car_insurance_premium.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8b9e947",
   "metadata": {},
   "source": [
    "# Reading and Preparing The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0079ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import arff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc2f69d8-2275-4f9a-aab3-96edd324ff26",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_freq = arff.load(\"data/freMTPL2freq.arff\")\n",
    "df_freq = pd.DataFrame(\n",
    "    data_freq,\n",
    "    columns=[\n",
    "        \"IDpol\",\n",
    "        \"ClaimNb\",\n",
    "        \"Exposure\",\n",
    "        \"Area\",\n",
    "        \"VehPower\",\n",
    "        \"VehAge\",\n",
    "        \"DrivAge\",\n",
    "        \"BonusMalus\",\n",
    "        \"VehBrand\",\n",
    "        \"VehGas\",\n",
    "        \"Density\",\n",
    "        \"Region\",\n",
    "    ],\n",
    ")\n",
    "data_sev = arff.load(\"data/freMTPL2sev.arff\")\n",
    "df_sev = pd.DataFrame(data_sev, columns=[\"IDpol\", \"ClaimAmount\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0799d94-d8e8-4cda-a013-dc72cc61e890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(678013, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDpol</th>\n",
       "      <th>ClaimNb</th>\n",
       "      <th>Exposure</th>\n",
       "      <th>Area</th>\n",
       "      <th>VehPower</th>\n",
       "      <th>VehAge</th>\n",
       "      <th>DrivAge</th>\n",
       "      <th>BonusMalus</th>\n",
       "      <th>VehBrand</th>\n",
       "      <th>VehGas</th>\n",
       "      <th>Density</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>'D'</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>'B12'</td>\n",
       "      <td>Regular</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>'R82'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>'D'</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>'B12'</td>\n",
       "      <td>Regular</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>'R82'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>'B'</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>'B12'</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>54.0</td>\n",
       "      <td>'R22'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>'B'</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>'B12'</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>76.0</td>\n",
       "      <td>'R72'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>'B'</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>'B12'</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>76.0</td>\n",
       "      <td>'R72'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IDpol  ClaimNb  Exposure Area  VehPower  VehAge  DrivAge  BonusMalus  \\\n",
       "0    1.0      1.0      0.10  'D'       5.0     0.0     55.0        50.0   \n",
       "1    3.0      1.0      0.77  'D'       5.0     0.0     55.0        50.0   \n",
       "2    5.0      1.0      0.75  'B'       6.0     2.0     52.0        50.0   \n",
       "3   10.0      1.0      0.09  'B'       7.0     0.0     46.0        50.0   \n",
       "4   11.0      1.0      0.84  'B'       7.0     0.0     46.0        50.0   \n",
       "\n",
       "  VehBrand   VehGas  Density Region  \n",
       "0    'B12'  Regular   1217.0  'R82'  \n",
       "1    'B12'  Regular   1217.0  'R82'  \n",
       "2    'B12'   Diesel     54.0  'R22'  \n",
       "3    'B12'   Diesel     76.0  'R72'  \n",
       "4    'B12'   Diesel     76.0  'R72'  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_freq.shape)\n",
    "df_freq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37621ba3-81a2-4910-81c7-c6badfd9ac7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26639, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDpol</th>\n",
       "      <th>ClaimAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1552.0</td>\n",
       "      <td>995.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1010996.0</td>\n",
       "      <td>1128.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4024277.0</td>\n",
       "      <td>1851.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4007252.0</td>\n",
       "      <td>1204.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4046424.0</td>\n",
       "      <td>1204.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IDpol  ClaimAmount\n",
       "0     1552.0       995.20\n",
       "1  1010996.0      1128.12\n",
       "2  4024277.0      1851.11\n",
       "3  4007252.0      1204.00\n",
       "4  4046424.0      1204.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_sev.shape)\n",
    "df_sev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2bb57a5-a709-4f53-a4ac-ac44d1f9fb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDpol</th>\n",
       "      <th>SumClaimAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139.0</td>\n",
       "      <td>303.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190.0</td>\n",
       "      <td>1981.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>414.0</td>\n",
       "      <td>1456.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424.0</td>\n",
       "      <td>10834.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>463.0</td>\n",
       "      <td>3986.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24945</th>\n",
       "      <td>6113521.0</td>\n",
       "      <td>1324.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24946</th>\n",
       "      <td>6113793.0</td>\n",
       "      <td>1769.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24947</th>\n",
       "      <td>6113817.0</td>\n",
       "      <td>1288.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24948</th>\n",
       "      <td>6113834.0</td>\n",
       "      <td>12230.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24949</th>\n",
       "      <td>6113971.0</td>\n",
       "      <td>1452.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24950 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           IDpol  SumClaimAmount\n",
       "0          139.0          303.00\n",
       "1          190.0         1981.84\n",
       "2          414.0         1456.55\n",
       "3          424.0        10834.00\n",
       "4          463.0         3986.67\n",
       "...          ...             ...\n",
       "24945  6113521.0         1324.40\n",
       "24946  6113793.0         1769.88\n",
       "24947  6113817.0         1288.28\n",
       "24948  6113834.0        12230.40\n",
       "24949  6113971.0         1452.00\n",
       "\n",
       "[24950 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum of claims per policyholder\n",
    "df_sev_agg = (\n",
    "    df_sev.groupby(\"IDpol\")[\"ClaimAmount\"]\n",
    "    .sum()\n",
    "    .to_frame()\n",
    "    .rename(columns={\"ClaimAmount\": \"SumClaimAmount\"})\n",
    "    .reset_index()\n",
    ")\n",
    "df_sev_agg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbcd79d-8c8d-4292-8a90-3985a174f772",
   "metadata": {},
   "source": [
    "### Merging The Two Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3244deac-2660-42f1-978d-7cebb9843a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SumClaimAmount\n",
       "False    653069\n",
       "True      24944\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = pd.merge(df_freq, df_sev_agg, on=\"IDpol\", how=\"left\")\n",
    "df_merged[\"SumClaimAmount\"].notna().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f29c55b-79db-4f4d-9770-0efcc4957053",
   "metadata": {},
   "source": [
    "It is expected that information for all contracts is found in `df_freq`. However, comparing the length of df_sev_agg with the non-null values in the merged dataframe, ‍‍`df_merged`, shows that for some claims, the corresponding contract information is missing in `df_freq`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a8a8e94-05c5-47fa-a82a-af1d3151d313",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDpol</th>\n",
       "      <th>SumClaimAmount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14138</th>\n",
       "      <td>2220367.0</td>\n",
       "      <td>84441.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14247</th>\n",
       "      <td>2227533.0</td>\n",
       "      <td>64093.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14512</th>\n",
       "      <td>2262511.0</td>\n",
       "      <td>151800.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14895</th>\n",
       "      <td>2277846.0</td>\n",
       "      <td>73440.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>2282134.0</td>\n",
       "      <td>330920.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15051</th>\n",
       "      <td>2286775.0</td>\n",
       "      <td>84017.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           IDpol  SumClaimAmount\n",
       "14138  2220367.0        84441.63\n",
       "14247  2227533.0        64093.76\n",
       "14512  2262511.0       151800.59\n",
       "14895  2277846.0        73440.72\n",
       "14973  2282134.0       330920.05\n",
       "15051  2286775.0        84017.43"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sev_agg[~df_sev_agg[\"IDpol\"].isin(df_freq[\"IDpol\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acab79a",
   "metadata": {},
   "source": [
    "### Separation of Policyholders with Filed Claims\n",
    "\n",
    "Out of 678,013 contracts, only 24,944 contracts have had a claim, which is about 3.7%. This indicates that the dataset is highly unbalanced.\n",
    "\n",
    "To analyze the variables, I will first focus on contracts with claim costs. When modeling, we can either analyze only contracts with claim costs or consider all contracts while being aware of the unbalanced classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "230ea70e-2fbc-4675-abbe-c7777c6e4ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the target variable\n",
    "df_merged[\"target\"] = df_merged[\"SumClaimAmount\"] / df_merged[\"Exposure\"]\n",
    "\n",
    "# Define numerical and categorical columns\n",
    "num_cols = [\n",
    "    \"Exposure\",\n",
    "    \"SumClaimAmount\",\n",
    "    \"VehPower\",\n",
    "    \"VehAge\",\n",
    "    \"DrivAge\",\n",
    "    \"BonusMalus\",\n",
    "    \"Density\",\n",
    "    \"target\",\n",
    "]\n",
    "cat_cols = [\"Area\", \"VehBrand\", \"VehGas\", \"Region\"]\n",
    "\n",
    "# Create a combined DataFrame with numerical columns and categorical columns converted to category type\n",
    "df_all = pd.concat(\n",
    "    [df_merged[num_cols]] + [df_merged[c].astype(\"category\") for c in cat_cols], axis=1\n",
    ")\n",
    "\n",
    "# Dataframe of contracts with a claim cost\n",
    "df = df_all[df_all[\"SumClaimAmount\"].notna()]\n",
    "\n",
    "# Add 'HasClaim' column to df_all indicating whether there was a claim\n",
    "df_all[\"HasClaim\"] = df_all[\"target\"].notna()\n",
    "\n",
    "# Fill missing 'target' values with 0.0\n",
    "df_all[\"target\"] = df_all[\"target\"].fillna(0.0)\n",
    "# df = df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc6d4a28-ea15-4624-a86a-d9ca4c098ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exposure           float64\n",
       "SumClaimAmount     float64\n",
       "VehPower           float64\n",
       "VehAge             float64\n",
       "DrivAge            float64\n",
       "BonusMalus         float64\n",
       "Density            float64\n",
       "target             float64\n",
       "Area              category\n",
       "VehBrand          category\n",
       "VehGas            category\n",
       "Region            category\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d3564eb-bf80-4f48-82b8-527d54362f74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Exposure</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>0.692669</td>\n",
