'],bins,labels=group,include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0ed04c1-335f-4ae1-8306-11c494ad768b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin customer age\n",
    "bins= [0,18,45,60,75]\n",
    "group=['0-17','18-45','45-60','61+']\n",
    "data['customers_age_binned']= pd.cut(data['customer_age'],bins,labels=group,include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8968d4ad-671e-4d86-88b0-01ea8574daa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customers_age_binned\n",
       "18-45    34822\n",
       "45-60    22264\n",
       "61+       1506\n",
       "0-17         0\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.customers_age_binned.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43f2e616-1237-4932-8c66-0d216c5903e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rwYIX1L79L2syHD+epyZNrrVv06TJtSopuaCzZ8+qQYMGbqq0oit5z6s25zJjY2P16aef6ttvv5XFYtHLL79sX8iiV69eWrp0qc6ePav9+/crPT1dPXv2VEBAgKKiopSamqqSkhKtXbtWBQUFat++vTp16qTc3Fxt3LjRvsph9+7dK0xBBAAAAHB1PP74E/ryy8/0zTcZkqTGjYN1/Hievf/48Vz5+vraT6DUdNUmaN1yyy2aM2eOEhISdNttt8lms2nq1KmSpGHDhummm25STEyM4uLiNHnyZPuCFsnJycrOzlZkZKReeeUVLVq0SEajUSaTSS+99JJefvlldezYUUePHlVSUpIbjxAAAACova65pokefnicNm/eIEmKjr5bb7+9Qrm5x1RcXKSXXlqg22+/Qz4+1WbS3RVx61GMGzeuwu27775bd9999yXb+fr6KjExUYmJiZf0BQcHa+nSpb+5/1tuuUUfffTRb/YBAAAAnsbP5Fut933PPf316acfS5J69eqrU6dO6dFHR6u4uEhRUbdrwoQpV/wY1YVnxEUAAACgFrPZpJLSMi1MuM+lj1NSWlbp7yw1bXqdvvgis0KbwWDQwoX//0mShx4apYceGuXMEqsNghYAAADgAc4WmF2++iALfVQeQQsAAADwEASh6qPaLIYBAAAAAJ6CoAUAAAAATkbQAgAAAAAnI2gBAAAAgJMRtAAAAADAyVh1EAAAAPAQLO9efRC0AAAAAA/QsIFJ3r6+Ln0Ma2mpzhRYXPoYnoKgBQAAANRwBoPk7eurPYsny1pidsljeBv91OaRFBkMlsue2UpImCyL5YKefz61QvvUqRMUGFhPiYkzXVJjdULQAgAAADyEtcSs8hL3n3GaOHGqHnjgb/rkk8266667JUmffrpFP/ywX2+88Z6bq7s6WAwDAAAAgFM1atRY48ZN0IIFL+j8+fM6f/68UlPnaurUBAUGBmrfvr0aNWqYYmLu0MSJY3X8eJ4k6dy5c5o8+THFxNyhwYPv1TvvvOnmI6k6ghYAAAAAp+vVq49atbpBy5Yt0bJlL6tjx9sUGdlZ58+f15Qpj+mBBx5UevoWdewYqRkznpQkvfPOGwoKaqj16z9RSsqLevPN15Sfn+/mI6kaghYAAAAAl5gyZZo2bVqvf/7zK40fP0mStH37V2rZ8np163anfHx89Le/DdGxYz/ryJFsBQQE6Pvv9+mrr75U48bBSk//REFBQW4+iqrhO1oAAAAAXOLaa5sqKqqzmjVrroCAAEnSiRPH9e9//0sxMd3s25WWlur48TwNGnS/CgsLtXDhPJ0+fUp33XW3Jk2aqjp1TG46gqojaAEAAABwGS8vb3l5/f8T6Ro1aqxOnW7TnDkv2NtycrJ13XXNlJ2dpf79/6YxYx7V4cOHNGPGk9qwIV333jvAHaVfEaYOAgAAAB7C2+gnL6PJJf+8jX5OqbFTpyjt3btHO3ZkyGazaevWTzRq1DCZzcVau3a1UlNf0IULF9S4cbC8vb1Ur149pzzu1cYZLQAAAKCGs9l+uZhwm0dSXPo41tLSy15D63KCgoKUnPycFiyYp6NHp6pp0+s0Z87zqlevvkaNeljPPDNT/frFyNvbSzExserePdo5xV9lBC0AAADAA5wpsMhgcO01tKoSshISki5pa9v2Vi1b9sYl7fXq1a8wpbAmI2gBAAAAHuJKzzbBefiOFgAAAAA4GUELAAAAAJyMoAUAAAC4BPP4aq4rf+0IWgAAAIATGQwGSQZZrVZ3l4Iq+uW1M/z3tawatwattLQ0JSQkXNK+aNEiDR061H7barUqKSlJHTp0UOfOnbVy5Up738mTJzV8+HBFRESod+/e2r17t71v165d6tOnj9q2bavhw4fr1KlTrj0gAAAA1HoGg0F+fgE6fz5fVqtV5eXl/KtB/6xWq86dy5efX8AVBS23rDpYUlKixYsX6+WXX1b//v0r9P34449aunSp2rRpY29bsWKFDh48qK1bt+qnn37S8OHD9de//lVhYWGaPn26wsPDtWTJEm3cuFETJ07UJ598otLSUo0fP15JSUnq2rWrnnnmGc2ZM0dz58692ocLAACAWiYwsIHOnDmukyd/cncpqAIfH6MCAxtc2T6cU4pjkpOTlZeXp8GDB6u0tNTebrVaNX36dA0YMEAHDhywt69bt05jx45VYGCgWrdurdjYWK1Zs0ajR4/Wtm3blJKSIqPRqH79+mnZsmXKyMhQSUmJmjRpoujoXy5wFh8fry5dumjWrFny9/evUt1XEGgB1DKe+n7hqcflKp74fHniMQGu4O3tpeDgprLZyllyvYYxGCSD4con/rklaI0bN07BwcFasGCB8vLy7O2vvvqqbrnlFt10000VglZOTo5CQ0Ptt0NDQ5WRkaEjR44oKChIgYGBFfoOHTqk8vLyCvdp0KCB/P39deTIEYWHhztcc1BQXYfvAwCeplGjwMtvBI/GGACAynFL0AoODr6kLSsrS2vWrNH777+vLVu2VOgzm80ymUz22yaTSWazWcXFxapTp06FbU0mkywWi8rKyi7p8/Pzk8VStatl5+cXyWotr9J9AdQ+BoNn/kJ6+vR5/jLrAE8cB4wBALWZt7dXpU/AuCVo/a/y8nIlJCQoISHhN6f1mUwmXbhwwX7bYrGobt268vPzq9B+sc/f31+lpaUqKSmp0Gc2m6s8bVDiStsAYLPxXljbMQYAoHKqRdDKy8vT3r17NX78eElSWVmZSkpK1KdPH61bt05hYWHKyclRWFiYJCk7O1thYWEKCQlRQUGBCgsLFRAQIOmXM2ODBw9WSUmJ0tPT7Y9RUFCgoqIitWjR4uofIAAAAIBapVpcR+u6667Tnj17tHPnTu3cuVMzZ85Uu3bttG7dOklSr169tHTpUp09e1b79+9Xenq6evbsqYCAAEVFRSk1NVUlJSVau3atCgoK1L59e3Xq1Em5ubnauHGjSkpKNH/+fHXv3r3CFEQAAAAAcIVqcUbrcoYNG6bc3FzFxMTI19dXkydPti9okZycrISEBEVGRqpZs2ZatGiRjEajJOmll17SjBkzNG3aNN16661KSUlx52EAAAAAqCUMNhszrSsjP79IZWUshgGgcgwGqXHjQI2cvlLmC2XuLueK+dXx0bKnB+nUKRZCcIQnjQPGAABIPj6VXwyjWkwdBAAAAABPQtACAAAAACcjaAEAAACAkxG0AAAAAMDJCFoAAAAA4GQELQAAAABwMoIWAAAAADgZQQsAAAAAnMzH3QV4MoPB3RU4DxenBAAAACqPoOUi9Rv4yejrOU9vSWmZzhaY3V0GAAAAUCN4ThKoRgwGyejro7GzV8tsKXV3OVfMz+SrhQn3yWDgzBYAAABQGQQtFzJbSmW+UObuMgAAAABcZSyGAQAAAABORtACAAAAACcjaAEAAACAkxG0AAAAAMDJCFoAAAAA4GQELQAAAABwMoIWAAAAADgZQQsAAAAAnIygBQAAAABORtACAAAAACcjaAEAAACAkxG0AAAAAMDJ3Bq00tLSlJCQIEmy2Wx64YUX1LlzZ3Xs2FGTJk3S+fPnJUlWq1VJSUnq0KGDOnfurJUrV9r3cfLkSQ0fPlwRERHq3bu3du/ebe/btWuX+vTpo7Zt22r48OE6derU1T1AAAAAALWSW4JWSUmJ5s+fr+eff97e9sEHH2jbtm368MMPtXXrVlksFqWkpEiSVqxYoYMHD2rr1q1KS0vTvHnzlJWVJUmaPn26wsPDlZmZqbi4OE2cOFFWq1UWi0Xjx4/X+PHj9c033ygkJERz5sxxx+ECAAAAqGXcErSSk5P1n//8R4MHD7a3nT17Vg8//LCCg4NVt25dDRgwQHv27JEkrVu3TiNGjFBgYKBat26t2NhYrVmzRoWFhdq2bZseeeQRGY1G9evXT4GBgcrIyND27dvVpEkTRUdHy2g0Kj4+Xps3b1ZxcbE7DhkAAABALeLjjgcdN26cgoODtWDBAuXl5UmSRo0aVWGbL774QjfeeKMkKScnR6Ghofa+0NBQZWRk6MiRIwoKClJgYGCFvkOHDqm8vLzCfRo0aCB/f38dOXJE4eHhVarbYHDudjWNpx4X4Aqe+vPiqcflKp74fHniMQGAKzgUtPLy8nTttdde0r5//36HwktwcPAf9q9atUobN27UBx98IEkym80ymUz2fpPJJLPZrOLiYtWpU6fCfU0mkywWi8rKyi7p8/Pzk8ViqXSdvxYUVLdK9/MkjRoFXn4jAB6N9wEwBgCgchwKWr169dK3335boe3ChQsaMmTIJe1VlZaWpmXLlumVV15R8+bNJf0Sni5cuGDfxmKxqG7duvLz86vQfrHP399fpaWlKikpqdBnNpvl7+9fpbry84tktZZXaluDwTM/iE6fPi+bzd1VADUD7wOQPHMcMAYA1Gbe3l6VPgFz2aB15MgRDRw4UKWlpSouLtatt95aob+0tFQ333xz1Sr9H3PmzNHHH3+st956S61atbK3h4WFKScnR2FhYZKk7OxshYWFKSQkRAUFBSosLFRAQIAkKSsrS4MHD1ZJSYnS09Pt+ygoKFBRUZFatGhR5fpq+weLzcZzANR2vA+AMQAAlXPZoNWiRQstX75c586dU1xcnF5++eUK/XXq1NENN9xwxYW8//772rhxo9577z01adKkQl+vXr20dOlSRUREKDc3V+np6Vq+fLkCAgIUFRWl1NRUPf7449q0aZMKCgrUvn17lZeXa9q0adq4caPuvPNOzZ8/X927d68wBREAAAAAXKFSUwdbt24tSdqyZYuuueYalxTy2muv6fTp04qJibG3NW/eXGvXrtWwYcOUm5urmJgY+fr6avLkyfbvhCUnJyshIUGRkZFq1qyZFi1aJKPRKEl66aWXNGPGDE2bNk233nqrfbl4AAAAAHAlh76jVV5erqeeeko5OTkqL6/4faUVK1Y4/ODjxo2z/3/Dhg2/u52vr68SExOVmJh4SV9wcLCWLl36m/e75ZZb9NFHHzlcFwAAAABcCYeC1pQpU2SxWHTXXXfJx8ctK8MDAAAAQLXnUFrat2+fPv/88wrXrQIAAAAAVOTlyMbXXnttla9DBQAAAAC1hUNntO655x7FxcXpb3/7mxo1alShr0ePHk4tDAAAAABqKoeC1jvvvCPpl4sK/5rBYCBoAQAAAMB/ORS0tm7d6qo6AAAAAMBjOPQdLUnav3+/nn76aT366KPKz8/X66+/7oq6AAAAAKDGcihoffLJJxo2bJjKysqUkZGhkpISvfrqq1q4cKGr6gMAAACAGsehoJWamqpFixZp5syZ8vLyUpMmTbRs2TJ98MEHrqoPAAAAAGoch4JWbm6u2rdvL+mXBTAkqVWrVioqKnJ+ZQAAAABQQzkUtFq1aqWNGzdWaPvyyy/VsmVLpxYFAAAAADWZQ6sOTpkyRaNGjdKqVatkNpsVHx+vr7/+WosXL3ZVfQAAAABQ4zgUtG699VatX79e6enpatGihZo0aaJJkyapefPmrqoPAAAAAGoch4KWJDVt2lSjR492RS0AAAAA4BEcClpff/215syZo2PHjqm8vLxC37fffuvUwgAAAACgpnIoaM2YMUM9evTQ7bffLi8vh691DAAAAAC1gkNBKz8/X5MmTZK3t7er6gEAAACAGs+h01J333231q9f76paAAAAAMAjOHRG67777tODDz6oF154QYGBgRX61q1b59TCAAAAAKCmcihoJSQkKCYmRpGRkUwfBAAAAIDf4VDQOnHihJ5//nlX1QIAAAAAHsGh72h16dJFX3/9tatqAQAAAACP4NAZLT8/P8XFxenmm29WUFBQhb6XX37ZqYUBAAAAQE3lUNBq3ry5Hn74YVfVAgAAAAAewaGgNXbsWFfVAQAAAAAew6GgNXToUBkMhkvafX19FRQUpNtvv119+/at9P7S0tKUnZ2t2bNnS5JWrlyphQsXqri4WH369NH06dPl7e0tq9Wqp59+WuvXr1edOnU0btw4DRo0SJJ08uRJTZkyRd99952uu+46JScnKyIiQpK0a9cuJSUl6ejRo4qIiFBKSooaN27syCEDAAAAgMMcWgzjL3/5i3744Qfdcsst6tWrlyIiInTw4EE1bNhQwcHBSklJ0SuvvHLZ/ZSUlGj+/PkVVjDcu3evUlNTtXz5cm3ZskX79u3T6tWrJUkrVqzQwYMHtXXrVqWlpWnevHnKysqSJE2fPl3h4eHKzMxUXFycJk6cKKvVKovFovHjx2v8+PH65ptvFBISojlz5jhyuAAAAABQJQ6d0dq5c6fS0tLUpk0be1tMTIxmzpyplJQUDRgwQKNHj9aoUaP+cD/JycnKy8vT4MGDVVpaKklav369+vTpo1atWkmS4uLitGzZMg0cOFDr1q3T2LFjFRgYqNatWys2NlZr1qzR6NGjtW3bNqWkpMhoNKpfv35atmyZMjIyVFJSoiZNmig6OlqSFB8fry5dumjWrFny9/d36Em66DdO5l3RdjWNpx4X4Aqe+vPiqcflKp74fHniMQGAKzgUtA4fPqzWrVtXaLv++ut14MABSVKrVq109uzZy+5n3LhxCg4O1oIFC5SXlydJys7OVteuXe3bhISE6PDhw5KknJwchYaG2vtCQ0OVkZGhI0eOKCgoSIGBgRX6Dh06pPLy8gr3adCggfz9/XXkyBGFh4c7ctiSpKCgug7fx9M0ahR4+Y0AeDTeB8AYAIDKcShohYeHa/78+ZowYYJ8fHxktVq1ePFi3XDDDZKkTZs2qUWLFpfdT3Bw8CVtZrNZJpPJftvPz09ms/k3+0wmk8xms4qLi1WnTp0K+zGZTLJYLCorK7ukz8/PTxaLpfIH/Cv5+UWyWssrta3B4JkfRKdPn5fN5u4qgJqB9wFInjkOGAMAajNvb69Kn4BxKGg9/fTTevjhh/Xuu++qcePGOnXqlJo2baoXX3xR33zzjRITE7Vw4cIqFW0ymXThwgX7bbPZbJ/i9799FotFdevWlZ+fX4X2i33+/v4qLS1VSUlJhb5f77MqavsHi83GcwDUdrwPgDEAAJXjUNAKCwvThg0b9N133+n48eO69tprFRERIYPBoGbNmikjI0M+Pg7tssK+s7Oz7bezs7PVsmVLe19OTo7CwsLsfWFhYQoJCVFBQYEKCwsVEBAgScrKytLgwYNVUlKi9PR0+/4KCgpUVFRUqTNuAAAAAHAlHFp1UJJ+/PFHbdiwQenp6QoLC9OKFSsk/XLWqaohS5J69uyptWvX6sCBA8rPz1daWpp69+4tSerVq5eWLl2qs2fPav/+/UpPT1fPnj0VEBCgqKgopaamqqSkRGvXrlVBQYHat2+vTp06KTc3Vxs3brSvcti9e/cKUxABAAAAwBUcClqffPKJhg0bprKyMmVmZqqkpESvvvpqlacL/lqbNm0UHx+vMWPG6O6779Ytt9yiIUOGSJKGDRumm266STExMYqLi9PkyZPtC1okJycrOztbkZGReuWVV7Ro0SIZjUaZTCa99NJLevnll9WxY0cdPXpUSUlJV1wnAAAAAFyOwWar/Ezrvn37avr06erQoYM6dOigHTt26ODBgxo1apQ+//xzF5bpfvn5RSorq/xiGI0bB2rk9JUyXyhzcWWu51fHR8ueHqRTp/gCNFBZvA9A8qxxwBgAAMnHp/KLYTh0Ris3N1ft27eXJBn+eyGNVq1aqaioyMESAQAAAMBzORS0WrVqpY0bN1Zo+/LLL+2LVgAAAAAAHFx1cMqUKRo1apRWrVols