       "      <td>0.313969</td>\n",
       "      <td>0.00274</td>\n",
       "      <td>0.4500</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SumClaimAmount</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>2401.748577</td>\n",
       "      <td>30264.753853</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>749.7875</td>\n",
       "      <td>1172.00</td>\n",
       "      <td>1346.400000</td>\n",
       "      <td>4075400.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VehPower</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>6.468970</td>\n",
       "      <td>2.012848</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VehAge</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>7.386225</td>\n",
       "      <td>5.173621</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DrivAge</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>45.139793</td>\n",
       "      <td>14.658487</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>34.0000</td>\n",
       "      <td>45.00</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BonusMalus</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>64.930164</td>\n",
       "      <td>19.864719</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>55.00</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>228.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Density</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>1984.306166</td>\n",
       "      <td>4119.719566</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>111.0000</td>\n",
       "      <td>495.00</td>\n",
       "      <td>2120.000000</td>\n",
       "      <td>27000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>24944.0</td>\n",
       "      <td>10417.566602</td>\n",
       "      <td>191698.891215</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1128.0000</td>\n",
       "      <td>1504.16</td>\n",
       "      <td>3352.009566</td>\n",
       "      <td>18524548.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean            std       min        25%  \\\n",
       "Exposure        24944.0      0.692669       0.313969   0.00274     0.4500   \n",
       "SumClaimAmount  24944.0   2401.748577   30264.753853   1.00000   749.7875   \n",
       "VehPower        24944.0      6.468970       2.012848   4.00000     5.0000   \n",
       "VehAge          24944.0      7.386225       5.173621   0.00000     3.0000   \n",
       "DrivAge         24944.0     45.139793      14.658487  18.00000    34.0000   \n",
       "BonusMalus      24944.0     64.930164      19.864719  50.00000    50.0000   \n",
       "Density         24944.0   1984.306166    4119.719566   2.00000   111.0000   \n",
       "target          24944.0  10417.566602  191698.891215   1.00000  1128.0000   \n",
       "\n",
       "                    50%          75%          max  \n",
       "Exposure           0.76     1.000000         2.00  \n",
       "SumClaimAmount  1172.00  1346.400000   4075400.56  \n",
       "VehPower           6.00     7.000000        15.00  \n",
       "VehAge             7.00    11.000000        99.00  \n",
       "DrivAge           45.00    54.000000        99.00  \n",
       "BonusMalus        55.00    76.000000       228.00  \n",
       "Density          495.00  2120.000000     27000.00  \n",
       "target          1504.16  3352.009566  18524548.00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0706aa-0376-4899-8687-1680b9e46bf2",
   "metadata": {},
   "source": [
    "# Analysis of the Dependent Variable\n",
    "\n",
    "First, we familiarize ourselves with the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec06d178-4898-474c-a6ca-99b61b6a1f1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot of Target')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
    "\n",
    "_ = axs[0].hist(df[\"target\"], bins=100)\n",
    "axs[0].set_title(\"Hist. of Target\")\n",
    "_ = axs[1].hist(df[\"target\"].map(np.log10), bins=100)\n",
    "axs[1].set_title(\"Hist. of log(Target)\")\n",
    "_ = axs[2].boxplot(df[\"target\"].map(np.log10), labels=[\"log(Target)\"])\n",
    "axs[2].set_title(\"Boxplot of Target\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b32d2a-defa-4de1-9ad5-27dfeef8e12d",
   "metadata": {},
   "source": [
    "The plots indicate that the dependent variable, `target`, exhibits a very wide range and is highly skewed. Additionally, there are some data points with exceptionally high or low values, which may be considered outliers. However, we should be cautious about labeling a data point as an \"outlier\", since we are dealing with an insurance problem.\n",
    "\n",
    "Could we investigate where these outliers come from? Since `target` is calculated by `SumClaimAmount` divided by `Exposure`, it is worth examining each of these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dde18a0-be8a-499d-be27-12d03320c267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n",
    "_ = axs[0].hist(df[\"Exposure\"], bins=50)\n",
    "axs[0].set_title(\"Exposure\")\n",
    "_ = axs[1].hist(df[\"SumClaimAmount\"], bins=50)\n",
    "axs[1].set_title(\"SumClaimAmount\")\n",
    "_ = axs[2].hist(df[\"SumClaimAmount\"].map(np.log10), bins=50)\n",
    "_ = axs[2].set_title(\"log(SumClaimAmount)\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beca0c8a",
   "metadata": {},
   "source": [
    "The plots reveal instances in the dataset where `Exposure` values are very low, resulting in disproportionately high values for the dependent variable. However, considering the low likelihood of raising a claim, defining the dependent variable as `SumClaimAmount` divided by `Exposure` as a measure of the expected claim cost for each policyholder appears to overestimate the cost. This, in turn, leads to numerous high values, making the distribution of the dependent variable highly skewed.\n",
    "\n",
    "On the other hand, there are cases with very high `SumClaimAmount`. Thus, removing instances that seem to be outliers requires careful consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "abb28d51-9c2c-4f82-b45f-9fc6d5d2db92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df[df['Exposure']<0.01].shape\n",
    "df[df[\"SumClaimAmount\"] > 100000].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54dde8e0",
   "metadata": {},
   "source": [
    "As `target` has an enormous range and is skewed, we could also consider analyzing and using the log-transformed values of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "397b29f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"targetLog\"] = df[\"target\"].map(np.log10)\n",
    "_ = sns.histplot(data=df, x=\"targetLog\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861a5d77",
   "metadata": {},
   "source": [
    "### Outliers\n",
    "If we decide to remove outliers, we can use the IQR or Z-Score method. Here, the latter is used on the log values of the response variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7b9ef96-3890-4cb3-883c-15523d61a7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 299\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_136033/573327837.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"IsOutlier\"] = outliers\n"
     ]
    }
   ],
   "source": [
    "# Z-Score\n",
    "z_scores = stats.zscore(df[\"targetLog\"])\n",
    "threshold = 3\n",
    "outliers = (z_scores < -threshold) | (z_scores > threshold)\n",
    "df[\"IsOutlier\"] = outliers\n",
    "\n",
    "print(f\"Number of outliers: {outliers.value_counts()[True]}\")\n",
    "\n",
    "# IRQ\n",
    "# q1 = df['target'].quantile(0.25)\n",
    "# q3 = df['target'].quantile(0.75)\n",
    "\n",
    "# outliers = (df['target'] <= q1) | (df['target'] >= q3)\n",
    "# print(f'Number of outliers: {outliers.value_counts()[True]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db37e85d-698a-4a19-9870-cc842b738380",
   "metadata": {},
   "source": [
    "# Analysis of Numerical Variables\n",
    "\n",
    "Focusing on contracts with claim costs, we analyze the numerical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2d4ec0",
   "metadata": {},
   "source": [
    "### VehPower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3df63c5b-6a3f-4f3e-acdc-c55232048706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
    "_ = sns.histplot(data=df, x=\"VehPower\", ax=axs[0])\n",
    "_ = sns.barplot(df, x=\"VehPower\", y=\"target\", errorbar=None, ax=axs[1])\n",
    "axs[1].set_ylabel(\"Mean Target\")\n",
    "plt.tight_layout()\n",
    "# df[df['VehPower']==9].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0dcd535-3e58-4ac2-8e84-b09a4d64be8c",
   "metadata": {},
   "source": [
    "### VehAge \n",
    "     \n",
    "Only 20 instances have `VehAge` > 30. These instances could be removed during modeling and treated differently later if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e088f164-6734-4d36-afff-e539a7af1cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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uMAAAAAosElLIV4mJieo5ebkkaWZ0lMqVK+fmiAAAcK1du3Zle9xms8nHx0cVKlRgcXMAAHDLIyGFfOfjH+TuEAAAcJs6derIZrNJkowxkmTtS5Knp6c6d+6s//znP/Lx8XFLjAAAAO7GfCoAAIB8tHDhQt1+++2aNm2adu7cqZ07d2ratGmqUqWK5s6dq+nTp2vNmjV69dVX3R0qAACA2zBCCgAAIB+98cYbmjhxoqKioqxjNWvWVPny5TV8+HBt2bJFxYoV04svvqh3333XjZECAAC4DyOkAAAA8tHu3bsVHh6e5Xh4eLh2794t6a9pfZkPAQEAALgVkZACAADIR1WrVtVbb72l9PR069ilS5f01ltvqWrVqpKkY8eOKTg42F0hAgAAuB1T9nBDGIfD6V9+Q0NDZbeT/wQA3PwmT56shx9+WOXLl1etWrUk/TVqKiMjQ0uWLJEkHT58WM8//7w7wwQAAHArElK4IS6eOaWXPkuWX1CoLqb8oZnRUSpXrpy7wwIA4IZr1KiRjhw5ojlz5ujnn3+WJD366KN6/PHHVaJECUlSt27d3BkiAACA25GQwg3j41dKRUuWdncYAAC4zKVLl1S1alUtWbJEffr0cXc4N426g2fnuG78O91vYCQAACC/5GkOVaVKlfTnn39mOX769GlVqlTpuoMCAAAojDw9PXXx4kV3hwEAAFDg5Skh9csvvygjIyPL8bS0NB07duy6gwIAACisoqOj9fbbb+vy5cvuDgUAAKDAytWUvS+//NL6/+XLl8vf39/az8jI0OrVq1WxYsV8Cw4AAKCw2bp1q1avXq0VK1aoZs2aKlasmFP5ggUL3BQZAABAwZGrhFSHDh0kSTabTT169HAq8/T0VMWKFfXee+/lW3AAAACFTUBAgDp16uTuMAAAAAq0XCWkHA6HJCkiIkJbt25VUFDQDQkKAACgsJo5c6a7QwAAACjw8vSUvSNHjuR3HAAAAAAAALhF5CkhJUmrV6/W6tWrdeLECWvkVKYZM2Zcd2AAAACF1RdffKHPP/9cCQkJSk9Pdyrbvn27m6ICAAAoOPL0lL3XXntNrVq10urVq/XHH3/o1KlTThsAAMCt6oMPPlDPnj0VHBysH374QfXr11epUqV0+PBhtWnTxt3hAQAAFAh5GiE1depUxcbGqlu3bvkdDwAAQKH20Ucfadq0aeratatiY2M1ZMgQVapUSSNGjNDJkyfdHR4AAECBkKcRUunp6WrUqFF+xwIAAFDoJSQkWJ+TfH19debMGUlSt27dNG/ePHeGBgAAUGDkKSH1zDPPaO7cufkdCwAAQKEXEhJijYSqUKGCNm3aJOmvh8IYY9wZGgAAQIGRpyl7Fy9e1LRp07Rq1SrVqlVLnp6eTuXjx4/Pl+AAAAAKi+bNm2vBggVq3ry5vvzyS911113q2bOnBg4cqC+++ELbtm3TI4884u4wAQAACoQ8JaR27dqlOnXqSJJ+/PFHpzKbzXbdQQEAABQ2a9euVXp6uqZNm2Y9gTg6OlqlSpXSxo0b9fDDD+u5555zc5QAAAAFQ54SUt9++21+xwEAAHBTsNvtstv/36oIXbp0UZcuXdwYEQAAQMGTp4QUAAAAstq7d6+SkpKuWadWrVouigYAAKDgylNC6v7777/m1Lw1a9bkOSAAAIDCqkWLFtdcuNxmsykjI8OFEQEAABRMeUpIZa4flenSpUvasWOHfvzxR/Xo0SM/4gIAACh0Nm/erNKlS7s7DAAAgAIvTwmpCRMmZHt81KhROnv27HUFBAAAUFhVqFBBZcqUcXcYAAAABZ79n6vk3JNPPqkZM2bk5ykBAAAAAABwk8nXhFRcXJx8fHzy85QAAACFQtOmTeXl5ZWv51y/fr0eeughlS1bVjabTYsWLXIqf+qpp2Sz2Zy21q1bO9U5efKknnjiCfn5+SkgIEC9evXKMqJ9165duu++++Tj46OwsDCNGzcuSyzz589X1apV5ePjo5o1a+rrr7/O12sFAAC3ljxN2XvkkUec9o0xSkxM1LZt2zR8+PB8CQwAAKAw+fbbb/P9nOfOnVPt2rX19NNPZ/n8lal169aaOXOmte/t7e1U/sQTTygxMVErV67UpUuX1LNnT/Xu3Vtz586VJKWmpqpVq1Zq2bKlpk6dqt27d+vpp59WQECAevfuLUnauHGjunbtqrFjx+rBBx/U3Llz1aFDB23fvl01atTI9+sGAAA3vzwlpPz9/Z327Xa7qlSpotGjR6tVq1b5EtitwuFwKDExUZL++u/VH8wDAABuMW3atFGbNm2uWcfb21shISHZlv30009atmyZtm7dqnr16kmSPvzwQ7Vt21bvvvuuypYtqzlz5ig9PV0zZsyQl5eX7rzzTu3YsUPjx4+3ElITJ05U69atNXjwYEnSmDFjtHLlSk2aNElTp07NxysGAAC3ijwlpK78Vzhcn8TERPWcvFw+/kE6/dsBFS0TrqLuDgoAABQaa9euVZkyZVSyZEk1b95cr7/+ukqVKiXpr+UUAgICrGSUJLVs2VJ2u12bN29Wx44dFRcXpyZNmjhNN4yKitLbb7+tU6dOqWTJkoqLi9OgQYOc+o2KisoyhRAAACCn8pSQyhQfH6+ffvpJknTnnXfqrrvuypegbjU+/kEqWrK0LqT86e5QAABAIdK6dWs98sgjioiI0KFDh/TKK6+oTZs2iouLk4eHh5KSkrI89a9IkSIKDAxUUlKSJCkpKUkRERFOdYKDg62ykiVLKikpyTp2ZZ3Mc2QnLS1NaWlp1n5qaup1XSsAALi55CkhdeLECXXp0kVr165VQECAJOn06dO6//779emnn6p06dL5GSMAAACy0aVLF+v/a9asqVq1aum2227T2rVr1aJFCzdGJo0dO1avvfaaW2MAAAAFV56estevXz+dOXNGe/bs0cmTJ3Xy5En9+OOPSk1NVf/+/fM7RgAAgEIjIyND06dP1+OPP66WLVuqefPmTtuNVKlSJQUFBengwYOSpJCQEJ04ccKpzuXLl3Xy5Elr3amQkBAlJyc71cnc/6c6V1u7SpKGDRumlJQUazt69Oj1XRwAALip5GmE1LJly7Rq1SpVq1bNOla9enVNnjyZRc0BAMAt7YUXXlBsbKzatWunGjVqyGazuazv3377TX/++adCQ0MlSZGRkTp9+rTi4+NVt25dSdKaNWvkcDjUoEEDq86///1vXbp0SZ6enpKklStXqkqVKipZsqRVZ/Xq1RowYIDV18qVKxUZGXnVWLy9vbM88Q+4WdQdPDvHdePf6X4DIwGAwitPCSmHw2F9YLmSp6enHA7HdQcFAABQWH366af6/PPP1bZt2+s+19mzZ63RTpJ05MgR7dixQ4GBgQoMDNRrr72mTp06KSQkRIcOHdKQIUNUuXJlRUVFSZKqVaum1q1b69lnn9XUqVN16dIlxcTEqEuXLipbtqwk6fHHH9drr72mXr16aejQofrxxx81ceJETZgwwer3hRdeUNOmTfXee++pXbt2+vTTT7Vt2zZNmzbtuq8RAADcmvI0Za958+Z64YUXdPz4cevYsWPHNHDgQLevVwAAAOBOXl5eqly5cr6ca9u2bbrrrrusB8cMGjRId911l0aMGCEPDw/t2rVLDz/8sO644w716tVLdevW1Xfffec0MmnOnDmqWrWqWrRoobZt26px48ZOiSR/f3+tWLFCR44cUd26dfXiiy9qxIgR6t27t1WnUaNGmjt3rqZNm6batWvriy++0KJFi1SjRo18uU4AAHDrydMIqUmTJunhhx9WxYoVFRYWJkk6evSoatSoof/7v//L1wABAAAKkxdffFETJ07UpEmTrnu6XrNmzWSMuWr58uXL//EcgYGBmjt37jXr1KpVS99999016zz66KN69NFH/7E/AACAnMhTQiosLEzbt2/XqlWrtG/fPkl/DQlv2bJlvgYHAABQ2GzYsEHffvutvvnmG915551ZljlYsGCBmyIDAAAoOHKVkFqzZo1iYmK0adMm+fn56YEHHtADDzwgSUpJSdGdd96pqVOn6r777rshwQIAABR0AQEB6tixo7vDAAAAKNBylZB6//339eyzz8rPzy9Lmb+/v5577jmNHz8+xwmp9evX65133lF8fLwSExO1cOFCdejQwSo3xmjkyJH6+OOPdfr0ad17772aMmWKbr/9dqvOyZMn1a9fP3311Vey2+3q1KmTJk6cqOLFi1t1du3apejoaG3dulWlS5dWv379NGTIkNxcOvKBw+FQYmKitR8aGiq7PU/LmAEAUGDNnDnT3SEAuIXl9AmAPP0PgLvlKhuwc+dOtW7d+qrlrVq1Unx8fI7Pd+7cOdWuXVuTJ0/OtnzcuHH64IMPNHXqVG3evFnFihVTVFSULl68aNV54okntGfPHq1cuVJLlizR+vXrnRbhTE1NVatWrRQeHq74+Hi98847GjVqFE+FcYPExET1nLxcff8vXj0nL3dKTgEAAAAAgFtHrkZIJScnZ1kHwelkRYro999/z/H52rRpozZt2mRbZozR+++/r1dffVXt27eXJM2ePVvBwcFatGiRunTpop9++knLli3T1q1bVa9ePUnShx9+qLZt2+rdd99V2bJlNWfOHKWnp2vGjBny8vLSnXfeqR07dmj8+PFOiSu4ho9/kIqWLO3uMAAAuKG++OILff7550pISFB6erpT2fbt290UFQAAQMGRqxFS5cqV048//njV8l27dik0NPS6g5KkI0eOKCkpyWmhdH9/fzVo0EBxcXGSpLi4OAUEBFjJKElq2bKl7Ha7Nm/ebNVp0qSJvLy8rDpRUVHav3+/Tp06lS+xAgAAZPrggw/Us2dPBQcH64cfflD9+vVVqlQpHT58+Kr/EAcAAHCryVVCqm3btho+fLjTlLlMFy5c0MiRI/Xggw/mS2BJSUmSpODgYKfjwcHBVllSUpLKlCnjVF6kSBEFBgY61cnuHFf28XdpaWlKTU112gAAAHLio48+0rRp0/Thhx/Ky8tLQ4YM0cqVK9W/f3+lpKS4OzwAAIACIVcJqVdffVUnT57UHXfcoXHjxmnx4sVavHix3n77bVWpUkUnT57Uv//97xsVq8uMHTtW/v7+1hYWFubukAAAQCGRkJCgRo0aSZJ8fX115swZSVK3bt00b948d4YGAABQYOQqIRUcHKyNGzeqRo0aGjZsmDp27KiOHTvqlVdeUY0aNbRhw4Yso5HyKiQkRNJf61ZdKTk52SoLCQnRiRMnnMovX76skydPOtXJ7hxX9vF3w4YNU0pKirUdPXr0+i8IAADcEkJCQnTy5ElJUoUKFbRp0yZJfy1HYIxxZ2gAAAAFRq4SUpIUHh6ur7/+Wn/88Yc2b96sTZs26Y8//tDXX3+tiIiIfAssIiJCISEhWr16tXUsNTVVmzdvVmRkpCQpMjJSp0+fdnqy35o1a+RwONSgQQOrzvr163Xp0iWrzsqVK1WlShWVLFky2769vb3l5+fntAEAAORE8+bN9eWXX0qSevbsqYEDB+qBBx5Q586d1bFjRzdHBwAAUDDk6il7VypZsqTuueee6+r87NmzOnjwoLV/5MgR7dixQ4GBgapQoYIGDBig119/XbfffrsiIiI0fPhwlS1bVh06dJAkVatWTa1bt9azzz6rqVOn6tKlS4qJiVGXLl1UtmxZSdLjjz+u1157Tb169dLQoUP1448/auLEiZowYcJ1xQ4AAJCdadOmyeFwSJKio6NVqlQpbdy4UQ8//LCee+45N0cHAABQMOQ5IZUftm3bpvvvv9/aHzRokCSpR48eio2N1ZAhQ3Tu3Dn17t1bp0+fVuPGjbVs2TL5+PhYbebMmaOYmBi1aNFCdrtdnTp10gcffGCV+/v7a8WKFYqOjlbdunUVFBSkESNGqHfv3q67UAAAcMuw2+2y2//fIPQuXbqoS5cubowIAACg4HFrQqpZs2bXXEvBZrNp9OjRGj169FXrBAYGau7cudfsp1atWvruu+/yHCcAAEBufPfdd/rPf/6jQ4cO6YsvvlC5cuX0ySefKCIiQo0bN3Z3eAAAAG6X6zWkAAAAcHX/+9//FBUVJV9fX/3www9KS0uTJKWkpOjNN990c3QAAAAFAwkpAACAfPT6669r6tSp+vjjj+Xp6Wkdv/fee7V9+3Y3RgYAAFBwuHXKHuBwOJSYmGjth4aGOq27AQBAYbN//341adIky3F/f3+dPn3a9QEBAAAUQCSk4FaJiYnqOXm5fPyDdDHlD82MjlK5cuXcHRYAAHkWEhKigwcPqmLFik7HN2zYoEqVKrknKAAAgAKGoShwOx//IBUtWVo+/kHuDgUAgOv27LPP6oUXXtDmzZtls9l0/PhxzZkzRy+99JL69u3r7vAAAAAKBEZIAQAA5KOXX35ZDodDLVq00Pnz59WkSRN5e3vrpZdeUr9+/dwdHoBCpO7g2TmqF/9O9xscCQDkPxJSAAAA+chms+nf//63Bg8erIMHD+rs2bOqXr26ihcv7u7QAAAACgwSUgAAADeAl5eXqlev7u4wAAAACiQSUgAAAPng6aefzlG9GTNm3OBIAAAACj4SUgAAAPkgNjZW4eHhuuuuu2SMcXc4AAAABRoJKQAAgHzQt29fzZs3T0eOHFHPnj315JNPKjAw0N1hAQAAFEh2dwcAAABwM5g8ebISExM1ZMgQffXVVwoLC9Njjz2m5cuXM2IKAADgb0hIAQAA5BNvb2917dpVK1eu1N69e3XnnXfq+eefV8WKFXX27Fl3hwcAAFBgkJACAAC4Aex2u2w2m4wxysjIcHc4AAAABQoJKQAAgHySlpamefPm6YEHHtAdd9yh3bt3a9KkSUpISFDx4sXdHR4AAECBwaLmAAAA+eD555/Xp59+qrCwMD399NOaN2+egoKC3B0WAABAgURCCgAAIB9MnTpVFSpUUKVKlbRu3TqtW7cu23oLFixwcWQAAAAFDwkpAACAfNC9e3fZbDZ3hwEAAFAokJACAADIB7Gxse4OAQAAoNBgUXMAAAAAAAC4FCOkAAAA4FJ1B8/OUb34d7rf4EgAAIC7kJACAAAAbjIk/QAABR0JKRQ4DodDiYmJ1n5oaKjsdmaXAgAAAABwsyAhhQInMTFRPScvl49/kC6m/KGZ0VEqV66cu8MCAAAAAAD5hIQUCiQf/yAVLVna3WEAAAAAAIAbgHlQAAAAAAAAcCkSUgAAAAXU+vXr9dBDD6ls2bKy2WxatGiRU7kxRiNGjFBoaKh8fX3VsmVLHThwwKnOyZMn9cQTT8jPz08BAQHq1auXzp4961Rn165duu++++Tj46OwsDCNGzcuSyzz589X1apV5ePjo5o1a+rrr7/O9+sFAAC3DhJSAAAABdS5c+dUu3ZtTZ48OdvycePG6YMPPtDUqVO1efNmFStWTFFRUbp48aJV54knntCePXu0cuVKLVmyROvXr1fv3r2t8tTUVLVq1Urh4eGKj4/XO++8o1GjRmnatGlWnY0bN6pr167q1auXfvjhB3Xo0EEdOnTQjz/+eOMuHgAA3NRYQwoAAKCAatOmjdq0aZNtmTFG77//vl599VW1b99ekjR79mwFBwdr0aJF6tKli3766SctW7ZMW7duVb169SRJH374odq2bat3331XZcuW1Zw5c5Senq4ZM2bIy8tLd955p3bs2KHx48dbiauJEyeqdevWGjx4sCRpzJgxWrlypSZNmqSpU6e64E4AAICbDSOkAAAACqEjR44oKSlJLVu2tI75+/urQYMGiouLkyTFxcUpICDASkZJUsuWLWW327V582arTpMmTeTl5WXViYqK0v79+3Xq1CmrzpX9ZNbJ7Cc7aWlpSk1NddoAAAAykZACAAAohJKSkiRJwcHBTseDg4OtsqSkJJUpU8apvEiRIgoMDHSqk905ruzjanUyy7MzduxY+fv7W1tYWFhuLxEAANzESEgBAAAg3w0bNkwpKSnWdvToUXeHBAAAChASUgAAAIVQSEiIJCk5OdnpeHJyslUWEhKiEydOOJVfvnxZJ0+edKqT3Tmu7ONqdTLLs+Pt7S0/Pz+nDQAAIBMJKQAAgEIoIiJCISEhWr16tXUsNTVVmzdvVmRkpCQpMjJSp0+fVnx8vFVnzZo1cjgcatCggVVn/fr1unTpklVn5cqVqlKlikqWLGnVubKfzDqZ/QAAAOQWCSkAAIAC6uzZs9qxY4d27Ngh6a+FzHfs2KGEhATZbDYNGDBAr7/+ur788kvt3r1b3bt3V9myZdWhQwdJUrVq1dS6dWs9++yz2rJli77//nvFxMSoS5cuKlu2rCTp8ccfl5eXl3r16qU9e/bos88+08SJEzVo0CArjhdeeEHLli3Te++9p3379mnUqFHatm2bYmJiXH1LAADATaKIuwMAAABA9rZt26b777/f2s9MEvXo0UOxsbEaMmSIzp07p969e+v06dNq3Lixli1bJh8fH6vNnDlzFBMToxYtWshut6tTp0764IMPrHJ/f3+tWLFC0dHRqlu3roKCgjRixAj17t3bqtOoUSPNnTtXr776ql555RXdfvvtWrRokWrUqOGCuwAAAG5GJKQAAAAKqGbNmskYc9Vym82m0aNHa/To0VetExgYqLlz516zn1q1aum77767Zp1HH31Ujz766LUDBgAAyCGm7AEAAAAAAMClSEgBAAAAAADApUhIAQAAAAAAwKVISAEAAAAAAMClSEgBAAAAAADApUhIAQAAAAAAwKVISAEAAAAAAMClirg7AAAAAKAwqjt4do7rxr/T/QZGAgBA4UNCCgAAAECu5TQhRzIOAJAdElIAAACASLAAAOBKrCEFAAAAAAAAlyIhBQAAAAAAAJciIQUAAAAAAACXIiEFAAAAAAAAlyIhBQAAAAAAAJfiKXsAAAAACqScPvlQ4umHeVVQny5ZUOMCkH8YIQUAAAAAAACXYoQUAAAAUIAxUgQAcDMiIQUAAADgpkECDwAKB6bsAQAAAAAAwKUKdEJq1KhRstlsTlvVqlWt8osXLyo6OlqlSpVS8eLF1alTJyUnJzudIyEhQe3atVPRokVVpkwZDR48WJcvX3b1pQAAAAAAAOD/V+Cn7N15551atWqVtV+kyP8LeeDAgVq6dKnmz58vf39/xcTE6JFHHtH3338vScrIyFC7du0UEhKijRs3KjExUd27d5enp6fefPNNl18L8s7hcCgxMdHaDw0Nld1eoPOpAAAAAP5/TKUE8HcFPiFVpEgRhYSEZDmekpKi6dOna+7cuWrevLkkaebMmapWrZo2bdqkhg0basWKFdq7d69WrVql4OBg1alTR2PGjNHQoUM1atQoeXl5ufpykEeJiYnqOXm5fPyDdDHlD82MjlK5cuXcHRYAAABygaQEACBTgR9icuDAAZUtW1aVKlXSE088oYSEBElSfHy8Ll26pJYtW1p1q1atqgoVKiguLk6SFBcXp5o1ayo4ONiqExUVpdTUVO3Zs8e1F4Lr5uMfpKIlS8vHP8jdoQAAAAAAgOtQoEdINWjQQLGxsapSpYoSExP12muv6b777tOPP/6opKQkeXl5KSAgwKlNcHCwkpKSJElJSUlOyajM8syyq0lLS1NaWpq1n5qamk9XBAAAAAAAgAKdkGrTpo31/7Vq1VKDBg0UHh6uzz//XL6+vjes37Fjx+q11167YecHAADArYkpawAA/KXAT9m7UkBAgO644w4dPHhQISEhSk9P1+nTp53qJCcnW2tOhYSEZHnqXuZ+dutSZRo2bJhSUlKs7ejRo/l7IQAAAAAAALewQpWQOnv2rA4dOqTQ0FDVrVtXnp6eWr16tVW+f/9+JSQkKDIyUpIUGRmp3bt368SJE1adlStXys/PT9WrV79qP97e3vLz83PaAAAAAAAAkD8K9JS9l156SQ899JDCw8N1/PhxjRw5Uh4eHuratav8/f3Vq1cvDRo0SIGBgfLz81O/fv0UGRmphg0bSpJatWql6tWrq1u3bho3bpySkpL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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
    "_ = sns.histplot(data=df, x=\"VehAge\", ax=axs[0])\n",
    "_ = sns.barplot(df, x=\"VehAge\", y=\"target\", errorbar=None, ax=axs[1])\n",
    "axs[1].set_ylabel(\"Mean Target\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf49b5e1",
   "metadata": {},
   "source": [
    "### DrivAge\n",
    "\n",
    "Only 14 instances have `DrivAge` > 90.\n",
    "These instances could be removed during modeling and treated differently later if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c38568a6-dc18-41ab-b8a8-95b6a6f126ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF7Fw4UKzHxciIiIiMl2JLUoFBgbC29sb8fHxyjKtVovExESEhIQAAEJCQnDr1i0kJSUpbXbs2IHs7GwEBwcrbXbv3o3MzEylTVxcHKpUqQI3Nzcz7Q0REVHp1LhxY8THx+PPP/8EABw7dgx79uxRblRizjkgExISdPrJaZPTDxERERGVLha9+96dO3fw119/Kc8vXLiAo0ePwt3dHf7+/hg+fDimTZuGypUrIzAwEBMnToSvry+6du0KAKhWrRratWuHQYMGITo6GpmZmYiKikLPnj3h6+sLAOjduzc++ugjDBw4EGPGjMGJEycwb948fP7555bYZSIiolJl7Nix0Gq1qFq1KqysrJCVlYXp06ejT58+AIp2DsjAwEC9beTE3Nzccp0nMrc5ItPT05Genq48541LiIiIiEoWixalDh06hJYtWyrPR44cCQCIiIjA8uXLMXr0aNy9exeDBw/GrVu30LRpU8TGxkKj0SjrrFy5ElFRUWjdujXUajXCw8Mxf/58Je7i4oJt27YhMjIS9erVQ9myZTFp0iSdeSyIiIjIsDVr1mDlypWIiYlBjRo1cPToUQwfPhy+vr6IiIiwdHp5mjlzJj766CNLp0FEREREubBoUapFixY6d+95kkqlwpQpUzBlypRc27i7uyMmJibPfmrXro3ff/+90HkSERE9q0aNGoWxY8eiZ8+eAIBatWrh33//xcyZMxEREaEzB6SPj4+yXkpKCurWrQug6OaAzK1NbnNEjhs3TvmHF/DoTCneUZeIiIio5LBoUYpKp6GjJ+DyDf1LIMp7OGP+7GkWyIiIiIrLvXv3oFbrTkFpZWWF7OxsALpzQOYUoXLmgBwyZAgA3Tkg69WrB8DwHJDjx49HZmYmbGxsAOjPARkSEoL4+HgMHz5cySUuLk6Za/JJdnZ2sLOzK5oDQURERERFjkUpKrDLN7SwCe6lvzxxlQWyISKi4tS5c2dMnz4d/v7+qFGjBo4cOYI5c+ZgwIABAB6d1WyuOSCHDRuG5s2b47PPPkPHjh2xevVqHDp0CEuWLDH7cSEiIiIi07EoRURERLlasGABJk6ciHfeeQfXrl2Dr68v3nrrLUyaNElpY645IBs3boyYmBhMmDABH3zwASpXrowNGzagZs2a5jkYRERERFSkWJQiIiKiXDk5OWHu3LmYO3durm3MOQdk9+7d0b179zzbEBEREVHpoDbehIiIiIiIiIiIqGixKEVERERERERERGbHohQREREREREREZkdi1JERERERERERGR2LEoREREREREREZHZsShFRERERERERERmx6IUERERERERERGZHYtSRERERERERERkdixKERERERERERGR2bEoRUREREREREREZseiFBERERERERERmR2LUkREREREREREZHYsShERERERERERkdlZWzoBIiqZho6egMs3tHrLy3s4Y/7saRbIiIiIiIiIiJ4mLEoRkUGXb2hhE9xLf3niKgtkQ0RERERERE8bXr5HRERERERERERmx6IUERERERERERGZHYtSRERERERERERkdixKERERERERERGR2bEoRUREREREREREZseiFBERERERERERmR2LUkREREREREREZHYsShERERERERERkdmxKEVERERERERERGbHohQREREREREREZkdi1JERESlyMWLFyEiestFBBcvXrRARkREREREhcOiFBERUSkSGBiI69ev6y1PTU1FYGCgBTIiIiIiIiocFqWIiIhKERGBSqXSW37nzh1oNBoLZEREREREVDjWlk6AiIiIjBs5ciQAQKVSYeLEiXBwcFBiWVlZSExMRN26dS2UHRERERFRwbEoRUREVAocOXIEwKMzpY4fPw5bW1slZmtrizp16uD999+3VHpERERERAXGohQREVEp8NtvvwEA+vfvj3nz5sHZ2dnCGVFxqDfqO4PLkz7pZ+ZMiIiIiIpfiZ5TKisrCxMnTkRgYCDs7e3x3HPPYerUqTp3HRIRTJo0CT4+PrC3t0doaCjOnTuns53U1FT06dMHzs7OcHV1xcCBA3Hnzh1z7w4REZHJli1bBmdnZ/z111/YunUr7t+/DwAG78hHRERERFSSleii1KxZs7B48WIsXLgQp0+fxqxZszB79mwsWLBAaTN79mzMnz8f0dHRSExMhKOjI8LCwvDgwQOlTZ8+fXDy5EnExcVh06ZN2L17NwYPHmyJXSIiIjJJamoqWrdujRdeeAEdOnTA1atXAQADBw7Ee++9Z+HsiIiIiIjyr0QXpfbt24cuXbqgY8eOqFixIl599VW0bdsWBw4cAPDov8Jz587FhAkT0KVLF9SuXRvfffcdrly5gg0bNgAATp8+jdjYWHz99dcIDg5G06ZNsWDBAqxevRpXrlyx4N4REREV3PDhw2FjY4OLFy/qTHbeo0cPxMbGWjAzIiIiIqKCKdFFqcaNGyM+Ph5//vknAODYsWPYs2cP2rdvDwC4cOECkpOTERoaqqzj4uKC4OBgJCQkAAASEhLg6uqK+vXrK21CQ0OhVquRmJhoxr0hIiIy3bZt2zBr1ixUqFBBZ3nlypXx77//WigrIiIiIqKCK9ETnY8dOxZarRZVq1aFlZUVsrKyMH36dPTp0wcAkJycDADw8vLSWc/Ly0uJJScnw9PTUydubW0Nd3d3pc2T0tPTkZ6erjzXarVFtk9ERESmuHv3rs4ZUjlSU1NhZ2dngYyIiIiIiAqnRJ8ptWbNGqxcuRIxMTE4fPgwvv32W3z66af49ttvi7XfmTNnwsXFRXn4+fkVa39ERET59dJLL+G77/7vDm0qlQrZ2dmYPXs2WrZsacHMiIiIiIgKpkSfKTVq1CiMHTsWPXv2BADUqlUL//77L2bOnImIiAh4e3sDAFJSUuDj46Osl5KSgrp16wIAvL29ce3aNZ3tPnz4EKmpqcr6Txo3bhxGjhypPNdqtSxMERFRiTB79my0bt0ahw4dQkZGBkaPHo2TJ08iNTUVe/futXR6RERERET5VqKLUvfu3YNarXsyl5WVFbKzswEAgYGB8Pb2Rnx8vFKE0mq1SExMxJAhQwAAISEhuHXrFpKSklCvXj0AwI4dO5CdnY3g4GCD/drZ2T2Vl0AMHT0Bl2/oX4pY3sMZ82dPs0BGRERUUDVr1sSff/6JhQsXwsnJCXfu3EG3bt0QGRmp8w8aIiIiIqKSrkQXpTp37ozp06fD398fNWrUwJEjRzBnzhwMGDAAwKNLFoYPH45p06ahcuXKCAwMxMSJE+Hr64uuXbsCAKpVq4Z27dph0KBBiI6ORmZmJqKiotCzZ0/4+vpacO/M7/INLWyCe+kvT1xlgWyIiKiwXFxcMH78eEunQURERERkkhJdlFqwYAEmTpyId955B9euXYOvry/eeustTJo0SWkzevRo3L17F4MHD8atW7fQtGlTxMbGQqPRKG1WrlyJqKgotG7dGmq1GuHh4Zg/f74ldomIiMgkf/zxh8HlKpUKGo0G/v7+T+XZvkRERET09CnRRSknJyfMnTsXc+fOzbWNSqXClClTMGXKlFzbuLu7IyYmphgyJCIiMq+6detCpVIBAEQEAJTnAGBjY4MePXrgyy+/1PkHDRERERFRSVOi775HREREutavX4/KlStjyZIlOHbsGI4dO4YlS5agSpUqiImJwTfffIMdO3ZgwoQJlk6ViIiIiChPJfpMKSIiItI1ffp0zJs3D2FhYcqyWrVqoUKFCpg4cSIOHDgAR0dHvPfee/j0008tmCkRERERUd54phQREVEpcvz4cQQEBOgtDwgIwPHjxwE8usTv6tWr5k6NiIiIiKhAWJQiIiIqRapWrYqPP/4YGRkZyrLMzEx8/PHHqFq1KgDg8uXL8PLyslSKRERERET5wqIUERFRKbJo0SJs2rQJFSpUQGhoKEJDQ1GhQgVs2rQJixcvBgCcP38e77zzTpH1efnyZfTt2xceHh6wt7dHrVq1cOjQISUuIpg0aRJ8fHxgb2+P0NBQnDt3Tmcbqamp6NOnD5ydneHq6oqBAwfizp07Om3++OMPvPTSS9BoNPDz88Ps2bP1clm7di2qVq