9ms+Ph4ff3111q8eLGr6gMAAACAGsehoHXrrbdq/fr1Sk9PV4sWLdSkSRNNmjRJzZs3d1V9AAAAAFDjOLwee9OmTTV69GhJ0o4dO2SxWJxeFAAAAADUZA59R+uLL75Qt27dJElLlizRyJEjNWDAAL3//vuuqA0AAAAAaiSHgtaCBQs0duxYlZeX6/XXX9eiRYv07rvvasmSJa6qDwAAAABqHIemDh49elQDBgzQnj17VFpaqqioKHl5eenMmTOuqg8AAAAAahyHzmjVq1dPOTk52rBhgzp27CgvLy9lZmYqODjYVfUBAAAAQI3j0BmtuLg4xcbGysfHRytWrNCuXbsUFxen5ORkV9UHAAAAADWOQ0Fr4MCBuvPOO2UymeTv76+zZ89qzZo1CgsLc1V9AAAAAFDjOLy8e8OGDe3/r1+/vurXr+/UggAAAACgpnPoO1oAAAAAgMsjaAEAAACAk1UqaN1///2SxIWJAQAAAKASKvUdrX379unQoUP6xz/+oR49eshms12yTYMGDZxdGwAAAADUSJUKWt26dVPv3r1lMBjUqVOnCn02m00Gg0Hff/+9SwoEAAAAgJqmUkFr/vz5On78uGJiYpSenu7qmgAAAACgRqv08u5NmjTRhx9+qGbNmqmwsFB5eXlq1KiRgoKCXFkfAAAAANQ4Dl1Hq3Hjxho/frw+/vhjSZKXl5duv/12paSkKDAw0CUFAgAAAEBN49Dy7s8995zMZrM2bNigPXv2aO3atbJarZozZ46r6gMAAACAGsehoPXZZ59p7ty5atmypYxGo66//no999xz+uSTT1xVHwAAAADUOA4FLavVKm9v7wptPj4+8vFxaAYiAAAAAHg0h4JWVFSUpk2bphMnTkiSjh8/roSEBEVFRbmkOAAAAACoiRwKWk888YROnDihrl276s9//rO6deumoqIiPfnkk04raPv27erdu7fatWunIUOG6NChQ5KklStXqkuXLmrXrp2SkpJktVol/XKWLSkpSR06dFDnzp21cuVK+75Onjyp4cOHKyIiQr1799bu3budVicAAAAA/B6HglajRo307rvv6uOPP9abb76pzz//XMuWLXPaEu9Wq1Xx8fGaPn26duzYoY4dO+qpp57S3r17lZqaquXLl2vLli3at2+fVq9eLUlasWKFDh48qK1btyotLU3z5s1TVlaWJGn69OkKDw9XZmam4uLiNHHiRHtAAwAAAABXcShoXdS8eXO1bdtWTZo0cWoxZ8+eVUFBgcrLy38pzstLJpNJ69evV58+fdSqVSs1bNhQcXFxWrVqlSRp3bp1GjFihAIDA9W6dWvFxsZqzZo1Kiws1LZt2/TII4/IaDSqX79+CgwMVEZGhlNrBgAAAID/Va1WsWjYsKHuu+8+DR8+XN7e3qpfv77effddzZkzR127drVvFxISosOHD0uScnJyFBoaau8LDQ1VRkaGjhw5oqCgoArX9woNDdWhQ4eq/J0yg8G529U0nnpcgCt46s+Lpx6Xq3ji8+WJxwQArlCtglZZWZkCAgK0fPly3XrrrVq8eLHi4+NVr149mUwm+3Z+fn4ym82SJLPZXKHPZDLJbDaruLhYderUqbB/k8kki8VSpdqCgupW6X6epFEjLkoN1Ha8D4AxAACV41DQ2rVrl9q1a3dJ+2effaY77rjjiov5+OOP9dNPPykyMlKSNH78eL355pu6+eabdeHCBft2ZrNZ/v7+kn4JT7/us1gsqlu3rvz8/Cq0X+y7eD9H5ecXyWotr9S2BoNnfhCdPn1eNpu7qwBqBt4HIHnmOGAMAKjNvL29Kn0C5rJBq7S0VEVFRZKk0aNH67PPPpPtV++w58+f18SJE52yot/x48crLFbh5eUlb29vNWjQQNnZ2fb27OxstWzZUpIUFhamnJwchYWF2fvCwsIUEhKigoICFRYWKiAgQJKUlZWlwYMHV7m+2v7BYrPxHAC1He8DYAwAQOVcdjGMM2fO6I477lBkZKTMZrM6deqkyMhI+78ePXqoU6dOTimmU6dOyszM1FdffSWr1aq0tDRdc801GjlypNauXasDBw4oPz9faWlp6t27tySpV69eWrp0qc6ePav9+/crPT1dPXv2VEBAgKKiopSamqqSkhKtXbtWBQUFat++vVNqBQAAAIDfc9kzWk2aNNEnn3wis9mse+65Rx999FGF/jp16qhx48ZOKaZ169aaPXu2nn76aZ0+fVo333yzFi1apJCQEMXHx2vMmDEqKipS3759NWTIEEnSsGHDlJubq5iYGPn6+mry5MkKDw+XJCUnJyshIUGRkZFq1qyZFi1aJKPR6JRaAQAAAOD3VOo7Wo0aNZIk7dy506XFSFJsbKxiY2MvaR84cKAGDhx4Sbuvr68SExOVmJh4SV9wcLCWLl3qkjoBAAAA4Pc4tBjG/v37NXfuXOXk5NivdXXRp59+6tTCAAAAAKCmcihoTZ8+Xddee63i4+Pl41OtVoYHAAAAgGrDobR06NAhvfHGGxWuWwUAAAAAqOiyqw7+WsuWLXX8+HFX1QIAAAAAHsGhM1qdO3fWgw8+qNjYWPsCGRcNHz7cqYUBAAAAQE3lUNDatWuXmjdvrn/9618V2g0GA0ELAAAAAP7LoaD1xhtvuKoOAAAAAPAYDgWtNWvW/G7fPffcc4WlAAAAAIBncChoLVu2rMLt/Px8nTlzRrfddhtBCwAAAAD+y6GgtW7dukva3n33XX3//fdOKwgAAAAAajqHlnf/LYMGDdKmTZucUQsAAAAAeIQrDlqbNm1S3bp1nVELAAAAAHgEh6YORkREyGAw2G+XlZXJarVq6tSpTi8MAAAAAGoqh4LWkiVLKtz28vJSSEiIgoODnVoUAAAAANRkDgWtv/71r5KkAwcOKDc3V40bNyZkAQAAAMD/cChonTx5Uo8++qj27dunoKAg5efnq1WrVkpLS1OTJk1cVSMAAAAA1CgOLYbxzDPPqFWrVtqxY4e++uorZWZm6uabb1ZycrKr6gMAAACAGsehM1qZmZn69NNP5efnJ0kKCAhQYmKiunXr5oraAAAAAKBGcuiMlre3t4qKiiq0FRUV2YMXAAAAAMDBoNWjRw+NHz9eu3fv1vHjx7Vr1y7Fx8erR48erqoPAAAAAGoch6YOTpo0SdOnT9fQoUNltVrl6+ure+65RxMnTnRVfQAAAABQ4zgUtPz9/ZWSkqKnn35aRUVF8vLyUlBQkLy8HDoxBgAAAAAezaGEtG/fPt1xxx368ccfFRwcrFdeeUV33XWXDh065Kr6AAAAAKDGcShozZo1S8OHD1ebNm0kSVOnTtVDDz2kpKQkV9QGAAAAADWSQ0Hr4MGDeuihh2QwGOxtQ4cO1ffff++0gn7++WeNGDFCHTp0UJ8+ffTdd99JklauXKkuXbqoXbt2SkpKktVqlSRZrVYlJSWpQ4cO6ty5s1auXGnf18mTJzV8+HBFRESod+/e2r17t9PqBAAAAIDf41DQaty4sb799tsKbf/+9791zTXXOKWY8vJyjRw5UlFRUcrMzNTw4cM1YcIE7d27V6mpqVq+fLm2bNmiffv2afXq1ZKkFStW6ODBg9q6davS0tI0b948ZWVlSZKmT5+u8PBwZWZmKi4uThMnTrQHNAAAAABwFYeC1ujRozV69GglJSVpyZIlmjlzpkaOHKkxY8Y4pZhvv/1WXl5eGjlypLy8vHTfffdp4cKFWrdunfr06aNWrVqpYcOGiouL06pVqyRJ69at04gRIxQYGKjWrVsrNjZWa9asUWFhobZt26ZHHnlERqNR/fr1U2BgoDIyMpxSKwAAAAD8HodWHRwwYICaNm2q9PR07dixQ9dee60WL16sDh06OKWY/fv3KywsTNOmTdOnn36qsLAwzZw5U0eOHFHXrl3t24WEhOjw4cOSpJycHIWGhtr7QkNDlZGRoSNHjigoKEiBgYEV+g4dOqSoqKgq1ferGZNO2a6m8dTjAlzBU39ePPW4XMUTny9PPCYAcAWHgpYkRUVFVTmoXM65c+f02Wefafbs2UpKStK7776rRx99VM2aNZPJZLJv5+fnJ7PZLEkym80V+kwmk8xms4qLi1WnTp0K+zeZTLJYLFWqLSiobpXu50kaNQq8/EYAPBrvA2AMAEDlOBy0XMloNCosLEz33nuvJGnYsGFatGiRysvLdeHCBft2ZrNZ/v7+kn4JT7/us1gsqlu3rvz8/Cq0X+y7eD9H5ecXyWotr9S2BoNnfhCdPn1eNpu7qwBqBt4HIHnmOGAMAKjNvL29Kn0CploFrdDQUJ0/f95+22azqby8XPXq1VN2dra9PTs7Wy1btpQkhYWFKScnR2FhYfa+sLAwhYSEqKCgQIWFhQoICJAkZWVlafDgwVWur7Z/sNhsPAdAbcf7ABgDAFA5Di2G4Wq33XabysrKtHz5clmtVr322mtq2LChxowZo7Vr1+rAgQPKz89XWlqaevfuLUnq1auXli5dqrNnz2r//v1KT09Xz549FRAQoKioKKWmpqqkpERr165VQUGB2rdv7+ajBAAAAODpqtUZLX9/fy1fvlxPPfWUUlNTFRYWptTUVN14442Kj4/XmDFjVFRUpL59+2rIkCGSfplemJubq5iYGPn6+mry5MkKDw+XJCUnJyshIUGRkZFq1qyZFi1aJKPR6M5DBAAAAFALVKugJUk33HCD3nnnnUvaBw4cqIEDB17S7uvrq8TERCUmJl7SFxwcrKVLl7qkTgAAAAD4PdVq6iAAAAAAeAKCFgAAAAA4GUELAAAAAJyMoAUAAAAATkbQAgAAAAAnI2gBAAAAgJMRtAAAAADAyQhaAAAAAOBkBC0AAAAAcDKCFgAAAAA4GUELAAAAAJyMoAUAAAAATkbQAgAAAAAnI2gBAAAAgJMRtAAAAADAyQhaAAAAAOBkBC0AAAAAcDKCFgAAAAA4GUELAAAAAJyMoAUAAAAATkbQAgAAAAAnI2gBAAAAgJMRtAAAAADAyQhaAAAAAOBkBC0AAAAAcLJqG7R27Nih8PBw++2VK1eqS5cuateunZKSkmS1WiVJVqtVSUlJ6tChgzp37qyVK1fa73Py5EkNHz5cERER6t27t3bv3n3VjwMAAABA7VMtg5bFYtH06dNls9kkSXv37lVqaqqWL1+uLVu2aN++fVq9erUkacWKFTp48KC2bt2qtLQ0zZs3T1lZWZKk6dOnKzw8XJmZmYqLi9PEiRPtAQ0AAAAAXMXH3QX8lvnz56tLly72wLR+/Xr16dNHrVq1kiTFxcVp2bJlGjhwoNatW6exY8cqMDBQrVu3VmxsrNasWaPRo0dr27ZtSklJkdFoVL9+/bRs2TJlZGQoKiqqSnUZDM7drqbx1OMCXMFTf1489bhcxROfL088JgBwhWoXtL777jt9++23mjdvnlasWCFJys7OVteuXe3bhISE6PDhw5KknJwchYaG2vtCQ0OVkZGhI0eOKCgoSIGBgRX6Dh06VKWgFRRUt4pH5DkaNQq8/EYAPBrvA2AMAEDlVKugVVJSohkzZui5556Tt7e3vd1sNstkMtlv+/n5yWw2/2afyWSS2WxWcXGx6tSpU2H/JpNJFoulSrXl5xfJai2v1LYGg2d+EJ0+fV7/nc0J4DJ4H4DkmeOAMVA1nnImkNcetZ23t1elT8BUq6C1YMECde/eXeHh4crLy7O3m0wmXbhwwX7bbDbL39//N/ssFovq1q0rPz+/Cu0X+y7erypq+5uLzcZzANR2vA+AMeC4+g38ZPStVr9yVVlJaZnOFpjdXQZQI1Srn/otW7bo5MmTevPNN+0LYbRv317R0dHKzs62b5edna2WLVtKksLCwpSTk6OwsDB7X1hYmEJCQlRQUKDCwkIFBARIkrKysjR48OCre1AAAKDWMhgko6+Pxs5eLbOl1N3lXBE/k68WJtwng4GwDVRGtQpamzZtsv8/Ly9PXbt21c6dO7Vnzx793//9n+69914FBwcrLS1N/fr1kyT16tVLS5cuVUREhHJzc5Wenq7ly5crICBAUVFRSk1N1eOPP65NmzapoKBA7du3d9fhAQCAWspsKZX5Qpm7ywBwFVWroPV72rRpo/j4eI0ZM0ZFRUXq27evhgwZIkkaNmyYcnNzFRMTI19fX02ePNl+/a3k5GQlJCQoMjJSzZo106JFi2Q0Gt15KAAAAABqgWobtK699lr98MMP9tsDBw7UwIEDL9nO19dXiYmJSkxMvKQvODhYS5cudWmdAAAAAPC/quUFiwEAAACgJiNoAQAAAICTEbQAAAAAwMkIWgAAAADgZNV2MQzAExgM7q7AebhmCgAAQOURtAAXqd/AT0Zfz/kRKykt09kCs7vLAAAAqBE857dAoBoxGCSjr4/Gzl4ts6XU3eVcMT+TrxYm3CeDgTNbAAAAlUHQAlzIbCmV+UKZu8sAAADAVcZiGAAAAADgZAQtAAAAAHAyghYAAAAAOBlBCwAAAACcjKAFAAAAAE5G0AIAAAAAJyNoAQAAAICTEbQAAAAAwMkIWgAAAADgZAQtAAAAAHAyghYAAAAAOBlBCwAAAACcjKAFAAAAAE5G0AIAAAAAJyNoAQAAAICTEbQAAAAAwMmqXdBav3697r77brVr107333+/Dh48KElauXKlunTponbt2ikpKUlWq1WSZLValZSUpA4dOqhz585auXKlfV8nT57U8OHDFRERod69e2v37t1uOSYAAAAAtUu1ClqHDh3SzJkzNXfuXH3zzTfq2rWrxo4dq7179yo1NVXLly/Xli1btG/fPq1evVqStGLFCh08eFBbt25VWlqa5s2bp6ysLEnS9OnTFR4erszMTMXFxWnixIn2gAYAAAAArlKtgtaxY8f0wAMP6C9/+Yu8vb11//33KysrS2vXrlWfPn3UqlUrNWzYUHFxcVq1apUkad26dRoxYoQCAwPVunVrxcbGas2aNSosLNS2bdv0yCOPyGg0ql+/fgoMDFRGRoabjxIAAACAp/NxdwG/1qVLF3Xp0sV++4svvtB1112no0ePqmvXrvb2kJAQHT58WJKUk5Oj0NBQe19oaKgyMjJ05MgRBQUFKTAwsELfoUOHFBUVVaX6DAbnblfTeOpxuYKnPleeelyu4KnPlacel6t44vPlicfkSp74fHniMQGuUK2C1q99//33SkpK0uzZs/XWW2/JZDLZ+/z8/GQ2myVJZrO5Qp/JZJLZbFZxcbHq1KlTYZ8mk0kWi6VK9QQF1a3S/TxJo0aBl98IHo0xAMYAGANgDACVUy2D1vbt2/XYY49p8uTJio6O1qpVq3ThwgV7v9lslr+/v6RfwtOv+ywWi+rWrSs/P78K7Rf7Lt7PUfn5RbJayyu1rcHgmW9Cp0+fl83m7ipqBsYAGAOQPHMcMAYcwxgAPIu3t1elT8BUu6C1efNmTZs2TXPmzFF0dLQkKSwsTNnZ2fZtsrOz1bJlS3tfTk6OwsLC7H1hYWEKCQlRQUGBCgsLFRAQIEnKysrS4MGDq1xbbX9Tsdl4Dmo7xgAYA2AMgDEAVE61Wgzjxx9/1BNPPKGFCxfaQ5Yk9ezZU2vXrtWBAweUn5+vtLQ09e7dW5LUq1cvLV26VGfPntX+/fuVnp6unj17KiAgQFFRUUpNTVVJSYnWrl2rgoICtW/f3l2HBwAAAKCWqFZntN566y1ZLBY98sgjFdo3bdqk+Ph4jRkzRkVFRerbt6+GDBkiSRo2bJhyc3MVExMjX19fTZ48WeHh4ZKk5ORkJSQkKDIyUs2aNdOiRYtkNBqv+nEBAAAAqF2qVdBKSkpSUlLSb/YNHDhQAwcOvKTd19dXiYmJSkxMvKQvODhYS5cudXaZAAAAAPCHqtXUQQAAAADwBAQtAAAAAHAyghYAAAAAOBlBCwAAAACcjKAFAAAAAE5G0AIAAAAAJ6tWy7sDAIDqzWBwdwXOY7O5uwIAnoygBQAALsvHx0vl1jI1ahTo7lKcxlpaqjMFFneXAcBDEbQAAMBl+Xh7ycvbR3sWT5a1xOzucq6Yt9FPbR5JkcFg4cwWrgrOBtc+BC0AQKXxiwKsJWaVl3AWCHBE/QZ+Mvp6zq/dJaVlOltQ8//g4mqe84oDAFyGaWMAUDUGg2T09dHY2atltpS6u5wr5mfy1cKE+2Qw8AeryyFoAQAui2ljAHBlzJZSmS+UubsMXEUELVSap0wZ4pcqoOqYNgYAQOUQtHBZnjZliOlCAAAAcDWCFi7Lk6YMMV0IAAAAVwNBC5XGlCEAAACgcrzcXQAAAAAAeBqCFgAAAAA4GUELAAAAAJyMoAUAAAAATkbQAgAAAAAnI2gBAAAAgJMRtAAAAADAybiOFoBKMxjcXYHzcMFqAKgaPguAyiFoAbgsHx8vlVvL1KhRoLtLcRpraanOFHABbgCoLD4LAMd4fNDatWuXkpKSdPToUUVERCglJUWNGzd2d1lAjeLj7SUvbx/tWTxZ1hKzu8u5Yt5GP7V5JEUGg4W/ZgJAJfFZADjGo4OWxWLR+PHjlZSUpK5du+qZZ57RnDlzNHfuXHeXBtRI1hKzykv4yx8A1GZ8FkDynCmkrgzZHh20tm/friZNmig6OlqSFB8fry5dumjWrFny9/d3aF/e3pVfN+TiwPt/ocG6cKHMocepjurVrSNJCrjuellLa/Ybq7evSdIv0x9c+YPFGKjersY4YAxUb7wXOI4xUDWMgeqLMVA1df18PWoKqbW0VOcKSyq9vUOZwGbz3JOly5cv1549e/TCCy/Y2zp27KjXX39d4eHhbqwMAAAAgCfz6OXdi4uLVadOnQptfn5+slhq/l9hAAAAAFRfHh20/Pz8VFJS8VSg2Wx2eNogAAAAADjCo4NWWFiYsrOz7bcLCgpUVFSkFi1auK8oAAAAAB7Po4NWp06dlJubq40bN6qkpETz589X9+7dZTKZ3F0aAAAAAA/m0YthSNK//vUvzZgxQ0eOHNGtt96qlJQUNWzY0N1lAQAAAPBgHh+0AAAAAOBq8+ipgwAAAADgDgQtAAAAAHAyghYAAAAAOBlBCwAAAACcjKAFAAAAAE5G0AIAAADwm44dO1bpbUtLS3XixAkXVlOzELRwiQ0bNmjgwIFq166dunTpolmzZqmwsFCS9Prrr+uOO+5Qhw4dNGbMGOXl5bm5WrjCH42Bi2bOnKnFixe7qUK4Q8+ePTVgwAB3l4Gr4KefftKNN96oiIgI+7/Bgwfr0KFDkqTt27erX79+uvXWW3Xvvfdq586dbq4Yzna5MXDRxo0bNXz4cDdViSt14403XvK7XGZmpqKjoyVJp06dUt++fe19EREROn78uFavXq2HHnpIkjRjxgylpaVJkiZNmqTt27dfneJrAIIWKnjttdf07LPP6vHHH9c333yjjz76SKdOndK4ceP09ddfa/ny5Vq+fLn++c9/qlmzZkpISHB3yXCyPxoDklRYWKjExES9/fbbbq4UV9N3332nBg0aKD8/X99//727y8FV4O3trd27d2v37t3asWOHOnbsqCeffFIFBQV67LHHNHHiRO3cuVMjRozQo48+quLiYneXDCf7vTEgSTabTW+88YamTJkiLsnquSwWS4Wf7d27d6tJkyYVtpk1a5ZGjx4tScrPz7+q9VV3BC3YFRYWav78+Zo9e7Y6duwob29vNWzYUM8884wCAwN14sQJjRo1SiEhITIajRo8eLD27Nnj7rLhRJcbA2fOnNGECRNUWlqqu+++293l4ir68MMP1a1bN/Xp00crV650dzm4ynx8fBQbG6v9+/fr2LFj6tmzp7p27SovLy/16dNHknTkyBE3VwlX+vUYkKS0tDStW7dOI0eOdHNlcKUHH3xQVqtVEREROn/+/G+eAXviiSe0ePFipaamaufOnUpMTNRbb72ltm3bKicnx77dk08+qWXLll3tQ3Arghbsdu/eLYPBoMjIyArtAQEBSk1N1T333KP777/f3v7FF1/oxhtvvNplwoUuNwYuhq5nn31W/v7+bqoSV1tJSYk2bdqkPn366N5771V6errMZrO7y8JVVFpaqo8++ki33367brrpJs2cOdPet2fPHl24cEEtWrRwY4VwtV+PAUm699579d577/G6e7jXX3/dfmYzMDDwD7cdP3682rdvr+TkZN1///26/fbbtWXLFkmS1WrVZ599Vuv+SOvj7gJQfZw9e1b16tWTt7f3Zbfdtm2bXnrpJb322mtXoTJcLZUZA8HBwVexIlQHn3zyiW6++WZdd911kn6Z079hwwb179/fzZXBlaxWq9q3by9JMpvNMhgMWrhwYYVtjh07pscee0wTJkzgjy8e6I/GAJ8FuJyYmBitWLFCo0aN0jfffKM//elP+tOf/uTusq4qghbsGjVqpIKCApWVlcnHp+LQOHPmjBo2bChJWrt2rWbNmqV58+bplltucUepcJHKjgHULh9++KF2796tqKgoSVJRUZHKysoIWh7O29vbvshFeXm5vvjiC40bN05vvvmmbrnlFu3fv1+jR4/WwIED9eCDD7q5WrjC5cYAaj5fX1+VlZVVaLNarTIajVe87zvuuEMJCQk6ceKENm/erJ49e17xPmsapg7CLiIiQt7e3vrnP/9Zob2wsFB33nmnvvvuO61YsUKzZ8/WkiVL1LVrVzdVClepzBhA7XLy5En7oihr1qzRmjVrtG7dOv3nP//RDz/84O7ycJV4eXnpjjvuUMuWLZWZmaldu3Zp6NChevjhhzV+/Hh3l4er4H/HADzDNddcc8ly7D///PMlC15UhZ+fn26//XZ99tln+vzzz2vdtEGJoIVfMZlMGjt2rJ566int2LFDNptNeXl5mjRpktq0aaOioiK9+OKLeuONN9SuXTt3lwsXuNwYaNu2rbtLxFX20UcfqWPHjmrRooWCg4MVHBys5s2bq2vXrnrvvffcXR6uoh07digrK0t/+tOf9Mgjj2jatGkVvrcLz3dxDHA2y3P06NFDL774oo4fPy6bzaYffvhBy5cvV0xMjCTJaDSqvLy80t/LNRqNKioqst/u2bOn3nzzTTVu3LjWTRuUmDqI/zFy5Ej5+/tr5syZOnbsmOrWrasePXpowoQJmjBhgsxmswYNGmTf/tfTCuAZ/mgMoPZZs2bNb14jp3fv3poxY4YmT54sk8nkhsrgahdXGruoUaNGeuKJJ/Tzzz+roKBAs2bN0qxZs+z9r776aoXtUfP93hjo2LGjG6uCM02YMEHPP/+8BgwYoPPnz6tx48a6//779be//U3SL9/Fu+2223Tbbbdp3bp1l91fTEyMnnnmGZ0/f15jxoxRt27d9OSTT2rs2LGuPpRqyWDj4gcAAAAAnMxms+mOO+7QW2+9pWbNmrm7nKuOM1oAAAAAnOrnn3/W1q1b1bx581oZsiSCFgAAAAAne/nll/X5559ryZIl7i7FbZg6CAAAAABOxqqDAAAAAOBkBC0AAAAAcDKCFgAAAAA4GUELAAAAAJyMoAUAQDWQk5Pj7hIAAE5E0AIAwM3efPNN/eMf/3B3GQAAJyJoAQDgZvn5+eJqKwDgWQhaAACPUF5eruTkZEVFRSkyMlIjRoxQdna2JOn9999XTEyM2rdvr6FDh+rgwYP2+3366afq2bOn2rdvr3Hjxmns2LFasGCBJGno0KFatGiR7rvvPrVt21ZDhw7V3r17NXToUEVERGjgwIH6+eef7fv6vcf56aefFBERoeXLl+v2229Xx44dNXXqVJWWlmrDhg1asmSJtm3bpt69e1+9JwwA4FIELQCAR9iyZYsyMzO1adMmffnll2rSpIkWLFigjz/+WPPnz9fzzz+v7du3Kzo6WiNGjJDZbFZOTo7i4+MVHx+vjIwMdevWTVu2bKmw3w8++EAvvviivvrqK504cUIjR45UQkKCvv76a/n6+urVV1+VpD98HEkqLi7W999/r82bN+utt97S1q1btXnzZvXq1UtjxoxRly5dtH79+qv+vAEAXIOgBQDwCPXq1dOxY8e0atUq/fzzz5o9e7aef/55vffeexo6dKhuvvlm+fr6atiwYfL399fnn3+u9evX669//avuvvtu+fj4qH///rrlllsq7LdXr15q3ry5AgIC1KZNG3Xp0kXh4eHy8/NTx44d7We0/uhxLho9erT8/Px0/fXXq02bNvYzbgAAz+Pj7gIAAHCGyMhIJSUl6Z133tHcuXPVrFkzTZkyRceOHdPLL7+sV155xb5tWVmZjh07pry8PDVt2rTCfpo1a1bhdlBQkP3/3t7e8vf3t9/28vKyf7fqjx7nL3/5iySpYcOG9j4fHx++lwUAHoygBQDwCEePHtUNN9ygt99+W4WFhXr77bcVHx+vtm3b6v7779f9999v3zY7O1vBwcFasWKFdu7cWWE/ubm5atmypf22wWCo1OM3adLkdx8nPz//Co8OAFDTMHUQAOARtm/frkcffVQ///yz6tatq3r16ikgIEADBw7U8uXL9eOPP8pms+mTTz5RbGyssrKy1LdvX+3cuVOfffaZrFar0tPTtXv37io9fv/+/X/3cS6nTp06KiwsrNLjAgCqJ85oAQA8woABA5Sdna1BgwapqKhIYWFhWrBggdq3b6+ioiKNGzdOx48f13XXXafnnntOf/7znyVJKSkpmj17tiZPnqyoqCj95S9/ka+vr8OPHxsbq/Pnz//m4/z0009/eN9u3brpzTffVOfOnbVt27ZKn0UDAFRfBhsTxAEAtdSxY8dUWFioG264wd523333adCgQRo0aJAbKwMA1HRMHQQA1FonT57UAw88oMOHD9un+/3444+KjIx0d2kAgBqOM1oAgFrtjTfe0Guvvab8/Hw1b95c8fHx6t69u7vLAgDUcAQtAAAAAHAypg4CAAAAgJMRtAAAAADAyQhaAAAAAOBkBC0AAAAAcDKCFgAAAAA4GUELAAAAAJyMoAUAAAAATkbQAgAAAAAn+/8AR8yD4G+FY84AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_eda= ['region_code','segment','model','fuel_type','claim_status','customers_age_binned','vehicle_age_binned','Members_binned','max_torque']\n",
    "for i in categorical_eda:\n",
    "    fig=plt.figure(figsize= (10,4))\n",
    "    sns.countplot(data=data, x= i, hue=\"claim_status_text\")\n",
    "    plt.xlabel(i)\n",
    "    plt.ylabel(f' count of {i}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "237250bc-a91a-4f7b-941e-f21991c7421a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data=data, x=\"customers_age_binned\")\n",
    "plt.xlabel(\"Age Distribution and Insurance Claim\")\n",
    "plt.ylabel(\"Count of age distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "572db6a0-1d68-4bc6-8fc6-6731c0515da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot= ['customer_age','']\n",
    "plt.boxplot(x='customer_age',data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "451c7443-8e34-41b9-8c05-46ee70497faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Percentage of Insurance claim made is 6.40%\n"
     ]
    }
   ],
   "source": [
    "claim = 1\n",
    "claim_insurance= len(data[data[\"claim_status\"] == claim])\n",
    "per_of_claim= (claim_insurance / len(data)) * 100\n",
    "print(f\" Percentage of Insurance claim made is{per_of_claim: .2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2813a739-9cca-4e13-b712-8987e26cc5eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax=plt.subplots()\n",
    "size=data[\"claim_status\"].value_counts()\n",
    "labels=size.index\n",
    "ax.pie(size,labels,autopct='%1.2f%%',shadow=True)\n",
    "plt.title(\"Claim status distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe58600-86de-435b-9142-53ab0b9b26eb",
   "metadata": {},
   "source": [
    "### Test of Hypothesis Using Chi-square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed6382b7-2ec1-4730-8378-a31d1513294d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claim_status_text     No   Yes\n",
      "fuel_type                     \n",
      "CNG                19095  1235\n",
      "Diesel             16580  1150\n",
      "Petrol             19169  1363\n",
      "Chi-square statistics: 5.759623054808867\n",
      "P-Values: 0.056145343695656484\n",
      "Degree of Freedom: 2\n",
      "Expected Frequencies\n",
      "[[19029.53509011  1300.46490989]\n",
      " [16595.85131076  1134.14868924]\n",
      " [19218.61359913  1313.38640087]]\n",
      "Fail to reject the null hypothesis: No association between fuel_type and claim status\n",
      "\n",
      "claim_status_text     No  Yes\n",
      "region_code                  \n",
      "C1                  1392   76\n",
      "C10                 3007  148\n",
      "C11                 1140   72\n",
      "C12                 1502   87\n",
      "C13                 3228  195\n",
      "C14                 3379  281\n",
      "C15                  733   38\n",
      "C16                  378   23\n",
      "C17                  473   19\n",
      "C18                  216   26\n",
      "C19                  881   71\n",
      "C2                  6822  520\n",
      "C20                  104    5\n",
      "C21                  350   29\n",
      "C22                  190   17\n",
      "C3                  5668  433\n",
      "C4                   614   51\n",
      "C5                  6576  403\n",
      "C6                   835   55\n",
      "C7                  2058  109\n",
      "C8                 12700  954\n",
      "C9                  2598  136\n",
      "Chi-square statistics: 96.08264061605902\n",
      "P-Values: 1.412557511595789e-11\n",
      "Degree of Freedom: 21\n",
      "Expected Frequencies\n",
      "[[1.37409530e+03 9.39046969e+01]\n",
      " [2.95318166e+03 2.01818337e+02]\n",
      " [1.13447105e+03 7.75289459e+01]\n",
      " [1.48735520e+03 1.01644798e+02]\n",
      " [3.20403830e+03 2.18961701e+02]\n",
      " [3.42587794e+03 2.34122064e+02]\n",
      " [7.21680844e+02 4.93191562e+01]\n",
      " [3.75348921e+02 2.56510786e+01]\n",
      " [4.60527854e+02 3.14721464e+01]\n",
      " [2.26519798e+02 1.54802021e+01]\n",
      " [8.91102676e+02 6.08973239e+01]\n",
      " [6.87234858e+03 4.69651420e+02]\n",