0KjUaDWrVq4ddffy2y/SQiIiIi82JRioiIqBRp3LgxLly4gClTpqB27dqoXbs2pkyZggsXLqBRo0YAgNdffx2jRo0qkv5u3ryJJk2awMbGBlu2bMGpU6fw2Wefwc3NTWkze/ZszJ8/H9HR0UhMTISjoyPCwsLw4MEDpU2fPn1w8uRJxMXFYdOmTdi9ezcGDx6sxLVaLdq2bYuAgAAkJSXhk08+weTJk7FkyRKlzb59+9CrVy8MHDgQR44cQdeuXdG1a1ecOHGiSPaViIiIiMyLE50TERGVEpmZmahatSo2bdqEt99+2yx9zpo1C35+fli2bJmyLDAwUPlZRDB37lxMmDABXbp0AQB899138PLywoYNG9CzZ0+cPn0asbGxOHjwIOrXrw8AWLBgATp06IBPP/0Uvr6+WLlyJTIyMrB06VLY2tqiRo0aOHr0KObMmaMUr+bNm4d27dopBbepU6ciLi4OCxcuRHR0tFmOBxEREREVnUKdKVWpUiXcuHFDb/mtW7dQqVIlk5MiIiIifTY2NjpnH5nDzz//jPr166N79+7w9PREUFAQvvrqKyV+4cIFJCcnIzQ0VFnm4uKC4OBgJCQkAAASEhLg6uqqFKQAIDQ0FGq1GomJiUqbZs2awdbWVmkTFhaGs2fP4ubNm0qbx/vJaZPTDxERERGVLoUqSv3zzz/IysrSW56eno7Lly+bnBQREREZFhkZiVmzZuHhw4dm6e/8+fNYvHgxKleujK1bt2LIkCEYOnQovv32WwBAcnIyAOhNrO7l5aXEkpOT4enpqRO3traGu7u7ThtD23i8j9za5MSflJ6eDq1Wq/MgIiIiopKjQJfv/fzzz8rPW7duhYuLi/I8KysL8fHxqFixYpElR0RERLoOHjyI+Ph4bNu2DbVq1YKjo6NOfN26dUXaX3Z2NurXr48ZM2YAAIKCgnDixAlER0cjIiKiSPsqajNnzsRHH31k6TSIiIiIKBcFKkp17doVAKBSqfQ+iNrY2KBixYr47LPPiiw5IiIi0uXq6orw8HCz9efj44Pq1avrLKtWrRp++uknAIC3tzcAICUlBT4+PkqblJQU1K1bV2lz7do1nW08fPgQqampyvre3t5ISUnRaZPz3FibnPiTxo0bh5EjRyrPtVot/Pz8jO80EREREZlFgYpS2dnZAB5NcHrw4EGULVu2WJIiIiIiwx6fcNwcmjRpgrNnz+os+/PPPxEQEADg0WcCb29vxMfHK0UorVaLxMREDBkyBAAQEhKCW7duISkpCfXq1QMA7NixA9nZ2QgODlbajB8/HpmZmbCxsQEAxMXFoUqVKsqd/kJCQhAfH4/hw4crucTFxSEkJMRg7nZ2drCzsyuaA0FERERERa5Qc0pduHCBBSkiIqJnwIgRI7B//37MmDEDf/31F2JiYrBkyRJERkYCeHT29PDhwzFt2jT8/PPPOH78OPr16wdfX1/lDOtq1aqhXbt2GDRoEA4cOIC9e/ciKioKPXv2hK+vLwCgd+/esLW1xcCBA3Hy5En88MMPmDdvns6ZTsOGDUNsbCw+++wznDlzBpMnT8ahQ4cQFRVl9uNCRERERKYr0JlSj4uPj0d8fDyuXbumnEGVY+nSpSYnRkRERIb9+OOPWLNmDS5evIiMjAyd2OHDh4u0rwYNGmD9+vUYN24cpkyZgsDAQMydOxd9+vRR2owePRp3797F4MGDcevWLTRt2hSxsbHQaDRKm5UrVyIqKgqtW7eGWq1GeHg45s+fr8RdXFywbds2REZGol69eihbtiwmTZqEwYMHK20aN26MmJgYTJgwAR988AEqV66MDRs2oGbNmkW6z0RERERkHoUqSn300UeYMmUK6tevDx8fH6hUqqLOi4iIiAyYP38+xo8fjzfeeAMbN25E//798ffff+PgwYPK2UtFrVOnTujUqVOucZVKhSlTpmDKlCm5tnF3d0dMTEye/dSuXRu///57nm26d++O7t27550wEREREZUKhSpKRUdHY/ny5Xj99deLOh8iIiLKwxdffIElS5agV69eWL58OUaPHo1KlSph0qRJSE1NtXR6RERERET5Vqg5pTIyMtC4ceOizoWIiIiMuHjxovI32N7eHrdv3wYAvP7661i1apUlUyMiIiIiKpBCFaXefPNNo6fgExERUdHz9vZWzojy9/fH/v37ATy6CYmIWDI1IiIiIqICKdTlew8ePMCSJUuwfft21K5dW7l1c445c+YUSXJERET0SKtWrbBu3Tq0atUKP//8M4KCgtC/f3+MGDECP/74Iw4dOoRu3bpZOk0iIiIionwrVFHqjz/+QN26dQEAJ06c0Ilx0nMi8xg6egIu39DqLT9x6gyCgi2QEBEVq507dyIjIwNLlixR7nobGRkJDw8P7Nu3Dy+//DLeeustC2dJRERERJR/hSpK/fbbb0WdBxEV0OUbWtgE99Jbfv/oeAtkQ0TmolaroVb/39X3PXv2RM+ePS2YERERERFR4RSqKEVEhXP82DGEDxyqt5xnNxFRfpw6dQrJycl5tqldu7aZsiEiIiIiMk2hilItW7bM8zK9HTt2FDohoqfZA7Hi2U1EVGitW7fOczJzlUqFrKwsM2ZET6o36juDy5M+6WfmTIiIiIhKvkIVpXLmk8qRmZmJo0eP4sSJE4iIiCiKvIiIiOgJiYmJKFeunKXTICIiIiIqEoUqSn3++ecGl0+ePBl37twxKSEiIiIyzN/fH56enpZOg4iIiIioSKiNN8m/vn37YunSpUW5SSIiIiIiIiIiegoVaVEqISEBGo2mKDdJREREAJo3bw5bW1tLp0FEREREVGQKdflet27ddJ6LCK5evYpDhw5h4sSJRZIYUWkwdPQEXL6h1VtelHfT4x37iAgAfvvtN0unQERERERUpApVlHJxcdF5rlarUaVKFUyZMgVt27YtksSISoPLN7TFfjc93rGPiIiIiIiInkaFKkotW7asqPMgIiIiIiIiIqJnSKGKUjmSkpJw+vRpAECNGjUQFBRUJEkREREREREREdHTrVBFqWvXrqFnz57YuXMnXF1dAQC3bt1Cy5YtsXr1apQrV64ocyQiIiIiIiIioqdMoYpS7777Lm7fvo2TJ0+iWrVqAIBTp04hIiICQ4cOxapVq4o0SSIiInokKysLy5cvR3x8PK5du4bs7Gyd+I4dOyyUGRERERFRwRSqKBUbG4vt27crBSkAqF69OhYtWsSJzomIiIrRsGHDsHz5cnTs2BE1a9aESqWydEpERERERIVSqKJUdnY2bGxs9Jbb2Njo/ceWiIiIis7q1auxZs0adOjQwdKpEBERERGZRF2YlVq1aoVhw4bhypUryrLLly9jxIgRaN26dZElR0RERLpsbW3x/PPPWzoNIiIiIiKTFaootXDhQmi1WlSsWBHPPfccnnvuOQQGBkKr1WLBggVFmuDly5fRt29feHh4wN7eHrVq1cKhQ4eUuIhg0qRJ8PHxgb29PUJDQ3Hu3DmdbaSmpqJPnz5wdnaGq6srBg4ciDt37hRpnkRERObw3nvvYd68eRARS6dCRERERGSSQl2+5+fnh8OHD2P79u04c+YMAKBatWoIDQ0t0uRu3ryJJk2aoGXLltiyZQvKlSuHc+fOwc3NTWkze/ZszJ8/H99++y0CAwMxceJEhIWF4dSpU9BoNACAPn364OrVq4iLi0NmZib69++PwYMHIyYmpkjzpafX8WPHED5wqN7yE6fOICjYAgkR0TNrz549+O2337BlyxbUqFFD73L6devWWSgzIiIiIqKCKVBRaseOHYiKisL+/fvh7OyMNm3aoE2bNgCAtLQ01KhRA9HR0XjppZeKJLlZs2bBz88Py5YtU5YFBgYqP4sI5s6diwkTJqBLly4AgO+++w5eXl7YsGEDevbsidOnTyM2NhYHDx5E/fr1AQALFixAhw4d8Omnn8LX17dIcqWn2wOxgk1wL73l94+Ot0A2RPQsc3V1xSuvvGLpNIiIiIiITFagotTcuXMxaNAgODs768VcXFzw1ltvYc6cOUVWlPr5558RFhaG7t27Y9euXShfvjzeeecdDBo0CABw4cIFJCcn65yh5eLiguDgYCQkJKBnz55ISEiAq6urUpACgNDQUKjVaiQmJhr8YJ+eno709HTluVarLZL9ISIiMtXj/6ghIiIiIirNClSUOnbsGGbNmpVrvG3btvj0009NTirH+fPnsXjxYowcORIffPABDh48iKFDh8LW1hYRERFITk4GAHh5eems5+XlpcSSk5Ph6empE7e2toa7u7vS5kkzZ87ERx99VGT7QfQsGzp6Ai7f0C/slvdwxvzZ0yyQEREREREREZUEBSpKpaSk6M1dobMxa2tcv37d5KRyZGdno379+pgxYwYAICgoCCdOnEB0dDQiIiKKrJ8njRs3DiNHjlSea7Va+Pn5FVt/xvBLPZVml29oDV76eDlxlQWyIXo6/Pjjj1izZg0uXryIjIwMndjhw4ctlBURERERUcEU6O575cuXx4kTJ3KN//HHH/Dx8TE5qRw+Pj6oXr26zrJq1arh4sWLAABvb28Aj4plj0tJSVFi3t7euHbtmk784cOHSE1NVdo8yc7ODs7OzjoPS8r5Uv/kw1ChioiInm7z589H//794eXlhSNHjqBhw4bw8PDA+fPn0b59e0unR0RERESUbwUqSnXo0AETJ07EgwcP9GL379/Hhx9+iE6dOhVZck2aNMHZs2d1lv35558ICAgA8GjSc29vb8THxytxrVaLxMREhISEAABCQkJw69YtJCUlKW127NiB7OxsBAfztmlERFS6fPHFF1iyZAkWLFgAW1tbjB49GnFxcRg6dCjS0tIsnR4RERERUb4V6PK9CRMmYN26dXjhhRcQFRWFKlWqAADOnDmDRYsWISsrC+PHF93dyEaMGIHGjRtjxowZeO2113DgwAEsWbIES5YsAQCoVCoMHz4c06ZNQ+XKlREYGIiJEyfC19cXXbt2BfDozKp27dph0KBBiI6ORmZmJqKiotCzZ0/eeY+IiEqdixcvonHjxgAAe3t73L59GwDw+uuvo1GjRli4cKEl0yMiIiIiyrcCFaW8vLywb98+DBkyBOPGjYOIAHhUHAoLC8OiRYv0Jh03RYMGDbB+/XqMGzcOU6ZMQWBgIObOnYs+ffoobUaPHo27d+9i8ODBuHXrFpo2bYrY2FhoNBqlzcqVKxEVFYXWrVtDrVYjPDwc8+fPL7I8iYiIzMXb2xupqakICAiAv78/9u/fjzp16uDChQvK32UiIiIiotKgQEUpAAgICMCvv/6Kmzdv4q+//oKIoHLlynBzcyuO/NCpU6c8LwlUqVSYMmUKpkyZkmsbd3d3xMTEFEd6VErlNnn8iVNnEPSUXtV5/NgxhA8cqrecE+YTlS6tWrXCzz//jKCgIPTv3x8jRozAjz/+iEOHDqFbt26WTo+IiIiIKN8KXJTK4ebmhgYNGhRlLkRmk9sd4e4fLbrLT0uaB2LFu+ARPQWWLFmC7OxsAEBkZCQ8PDywb98+vPzyy3jrrbcsnB0RERERUf4VuihFZAkFPcMpt7ODnuYzoojo6aZWq6FW/999Snr27ImePXtaMCMiIiIiosJhUYpKlYKe4ZTb2UFP8xlRRPT0+/333/Hll1/i77//xo8//ojy5ctjxYoVCAwMRNOmTS2dHhERERFRvqiNNyEiIqKS4qeffkJYWBjs7e1x5MgRpKenAwDS0tIwY8YMC2dHRERERJR/LEoRERGVItOmTUN0dDS++uor2NjYKMubNGmCw4cPWzAzIiIiIqKCYVGKiIioFDl79iyaNWumt9zFxQW3bt0yf0JERERERIXEOaWoROIE5UREhnl7e+Ovv/5CxYoVdZbv2bMHlSpVskxSRERERESFwKIUlUicoJyIyLBBgwZh2LBhWLp0KVQqFa5cuYKEhAS8//77mDhxoqXTIyIiIiLKNxaliIiISpGxY8ciOzsbrVu3xr1799CsWTPY2dnh/fffx7vvvmvp9IiIiIiI8o1FKSIiolJEpVJh/PjxGDVqFP766y/cuXMH1atXR5kyZSydGhERERFRgbAoRUREVArZ2tqievXqlk6DiIiIiKjQWJQiIiIqBQYMGJCvdkuXLi3mTIiIiIiIigaLUkRERKXA8uXLERAQgKCgIIiIpdMhIiIiIjIZi1JERESlwJAhQ7Bq1SpcuHAB/fv3R9++feHu7m7ptIiIiIiICk1t6QSIiIjIuEWLFuHq1asYPXo0fvnlF/j5+eG1117D1q1bzXbm1McffwyVSoXhw4cryx48eIDIyEh4eHigTJkyCA8PR0pKis56Fy9eRMeOHeHg4ABPT0+MGjUKDx8+1Gmzc+dOvPjii7Czs8Pzzz+P5cuX6/W/aNEiVKxYERqNBsHBwThw4EBx7CYRERERmQmLUkRERKWEnZ0devXqhbi4OJw6dQo1atTAO++8g4oVK+LOnTvF2vfBgwfx5Zdfonbt2jrLR4wYgV9++QVr167Frl27cOXKFXTr1k2JZ2VloWPHjsjIyMC+ffvw7bffYvny5Zg0aZLS5sKFC+jYsSNatmyJo0ePYvjw4XjzzTexdetWpc0PP/yAkSNH4sMPP8Thw4dRp04dhIWF4dq1a8W630RERERUfHj5HtEz7vixYwgfOFRv+YlTZxAUbIGEiChf1Go1VCoVRARZWVnF2tedO3fQp08ffPXVV5g2bZqyPC0tDd988w1iYmLQqlUrAMCyZctQrVo17N+/H40aNcK2bdtw6tQpbN++HV5eXqhbty6mTp2KMWPGYPLkybC1tUV0dDQCAwPx2WefAQCqVauGPXv24PPPP0dYWBgAYM6cORg0aBD69+8PAIiOjsbmzZuxdOlSjB07tlj3n4iIiIiKB4tSVGRyK26c//M0Kr1QzeA6LHxY3gOxgk1wL73l94+Ot0A2RJSX9PR0rFu3DkuXLsWePXvQqVMnLFy4EO3atYNaXXwnP0dGRqJjx44IDQ3VKUolJSUhMzMToaGhyrKqVavC398fCQkJaNSoERISElCrVi14eXkpbcLCwjBkyBCcPHkSQUFBSEhI0NlGTpucywQzMjKQlJSEcePGKXG1Wo3Q0FAkJCQU014TERERUXFjUYqKTG7FjRtHx6OKgeUACx9ERPn1zjvvYPXq1fDz88OAAQOwatUqlC1bttj7Xb16NQ4fPoyDBw/qxZKTk2FrawtXV1ed5V5eXkhOTlbaPF6QyonnxPJqo9Vqcf/+fdy8eRNZWVkG25w5cybX3NPT05Genq4812q1RvaWiIiIiMyJRSkiIqJSIDo6Gv7+/qhUqRJ27dqFXbt2GWy3bt26Iuvz0qVLGDZsGOLi4qDRaIpsu+Yyc+ZMfPTRR5ZOg4iIiIhywaIUERFRKdCvXz+oVCqz9pmUlIRr167hxRdfVJZlZWVh9+7dWLhwIbZu3YqMjAzcunVL52yplJQUeHt7AwC8vb317pKXc3e+x9s8ece+lJQUODs7w97eHlZWVrCysjLYJmcbhowbNw4jR45Unmu1Wvj5+RXgCBARERFRcWJRioiIqBRYvny52fts3bo1jh8/rrOsf//+qFq1KsaMGQM/Pz/Y2NggPj4e4eHhAICzZ8/i4sWLCAkJAQCEhIRg+vTpuHbtGjw9PQEAcXFxcHZ2RvXq1ZU2v/76q04/cXFxyjZsbW1Rr149xMfHo2vXrgCA7OxsxMfHIyoqKtf87ezsYGdnZ/qBICIiIqJiwaIUERERGeTk5ISaNWvqLHN0dISHh4eyfODAgRg5ciTc3d3h7OyMd999FyEhIWjUqBEAoG3btqhevTpef/11zJ49G8nJyZgwYQIiIyOVgtHbb7+NhQsXYvTo0RgwYAB27NiBNWvWYPPmzUq/I0eOREREBOrXr4+GDRti7ty5uHv3rnI3PiIiIiIqfViUIiIiokL7/PPPoVarER4ejvT0dISFheGLL75Q4lZWVti0aROGDBmCkJAQODo6IiIiAlOmTFHaBAYGYvPmzRgxYgTmzZuHChUq4Ouvv0ZYWJjSpkePHrh+/TomTZqE5ORk1K1bF7GxsXqTnxMRERFR6cGiFBEREeXbzp07dZ5rNBosWrQIixYtynWdgIAAvcvzntSiRQscOXIkzzZRUVF5Xq5HRERERKWL2tIJEBERERERERHRs4dnShFRqTB09ARcvqHVW17ewxnzZ0+zQEZERERERERkChalKFe5FQFOnDqDoGALJETPtMs3tLAJ7qW/PHGVBbIhIiIiIiIiU7EoRbnKrQhw/+h4C2RDRERERERERE8TzilFRERERERERERmx6IUERERERERERGZHYtSRERERERERERkdixKERERERERERGR2bEoRUREREREREREZseiFBERERERERERmV2pKkp9/PHHUKlUGD58uLLswYMHiIyMhIeHB8qUKYPw8HCkpKTorHfx4kV07NgRDg4O8PT0xKhRo/Dw4UMzZ09ERERERERERDlKTVHq4MGD+PLLL1G7dm2d5SNGjMAvv/yCtWvXYteuXbhy5Qq6deumxLOystCxY0dkZGRg3759+Pbbb7F8+XJMmjTJ3LtARERERERERET/X6koSt25cwd9+vTBV199BTc3N2V5WloavvnmG8yZMwetWrVCvXr1sGzZMuzbtw/79+8HAGzbtg2nTp3C999/j7p166J9+/aYOnUqFi1ahIyMDEvtEhERERERERHRM61UFKUiIyPRsWNHhIaG6ixPSkpCZmamzvKqVavC398fCQkJAICEhATUqlULXl5eSpuwsDBotVqcPHnSPDtAREREREREREQ6rC2dgDGrV6/G4cOHcfDgQb1YcnIybG1t4erqqrPcy8sLycnJSpvHC1I58ZyYIenp6UhPT1eea7VaU3aBiIiIiIiIiIieUKLPlLp06RKGDRuGlStXQqPRmK3fmTNnwsXFRXn4+fmZrW8iIiIiIiIiomdBiS5KJSUl4dq1a3jxxRdhbW0Na2tr7Nq1C/Pnz4e1tTW8vLyQkZGBW7du6ayXkpICb29vAIC3t7fe3fhynue0edK4ceOQlpamPC5dulT0O0dERERERERE9Awr0ZfvtW7dGsePH9dZ1r9/f1StWhVjxoyBn58fbGxsEB8fj/DwcADA2bNncfHiRYSEhAAAQkJCMH36dFy7dg2enp4AgLi4ODg7O6N69eoG+7Wzs4OdnV0x7lnJcvzYMYQPHKq3/MSpMwgKtkBCRERERERERPTUK9FFKScnJ9SsWVNnmaOjIzw8PJTlAwcOxMiRI+Hu7g5nZ2e8++67CAkJQaNGjQAAbdu2RfXq1fH6669j9uzZSE5OxoQJExAZGflMFZ7y8kCsYBPcS2/5/aPjLZANUfEbOnoCLt/QnyuuvIcz5s+eZoGMiIiIiIiInj0luiiVH59//jnUajXCw8ORnp6OsLAwfPHFF0rcysoKmzZtwpAhQxASEgJHR0dERERgypQpFsyaiCzp8g2twULs5cRVFsiGiIiIiIjo2VTqilI7d+7Uea7RaLBo0SIsWrQo13UCAgLw66+/FnNmRERERERERESUXyV6onMiIiIiIiIiIno6sShFRERERERERERmx6IUERERERERERGZHYtSRERERERERERkdqVuonMisqzjx44hfOBQveXlPZwxf/Y0C2REREREREREpRGLUkRUIA/ECjbBvfSWX05cZYFsiIiIiIiIqLRiUYqIiIgon+qN+i7XWNIn/cyYCREREVHpx6LUU2jo6Am4fEOrt/zEqTMICrZAQkRERERERERET2BRqhTLbW6fE6fOIKj/R3rL7x8db460iIiIiIiIiIiMYlGqFMttbh8Wn4iIiIiIiIiopFNbOgEiIiIiIiIiInr2sChFRERERERERERmx6IUERERERERERGZHYtSRERERERERERkdpzonIiKRJ53gwy2QEJmMHT0BFy+odVbXt7DGfNnT7NARkRERERERKUHi1JEVCSexbtBXr6hNbjPlxNXWSAbIiIiIiKi0oWX7xERERERERERkdmxKEVERERERERERGbHy/dKkNzmp3ma5+QhIiIiIiIiomcTi1IlSG7z0zzNc/IQERERERER0bOJRSkisojc7tbHO9cRERERERE9G1iUsgBepkeU+936eOc6IiIiIiKiZwOLUhbAy/SIiIiIiIiI6FnHu+8REREREREREZHZsShFRERERERERERmx6IUERER5WrmzJlo0KABnJyc4Onpia5du+Ls2bM6bR48eIDIyEh4eHigTJkyCA8PR0pKik6bixcvomPHjnBwcICnpydGjRqFhw8f6rTZuXMnXnzxRdjZ2eH555/H8uXL9fJZtGgRKlasCI1Gg+DgYBw4cKDI95mIiIiIzINzShERmUluNzngHQepJNu1axciIyPRoEEDPHz4EB988AHatm2LU6dOwdHREQAwYsQIbN68GWvXroWLiwuioqLQrVs37N27FwCQlZWFjh07wtvbG/v27cPVq1fRr18/2NjYYMaMGQCACxcuoGPHjnj77bexcuVKxMfH480334SPjw/CwsIAAD/88ANGjhyJ6OhoBAcHY+7cuQgLC8PZs2fh6elpmQNERERERIXGohQRkZnkdpMD3nGQSrLY2Fid58uXL4enpyeSkpLQrFkzpKWl4ZtvvkFMTAxatWoFAFi2bBmqVauG/fv3o1GjRti2bRtOnTqF7du3w8vLC3Xr1sXUqVMxZswYTJ48Gba2toiOjkZgYCA+++wzAEC1atWwZ88efP7550pRas6cORg0aBD69+8PAIiOjsbmzZuxdOlSjB071oxHhYiIiIiKAotSRERElG9paWkAAHd3dwBAUlISMjMzERoaqrSpWrUq/P39kZCQgEaNGiEhIQG1atWCl5eX0iYsLAxDhgzByZMnERQUhISEBJ1t5LQZPnw4ACAjIwNJSUkYN26cEler1QgNDUVCQkJx7S4REZVC9UZ9Z3B50if9zJwJERnDohQRERHlS3Z2NoYPH44mTZqgZs2aAIDk5GTY2trC1dVVp62XlxeSk5OVNo8XpHLiObG82mi1Wty/fx83b95EVlaWwTZnzpwxmG96ejrS09OV51qt/uWzRERERGQ5nOiciIiI8iUyMhInTpzA6tWrLZ1KvsycORMuLi7Kw8/Pz9IpEREREdFjeKYUERERGRUVFYVNmzZh9+7dqFChgrLc29sbGRkZuHXrls7ZUikpKfD29lbaPHmXvJy78z3e5sk79qWkpMDZ2Rn29vawsrKClZWVwTY523jSuHHjMHLkSOW5VqtlYYqIiMiA3C55BHjZIxUvnilFREREuRIRREVFYf369dixYwcCAwN14vXq1YONjQ3i4+OVZWfPnsXFixcREhICAAgJCcHx48dx7do1pU1cXBycnZ1RvXp1pc3j28hpk7MNW1tb1KtXT6dNdnY24uPjlTZPsrOzg7Ozs86DiIiIiEoOnilFREREuYqMjERMTAw2btwIJycnZQ4oFxcX2Nvbw8XFBQMHDsTIkSPh7u4OZ2dnvPvuuwgJCUGjRo0AAG3btkX16tXx+uuvY/bs2UhOTsaECRMQGRkJOzs7AMDbb7+NhQsXYvTo0RgwYAB27NiBNWvWYPPmzUouI0eOREREBOrXr4+GDRti7ty5uHv3rnI3PsobJ/4lIiKikoZFKSIqUY4fO4bwgUP1lp84dQZBwRZIiOgZt3jxYgBAixYtdJYvW7YMb7zxBgDg888/h1qtRnh4ONLT0xEWFoYvvvhCaWtlZYVNmzZhyJAhCAkJgaOjIyIiIjBlyhSlTWBgIDZv3owRI0Zg3rx5qFChAr7++muEhYUpbXr06IHr169j0qRJSE5ORt26dREbG6s3+TkRERERlQ4sShFRifJArGAT3Etv+f2j4y2QDRGJiNE2Go0GixYtwqJFi3JtExAQgF9//TXP7bRo0QJHjhzJs01UVBSioqKM5kREREREJV+JnlNq5syZaNCgAZycnODp6YmuXbvi7NmzOm0ePHiAyMhIeHh4oEyZMggPD9ebBPXixYvo2LEjHBwc4OnpiVGjRuHhw4fm3BUiIiIiIiIiInpMiS5K7dq1C5GRkdi/fz/i4uKQmZmJtm3b4u7du0qbESNG4JdffsHatWuxa9cuXLlyBd26dVPiWVlZ6NixIzIyMrBv3z58++23WL58OSZNmmSJXSIiIiIiIiIiIpTwy/diY2N1ni9fvhyenp5ISkpCs2bNkJaWhm+++QYxMTFo1aoVgEdzXFSrVg379+9Ho0aNsG3bNpw6dQrbt2+Hl5cX6tati6lTp2LMmDGYPHkybG1tLbFrRERERERERETPtBJdlHpSWloaAMDd3R0AkJSUhMzMTISGhiptqlatCn9/fyQkJKBRo0ZISEhArVq1dCZBDQsLw5AhQ3Dy5EkEBQXp9ZOeno709HTluVarLa5dIiIiIiIiIgLAO6XSs6fUFKWys7MxfPhwNGnSBDVr1gQAJCcnw9bWFq6urjptvby8lFtWJycn692VJ+d5TpsnzZw5Ex999FER7wERFYfc7tYHAOU9nDF/9jQzZ0RERERERET5UWqKUpGRkThx4gT27NlT7H2NGzcOI0eOVJ5rtVr4+fkVe79EVHC53a0PAC4nrjJzNkRERERERJRfpaIoFRUVhU2bNmH37t2oUKGCstzb2xsZGRm4deuWztlSKSkp8Pb2VtocOHBAZ3s5d+fLafMkOzs72NnZFfFeEBEVjaGjJ+DyDf3LinlmGBERERERlSYl+u57IoKoqCisX78eO3bsQGBgoE68Xr16sLGxQXx8vLLs7NmzuHjxIkJCQgAAISEhOH78OK5du6a0iYuLg7OzM6pXr26eHSEiKkKXb2hhE9xL72GoUEVERERERFRSlegzpSIjIxETE4ONGzfCyclJmQPKxcUF9vb2cHFxwcCBAzFy5Ei4u7vD2dkZ7777LkJCQtCoUSMAQNu2bVG9enW8/vrrmD17NpKTkzFhwgRERkbybCiip1xu802dOHUGQcH5b88zkIiIiIiIiIpeiS5KLV68GADQokULneXLli3DG2+8AQD4/PPPoVarER4ejvT0dISFheGLL75Q2lpZWWHTpk0YMmQIQkJC4OjoiIiICEyZMsVcu0FEFpLbfFP3j44vUHvOTUVEREREpR3v7EclUYkuSomI0TYajQaLFi3CokWLcm0TEBCAX3/9tShTIyIiIiIiIiIiE5ToohQRERERlXz87zsREREVBotSREREREQGsNhGRERUvEr03feIiIiIiIiIiOjpxDOliIiIiJ5xuZ0RBPCsICIiIio+PFOKiIiIiIiIiIjMjmdKEREZcfzYMYQPHKq3/MSpMwgKtkBCREREREREhVSS5kxkUYqIyIgHYgWb4F56y+8fHW+BbIiI6HEl6YM1ERERFQwv3yMiIiIiIiIiIrPjmVJERERERERERAXEs3VNxzOliIiIiIiIiIjI7HimFBERERERPdNyO9sB4BkPRETFiUUpIqIixrv1EdHThpcnkLlwrBERPVtYlCIiKmK8Wx8RUdEprUUKnnlDRIaU1vc0ouLCohQRERERWQyLN0RERM8uTnRORERERERERERmxzOliIiIiIhKGF7iQ0REzwIWpYiILCy3idHP/3kalV6oprecE6YTERUNFn6I8sbfEcrBsUDFhUUpIiILy21i9BtHx6MKJ0wneqrwQz0RPSs4XxwR5QeLUkRERESlAL/g0eOKs8BZWounxvIurftFRPQ0Y1GKiIiIiIiIiEolFpxLNxaliIiIiIjMzJJfovgFjoiISgoWpYiIiIiIniIsOhU9XhpIRFQ8WJQiIiIiIiKzYPGm4Dh/GNGz6Vn5/WRRioiIiIieWs/Kh3oiKjp83yAyHxaliIieYUNHT8DlG1q95eU9nDF/9jQLZERERKUZv8yTOfBupERPDxaliIieYZdvaGET3Et/eeIqC2RDRKYoycWAkpwblR4cR0RETx8WpYiIiIiIKF94hkrhsKBGRGQYi1JERE+53C7RA4ATp84gKNjMCREREdEzr7QW6ixZmC2tx4woLyxKERE9JY4fO4bwgUP1lp84dQZB/T8yuM79o+OLOy0ioqcWvyBSSWDJccjfAX3P6uvBsUCFxaIUEdFT4oFYGZwfioUnIiKikqs4v8zzckvDWEAhKjnUlk6AiIiIiIiIiIiePTxTioiIiIiIiEoUns30dMnr9SzJr3VJzu1pwTOliIiIiIiIiIjI7HimFBERERERERE9c3gmlOWxKEVERCYbOnoCLt/Q6i0v7+GM+bOnWSAjIiIiInoamFI44mT/Jd8zVZRatGgRPvnkEyQnJ6NOnTpYsGABGjZsaOm0iIhKjdyKTydOnUFQ/4/0lscuGYvwgUP1lrNYRabg33MiIiIq6Z7Ws7By26/fJnYt1PaemaLUDz/8gJEjRyI6OhrBwcGYO3cuwsLCcPbsWXh6elo6PSKiUuHyDS1sgnvpLb9/dLzB9g/EymD7y4mrijw3ejbw7zkRERE9DUryGWDmLKg9M0WpOXPmYNCgQejfvz8AIDo6Gps3b8bSpUsxduxYC2dHRFSyHD92zOAZTidOnUFQsAUSIvr/+PeciIiI6OnxTBSlMjIykJSUhHHjxinL1Go1QkNDkZCQYMHMiIhKptzOcMrtjKiCyq3odf7P06j0QjW95bzcjwD+PSciIiJ62jwTRan//vsPWVlZ8PLy0lnu5eWFM2fO6LVPT09Henq68jwtLQ0AoNXqz6NSGJkZGcD9u3rLs7MeIvMZWl4Scyppy0tiTqVleUnMqaQtt2Tf9x4KUPtlveXXk46ikoHl/xz6sUDvwaMnTsWV1Nt6y33dnTB76kST2xdUTu4iYvK2nmVF9fc8K+O+we3nvE5Z6bnHc4sZi+dn25bs+2ndr8L2zf3ifpWEvp/W/SqKvp/W/Sps39wv7teT8WYTDE+VsXtar2Lru9Cfd+UZcPnyZQEg+/bt01k+atQoadiwoV77Dz/8UADwwQcffPDBR5E+Ll26ZK4/fU8l/j3ngw8++OCDDz74KNmPgn7efSbOlCpbtiysrKyQkpKiszwlJQXe3t567ceNG4eRI0cqz7Ozs5GamgoPDw+oVKpiz1er1cLPzw+XLl2Cs7NzsfdXFJizeTBn82DO5vEs5SwiuH37Nnx9fYsxu6dfUf89N/Z6mhIvrdvmfpWubXO/nq6+uV+lq2/uV+nq+2ndL0v2/WSssJ93n4milK2tLerVq4f4+Hh07doVwKMPpvHx8YiKitJrb2dnBzs7O51lrq6uZshUl7Ozc6n5opaDOZsHczYP5mwez0rOLi4uxZTNs6O4/p4bez1NiZfWbVuyb+5X6er7ad0vS/bN/SpdfXO/SlffT+t+WbLvx2OF+bz7TBSlAGDkyJGIiIhA/fr10bBhQ8ydOxd3795V7t5DREREJR//nhMRERE9PZ6ZolSPHj1w/fp1TJo0CcnJyahbty5iY2P1JkslIiKikot/z4mIiIieHs9MUQoAoqKiDJ7eX9LY2dnhww8/1LvkoCRjzubBnM2DOZsHc6bCKqq/58ZeT1PipXXb3K/StW3u19PVN/erdPXN/SpdfT+t+2XJvo2tm18qEd6fmoiIiIiIiIiIzEtt6QSIiIiIiIiIiOjZw6IUERERERERERGZHYtSRERERERERERkdixKERERERERERGR2bEoZSEzZ85EgwYN4OTkBE9PT3Tt2hVnz57VafPgwQNERkbCw8MDZcqUQXh4OFJSUiyUMbB48WLUrl0bzs7OcHZ2RkhICLZs2VJi8zXk448/hkqlwvDhw5VlJS3vyZMnQ6VS6TyqVq1aYvPNcfnyZfTt2xceHh6wt7dHrVq1cOjQISUuIpg0aRJ8fHxgb2+P0NBQnDt3zmL5VqxYUe84q1QqREZGAiiZxzkrKwsTJ05EYGAg7O3t8dxzz2Hq1Kl4/H4VJe04A8Dt27cxfPhwBAQEwN7eHo0bN8bBgweVuKVz3r17Nzp37gxfX1+oVCps2LBBJ56f/FJTU9GnTx84OzvD1dUVAwcOxJ07d8y2D0REREREpZG1pRN4Vu3atQuRkZFo0KABHj58iA8++ABt27bFqVOn4OjoCAAYMWIENm/ejLVr18LFxQVRUVHo1q0b9u7da5GcK1SogI8//hiVK1eGiODbb79Fly5dcOTIEdSoUaPE5fukgwcP4ssvv0Tt2rV1lpfEvGvUqIHt27crz62t/+9XtSTme/PmTTRp0gQtW7bEli1bUK5cOZw7dw5ubm5Km9mzZ2P+/Pn49ttvERgYiIkTJyIsLAynTp2CRqMxe84HDx5EVlaW8vzEiRNo06YNunfvDqBkHudZs2Zh8eLF+Pbbb1GjRg0cOnQI/fv3h4uLC4YOHQqg5B1nAHjzzTdx4sQJrFixAr6+vvj+++8RGhqKU6dOoXz58hbP+e7du6hTpw4GDBiAbt266cXzk1+fPn1w9epVxMXFITMzE/3798fgwYMRExNT7PmT+WRkZGDDhg1ISEhAcnIyAMDb2xuNGzdGkyZN8P333xuMvfHGG3Bxccl13S5dugBArvH27dvj119/LdS6Xbp0gVarxdKlSwuVm7H9UqlUeW67XLlyxfuiPIU4zjjOzOG///7L85hev34dCxcu1IuHhIQgKioKnp6eua7fuXNn/Pzzz4V+PY3F88qtT58+2Lt3b6H3C0Ce8erVqxf7a/O0yWushYSEICYmplDjzNhYMXUcmvI7YGy/gLzHmSnbNuWYGYsb67uwvx8qefxf7GQx169fh6enJ3bt2oVmzZohLS0N5cqVQ0xMDF599VUAwJkzZ1CtWjUkJCSgUaNGFs74EXd3d3zyySd49dVXS3S+d+7cwYsvvogvvvgC06ZNQ926dTF37twSeZwnT56MDRs24OjRo3qxkpgvAIwdOxZ79+7F77//bjAuIvD19cV7772H999/H8CjffHy8sLy5cvRs2dPc6Zr0PDhw7Fp0yacO3cOWq22RB7nTp06wcvLC998842yLDw8HPb29vj+++9L5HG+f/8+nJycsHHjRnTs2FFZXq9ePbRv3x5Tp04tUTmrVCqsX78eXbt2BZC/sXv69GlUr14dBw8eRP369QEAsbGx6NChA/73v//B19fXrPtAeTt16lSuHwTbtWuH2NhYg7HOnTsjKioKV65cQXBwMLy8vAAAKSkpSEhIwMOHD1GuXDm0a9dOJxYfH4/bt2+jTJkyuHHjht66iYmJ8PT0hIjg+vXrBrednZ0NKysrNGrUqEDrJiYmomzZskr/oaGhBcrN2H5ptVoAgJOTk8Ft37t3D5s2bcKlS5cKVcjo3bt3rl8u81MkySturABjSoHF2Pp5fVFq1qwZevfuzXFmxnFm7PU0tViXV9zUIqOx/crty6Wfnx9WrFgBR0dHg8f01q1bSE9PR7169RAWFqYTj4uLw8GDB2FnZwcXFxe99bds2YLr16+jXLlyaN++fYFfT2PxvHJbt24djh8/jrJly6Jjx44F3q8DBw5ApVLlGk9KSsKPP/6Ie/fusWiczyJGnTp10LNnTzg4OOi9nps2bcKNGzdQq1YtdOvWrUDjzNhYMXUcmvI7YGy/jI0zU7ZtyjEzFjfWd1JSEjZu3IiwsDAUFItSJcRff/2FypUr4/jx46hZsyZ27NiB1q1b4+bNm3B1dVXaBQQEYPjw4RgxYoTlksWjy4jWrl2LiIgIHDlyBMnJySU634iICLi7u+Pzzz9HixYtlKJUSTzOkydPxieffAIXFxdoNBqEhIRg5syZ8Pf3L5H5AkD16tURFhaG//3vf9i1axfKly+Pd955B4MGDQIAnD9/Hs899xyOHDmCunXrKus1b94cdevWxbx58yySd46MjAz4+vpi5MiR+OCDD0rscZ4xYwaWLFmCbdu24YUXXsCxY8fQtm1bzJkzB3369CmRx/n27dtwdnbG9u3b0bp1a2V506ZNYW1tjaVLl5aonJ8sSuXnmC5duhTvvfcebt68qcQfPnwIjUaDtWvX4pVXXjHrPlDutmzZgq5du+LFF1/U+yC4Zs0anD59GlWrVkWPHj30PmzlFKRjY2Ph7Oyss90GDRrg+vXreOGFF7Bt2zadmIjAz88P9+7dwz///KO3rlarxfPPPw/g0WeBJ+MtW7bEmTNnUL16dcTHxxdoXa1Wi4oVK8LR0REXL16ESqUqUG7G9svb2xsAkJycbHDbvXr1woYNG6BSqQpcyNi6dSuuXbsGGxubQhVJTCnAmFJgMba+sS9KycnJaNKkCX7++WeOMxT/OMtPwcyUYp2xcWhKkdHYfuX15XLVqlVQqVT4/fff0aBBA71jWrZsWdjb2+N///sfDKlQoQLu37+P//77T+81adSoEdLS0uDi4oL9+/cX+PU0Fs8rt0aNGiEzMxMZGRk4fvx4gffL29sbIpLrdA1Dhw7Fl19+CbVazaJxPoscV69excsvv4yffvpJ7/WsU6cOrK2tYWtri4SEBL3jndc4MzZWTB2HpvwOGNsvY+PMlG2bcsyMxY31PXnyZKxbtw5//PGHwf3Kk5DFZWVlSceOHaVJkybKspUrV4qtra1e2wYNGsjo0aPNmZ6OP/74QxwdHcXKykpcXFxk8+bNIlJy8xURWbVqldSsWVPu378vIiLNmzeXYcOGiUjJzPvXX3+VNWvWyLFjxyQ2NlZCQkLE399ftFpticxXRMTOzk7s7Oxk3LhxcvjwYfnyyy9Fo9HI8uXLRURk7969AkCuXLmis1737t3ltddes0TKOn744QexsrKSy5cvi0jJHBcij94rxowZIyqVSqytrUWlUsmMGTOUeEk9ziEhIdK8eXO5fPmyPHz4UFasWCFqtVpeeOGFEpczAFm/fr3yPD/5TZ8+XV544QW9bZUrV06++OKLYs2XCqZ27doyceLEXGPNmjWTWrVqGYxbW1tL5cqVDcY0Go1s3LhR7O3tc40bek95PG5nZ2cwZm9vLz/99FOe285tXZH/e3/Oq+/ccjO2X3Z2dnnuV0hIiKjVaklLS9OLpaWliZubm1SoUEGys7P14qGhoRIQECD169c3uG65cuWkXLlyuW47r3iLFi3E29tbWrVqVeC8srOzpXz58uLm5lao/QoODpaqVatKcHCwwW1bWVlJ7dq19WIiHGe5MWWcGXs9jY0VU8Zh/fr1JSAgQNq0aVPgvPKzX56enuLp6WkwrtFo5LXXXpNGjRrpxUSMH/O84hqNRjZv3iwajabIt52fvn/99ddi67tx48a5jjVTX09T3rOMjUNTfgeM7Vde4yznPa1OnTp6MZHifb2e1nFY3GPclL7PnDmTa8wYzilVAkRGRuLEiRPYs2ePpVMxqkqVKjh69CjS0tLw448/IiIiArt27bJ0Wrm6dOkShg0bhri4OIvNp1NQ7du3V36uXbs2goODERAQgDVr1sDe3t6CmeUuOzsb9evXx4wZMwAAQUFBOHHiBKKjoxEREWHh7Iz75ptv0L59+xJ/mdWaNWuwcuVKxMTEoEaNGjh69CiGDx8OX1/fEn2cV6xYgQEDBqB8+fKwsrLCiy++iF69eiEpKcnSqdEz5s8//0SfPn1yja1bt87gvGIA4Obmhn/++cdgzNvbG7/99pvOmZWP02g0sLGxyTUvBwcHnRsWPM7V1RVHjx7Nddt5rQsALi4uSE9PzzWeV27G9svJySnPvpOSkuDl5aV3ZgwAODs74969e7h3757ef2MBYO/evVi9ejV69OhhcN3bt29DRHLddl7xxMREfP/99+jbt2+B81KpVLhx4ways7MLtV/Hjh3DTz/9hPDwcIPbdnd3x+nTp/ViAMdZbkwZZ8ZeT2NjxZRxeOLECfzwww8GL1M3dZypVCqkpaVBRAzGvb29ERQUhJ9//lkvBjyanuPBgwcGYwDg6OgIW1tbgzFvb29s2LBBOWPmScZeT2PxvHLz9vbGihUrEBAQUOB1gUfjOK++Dx48CH9//2J5PU15zzI2Dk35HTC2X3mNM5VKBW9vb5w6dUovBjy66dCKFStyHSt5jTMg77Fi6jg05XfA2H4ZG2embNuUY2YsbqzvzZs35/q7ZwyLUhYWFRWFTZs2Yffu3ahQoYKy3NvbGxkZGbh165bOH+mUlBTltDpLsLW1VU7hrlevHg4ePIh58+ahR48eJTLfpKQkXLt2DS+++KKyLCsrC7t378bChQuxdevWEpn341xdXfHCCy/gr7/+Qps2bUpkvj4+PnoT21WrVg0//fQTACi5paSkwMfHR2mTkpKic0mUJfz777/Yvn071q1bpywrqb9/o0aNwtixY5UPBrVq1cK///6LmTNnIiIiosQe5+eeew67du3C3bt3odVq4ePjgx49eqBSpUolNucc+cnP29sb165d01nv4cOHSE1NLTHvI/RIxYoVsXnzZlSpUsVgLDo6OtcPVHXr1kV8fDw+//xztG7dWucShRdffBFz585Fw4YN8fPPP+tdvnDnzh3Y2toaXDdnThwABuM1a9bEtGnT0KZNG/zxxx8FWjen7/T0dAwbNizXeG65GduvnC8juW07IyNDmevEkLwKGa6urti5c2euH36NFUlMKcCYUmAxtr6xL0qNGzfGL7/8wnEG84wzIO/X09RiXV5xU4uMxvYrry+X77//PkaMGAGNRmPwmN+4cQMPHz7Eyy+/bPCSrLS0NKjVaoOvSUBAAL766iu0aNGiUK+nsXheuTk5OWHVqlXo0KFDofYrNTUVIpJrPDMzM9e5Llk0NqxVq1ZYsWKFwdczMDAQq1atQq1atTB//vwCjTNjY8XUcWjK74Cx/TI2zkzZtinHzFjcWN+xsbGFv8FPoc6vIpNlZ2dLZGSk+Pr6yp9//qkXv3XrltjY2MiPP/6oLDtz5owAkISEBHOmmqeWLVtKREREic1Xq9XK8ePHdR7169eXvn37yvHjx0ts3o+7ffu2uLm5ybx580psvr169ZKmTZvqLBs+fLiEhISIyKPx7u3tLZ9++qkST0tLEzs7O1m1apVZc33Shx9+KN7e3pKZmaksK6nH2d3dXe9ysBkzZiiXFJXk4/y41NRUcXFxkS+//LLE5YwnLt/LT36nTp0SAHLo0CGlzdatW0WlUimXhFLJsGbNGrG2tpbOnTvLvHnzZPXq1bJ69WqZN2+e1K9fXwBI/fr19WIvv/yy2NraSp8+fcTHx0dUKpWo1WpRq9WiUqnEx8dHevfuLcHBwcqltTmX2QYHB8sPP/wgH3/8ca7rzpo1K894+/btC73urFmzZPXq1YXOzdh+5bXt8PBwcXNzkzlz5sixY8ckOTlZkpOT5dixYzJnzhxxcHAQKysrGTp0qGzcuFH2798v+/fvl40bN0rDhg0FgISHhxtcV6PRiEajyXXbecXbtGkjKpVK2rZtW+C8hg4dKtbW1uLg4FCo/WrevLkAkBYtWhjctr29vXTp0oXjzEzjzNjraWysmDIOu3XrJgCkYcOGRT7Ohg4dKjY2NmJtbZ1nvGLFirke871790qPHj3E399fbG1txdbWVvz9/aVHjx6yb9++PF+TYcOGFfr1zE88r9ymTJlS6HX37duXZ7x///65jjVTX09T3rOMjUNTfgeM7ZexcWZvby8DBgzI9TWZMmVKoceZsbFi6jg05XfA2H4V57ZN/f0ype/CYlHKQoYMGSIuLi6yc+dOuXr1qvK4d++e0ubtt98Wf39/2bFjhxw6dEhCQkKUL/mWMHbsWNm1a5dcuHBB/vjjDxk7dqyoVCrZtm1bicw3N4/PKSVS8vJ+7733ZOfOnXLhwgXZu3evhIaGStmyZeXatWslMl8RkQMHDoi1tbVMnz5dzp07JytXrhQHBwf5/vvvlTYff/yxuLq6ysaNG+WPP/6QLl26SGBgoDLXlyVkZWWJv7+/jBkzRi9WEo9zRESElC9fXjZt2iQXLlyQdevWSdmyZXXmuSqJxzk2Nla2bNki58+fl23btkmdOnUkODhYMjIySkTOt2/fliNHjsiRI0cEgMyZM0eOHDki//77b77za9eunQQFBUliYqLs2bNHKleuLL169TJL/lQweX0Q/PLLL/P1Yev8+fOyb98+2bdvn5w/f15n+xkZGXLlyhW5cuWKMsYfl9e6xuKmrGtqbsbWzS1uSiGjd+/eJhVJTCnAmFJgMba+sS9KRfF6cJwV3etparHOlGKcqftlLJ6fY25MXusX9vXMb7yweZmiOF/Pp7Fo/Ph7WnG+nsU5Dk3NzRSWOmam9l1QLEpZCACDj2XLlilt7t+/L++88464ubmJg4ODvPLKK3L16lWL5TxgwAAJCAgQW1tbKVeunLRu3VopSJXEfHPzZFGqpOXdo0cP8fHxEVtbWylfvrz06NFD/vrrrxKbb45ffvlFatasKXZ2dlK1alVZsmSJTjw7O1smTpwoXl5eYmdnJ61bt5azZ89aKNtHtm7dKgAM5lESj7NWq5Vhw4aJv7+/aDQaqVSpkowfP17S09OVNiXxOP/www9SqVIlsbW1FW9vb4mMjJRbt24pcUvn/Ntvvxl8P46IiMh3fjdu3JBevXpJmTJlxNnZWfr37y+3b9822z4QlXSmFDJMLZKYUoAxtfhjyhclKrjifD2LcxwW5zjLT5wKjkVjjjMqOiqRPC4CJSIiIiKjNm7ciLS0NPTr108v9sUXX+C///7DpEmTCryusbgp65qam7F1jcWp4DjOCr5tKjhjx/SDDz5AcnIyli5dWuD1TX09TcnN1P0yFqeCy+s1MWWcGYsX5zgzdb+Kc9vF+ftn0u+HpatiRERERCXBuHHjpH///gWOiYhUqVJF1Gq1wVirVq0kMDCwUOsai5uyrqm5GVvXWHzDhg3y7bff5hpftGiRfPTRR4Va15S4KXmZur6xbXOcFTxenK9ncY7D4hxnxuLGjmm/fv2kZcuWucbzWt/U19OU3EzdL2Px4nw9i3McmpKbKeNMJO/XxJRxZixenOPM2PqmjjNLHTNT+84Lz5QiIiIiAhAREYFLly5hx44dBYpR4VStWhXnzp1DVlaWwXjr1q1x4cIFnD9/vsDrmhI3JS9T1ze2bSq44nw9i3McFuc4y0+cCq44X8/iHIem5MZxRkWBRSkiIiIiIiIiIjI7a0snQERERFRaHDhwAAkJCUhOTgYAeHt7IyQkBA0bNsx1nZs3b+KXX35B3759oVar9eLZ2dm4dOkSsrOz4efnB2tra2RkZGD9+vVIT09Hhw4dULZsWZ11WrVqhWXLliEgIEBvexcuXMBff/0FHx8fVK5cGWq1GjY2NgCAv//+G0uXLsXFixcREBAAX19fvPHGG3BwcDCY+7Fjx5CUlIQWLVqgUqVKOHnyJBYtWoTs7Gy88sorCAsLw44dO7Bnzx5cvXoVarUalSpVwssvv4zKlSvn+7iSLo4zjrPilpGRgQ0bNuiNs8aNG6NLly6wtbXNdd2UlBR8+eWXGDBgAFxdXVGmTBmdeHJyMlavXo1+/frB3d0d//33H7755hukp6eje/fuqFatmt42K1WqhK1bt+q9niKCnTt3KmOtRo0acHJyUsbq77//jujoaFy8eBH37t3D+PHj0a1bt1xz37RpEw4cOICwsDA0adIEO3bswKeffors7Gx069YNr7/+OlatWqU31rp27YrWrVvn+/jS/ynsWDM2zjIzMxEbG4syZcqgTp06+RprRTHOAgIC4Obmhvfff9/geyNg+jgz5Zh9/vnnCAsLy/cxKchxadasGX788cci//3gmVJERET0zMjrg2KnTp3g4OBg8MNWrVq1EB4ejr1798Lf3x9eXl4AHn0AvHjxIpo0aYKffvoJnp6een3u3bsXTZs2hUajgbOzM9566y18+OGHsLKy0onn9Ldt2zZ0794dZ86cQWZmJuzs7DB79mz4+voq2+zWrRvmzZuHzZs3IyIiAj169MD9+/fx+uuvY/369RARqFQqODs7Y8GCBejbty/27t2L1q1bo0qVKqhWrRr+/PNPHDlyBI6OjujduzcGDhyI4OBgpY9169bhtddeg6urK9LT07F+/Xp0794d9evXh5WVFeLi4hAYGIgLFy5ArVYjOzsbQUFBuHz5Mq5fv46RI0di9uzZhSqwAI+KLBs3bsQbb7yhFytMgQXIvchSlAUWwHiRxcbGxmCBxcXFhePMAuMsr2JeVlYWEhIS0KhRI5PHGfB/Y83DwwO1atUq1nGWWzGvTp06eOutt3DlyhUEBwfrjLPExERUqFABW7ZswfPPP2+w3+3bt6NNmzZQq9VQqVTo3bs3vvjiC5QpUwYHDhxAmzZtoNVq4ebmhri4OHTv3h3W1tbIzs7GxYsXMWzYMPj5+elsc+TIkRg9ejQ2bNiAiIgIjBkzBqmpqejQoQMOHDiAsmXL4saNG7C1tcXXX3+NPn36YOPGjejWrRs6deqEatWqYdasWQCAunXrYuzYsXjllVd0vrx/+eWXiIqKQp06dXDu3DksWrQI77zzDnr06AErKyssX74cDg4OsLGxgZ2dHf73v/+hQ4cO+O+//3Do0CF069YNMTExOHz4MIvG+Swaq1QqhIWFFWqs5TXOAGDLli3o0KEDVCoVXF1ddcZaamoqtFqt3lgrinH2559/Yv369VCr1WjVqhXefPNNnbFm6jhr27Ytzp49W6hj9v333+P11183eExM/f3777//YGVlBVdXV9jb2+f6+2FtXYjzngo1ExURERFRKRMdHS3W1tZSr149cXZ2lhUrVoiTk5O8+eab0rNnT1GpVFKmTBnx8/MTlUolHTt2lODgYLGyspLy5ctLo0aN5MyZM3rbPXTokDRs2FC6dOkiaWlpeo9XX31VAMjatWvlq6++koCAAOnYsaOkp6eLiEi7du0EgPzxxx8yfPhwqVatmnTp0kUyMjJEpVIJAAEgKpVK75GzXOTRZOwVKlSQHTt2yN27d2XPnj2iVqtl8ODBIiLSvHlzGTFihE7uAMTf31+CgoJEpVJJjRo15PPPP5f//vtPXnzxRZk2bZqIiKxatUpcXV1lypQpyrp169YVFxcXSUtLkwcPHkhUVJT069dPRETi4+PFzc1NKlWqJCqVSgICAqRhw4bSsGFDCQgIEJVKJU2bNpWUlBSDr1VaWpq0adNGAIinp6dMnDhRHj58qMT37NkjAEStVsvzzz8v58+fl3r16omjo6M4ODiIk5OTLF68WDZu3KjzsLKykpdeeklWr14tGzdulHv37kl4eLio1WpRqVSiVqvF1dVVVqxYofRjZ2cntWvXlh49ekhQUJAAEEdHRxk0aJDs379fL/effvpJrKysxMPDQ8qUKSNxcXHi6uoqoaGh0qJFC+U1s7a2FrVaLfXq1RNvb2+xsrKSF154QUJCQjjOzDTOHh9LGo1Gb6ydOXNGKlSoIAAKPM4WLlwo7du3l9WrV4uI6I01AFKjRg25fft2kY+zsLAwUavV8txzz4lardYbazn7k5aWprfdtLQ0adGihYSEhMixY8cMPpo1ayYA5ODBgxIXFyf16tWT+vXrS2pqqoSGhkqfPn0EgHzyySdSoUIFefPNN3XGgr29vVSsWFHnoVKppHz58spYEREZMmSIVK9eXc6fPy8iIpcuXRK1Wi29e/cWEZHg4GD5+OOPlW2rVCrp06ePuLi4iI2NjXh4eMiwYcPk+PHjIiJSvXp1WbJkiYiI7NixQzQajSxatEhZv1atWuLq6irZ2dkiIvLxxx9L+/btRUTkzz//FD8/P/H39y/UWMtrnD0eN/SeZmtra3Cs5WecmfqeZso4s7KykoCAAOU960l79+7Nc6zlNc5ERIlrtVq9saZSqcTBwUEcHByKfJzlbN/f31+6dOmiN9ZMHWcajUaqVKlSqGNWq1atXI+JiGm/fy1btpSyZcvKW2+9ZTDvihUryocffqiXc36wKEVERETPhLw+KLZv315atGghVatWFRH9D1sqlUr50v2knA/+OV8onnw8/oVeROT69evSsGFDadu2rTx48EA8PDyU+J07d0SlUsnvv/8uIo8KCY0bN5by5cvr9GltbS0nT54UlUqlfAmqWbOmxMTE6LTTaDRSsWJFERHx8vKSo0eP6uXu4OAgIo+KHkOGDBFXV1exs7MTKysr+e6770REJDs7W2xsbOSPP/5Q1i1TpozY29srz+/cuSM2NjbKB+kGDRqIRqMxWGA5c+ZMngWWt99+WykOFrTA8uDBg1yLK48XX9RqdZEXWEQkzyJLjx49pGbNmlKrVi2DBRYA8v777+sdr5zXiuOsaMeZsWJely5dJCwsTAAUeJwZK+Y5OjqKn5+fjB07tsjHmUjexTw7OztxcXGRuXPn6h2znNcrt30zNNYePHggnTt3lrp164qrq6vs3r1b1Gq1ZGRkiFqtlsTERKVteHi42NjYyKlTp3T6NDTWqlSpIhs3btRp5+joqIxTT09POXbsmE7eiYmJ4uDgICkpKTJr1iypWrWqqNVqadCggdjY2MjJkyeV9jY2NkrBSkTE3t5eNBqN8jw9PV1sbGyUYx4SEiJ2dnYsGhegaAxAxowZo3e8cl6vx8dTQcbZjRs3xNXVVYk/OdbeeustqVKlinh6eur0WRTjLCf3nPelJ8eaWq2WmTNnilarFZGCjzNbW1vx9fU1+ZgV9e+fg4ODLF++XLn73pN5b9iwQfk7UFAsShFRrn777TcBIDdv3rR0KkREJrO3t5d///1Xef74B0UHBwfZsWOH8sX5yQ9bTk5O4u3tbXC7zs7OMnjwYHF2dpadO3fqPWxtbXU+WIuIaLVaCQkJkVatWolGo9GJlylTRv766y/l+aRJkwSA/PLLL8qyxz9AXrt2TUREypYtKydOnNDpp3HjxmJtba38/ORtvXP+O/q4+/fvy3fffafkXbFiRUlNTRWVSiW//fab0s7NzU3Kli2rPL93756o1Wq5ceOGiDz6UG9jY2PwmOX0nVeBJScmUrACi4hIo0aNRKPR6J21YG1tXawFlu7du4tGo5ELFy6IiH6RxdnZWbZs2SJlypRRcn+8wJJzpp4hHGePFOU4M1bMK1eunGzfvl3UanWBx5mxYp6jo6MsWrRIXnjhhSIfZyJ5F/N8fHxk5MiRUqVKFYPHzMnJSVxcXOSff/4x+HhyLImIZGZmSteuXUWtVsuWLVuU390yZcrI33//rbT7999/xcbGRvz8/GTBggUGj1nOWPP09NQba6GhoWJlZSUiImFhYTJv3jydY/bZZ59J5cqVddbZvXu3REREiEqlUooBly9fFpVKJZs3b1balS1bVqeIcfPmTVGpVEpxIa+xxqKxKLk//p7m6uqql3cODw8Peffdd6VcuXIFHme1a9cWe3t7vfe0x8fal19+KQCKfJzlHLOc4szjdu/eLQ4ODqLRaMTR0bFQ48zT01N5PzX1mBXl75+vr69s2rRJ7OzsDOZ9/vx5JVZQLEoRPeUiIiKUD/fW1tbi6ekpoaGh8s0330hWVlae66anp8vVq1eV00sLYvDgwaJWq2XNmjWFTZ2IqEhVqFBBdu/eLSL6X0h8fX1lyZIlUqFCBRHR/7DVt29fASDr1q3TOaU+LS1NatasKW5ubhIVFWWw34oVK4qhGRNu374tISEhesWEL774QulXRCQpKUk8PDykevXqMnjwYLl7967OB8i33npLRowYIZ6enrJt2zadPpYtWyYqlUo+/PBDWbBggZQtW1YmTJggK1euVIoQEydONJh33759pU6dOvLyyy9L586dJSwsTBo1aiSnT5+WM2fOSNmyZaV8+fJy584dycjIkOHDh8vzzz+vrO/i4iLu7u65vh45l0AZKrDY2dnJjBkzlC+2IgUrsFy8eFGsrKzEz89Pr8hSnAWWnMvzfHx8RET0iizlypWT1atXKwXOJwssHGfmHWfGinlqtVp+//13nQJLfseZsWJeq1atZNy4cWJvb1/k40wk72LexIkTxcXFRaysrOTYsWOSnJwsycnJcuzYMZkzZ45YW1tLy5Ytcz2mlStXNjjWMjMzpUyZMuLp6akcs02bNsm9e/eUNvv375cKFSrI//73P2nVqpW0a9dOrl69qnPMOnToIK+88oq4ubnpHFeRR2frqFQq6devn0ydOlXKlCkjffv2lenTp4tKpRJbW1tZtmyZwbwHDRoknp6eMm3aNGnYsKFERERI1apVZcuWLRIbGyuurq7i7e0tp0+flvPnzyuXt+VwdnaWcuXKGdw2i8aPPPmeFhUVJQBkzpw5emOtSpUqYm9vn+slX3mNs65duyrv5zkMjTUfH58iH2f9+vUTAHqFqhyRkZHy3HPPSZcuXQo1zvr16ydqtbpQxywwMFDnmBXl719ERIQEBQWJh4eHwbx37tyZ6z9VjGFRiugpFxERobzh/O9//5OkpCSZPn26lClTRtq3by+ZmZkG18vIyCh0n3fv3hVnZ2cZO3astGvXrtDbISIqSpGRkVK5cmWDX0hCQ0PF0dFRunXrZvDD1rZt25S5PdRqtWg0GtFoNKJWq8XKykqaNGkiDx48MNjvwIEDpXr16gZjWq1WPD099b6sPG7mzJnSoUMHuXfvnrz11ltSuXJlsbKykpMnT0rz5s2lRYsWyuOrr77SWXfq1KlSt25dadSokd6p/uXLl9f5D/uTkpOTpU2bNlKmTBkJCwuTW7duSVRUlHJGQMWKFcXf31+sra3FxsZGXF1dJS4uTlm/VatW4uzsbLDAsm7dOtFoNNK4cWODfVepUkUWLFigd1wKUmDx9vaWI0eO6BVZirPAIiLSpUsX8fX1le+//16vyNKmTRspW7asdO3a1WCBZffu3WJvb683zlQqFcdZMYwzkbyLeXZ2dsp8OSIFG2fGinn79u1TzjIp6nGWn2LeO++8ozPvUM7PPj4+8vrrrytzEBkydOhQqVmzpsHYpEmTpF69ejoF5cd98MEH0q1bNxF5dObNjBkzlPmHTp48KW+88YbO44cfftBZf9SoUfLSSy9Jz549xcnJSRlnNjY2AiDXgpTIo7N4Bg0aJDVr1pTBgwdLenq6fPLJJ8r7SePGjaVevXrKMQkICJDDhw8r64eFhYm7uzuLxgUYZ/v375cyZcqIj4+P3lhzc3OTHj165Pp65TXOMjMzpUqVKnm+p+WMtaIeZ40bN87zPc3UcbZ27Vrp3LlzoY7ZmDFj5O233zZ6TEQK/vuXkpIiPj4+yll/AQEBkpSUpJP3/Pnzc+07LyxKET3lIiIipEuXLnrLc67zzvlgCUC++OIL6dy5szg4OMiHH36oc/leWlqaaDQa+fXXX3W2s27dOilTpozcvXtXWbZ8+XJp1KiR3Lp1SxwcHOTixYs662RmZsq7776r/Hdz9OjR0q9fP508s7KyZMaMGVKxYkXRaDRSu3ZtWbt2bdEdGCJ65uT1QRGAODs75/khcf78+ZKWliY7duyQmJgYiYmJkR07dhicjPRxqampev+5fpxWq5WdO3fmGj9//rxcuXJFeb5x40YZPnx4npM35/j777/l0qVLIiJy7do12b9/v+zbt0+57Oeff/4p8Nmwf//9txw/flwyMzPl7t27snXrVvnll1/k+vXrOu0ePHggb7/9tsFCnq2trTRv3lw+/fRTg328++670qlTJ5k8ebJerCAFFhHRK7I0aNCg2AosInkXWQCIjY1NrgWWZcuWydixYyUtLU3i4+OVcRYfH5/rOMt5/XIbZznx3MZZTtzQOBs6dGie+5qzbm7jLGeC3H/++cfg2dl5jT1j4yxn3fyMs88++yzXfvIq5g0YMEACAwNzLbDkNc7yU8wbNGiQzhfeohpnuRXzHi9W5Iy18+fPy759+3ReL2MyMzPzfN/LzMyUf/75x2Ds7t27eoXVQ4cOydy5c5UJrPNy584duX//vog8GgPJycly5coVk/6Zev/+fZ0C0J9//qmMvcflNdaMFY0HDBjwTBaNc8aZiBR4rJkyzkT0x1ppGWePK8zvZ14M/f4lJSUV6LgcP35cjh8/rjNRv6lUIiJ53p6PiEq1N954A7du3cKGDRv0YnXr1oWvry9+/fVXqFQqeHp64uOPP0bz5s1hbW2N8+fPo2XLlrh58yZcXV3RvXt3aDQarFixQtnGq6++Cnt7e51lzZo1Q48ePRAZGYlXX30VderUwcSJE5X49OnTMWfOHHz99deoVq0a5s2bh5iYGLRs2VLJc/r06fj+++8xd+5cVK5cGbt378bbb7+NrVu3onnz5sV2vIjo2fPgwQNkZmbCyckJ586dQ3p6OqpWrVq42xqTHq1Wi6SkJJ3bp9erVw/Ozs65rnPz5k1cuXIFNWrUMBi/ffs2Dh8+nOvfgwsXLkCj0cDHx0dZ9vPPP+O3337DuHHj4OnpmWvf58+fh62tLSpUqIDr16/j/PnzyM7Oho+PDypWrIh///0X/v7+UKlU+dl9ne3eu3cP/v7+SExMRHp6Oho1aqR3m3dDbG1tcezYMVSrVq1AMVPjxbntou67MOMMMG2sGRpnv/zyC3bs2GF0nAH/N9bs7OyKfJxVrVoVGRkZ2Lt3b4HGGhmn1Wpx6NAhpKSkALDMe1p+38+A4nlPe3Kc7dmzBxkZGRxnReTq1atYvHgx9uzZg6tXr0KtVqNSpUro2rUrwsLCsGTJEoOxN954A9euXct1XVPjxvq2srIq1P6yKEX0lMurKNWzZ0/88ccfOHXqFFQqFYYPH47PP/9cie/cuVOnKLVhwwa8/vrrSElJgYODA7RaLby8vLB+/Xq0a9cOAHDu3DnUqFEDV65cQdmyZbFhwwaMHDkSf//9t/LHztvbG++//z7ef/99AEBWVhYqVaqEoKAgbNiwAenp6XB3d8f27dsREhKi5PPmm2/i3r17iImJKcYjRkTPqkuXLuHDDz/E0qVLDcbGjx+PwYMHw93dHdWrV9eJ37x5E5999hl69+6tF3vw4AG+//57VK1a1eC6xuLFue3i7nvu3Lnw8vJCSEgIqlatijNnzmDevHlIT09H37594ePjg/379xuMv/TSSwBQqHVzi8+dOxcZGRnKths3bowqVarkuq6xuLG+Da2f135ptVr4+/vjSfPmzUOVKlVgb28PADpfXOfNm4e+ffvixIkTerH8xotz28Xdt4eHBwBgzpw5AIC7d+9izZo1+Ouvv+Dr64uePXsqbZ6M+/j4oFevXvmOm7JuQbZtat7G1n/48CF69OiBF198EQCwYsUKREdH4+LFiwgICEDHjh3Rs2dPBAYGFjju4eGBAQMGYOjQoUW+bUv37ejoiAkTJii/w49799138dprrxmMmRovzm0Xd98AsHDhQhw4cAAdOnRAz549sWLFCsycORPZ2dnw9/eHp6cnOnXqpBfr1q0bvLy8kJSUZHBdY/Hi3HZx9h0SEoL169fj+eefh729PRISEtC7d29kZGRg8+bNuH//PmrXrg1HR0ed2NatW+Hv749///3X4Lqmxo31Xb16dcTGxsLJySnXsZCrIjvniohKpNwu3xMRee2115RTiQHI999/rxN/8u576enp4ubmJqtWrRIRkaVLl4qnp6fOaadjx46VTp06Kc/T09PF3d1dtm/fLiIit27dEgCya9cunb5eeeUVJc8TJ04IAHF0dNR52NjYSMOGDQt9LIiI8nL06NFcL9PZuHGjMo+CWq2WZs2ayeXLl0VE5OzZs1KhQgXlrkvNmjXTuQxq7969euvmN16c2y7uvmNiYgSAuLu7i0ajkS1btki5cuUkNDRUWrVqJWq1WmxsbAzG69SpIwDEycmpwOtactum9g1Ann/+eZ1LcVq0aKFc+lemTBlxdXXVizVo0ECJG1rXWLw4t13cfTs4OEjTpk1F5NHE4xUrVhQXFxdp0KCBuLu7i5WVlXIpbkHjarVaypYtK+fPn8/XugEBAfmOF3TbRdm3lZWVuLq6yvnz5+Wrr74Se3t7GTp0qCxevFiGDx8uarVa3nvvPRGRAsfLli0rGo1GvvnmmyLftqX7znm/q1y5snz88cdy9epV5f0u533QUMzUeHFuu7j7njp1qjg5OUl4eLh4e3vLxx9/LB4eHjJt2jQJDQ0VlUol1apV04vNmDFDHBwcxNbW1uC6xuLFue3i7tva2lqaN2+uHMMVK1ZIcHCwiIgEBweLt7e3DB06VC+Wmpoqjo6OyvOijhvru27dukqsoFiUInrK5VWUqlWrlnTs2FFEHhWl1q9frxN/sigl8mj+g86dO4vIo9ulvvvuu0rs4cOHyqR8VlZWygOA9O7dW0TyV5Tav3+/AJCdO3fKuXPndB5Pzk9FRJRfGzduzPXxwQcfyMCBA0WlUhmM16pVSwDI9evX5dy5c9KxY0cJDAyUf//9V7p27ap8CH0yJiLSvn37XNc1Fi/ObRd33/Xr11cm9l21apW4ubnJBx98oLwe5cuXV26p/WQ8JCREGjduLG3atCnwupbctql9N2/eXOzt7SU+Pl5n7FpbW8vw4cMlMDDQYOzkyZMyc+bMQseLc9vF3ffj8+H06dNHGjduLLdu3RKRR5NIA5BXXnmlUHGVSiXNmjWTXr16laptG4vb29tLkyZNpFevXhIUFCRLlizROba2trbKZNUFjdvb28vcuXOlevXqRb5tS/etUqnE399fhg0bJmXLlhUbGxt5+eWX5ZdffhGVSiXbt283GMvKyjIpXpzbLu6+n3vuOfnpp59E5NE/fqysrJR/gj/33HMyevRoef755/ViIiLe3t7KnUoLGi/ObRd337a2thIQEKA8z8rKEhsbG0lOThZ7e3v59ttvxdfXVy8mImJnZyeenp4G1zU1bqzvbdu2KbGCYlGK6ClnbKLzpUuXikj+i1I7d+4UGxsbOXHihKjVatm/f78S+/nnn6VMmTJy9OhRZRK848ePy6pVq0Sj0Sjb8fLy0pnc9uHDhxIQEKDkqdVqxc7OTr777rsiOQZERCL/9x/dJyd6zTlbI+eRW/zxSWizs7Pl7bffFn9/f/Hw8JDffvtNOcvq8djff/8tZcuWzXVdY/Hi3HZx950zgbPIow+v1tbWOpPHlylTRjw8PAzGnZ2dZfPmzeLl5VXgdS25bVP7Pn78uLi7u8sLL7wg7733njKpbk4B5sCBA7nGRMSkeHFuuzj7frwoValSJb07j6lUKuWLUkHjKpVKNm3aJH5+fqVq28biHh4e8s0334ifn594enrK0aNHddZ1c3MTOzs7EZECxz08PGTDhg1ib29f5Nu2dN8qlUo0Go2IPLpL9Q8//CBhYWHKP2CHDRsm586d04v5+voKAOUzc0Hjxbnt4u7b2tpa5x/ROd8hRB4VEffu3SsODg56MRFRJpI3tK6xeHFuu7j7Ll++vDIORUSuXLkiKpVK7t27JwEBAbJ27Vpl/cdjOeva2toaXNfUuLG+L1y4oLNfBaEu+AV/RFTapKenIzk5GZcvX8bhw4cxY8YMdOnSBZ06dUK/fv0KtK1mzZrB29sbffr0QWBgIIKDg5XYN998g44dO6JOnTqoWbOm8njttdfg6uqKlStXAnh0/fnMmTOxceNGnD17FsOGDcPNmzeVOaecnJzw/vvvY8SIEfj222/x999/4/Dhw1iwYAG+/fbbojswRPRM8fHxwbp165Cdna338PX1xZw5c6BWqw3GHR0ddbalUqmwePFidO7cGampqbh06ZLBWPPmzXHv3r1c1zUWL85tF3ff2dnZSkytVkOj0cDFxUWn/e3bt3ONlylTBmlpaYVa15LbNqVvJycn3Lt3D0lJSbh+/Trq16+PEydOKH8fGzRokGvM1Hhxbru4+875+cGDBzqTjue4ceNGoeM+Pj64fv16qdt2XvH27dtj27ZtuH79Opo3b44ff/xRZ72AgAA4ODgAQIHj7du3x7Rp0/D8888X+bYt3TcAZb4pGxsbvPbaa4iNjcX58+cBAD/99BOqVKmiFxs0aBAAKPOkFjRenNs2R98tWrQA8Gje2aysLJw6dQrAozlmt2/fDk9PT70YALi4uCiTxxc0XpzbLu6+69evj+zsbMTGxuK3335Dnz590Lx5c9jb26Nr164YNWoU3Nzc9GLAo/dKAAbXNTVurO+zZ8+ifPnyKJRClbKIqNSIiIhQ/vtvbW2tzF2xdOlSndszI59nSomIjB49WgDIpEmTlGXJyclibW0ta9asMZjHkCFDJCgoSEQe3cI1KipKnJ2dxc3NTcaMGSPdu3eXnj17Ku2zs7Nl7ty5UqVKFbGxsZFy5cpJWFiY3mV/RET51blzZ5k4cWKuscGDB+d6S+4aNWpIbh+bPD09xcHBweB8VJGRkWJlZZXrdo3Fi3Pbxd13zllpOZ689fVzzz0nXl5eBuO1a9eWTz75RLkMriDrWnLbpva9e/duJSby6PI/Ly8vUavVyllD+YmZGi/ObRd13yqVSmrVqiVBQUFSpkwZ+fHHH3Xaq1Qqsba2LlRcpVJJpUqVxMbGplRt21j88uXL4u3tLba2tjJy5Eixt7eXpk2byqBBg6RZs2ZiY2MjXl5e0qxZswLHg4ODBYDUrFmzyLdt6b4ByMqVK/XGZM7xTk5O1jtj7fH4Dz/8YDBmLF6c2y7uvsePHy8uLi7y5ptvSmBgoIwdO1b8/f1l8eLF0qFDB1Gr1VKzZk29WHR0tDg7O4u9vb3BdY3Fi3Pbxd13+fLl5YUXXhBra2tRqVTSuHFjOX/+vIg8ujS3WbNmyt/fx2MiIhs2bJCQkBCD65oaN9b31q1bc/0eaAyLUkRkcVlZWfLCCy/IhAkTLJ0KET3Fdu/eLVu2bMk1tn79etm5c6fB+OTJk3Um/3zcjBkzxN/fP9cCTc4XpdzkFS/ObRd33zlf4nITFhYmbdq0MRhbvHixdO/eXQYOHFjgdS25bVP7HjdunF7s0qVLsmHDBrlz545e+7xipsaLc9tF2ffkyZN1HrGxsTptGzduLDVr1ixUfPLkyTrx0rLt/MSjoqKkWrVqUr16ddFoNMo8Nr1795aDBw/KzZs3ZcyYMYWK79ixo9DrluS+fX195b///jM4JitWrJhrzNR4cW67uPvOysqS6dOnS6dOnWTGjBmSnZ0tq1atEj8/P3F3d5cXX3xR2rdvrxfz8PCQiIgI+fDDDw2uayxenNsu7r7feOMNuXPnjty/f19u375t8LjmFSvuuLF1C0MlIlK4c6yIiArn33//xbZt29C8eXOkp6dj4cKFWLZsGY4dO4Zq1apZOj0iIiIiIiIyA84pRURmp1arsXz5cjRo0ABNmjTB8ePHsX37dhakiIiIiIiIniE8U4qIiIiIiIiIiMyOZ0oREREREREREZHZsShFRERERERERERmx6IUERERERGRmezcuRMqlQq3bt2ydCpERBbHohQREREREVEBvPHGG1CpVFCpVLCxsYGXlxfatGmDpUuXIjs7O891GzdujKtXr8LFxaXA/b711luwsrLC2rVrC5s6EVGJwqIUERERERFRAbVr1w5Xr17FP//8gy1btqBly5YYNmwYOnXqhIcPHxpcJzMzE7a2tvD29oZKpSpQf/fu3cPq1asxevRoLF26tCh2gYjI4liUIiIiIiIiKiA7Ozt4e3ujfPnyePHFF/HBBx9g48aN2LJlC5YvXw4AUKlUWLx4MV5++WU4Ojpi+vTpOpfvabVa2NvbY8uWLTrbXr9+PZycnHDv3j1l2dq1a1G9enWMHTsWu3fvxqVLl3TWefjwIYYOHQpXV1d4eHhgzJgxiIiIQNeuXZU22dnZmDlzJgIDA2Fvb486dergxx9/LLZjRERkDItSRERERERERaBVq1aoU6cO1q1bpyybPHkyXnnlFRw/fhwDBgzQae/s7IxOnTohJiZGZ/nKlSvRtWtXODg4KMu++eYb9O3bFy4uLmjfvr1S+Moxa9YsrFy5EsuWLcPevXuh1WqxYcMGnTYzZ87Ed999h+joaJz8f+3dOUhjWxzH8Z+JS+FCBEVFEBSLaKO4FooWKoLaCOKW4NLYiEVAEC1sFAOWJo2FK2gniBARkYCFIAhaqFi4WyWVRkVIoXnFMJeReTOYMcQ3j+8HUpxz/udy7uny49x7T0/lcDhkt9u1u7sbmQ0AgDARSgEAAABAhFitVt3c3Bjt7u5u9ff3Ky8vTzk5OT/V22w2ra+vG6eiHh8f5fF4ZLPZjJrz83Pt7++ro6NDkmS327WwsKBQKGTUuFwujY6OqrW1VVarVW63WxaLxRgPBoOamprS/Py8GhsblZeXp76+Ptntds3OzkZ4FwDgYwilAAAAACBCQqHQu/dFlZWV/ba+qalJcXFx2tjYkCStra0pJSVF9fX1Rs33ICktLc2YEwgE5PV6JUmBQEB+v18VFRXGHLPZrNLSUqN9cXGhl5cXNTQ0KCkpyfgtLy/r8vLy8zcOAH8g9qsXAAAAAAD/F2dnZ8rNzTXaiYmJv62Pj49XW1ubVldX1dnZqdXVVXV0dCg29ttftdfXVy0tLcnn8xl93/vn5+dVV1f3oXU9Pz9Lkjwej7Kzs9+NJSQkfOgaABBphFIAAAAAEAFer1fHx8dyOBxhzbPZbGpoaNDp6am8Xq8mJyeNsc3NTT09Peno6Ehms9noPzk5UX9/vx4eHmSxWJSRkaGDgwPV1NRI+hZaHR4eqri4WJJUWFiohIQE3d3dqba29vM3CwARQCgFAAAAAGEKBoPy+Xx6fX2V3+/X1taWnE6nWlpa1NPTE9a1ampqlJmZKZvNptzcXFVWVhpjc3Nzam5uVlFR0bs5hYWFcjgcWllZ0eDgoIaGhuR0OpWfny+r1SqXy6X7+3vjUcLk5GQNDw/L4XDo7e1N1dXVCgQC2tvbU0pKinp7ez+/KQAQJkIpAAAAAAjT1taWsrKyFBsbq9TUVBUVFWlmZka9vb0ymcJ7dW9MTIy6uro0PT2t8fFxo9/v98vj8fz0dT5JMplMam1t1dzcnAYHBzUyMiKfz6eenh6ZzWYNDAyosbHx3emqiYkJpaeny+l06urqShaLRSUlJRobG/vzjQCAT4gJ/fjJBgAAAADAX+/t7U0FBQVqb2/XxMTEVy8HAP4VJ6UAAAAA4C93e3ur7e1t1dbWKhgMyu126/r6Wt3d3V+9NAD4pfDOlQIAAAAA/nNMJpMWFxdVXl6uqqoqHR8fa2dnRwUFBV+9NAD4JR7fAwAAAAAAQNRxUgoAAAAAAABRRygFAAAAAACAqCOUAgAAAAAAQNQRSgEAAAAAACDqCKUAAAAAAAAQdYRSAAAAAAAAiDpCKQAAAAAAAEQdoRQAAAAAAACijlAKAAAAAAAAUfcPk5+ivnjf164AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4), width_ratios=(1, 1.5))\n",