      " [1.02027512e+02 6.97248771e+00]\n",
      " [3.54756212e+02 2.42437875e+01]\n",
      " [1.93758670e+02 1.32413299e+01]\n",
      " [5.71073259e+03 3.90267409e+02]\n",
      " [6.22461428e+02 4.25385718e+01]\n",
      " [6.53256888e+03 4.46431117e+02]\n",
      " [8.33068678e+02 5.69313217e+01]\n",
      " [2.02838183e+03 1.38618173e+02]\n",
      " [1.27805840e+04 8.73416029e+02]\n",
      " [2.55911210e+03 1.74887903e+02]]\n",
      "Reject the null hypothesis: There is an association between region_code type and claim status\n",
      "claim_status_text     No   Yes\n",
      "cylinder                      \n",
      "3                  20552  1305\n",
      "4                  34292  2443\n",
      "Chi-square statistics: 10.46038099301418\n",
      "P-Values: 0.0012196212137077615\n",
      "Degree of Freedom: 1\n",
      "Expected Frequencies\n",
      "[[20458.85629437  1398.14370563]\n",
      " [34385.14370563  2349.85629437]]\n",
      "Reject the null hypothesis: There is an association between cylinder type and claim status\n",
      "claim_status_text     No   Yes\n",
      "steering_type                 \n",
      "Electric           22284  1597\n",
      "Manual              1136    73\n",
      "Power              31424  2078\n",
      "Chi-square statistics: 5.736127029443465\n",
      "P-Values: 0.05680882959490323\n",
      "Degree of Freedom: 2\n",
      "Expected Frequencies\n",
      "[[22353.38551338  1527.61448662]\n",
      " [ 1131.6629574     77.3370426 ]\n",
      " [31358.95152922  2143.04847078]]\n",
      "Fail to reject the null hypothesis: No association between steering_type and claim status\n",
      "\n",
      "claim_status_text     No  Yes\n",
      "model                        \n",
      "M1                 14030  918\n",
      "M10                 1136   73\n",
      "M11                  348   15\n",
      "M2                  1000   80\n",
      "M3                  2245  128\n",
      "M4                 13117  901\n",
      "M5                  1482  116\n",
      "M6                 12837  939\n",
      "M7                  2739  201\n",
      "M8                  3929  244\n",
      "M9                  1981  133\n",
      "Chi-square statistics: 19.97498032832033\n",
      "P-Values: 0.02949022219663932\n",
      "Degree of Freedom: 10\n",
      "Expected Frequencies\n",
      "[[13991.80966685   956.19033315]\n",
      " [ 1131.6629574     77.3370426 ]\n",
      " [  339.77969689    23.22030311]\n",
      " [ 1010.91480066    69.08519934]\n",
      " [ 2221.20446477   151.79553523]\n",
      " [13121.29969962   896.70030038]\n",
      " [ 1495.77949208   102.22050792]\n",
      " [12894.77990169   881.22009831]\n",
      " [ 2751.93473512   188.06526488]\n",
      " [ 3906.06246587   266.93753413]\n",
      " [ 1978.77211906   135.22788094]]\n",
      "Reject the null hypothesis: There is an association between model type and claim status\n",
      "claim_status_text     No   Yes\n",
      "airbags                       \n",
      "1                   1136    73\n",
      "2                  37852  2573\n",
      "6                  15856  1102\n",
      "Chi-square statistics: 0.6210927084192801\n",
      "P-Values: 0.733046343840305\n",
      "Degree of Freedom: 2\n",
      "Expected Frequencies\n",
      "[[ 1131.6629574     77.3370426 ]\n",
      " [37839.10260786  2585.89739214]\n",
      " [15873.23443474  1084.76556526]]\n",
      "Fail to reject the null hypothesis: No association between airbags and claim status\n",
      "\n",
      "claim_status_text             No  Yes\n",
      "engine_type                          \n",
      "1.0 SCe                     2245  128\n",
      "1.2 L K Series Engine       2739  201\n",
      "1.2 L K12N Dualjet          1000   80\n",
      "1.5 L U2 CRDi              13117  901\n",
      "1.5 Turbocharged Revotorq   1482  116\n",
      "1.5 Turbocharged Revotron    348   15\n",
      "F8D Petrol Engine          14030  918\n",
      "G12B                        1136   73\n",
      "K Series Dual jet          12837  939\n",
      "K10C                        3929  244\n",
      "i-DTEC                      1981  133\n",
      "Chi-square statistics: 19.974980328320328\n",
      "P-Values: 0.029490222196639326\n",
      "Degree of Freedom: 10\n",
      "Expected Frequencies\n",
      "[[ 2221.20446477   151.79553523]\n",
      " [ 2751.93473512   188.06526488]\n",
      " [ 1010.91480066    69.08519934]\n",
      " [13121.29969962   896.70030038]\n",
      " [ 1495.77949208   102.22050792]\n",
      " [  339.77969689    23.22030311]\n",
      " [13991.80966685   956.19033315]\n",
      " [ 1131.6629574     77.3370426 ]\n",
      " [12894.77990169   881.22009831]\n",
      " [ 3906.06246587   266.93753413]\n",
      " [ 1978.77211906   135.22788094]]\n",
      "Reject the null hypothesis: There is an association between engine_type type and claim status\n",
      "claim_status_text     No   Yes\n",
      "is_brake_assist               \n",
      "No                 24803  1612\n",
      "Yes                30041  2136\n",
      "Chi-square statistics: 6.863159393489996\n",
      "P-Values: 0.008799083467312552\n",
      "Degree of Freedom: 1\n",
      "Expected Frequencies\n",
      "[[24725.29116603  1689.70883397]\n",
      " [30118.70883397  2058.29116603]]\n",
      "Reject the null hypothesis: There is an association between is_brake_assist type and claim status\n"
     ]
    }
   ],
   "source": [
    "# create contigrncy table between two observed variables \n",
    "stats_list=[\"fuel_type\",\"region_code\",\"cylinder\",\"steering_type\",\"model\",\"airbags\",\"engine_type\",\"is_brake_assist\"]\n",
    "for i in stats_list:\n",
    "    contingency_table= pd.crosstab(data[i], data[\"claim_status_text\"])\n",
    "    print(contingency_table)\n",
    "    #perform chi-square test of Independence \n",
    "    chi2,p,dof,expected =chi2_contingency(contingency_table)\n",
    "\n",
    "    print(f\"Chi-square statistics: {chi2}\")\n",
    "    print(f\"P-Values: {p}\")\n",
    "    print(f\"Degree of Freedom: {dof}\")\n",
    "    print(\"Expected Frequencies\")\n",
    "    print(expected)\n",
    "\n",
    "\n",
    "    alpha= 0.05\n",
    "\n",
    "    if p <= alpha:\n",
    "        print(f\"Reject the null hypothesis: There is an association between {i} type and claim status\")\n",
    "    else:\n",
    "        print(f\"Fail to reject the null hypothesis: No association between {i} and claim status\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5629835e-4b0b-4d7e-a487-4338fb9ac52e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-Statistic: 95987705.0\n",
      "P-Value: 1.0997711491797156e-11\n",
      "Reject the null hypothesis: There is a significant difference in car age between claim and no claim groups.\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "# Split the data into two groups based on the claim status\n",
    "age_yes = data[data['claim_status_text'] == 'Yes']['vehicle_age']\n",
    "age_no = data[data['claim_status_text'] == 'No']['vehicle_age']\n",
    "\n",
    "# Perform Mann-Whitney U Test\n",
    "u_stat, p_value = mannwhitneyu(age_yes, age_no)\n",
    "\n",
    "print(f\"U-Statistic: {u_stat}\")\n",
    "print(f\"P-Value: {p_value}\")\n",
    "\n",
    "# Conclusion\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"Reject the null hypothesis: There is a significant difference in car age between claim and no claim groups.\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis: No significant difference in car age between claim and no claim groups.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64425fa-117b-4dfc-b845-1f4abc8767c4",
   "metadata": {},
   "source": [
    "### Encoding Categorical variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "997484fb-a612-4ded-8b20-24b1d9178db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to encode categorrical variabe\n",
    "def car(x):\n",
    "    if x == \"No\":\n",
    "        return 0\n",
    "    else : \n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "372c4eb9-2330-41b7-a4a8-fd2bc0b7a32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "category= [\"is_esc\",\"is_adjustable_steering\",\"is_power_door_locks\",\"is_parking_sensors\",\n",
    "           \"is_parking_camera\",\"rear_brakes_type\",\"is_front_fog_lights\",\"is_rear_window_wiper\",\"is_rear_window_washer\",\"is_tpms\",\n",
    "           'is_rear_window_defogger', 'is_brake_assist','is_power_door_locks',\"is_central_locking\",\"is_power_steering\",\"is_driver_seat_height_adjustable\",\n",
    "           \"is_day_night_rear_view_mirror\",\"is_ecw\",\"is_speed_alert\"]\n",
    "\n",
    "for i in category:\n",
    "    data[i]=data[i].apply(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ea0b2436-a97a-4ae0-aaad-76f2612d3ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policy_id                             object\n",
       "subscription_length                  float64\n",
       "vehicle_age                          float64\n",
       "customer_age                           int64\n",
       "region_code                           object\n",
       "region_density                         int64\n",
       "segment                               object\n",
       "model                                 object\n",
       "fuel_type                             object\n",
       "max_torque                            object\n",
       "max_power                             object\n",
       "engine_type                           object\n",
       "airbags                                int64\n",
       "is_esc                                 int64\n",
       "is_adjustable_steering                 int64\n",
       "is_tpms                                int64\n",
       "is_parking_sensors                     int64\n",
       "is_parking_camera                      int64\n",
       "rear_brakes_type                       int64\n",
       "displacement                           int64\n",
       "cylinder                               int64\n",
       "transmission_type                     object\n",
       "steering_type                         object\n",
       "turning_radius                       float64\n",
       "length                                 int64\n",
       "width                                  int64\n",
       "gross_weight                           int64\n",
       "is_front_fog_lights                    int64\n",
       "is_rear_window_wiper                   int64\n",
       "is_rear_window_washer                  int64\n",
       "is_rear_window_defogger                int64\n",
       "is_brake_assist                        int64\n",
       "is_power_door_locks                    int64\n",
       "is_central_locking                     int64\n",
       "is_power_steering                      int64\n",
       "is_driver_seat_height_adjustable       int64\n",
       "is_day_night_rear_view_mirror          int64\n",
       "is_ecw                                 int64\n",
       "is_speed_alert                         int64\n",
       "ncap_rating                            int64\n",
       "claim_status                           int64\n",
       "claim_status_text                     object\n",
       "Members_binned                      category\n",
       "vehicle_age_binned                  category\n",
       "customers_age_binned                category\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b71a336-9ab3-4c56-87d0-cb501a1eae22",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1e7c0147-05a1-4125-9b3a-73fdeb295059",
   "metadata": {},
   "outputs": [],
   "source": [
    "column=[\"segment\",\"model\",\"fuel_type\",\"max_torque\",\"max_power\",\"engine_type\",\"transmission_type\",\"steering_type\"]\n",
    "for i in column:\n",
    "    data_encoded=pd.get_dummies(data,columns=column,drop_first=False)\n",
    "\n",
    "for col in data_encoded.columns:\n",
    "    if data_encoded[col].dtype == bool:\n",
    "        data_encoded[col] = data_encoded[col].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "485b9ee2-33c7-4823-87b7-f85e02303d57",
   "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>policy_id</th>\n",
       "      <th>subscription_length</th>\n",
       "      <th>vehicle_age</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>region_code</th>\n",
       "      <th>region_density</th>\n",
       "      <th>airbags</th>\n",
       "      <th>is_esc</th>\n",
       "      <th>is_adjustable_steering</th>\n",
       "      <th>is_tpms</th>\n",
       "      <th>...</th>\n",
       "      <th>engine_type_F8D Petrol Engine</th>\n",
       "      <th>engine_type_G12B</th>\n",
       "      <th>engine_type_K Series Dual jet</th>\n",
       "      <th>engine_type_K10C</th>\n",
       "      <th>engine_type_i-DTEC</th>\n",
       "      <th>transmission_type_Automatic</th>\n",
       "      <th>transmission_type_Manual</th>\n",
       "      <th>steering_type_Electric</th>\n",
       "      <th>steering_type_Manual</th>\n",
       "      <th>steering_type_Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POL045360</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>41</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>POL016745</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>35</td>\n",
       "      <td>C2</td>\n",
       "      <td>27003</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POL007194</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POL018146</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>44</td>\n",
       "      <td>C10</td>\n",
       "      <td>73430</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POL049011</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>C13</td>\n",
       "      <td>5410</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>58587</th>\n",
       "      <td>POL019269</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>48</td>\n",
       "      <td>C5</td>\n",
       "      <td>34738</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58588</th>\n",
       "      <td>POL001254</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>37</td>\n",
       "      <td>C3</td>\n",
       "      <td>4076</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58589</th>\n",
       "      <td>POL019859</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>35</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58590</th>\n",
       "      <td>POL014061</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>44</td>\n",
       "      <td>C8</td>\n",
       "      <td>8794</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58591</th>\n",
       "      <td>POL054184</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>49</td>\n",
       "      <td>C12</td>\n",
       "      <td>34791</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58592 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       policy_id  subscription_length  vehicle_age  customer_age region_code  \\\n",
       "0      POL045360                  9.3          1.2            41          C8   \n",
       "1      POL016745                  8.2          1.8            35          C2   \n",
       "2      POL007194                  9.5          0.2            44          C8   \n",
       "3      POL018146                  5.2          0.4            44         C10   \n",
       "4      POL049011                 10.1          1.0            56         C13   \n",
       "...          ...                  ...          ...           ...         ...   \n",
       "58587  POL019269                 10.6          2.6            48          C5   \n",
       "58588  POL001254                  2.3          2.2            37          C3   \n",
       "58589  POL019859                  6.6          2.2            35          C8   \n",
       "58590  POL014061                  4.1          3.6            44          C8   \n",
       "58591  POL054184                  3.1          0.4            49         C12   \n",
       "\n",
       "       region_density  airbags  is_esc  is_adjustable_steering  is_tpms  ...  \\\n",
       "0                8794        6       1                       1        1  ...   \n",
       "1               27003        2       0                       1        0  ...   \n",
       "2                8794        6       1                       1        1  ...   \n",
       "3               73430        2       0                       0        0  ...   \n",
       "4                5410        2       0                       1        0  ...   \n",
       "...               ...      ...     ...                     ...      ...  ...   \n",
       "58587           34738        2       0                       1        0  ...   \n",
       "58588            4076        6       1                       1        1  ...   \n",
       "58589            8794        2       0                       1        0  ...   \n",
       "58590            8794        2       0                       1        0  ...   \n",
       "58591           34791        2       0                       1        0  ...   \n",
       "\n",