    "_ = sns.histplot(data=df, x=\"DrivAge\", ax=axs[0])\n",
    "_ = sns.barplot(df, x=\"DrivAge\", y=\"target\", errorbar=None, ax=axs[1])\n",
    "axs[1].set_ylabel(\"Mean Target\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7befe0ba",
   "metadata": {},
   "source": [
    "### BonusMalus\n",
    "\n",
    "Only 40 instances have `BonusMalus` > 150.\n",
    "These instances could be removed during modeling and treated differently later if necessary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43fd9f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4), width_ratios=(1, 1.5))\n",
    "bins = pd.cut(df[\"BonusMalus\"], right=False, bins=range(50, 240, 10))\n",
    "df_tmp = df.groupby(bins, observed=True)[\"target\"].mean().to_frame().reset_index()\n",
    "_ = sns.histplot(data=df, x=\"BonusMalus\", ax=axs[0])\n",
    "_ = sns.barplot(df_tmp, x=\"BonusMalus\", y=\"target\", errorbar=None, ax=axs[1])\n",
    "axs[1].set_ylabel(\"Mean Target\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd18ce9",
   "metadata": {},
   "source": [
    "### Density \n",
    "\n",
    "We see a very wide range for this variable, and its distribution seems to follow a power law. So, we can also convert it into a log scale to resemble a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7fef767-5ffd-42ed-be99-a73a863871f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_136033/2362594542.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"DensityLog\"] = df[\"Density\"].map(np.log10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8), width_ratios=(1, 1.5))\n",
    "axs = axs.flatten()\n",
    "_ = sns.histplot(data=df, x=\"Density\", ax=axs[0])\n",
    "\n",
    "bins = pd.cut(df[\"Density\"], right=False, bins=range(0, 30000, 500))\n",
    "df_tmp = df.groupby(bins, observed=True)[\"target\"].mean().to_frame().reset_index()\n",
    "_ = sns.barplot(df_tmp, x=\"Density\", y=\"target\", errorbar=None, ax=axs[1])\n",
    "axs[1].set_ylabel(\"Mean Target\")\n",
    "axs[1].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "df[\"DensityLog\"] = df[\"Density\"].map(np.log10)\n",
    "df_all[\"DensityLog\"] = df_all[\"Density\"].map(np.log10)\n",
    "\n",
    "_ = sns.histplot(df[\"DensityLog\"], ax=axs[2])\n",
    "\n",
    "bins = pd.cut(df[\"DensityLog\"], right=False, bins=np.linspace(0, 5, 50))\n",
    "df_tmp = df.groupby(bins, observed=True)[\"target\"].mean().to_frame().reset_index()\n",
    "_ = sns.barplot(df_tmp, x=\"DensityLog\", y=\"target\", errorbar=None, ax=axs[3])\n",
    "axs[3].tick_params(axis=\"x\", rotation=90)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c46756-27c4-4545-a01e-4997978cdf27",
   "metadata": {},
   "source": [
    "### Statistical Analysis of Numerical Variables\n",
    "\n",
    "As shown in the plot below, the numerical variables exhibit very low correlation with both `target` and `targetLog`. Therefore, these features may not be highly effective in predicting the dependent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0344f60-29a6-4e74-95f4-4633550b6ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "cols = [\n",
    "    \"target\",\n",
    "    \"targetLog\",\n",
    "    \"SumClaimAmount\",\n",
    "    \"Exposure\",\n",
    "    \"VehPower\",\n",
    "    \"VehAge\",\n",
    "    \"DrivAge\",\n",
    "    \"BonusMalus\",\n",
    "    \"Density\",\n",
    "    \"DensityLog\",\n",
    "]\n",
    "_ = sns.heatmap(df[cols].corr(), annot=True, linewidths=1, fmt=\".3f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52073ab6-b1bc-483c-b8c2-58998df4ffee",
   "metadata": {},
   "source": [
    "# Analysis of Categorical Variables\n",
    "\n",
    "At this step, we analyze categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c18f1beb-8eb5-485d-a377-1e8c5214ce40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axs = axs.flatten()\n",
    "for i, c in enumerate(cat_cols):\n",
    "    sns.countplot(df, x=c, ax=axs[i])\n",
    "    axs[i].tick_params(axis=\"x\", rotation=45)\n",
    "    axs[i].set_title(c)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3ac1346-206e-45aa-8b85-198978bd2ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axs = axs.flatten()\n",
    "for i, c in enumerate(cat_cols):\n",
    "    sns.barplot(data=df, x=c, y=\"target\", errorbar=None, ax=axs[i])\n",
    "    axs[i].set_ylabel(\"Mean Targer\")\n",
    "axs[-1].tick_params(axis=\"x\", rotation=90)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8710fcce",
   "metadata": {},
   "source": [
    "Similar to the numerical variables, these plots suggest that the dataset can be clustered into various groups, and each group can be treated differently.\n",
    "\n",
    "One more question here: Is each region contained within a single area, so we could remove the `Area` column? NO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68aa16f3",
   "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>Region</th>\n",
       "      <th>'R11'</th>\n",
       "      <th>'R21'</th>\n",
       "      <th>'R22'</th>\n",
       "      <th>'R23'</th>\n",
       "      <th>'R24'</th>\n",
       "      <th>'R25'</th>\n",
       "      <th>'R26'</th>\n",
       "      <th>'R31'</th>\n",
       "      <th>'R41'</th>\n",
       "      <th>'R42'</th>\n",
       "      <th>...</th>\n",
       "      <th>'R53'</th>\n",
       "      <th>'R54'</th>\n",
       "      <th>'R72'</th>\n",
       "      <th>'R73'</th>\n",
       "      <th>'R74'</th>\n",
       "      <th>'R82'</th>\n",
       "      <th>'R83'</th>\n",
       "      <th>'R91'</th>\n",
       "      <th>'R93'</th>\n",
       "      <th>'R94'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Region  'R11'  'R21'  'R22'  'R23'  'R24'  'R25'  'R26'  'R31'  'R41'  'R42'  \\\n",
       "Area        6      5      5      5      5      5      5      5      5      5   \n",
       "\n",
       "Region  ...  'R53'  'R54'  'R72'  'R73'  'R74'  'R82'  'R83'  'R91'  'R93'  \\\n",
       "Area    ...      5      5      5      5      4      5      5      5      5   \n",
       "\n",
       "Region  'R94'  \n",
       "Area        4  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Region\"], observed=True)[\"Area\"].nunique().to_frame().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e186feda-697c-4ebf-b643-b165b695db51",
   "metadata": {},
   "source": [
    "## Statistical Test of Categorical Variables\n",
    "\n",
    "An ANOVA test is conducted on the categorical variables. The results indicate that the distribution of the `target` variable is not significantly different across all categorical variables. However, some categorical variables show a significant difference for `targetLog`. Therefore, it is expected that modeling `targetLog` will lead to better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "caf3f585-f7d4-4918-bba8-6eaf87775f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependent Variable: Target\n",
      "Variable: Area\tp-value: 0.5621655789253363\n",
      "Variable: VehBrand\tp-value: 0.6401726096346133\n",
      "Variable: VehGas\tp-value: 0.22047970212461002\n",
      "Variable: Region\tp-value: 0.9735818647330804\n",
      "\n",
      "\n",
      "Dependent Variable: log(Target)\n",
      "Variable: Area\tp-value: 2.864841485854489e-08\n",
      "Variable: VehBrand\tp-value: 3.6498732677036494e-60\n",
      "Variable: VehGas\tp-value: 0.8569818310183478\n",
      "Variable: Region\tp-value: 6.55718358261526e-36\n"
     ]
    }
   ],
   "source": [
    "def ANOVA_test(target_col):\n",
    "    for c in cat_cols:\n",
    "        df_tmp = [g[1] for g in df.groupby([c], observed=True)[target_col]]\n",
    "        _, pvalue = stats.f_oneway(*df_tmp)\n",
    "        print(f\"Variable: {c}\\tp-value: {pvalue}\")\n",
    "\n",
    "\n",
    "print(\"Dependent Variable: Target\")\n",
    "ANOVA_test(\"target\")\n",
    "\n",
    "print(\"\\n\\nDependent Variable: log(Target)\")\n",
    "ANOVA_test(\"targetLog\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcc4fe3-07c9-4754-9ad6-d4fbfa4ff839",
   "metadata": {},
   "source": [
    "# Regession\n",
    "\n",
    "The statistical analysis of the features revealed that we do not have any fantastic features that can be used for predicting the dependent variable.\n",
    "\n",
    "First, we attempt to predict the expected claim amount for a policyholder given he or she has a claim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3bee754-f0b3-428d-9659-2c6a4e6bb5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn import preprocessing\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.metrics import mean_squared_error, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "\n",
    "def get_SVR_model():\n",
    "    preprocess = ColumnTransformer(\n",
    "        transformers=[\n",
    "            (\n",
    "                \"Cat\",\n",
    "                Pipeline(\n",
    "                    [\n",
    "                        (\"OneHot\", preprocessing.OneHotEncoder()),\n",
    "                        (\"Normalizer\", preprocessing.StandardScaler(with_mean=False)),\n",
    "                    ]\n",
    "                ),\n",
    "                cat_cols,\n",
    "            ),\n",
    "            (\"Num-Normalizer\", preprocessing.StandardScaler(), num_cols),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    model = SVR()\n",
    "    pipeline = Pipeline([(\"preprocess\", preprocess), (\"svr_model\", model)])\n",
    "    return pipeline\n",
    "\n",
    "\n",
    "def get_XGB_model():\n",
    "    preprocess = ColumnTransformer(\n",
    "        [\n",
    "            (\"OrdinalEncoder\", preprocessing.OrdinalEncoder(), cat_cols),\n",
    "            (\"Normalizer\", preprocessing.StandardScaler(), num_cols),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    model = xgb.XGBRegressor()\n",
    "    pipeline = Pipeline([(\"preprocess\", preprocess), (\"xgb_model\", model)])\n",
    "    return pipeline\n",
    "\n",
    "\n",
    "def get_RF_model():\n",
    "    preprocess = ColumnTransformer(\n",
    "        [\n",
    "            (\"OrdinalEncoder\", preprocessing.OrdinalEncoder(), cat_cols),\n",
    "            (\"Normalizer\", preprocessing.StandardScaler(), num_cols),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    model = RandomForestRegressor()\n",