       "       engine_type_F8D Petrol Engine  engine_type_G12B  \\\n",
       "0                                  0                 0   \n",
       "1                                  0                 0   \n",
       "2                                  0                 0   \n",
       "3                                  1                 0   \n",
       "4                                  0                 0   \n",
       "...                              ...               ...   \n",
       "58587                              0                 0   \n",
       "58588                              0                 0   \n",
       "58589                              0                 0   \n",
       "58590                              0                 0   \n",
       "58591                              0                 0   \n",
       "\n",
       "       engine_type_K Series Dual jet  engine_type_K10C  engine_type_i-DTEC  \\\n",
       "0                                  0                 0                   0   \n",
       "1                                  0                 0                   1   \n",
       "2                                  0                 0                   0   \n",
       "3                                  0                 0                   0   \n",
       "4                                  0                 0                   0   \n",
       "...                              ...               ...                 ...   \n",
       "58587                              1                 0                   0   \n",
       "58588                              0                 0                   0   \n",
       "58589                              1                 0                   0   \n",
       "58590                              1                 0                   0   \n",
       "58591                              1                 0                   0   \n",
       "\n",
       "       transmission_type_Automatic  transmission_type_Manual  \\\n",
       "0                                1                         0   \n",
       "1                                0                         1   \n",
       "2                                1                         0   \n",
       "3                                0                         1   \n",
       "4                                0                         1   \n",
       "...                            ...                       ...   \n",
       "58587                            0                         1   \n",
       "58588                            1                         0   \n",
       "58589                            0                         1   \n",
       "58590                            0                         1   \n",
       "58591                            0                         1   \n",
       "\n",
       "       steering_type_Electric  steering_type_Manual  steering_type_Power  \n",
       "0                           0                     0                    1  \n",
       "1                           1                     0                    0  \n",
       "2                           0                     0                    1  \n",
       "3                           0                     0                    1  \n",
       "4                           1                     0                    0  \n",
       "...                       ...                   ...                  ...  \n",
       "58587                       1                     0                    0  \n",
       "58588                       0                     0                    1  \n",
       "58589                       1                     0                    0  \n",
       "58590                       1                     0                    0  \n",
       "58591                       1                     0                    0  \n",
       "\n",
       "[58592 rows x 91 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4efafc9c-1216-4809-90f7-42a045d55ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Droped columns not needed for machine learning\n",
    "dropped_columns=['policy_id', 'region_code',\"claim_status_text\",\"customers_age_binned\",\"vehicle_age_binned\",\"Members_binned\"]\n",
    "\n",
    "data_encoded.drop(columns=dropped_columns,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aa4adfdb-c356-4420-aac6-30201d506f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['subscription_length', 'vehicle_age', 'customer_age', 'region_density',\n",
       "       'airbags', 'is_esc', 'is_adjustable_steering', 'is_tpms',\n",
       "       'is_parking_sensors', 'is_parking_camera', 'rear_brakes_type',\n",
       "       'displacement', 'cylinder', 'turning_radius', 'length', 'width',\n",
       "       'gross_weight', 'is_front_fog_lights', 'is_rear_window_wiper',\n",
       "       'is_rear_window_washer', 'is_rear_window_defogger', 'is_brake_assist',\n",
       "       'is_power_door_locks', 'is_central_locking', 'is_power_steering',\n",
       "       'is_driver_seat_height_adjustable', 'is_day_night_rear_view_mirror',\n",
       "       'is_ecw', 'is_speed_alert', 'ncap_rating', 'claim_status', 'segment_A',\n",
       "       'segment_B1', 'segment_B2', 'segment_C1', 'segment_C2',\n",
       "       'segment_Utility', 'model_M1', 'model_M10', 'model_M11', 'model_M2',\n",
       "       'model_M3', 'model_M4', 'model_M5', 'model_M6', 'model_M7', 'model_M8',\n",
       "       'model_M9', 'fuel_type_CNG', 'fuel_type_Diesel', 'fuel_type_Petrol',\n",
       "       'max_torque_113Nm@4400rpm', 'max_torque_170Nm@4000rpm',\n",
       "       'max_torque_200Nm@1750rpm', 'max_torque_200Nm@3000rpm',\n",
       "       'max_torque_250Nm@2750rpm', 'max_torque_60Nm@3500rpm',\n",
       "       'max_torque_82.1Nm@3400rpm', 'max_torque_85Nm@3000rpm',\n",
       "       'max_torque_91Nm@4250rpm', 'max_power_113.45bhp@4000rpm',\n",
       "       'max_power_118.36bhp@5500rpm', 'max_power_40.36bhp@6000rpm',\n",
       "       'max_power_55.92bhp@5300rpm', 'max_power_61.68bhp@6000rpm',\n",
       "       'max_power_67.06bhp@5500rpm', 'max_power_88.50bhp@6000rpm',\n",
       "       'max_power_88.77bhp@4000rpm', 'max_power_97.89bhp@3600rpm',\n",
       "       'engine_type_1.0 SCe', 'engine_type_1.2 L K Series Engine',\n",
       "       'engine_type_1.2 L K12N Dualjet', 'engine_type_1.5 L U2 CRDi',\n",
       "       'engine_type_1.5 Turbocharged Revotorq',\n",
       "       'engine_type_1.5 Turbocharged Revotron',\n",
       "       'engine_type_F8D Petrol Engine', 'engine_type_G12B',\n",
       "       'engine_type_K Series Dual jet', 'engine_type_K10C',\n",
       "       'engine_type_i-DTEC', 'transmission_type_Automatic',\n",
       "       'transmission_type_Manual', 'steering_type_Electric',\n",
       "       'steering_type_Manual', 'steering_type_Power'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a614866e-1d0d-46d4-9993-b0e53c77139a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= data_encoded.drop(columns=['claim_status'])\n",
    "y= data_encoded[\"claim_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f23a6f33-141c-4972-8eed-b79231b04ad5",
   "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>subscription_length</th>\n",
       "      <th>vehicle_age</th>\n",
       "      <th>customer_age</th>\n",
       "      <th>region_density</th>\n",
       "      <th>airbags</th>\n",
       "      <th>is_esc</th>\n",
       "      <th>is_adjustable_steering</th>\n",
       "      <th>is_tpms</th>\n",
       "      <th>is_parking_sensors</th>\n",
       "      <th>is_parking_camera</th>\n",
       "      <th>...</th>\n",
       "      <th>engine_type_F8D Petrol Engine</th>\n",
       "      <th>engine_type_G12B</th>\n",
       "      <th>engine_type_K Series Dual jet</th>\n",
       "      <th>engine_type_K10C</th>\n",
       "      <th>engine_type_i-DTEC</th>\n",
       "      <th>transmission_type_Automatic</th>\n",
       "      <th>transmission_type_Manual</th>\n",
       "      <th>steering_type_Electric</th>\n",
       "      <th>steering_type_Manual</th>\n",
       "      <th>steering_type_Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>35</td>\n",
       "      <td>27003</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>44</td>\n",
       "      <td>8794</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>44</td>\n",
       "      <td>73430</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56</td>\n",
       "      <td>5410</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>58587</th>\n",