    "    pipeline = Pipeline([(\"preprocess\", preprocess), (\"rf_model\", model)])\n",
    "    return pipeline\n",
    "\n",
    "\n",
    "def get_test_train(df, cols, target):\n",
    "    X = df[cols]\n",
    "    y = df[target]\n",
    "    return train_test_split(X, y, test_size=0.20, random_state=37)\n",
    "\n",
    "\n",
    "def train(model):\n",
    "    model.fit(train_x, train_y)\n",
    "\n",
    "    pred_y = model.predict(test_x)\n",
    "    mse = mean_squared_error(test_y, pred_y)\n",
    "\n",
    "    return mse\n",
    "\n",
    "\n",
    "def filter_dataset(df):\n",
    "    df_tmp = df.copy()\n",
    "    df_tmp = df_tmp[df_tmp[\"VehAge\"] <= 30]\n",
    "    df_tmp = df_tmp[df_tmp[\"DrivAge\"] <= 90]\n",
    "    df_tmp = df_tmp[df_tmp[\"BonusMalus\"] <= 150]\n",
    "    df_tmp = df_tmp[~df_tmp[\"IsOutlier\"]]\n",
    "    return df_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9fb8338e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline - Root mean squared error (RMSE): 9983.06\n",
      "\n",
      "RandomForest - Root mean squared error (RMSE): 10419.19\n",
      "\n",
      "XGB - Root mean squared error (RMSE): 10568.72\n",
      "\n",
      "SVR - Root mean squared error (RMSE): 10333.84\n"
     ]
    }
   ],
   "source": [
    "num_cols = [\"VehPower\", \"VehAge\", \"DrivAge\", \"BonusMalus\", \"DensityLog\"]\n",
    "cat_cols = [\"Area\", \"VehBrand\", \"VehGas\", \"Region\"]\n",
    "cols = cat_cols + num_cols\n",
    "\n",
    "df_tmp = filter_dataset(df)\n",
    "target = \"target\"\n",
    "train_x, test_x, train_y, test_y = get_test_train(df_tmp, cols, target)\n",
    "\n",
    "mse = mean_squared_error(test_y, [train_y.mean()] * len(test_y))\n",
    "print(f\"Baseline - Root mean squared error (RMSE): {np.sqrt(mse):.2f}\")\n",
    "\n",
    "rf_model = get_RF_model()\n",
    "mse = train(rf_model)\n",
    "print(f\"\\nRandomForest - Root mean squared error (RMSE): {np.sqrt(mse):.2f}\")\n",
    "\n",
    "xgb_model = get_XGB_model()\n",
    "mse = train(xgb_model)\n",
    "print(f\"\\nXGB - Root mean squared error (RMSE): {np.sqrt(mse):.2f}\")\n",
    "\n",
    "svr_model = get_SVR_model()\n",
    "mse = train(svr_model)\n",
    "print(f\"\\nSVR - Root mean squared error (RMSE): {np.sqrt(mse):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b0ae6f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline - Root mean squared error (RMSE): 3.69\n",
      "\n",
      "RandomForest - Root mean squared error (RMSE): 3.73\n",
      "\n",
      "XGB - Root mean squared error (RMSE): 3.74\n",
      "\n",
      "SVR - Root mean squared error (RMSE): 3.64\n"
     ]
    }
   ],
   "source": [
    "target = \"targetLog\"\n",
    "train_x, test_x, train_y, test_y = get_test_train(df_tmp, cols, target)\n",
    "\n",
    "mse = mean_squared_error(test_y, [train_y.mean()] * len(test_y))\n",
    "print(f\"Baseline - Root mean squared error (RMSE): {np.power(10, np.sqrt(mse)):.2f}\")\n",
    "\n",
    "rf_model = get_RF_model()\n",
    "mse = train(rf_model)\n",
    "print(\n",
    "    f\"\\nRandomForest - Root mean squared error (RMSE): {np.power(10, np.sqrt(mse)):.2f}\"\n",
    ")\n",
    "\n",
    "xgb_model = get_XGB_model()\n",
    "mse = train(xgb_model)\n",
    "print(f\"\\nXGB - Root mean squared error (RMSE): {np.power(10, np.sqrt(mse)):.2f}\")\n",
    "\n",
    "svr_model = get_SVR_model()\n",
    "mse = train(svr_model)\n",
    "print(f\"\\nSVR - Root mean squared error (RMSE): {np.power(10, np.sqrt(mse)):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940f244a",
   "metadata": {},
   "source": [
    "The above models did not perform satisfactorily. In fact, they performed worse than the baseline, which was simply the mean of the target variable. However, these results are not surprising given the outcomes of the statistical tests we observed previously.\n",
    "\n",
    "We may attempt parameter optimization here. In this [post](https://medium.com/@mohsenim/tracking-machine-learning-experiments-with-mlflow-and-dockerizing-trained-models-germany-car-price-e539303b6f97) and [this](https://github.com/mohsenim/MLflow-XGBoost-Docker) GitHub repository, I have explained how to optimize hyperparameters for XGBoost, and the method can easily be generalized for other models as well. However, given the poor performance of the models with the default settings, I don't think parameter optimization will change anything here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "467391c4",
   "metadata": {},
   "source": [
    "# Classification\n",
    "\n",
    "We didn't get satisfactory results with regression. Now, I'm interested in investigating whether we can predict if a policyholder files a claim using the features. Thus, we convert the problem into a binary classification problem. \n",
    "\n",
    "We already know that in the dataset, only 3.7% of policyholders have filed a claim, while 96.3% have not. Such a highly imbalanced dataset requires careful handling of the imbalanced classes. For this classification task, I use logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9d1be9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "\n",
    "num_cols = [\"VehPower\", \"VehAge\", \"DrivAge\", \"BonusMalus\", \"DensityLog\"]\n",
    "cat_cols = [\"Area\", \"VehBrand\", \"VehGas\", \"Region\"]\n",
    "\n",
    "# Preprocess the data\n",
    "preprocess = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\n",
    "            \"cat\",\n",
    "            Pipeline(\n",
    "                [\n",
    "                    (\"OneHot\", preprocessing.OneHotEncoder()),\n",
    "                    (\n",
    "                        \"Normalizer\",\n",
    "                        preprocessing.StandardScaler(with_mean=False),\n",
    "                    ),  \n",
    "                ]\n",
    "            ),\n",
    "            cat_cols,\n",
    "        ),\n",
    "        (\"num\", preprocessing.StandardScaler(), num_cols),\n",
    "    ]\n",
    ")\n",
    "\n",
    "target = \"HasClaim\"\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X = df_all[num_cols + cat_cols]\n",
    "y = df_all[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=37\n",
    ")\n",
    "\n",
    "X_train = preprocess.fit_transform(X_train).toarray()\n",
    "X_test = preprocess.fit_transform(X_test).toarray()\n",
    "\n",
    "X_train_torch = torch.tensor(X_train, dtype=torch.float32)\n",
    "y_train_torch = torch.tensor(list(y_train), dtype=torch.float32)\n",
    "X_test_torch = torch.tensor(X_test, dtype=torch.float32)\n",
    "y_test_torch = torch.tensor(list(y_test), dtype=torch.float32)\n",
    "\n",
    "# Create datasets\n",
    "train_dataset = TensorDataset(X_train_torch, y_train_torch)\n",
    "test_dataset = TensorDataset(X_test_torch, y_test_torch)\n",
    "\n",
    "# Create data loaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8c20ee5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from tqdm import tqdm\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "\n",
    "class LogisticRegression(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(LogisticRegression, self).__init__()\n",
    "        self.linear = nn.Linear(input_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return torch.sigmoid(self.linear(x))\n",
    "\n",
    "\n",
    "def train_model(num_epochs, positive_class_weight=None):\n",
    "    if positive_class_weight is not None:\n",
    "        class_weights = compute_class_weight(\n",
    "            \"balanced\", classes=np.unique(y_train), y=y_train\n",
    "        )\n",
    "        class_weight_tensor = torch.tensor(\n",
    "            [positive_class_weight * class_weights[1]], dtype=torch.float32\n",
    "        )\n",
    "\n",
    "        # Define the loss function with class weight\n",
    "        criterion = nn.BCEWithLogitsLoss(pos_weight=class_weight_tensor)\n",
    "    else:\n",
    "        criterion = nn.BCEWithLogitsLoss()\n",
    "\n",
    "    # Move the criterion to GPU if available\n",
    "    model = LogisticRegression(X_train.shape[1]).to(device)\n",
    "    criterion = criterion.to(device)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "    print(\"Training ...\")\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        for data, target in train_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            loss = criterion(output, target.unsqueeze(1))\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        if (epoch + 1) % 5 == 0:\n",
    "            print(f\"Epoch {epoch+1}, Loss: {loss.item()}\")\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def evaluate(model):\n",
    "    model.eval()\n",
    "    y_pred = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            outputs = model(data)\n",
    "            predicted = (outputs.data > 0.5).float()\n",
    "            y_pred += predicted.tolist()\n",
    "\n",
    "    print(classification_report(y_test, y_pred, zero_division=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fd31d4a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training ...\n",
      "Epoch 5, Loss: 0.6931471824645996\n",
      "Epoch 10, Loss: 0.6931471824645996\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      1.00      0.98    130699\n",
      "        True       0.00      0.00      0.00      4904\n",
      "\n",
      "    accuracy                           0.96    135603\n",
      "   macro avg       0.48      0.50      0.49    135603\n",
      "weighted avg       0.93      0.96      0.95    135603\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = train_model(5, positive_class_weight=None)\n",
    "print(\"\")\n",
    "evaluate(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5eb55299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training ...\n",
      "Epoch 5, Loss: 1.093119740486145\n",
      "Epoch 10, Loss: 1.0352026224136353\n",
      "Epoch 15, Loss: 1.1928868293762207\n",
      "Epoch 20, Loss: 2.6955227851867676\n",
      "Epoch 25, Loss: 1.1254448890686035\n",
      "Epoch 30, Loss: 0.9496679306030273\n",
      "Epoch 35, Loss: 0.9839045405387878\n",
      "Epoch 40, Loss: 1.033744215965271\n",
      "Epoch 45, Loss: 0.9856802225112915\n",
      "Epoch 50, Loss: 1.0736278295516968\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.98      0.42      0.58    130699\n",
      "        True       0.05      0.73      0.08      4904\n",
      "\n",
      "    accuracy                           0.43    135603\n",
      "   macro avg       0.51      0.57      0.33    135603\n",
      "weighted avg       0.94      0.43      0.56    135603\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = train_model(50, positive_class_weight=4)\n",
    "evaluate(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7b604e",
   "metadata": {},
   "source": [
    "It seems that detecting policyholders who file a claim, i.e., recall, is more important than the accuracy or precision of the model. By using class weights and weighting the positive class (those instances with `HasClaim=True`), we can guide the model towards our desired outcome.\n",
    "\n",
    "The insurance premium for each policyholder can initially be set based on the probability of filing a claim, with a safe margin included. Then, we use the classification model to adjust the premium: if the policyholder is classified as positive, we increase their premium proportionally based on the statistics of previously filed `ClaimAmount`s.\n",
    "\n",
    "In the next step, we could go beyond the two positive and negative classes and cluster policyholders into groups. We can detect the cluster of each policyholder and adjust the insurance premium according to the statistics of previously filed `ClaimAmount`s in that cluster."
   ]
  }
 ],
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