       "      <td>10.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>48</td>\n",
       "      <td>34738</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>58588</th>\n",
       "      <td>2.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>37</td>\n",
       "      <td>4076</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58589</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>35</td>\n",
       "      <td>8794</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>4.1</td>\n",
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       "      <td>44</td>\n",
       "      <td>8794</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58591</th>\n",
       "      <td>3.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>49</td>\n",
       "      <td>34791</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58592 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subscription_length  vehicle_age  customer_age  region_density  \\\n",
       "0                      9.3          1.2            41            8794   \n",
       "1                      8.2          1.8            35           27003   \n",
       "2                      9.5          0.2            44            8794   \n",
       "3                      5.2          0.4            44           73430   \n",
       "4                     10.1          1.0            56            5410   \n",
       "...                    ...          ...           ...             ...   \n",
       "58587                 10.6          2.6            48           34738   \n",
       "58588                  2.3          2.2            37            4076   \n",
       "58589                  6.6          2.2            35            8794   \n",
       "58590                  4.1          3.6            44            8794   \n",
       "58591                  3.1          0.4            49           34791   \n",
       "\n",
       "       airbags  is_esc  is_adjustable_steering  is_tpms  is_parking_sensors  \\\n",
       "0            6       1                       1        1                   1   \n",
       "1            2       0                       1        0                   1   \n",
       "2            6       1                       1        1                   1   \n",
       "3            2       0                       0        0                   1   \n",
       "4            2       0                       1        0                   1   \n",
       "...        ...     ...                     ...      ...                 ...   \n",
       "58587        2       0                       1        0                   1   \n",
       "58588        6       1                       1        1                   1   \n",
       "58589        2       0                       1        0                   1   \n",
       "58590        2       0                       1        0                   1   \n",
       "58591        2       0                       1        0                   1   \n",
       "\n",
       "       is_parking_camera  ...  engine_type_F8D Petrol Engine  \\\n",
       "0                      1  ...                              0   \n",
       "1                      1  ...                              0   \n",
       "2                      1  ...                              0   \n",
       "3                      0  ...                              1   \n",
       "4                      0  ...                              0   \n",
       "...                  ...  ...                            ...   \n",
       "58587                  0  ...                              0   \n",
       "58588                  1  ...                              0   \n",
       "58589                  0  ...                              0   \n",
       "58590                  0  ...                              0   \n",
       "58591                  0  ...                              0   \n",
       "\n",
       "       engine_type_G12B  engine_type_K Series Dual jet  engine_type_K10C  \\\n",
       "0                     0                              0                 0   \n",
       "1                     0                              0                 0   \n",
       "2                     0                              0                 0   \n",
       "3                     0                              0                 0   \n",
       "4                     0                              0                 0   \n",
       "...                 ...                            ...               ...   \n",
       "58587                 0                              1                 0   \n",
       "58588                 0                              0                 0   \n",
       "58589                 0                              1                 0   \n",
       "58590                 0                              1                 0   \n",
       "58591                 0                              1                 0   \n",
       "\n",
       "       engine_type_i-DTEC  transmission_type_Automatic  \\\n",
       "0                       0                            1   \n",
       "1                       1                            0   \n",
       "2                       0                            1   \n",
       "3                       0                            0   \n",
       "4                       0                            0   \n",
       "...                   ...                          ...   \n",
       "58587                   0                            0   \n",
       "58588                   0                            1   \n",
       "58589                   0                            0   \n",
       "58590                   0                            0   \n",
       "58591                   0                            0   \n",
       "\n",
       "       transmission_type_Manual  steering_type_Electric  steering_type_Manual  \\\n",
       "0                             0                       0                     0   \n",
       "1                             1                       1                     0   \n",
       "2                             0                       0                     0   \n",
       "3                             1                       0                     0   \n",
       "4                             1                       1                     0   \n",
       "...                         ...                     ...                   ...   \n",
       "58587                         1                       1                     0   \n",
       "58588                         0                       0                     0   \n",
       "58589                         1                       1                     0   \n",
       "58590                         1                       1                     0   \n",
       "58591                         1                       1                     0   \n",
       "\n",
       "       steering_type_Power  \n",
       "0                        1  \n",
       "1                        0  \n",
       "2                        1  \n",
       "3                        1  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "58587                    0  \n",
       "58588                    1  \n",
       "58589                    0  \n",
       "58590                    0  \n",
       "58591                    0  \n",
       "\n",
       "[58592 rows x 84 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4b24e24a-24f2-41f2-ae9d-c883d1bd87a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38084, 84)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84161fa-3551-4c43-b671-a33f55a1f99f",
   "metadata": {},
   "source": [
    "### Model Trainig and Tracking with Mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7de4558c-c82c-4180-b6d1-73e957e0c6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set our tracking server uri for logging\n",
    "mlflow.set_tracking_uri(uri=\"http://127.0.0.1:8080\")\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "99104e61-cb65-42c0-9408-5a4e9422296e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/08/22 16:15:02 INFO mlflow.tracking.fluent: Experiment with name 'Insurance_Claim' does not exist. Creating a new experiment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='mlflow-artifacts:/190918915500080902', creation_time=1724339702574, experiment_id='190918915500080902', last_update_time=1724339702574, lifecycle_stage='active', name='Insurance_Claim', tags={}>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new MLflow Experiment\n",
    "mlflow.set_experiment(\"Insurance_Claim\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b644519e-2c63-459d-85c1-b27c6e0e1a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define evaluation metrics \n",
    "def eval_metrics(actual, pred):\n",
    "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
    "    mae = mean_absolute_error(actual, pred)\n",
    "    r2 = r2_score(actual, pred)\n",
    "    acc=accuracy_score(actual,pred)\n",
    "    return rmse, mae, r2,acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f525dbee-eebf-49ae-b5e2-da77dcd28049",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a temporary directory for storing confusion matrix images\n",
    "artifact_dir = \"confusion_matrices\"\n",
    "os.makedirs(artifact_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "fc395c23-2df5-480f-a82e-e694cb776556",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test train split\n",
    "\n",
    "X_train,X_test,y_train,y_test= train_test_split (X,y, test_size=0.35, random_state=42)\n",
    "\n",
    "# Initialize the scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data and transform both the training and test data\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8d11bddf-59d5-45d0-8875-ff7d188e3641",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/08/22 16:29:08 WARNING mlflow.models.model: Input example should be provided to infer model signature if the model signature is not provided when logging the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.56      0.71     19185\n",
      "           1       0.09      0.63      0.16      1323\n",
      "\n",
      "    accuracy                           0.57     20508\n",
      "   macro avg       0.52      0.59      0.43     20508\n",
      "weighted avg       0.90      0.57      0.67     20508\n",
      "\n",
      "[[10784  8401]\n",
      " [  493   830]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/08/22 16:29:09 INFO mlflow.tracking._tracking_service.client: 🏃 View run Random forest at: http://127.0.0.1:8080/#/experiments/190918915500080902/runs/2eed33c730c242e79c0c040a0226f7aa.\n",
      "2024/08/22 16:29:09 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:8080/#/experiments/190918915500080902.\n",
      "2024/08/22 16:29:14 WARNING mlflow.models.model: Input example should be provided to infer model signature if the model signature is not provided when logging the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97     19185\n",
      "           1       0.13      0.00      0.01      1323\n",
      "\n",
      "    accuracy                           0.93     20508\n",
      "   macro avg       0.53      0.50      0.49     20508\n",
      "weighted avg       0.88      0.93      0.90     20508\n",
      "\n",
      "[[19159    26]\n",
      " [ 1319     4]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/08/22 16:29:15 INFO mlflow.tracking._tracking_service.client: 🏃 View run Decision tree at: http://127.0.0.1:8080/#/experiments/190918915500080902/runs/884246d67d7f442cac72a2c4ed44492f.\n",
      "2024/08/22 16:29:15 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:8080/#/experiments/190918915500080902.\n"
     ]
    }
   ],
   "source": [
    "# define model parameters\n",
    "random_param={'n_estimators': 100,\n",
    "              'class_weight':'balanced', \n",
    "            'max_depth':2, \n",
    "            'random_state':4}\n",
    "\n",
    "decision_param={\"random_state\": 24,\n",
    "                'splitter':'best',\n",
    "                'max_depth':10,\n",
    "                'min_samples_split':5,\n",
    "                'min_samples_leaf':4,\n",
    "                'max_features':'sqrt',\n",
    "                'random_state':42}\n",
    "\n",
    "\n",
    "\n",
    "# Initialize models with parameters\n",
    "mlmodel= {\"Random forest\":RandomForestClassifier(**random_param),\n",
    "         \"Decision tree\":DecisionTreeClassifier(**decision_param)}\n",
    "\n",
    "# store results\n",
    "results= []\n",
    "\n",
    "# store column name\n",
    "\n",
    "column_names= X.columns.tolist()\n",
    "\n",
    "for name,model in mlmodel.items():\n",
    "    with mlflow.start_run(run_name=name):\n",
    "        model.fit(X_train,y_train)\n",
    "        \n",
    "        #make predictions\n",
    "        y_pred_train = model.predict(X_train)\n",
    "        y_pred_test = model.predict(X_test)\n",
    "    \n",
    "        #evaluate train model performance \n",
    "        train_rmse,train_r2,train_mae,train_acc = eval_metrics(y_train,y_pred_train)\n",
    "    \n",
    "        #evaluate test model performance \n",
    "        test_rmse,test_r2,test_mae,test_acc = eval_metrics(y_test,y_pred_test)\n",
    "    \n",
    "        #store test model results\n",
    "    \n",
    "        results.append({\"model\":name,\n",
    "                   \"Accuracy\": test_acc})\n",
    "        \n",
    "        #log parameters parameters\n",
    "        if name == \"Random forest\":\n",
    "            for param_name, param_value in random_param.items():\n",
    "                mlflow.log_param(param_name, param_value)\n",
    "        elif name == \"Decision tree\":\n",
    "            for param_name, param_value in decision_param.items():\n",
    "                mlflow.log_param(param_name, param_value)\n",
    "       \n",
    "        # log model \n",
    "        mlflow.sklearn.log_model(model,\"model\")\n",
    "        \n",
    "        # Log metrics to MLflow\n",
    "        mlflow.log_metric(\"train_rmse\", train_rmse)\n",
    "        mlflow.log_metric(\"train_r2\", train_r2)\n",
    "        mlflow.log_metric(\"train_mae\", train_mae)\n",
    "        mlflow.log_metric(\"train_acc\", train_acc)\n",
    "        mlflow.log_metric(\"test_rmse\", test_rmse)\n",
    "        mlflow.log_metric(\"test_r2\", test_r2)\n",
    "        mlflow.log_metric(\"test_mae\", test_mae)\n",
    "        mlflow.log_metric(\"test_acc\", test_acc)\n",
    "        \n",
    "        # Log the classification report as an artifact\n",
    "        classification_rep = classification_report(y_test, y_pred_test, output_dict=True)\n",
    "        classification_df = pd.DataFrame(classification_rep).transpose()\n",
    "        classification_report_path = os.path.join(artifact_dir, f\"{name}_classification_report.csv\")\n",
    "        classification_df.to_csv(classification_report_path)\n",
    "        mlflow.log_artifact(classification_report_path)\n",
    "    \n",
    "        print(classification_report(y_test, y_pred_test))\n",
    "    \n",
    "        # Compute the confusion matrix\n",
    "        cm = confusion_matrix(y_test, y_pred_test)\n",
    "        print(cm)\n",
    "\n",
    "        # Visualize the confusion matrix\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "                    xticklabels=['Noclaim','Claim'],  \n",
    "                    yticklabels=['Noclaim', 'Claim'])\n",
    "        plt.xlabel('Predicted Label')\n",
    "        plt.ylabel('True Label')\n",
    "        plt.title(f'{name} Confusion Matrix')\n",
    "\n",
    "        # Save confusion matrix plot as an image file and log as an artifact\n",
    "        confusion_matrix_path = os.path.join(artifact_dir, f\"{name}_confusion_matrix.png\")\n",
    "        plt.savefig(confusion_matrix_path)\n",
    "        mlflow.log_artifact(confusion_matrix_path)\n",
    "        plt.show()\n",
    "        \n",
    "        column_file=\"columns.txt\"\n",
    "        with open(column_file,\"w\") as f:\n",
    "            for col in column_names:\n",
    "                f.write(f\"{col}\\n\")\n",
    "        #log text file\n",
    "        mlflow.log_artifact(column_file)\n",
    "        \n",
    "# Clean up the temporary directory\n",
    "import shutil\n",
    "shutil.rmtree(artifact_dir)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5f827999-859d-4cad-8103-19739ca8b29a",
   "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>model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random forest</td>\n",
       "      <td>0.566316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision tree</td>\n",
       "      <td>0.934416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  Accuracy\n",
       "0  Random forest  0.566316\n",
       "1  Decision tree  0.934416"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df=pd.DataFrame(results)\n",
    "results_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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