P_mera_script1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mera Housing Characteristics - Python "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        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>Customer_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Home_Age</th>\n",
       "      <th>Home_Size_sqft</th>\n",
       "      <th>Security_System</th>\n",
       "      <th>Fire_Safety</th>\n",
       "      <th>Roof_Condition</th>\n",
       "      <th>Swimming_Pool</th>\n",
       "      <th>Credit_Score</th>\n",
       "      <th>Claims_History</th>\n",
       "      <th>Home_Use</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Proximity_to_Water</th>\n",
       "      <th>Local_Weather_Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76</td>\n",
       "      <td>2164</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Vacation Home</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-3</td>\n",
       "      <td>507</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000002</td>\n",
       "      <td>Urban</td>\n",
       "      <td>45</td>\n",
       "      <td>1365</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>New</td>\n",
       "      <td>True</td>\n",
       "      <td>444</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>732</td>\n",
       "      <td>High</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000003</td>\n",
       "      <td>Rural</td>\n",
       "      <td>45</td>\n",
       "      <td>2697</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>475</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>1221</td>\n",
       "      <td>Low</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000004</td>\n",
       "      <td>Urban</td>\n",
       "      <td>99</td>\n",
       "      <td>1199</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>New</td>\n",
       "      <td>False</td>\n",
       "      <td>510</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>False</td>\n",
       "      <td>HO-6</td>\n",
       "      <td>1326</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Far</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000005</td>\n",
       "      <td>Urban</td>\n",
       "      <td>21</td>\n",
       "      <td>3351</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>563</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Primary Residence</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-1</td>\n",
       "      <td>344</td>\n",
       "      <td>High</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID Location  Home_Age  Home_Size_sqft  Security_System  \\\n",
       "0      1000001    Rural        76            2164            False   \n",
       "1      1000002    Urban        45            1365             True   \n",
       "2      1000003    Rural        45            2697            False   \n",
       "3      1000004    Urban        99            1199            False   \n",
       "4      1000005    Urban        21            3351            False   \n",
       "\n",
       "   Fire_Safety Roof_Condition  Swimming_Pool  Credit_Score Claims_History  \\\n",
       "0        False            Old          False           719         Medium   \n",
       "1         True            New           True           444           High   \n",
       "2        False            Old           True           475           High   \n",
       "3        False            New          False           510           High   \n",
       "4         True            Old           True           563         Medium   \n",
       "\n",
       "            Home_Use   Pets Policy_Type  Deductible Crime_Rate  \\\n",
       "0      Vacation Home   True        HO-3         507        Low   \n",
       "1             Rental   True        HO-5         732       High   \n",
       "2             Rental   True        HO-5        1221        Low   \n",
       "3             Rental  False        HO-6        1326     Medium   \n",
       "4  Primary Residence   True        HO-1         344       High   \n",
       "\n",
       "  Proximity_to_Water Local_Weather_Conditions  \n",
       "0           Moderate                   Normal  \n",
       "1              Close                   Severe  \n",
       "2              Close                   Severe  \n",
       "3                Far                   Normal  \n",
       "4           Moderate                   Normal  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# import folium\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# Specify the file path\n",
    "#file_path = 'C:\\\\temp\\\\marketing_campaign.xlsx'\n",
    "\n",
    "# 'C:\\\\efrayer\\\\Python_Projects\\\\P_Mera_Housing_Characteristics\\\\MERA_Insurance_Home_Owner_Data.csv'\n",
    "\n",
    "#file_path = 'C:\\\\efrayer\\\\Python_Projects\\\\P_Mera_Housing_Characteristics\\\\MERA_Insurance_Home_Owner_Data.csv' \n",
    "\n",
    "file_path = 'C:\\\\Users\\\\Eric Frayer\\\\GitHub\\\\PythonFiles\\\\P_Mera_Housing_Characteristic_Data\\\\MERA_Insurance_Home_Owner_Data.csv'\n",
    "\n",
    "\n",
    "\n",
    "# Load the csv file\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "df.head()\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "# print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Customer_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Home_Age</th>\n",
       "      <th>Home_Size_sqft</th>\n",
       "      <th>Security_System</th>\n",
       "      <th>Fire_Safety</th>\n",
       "      <th>Roof_Condition</th>\n",
       "      <th>Swimming_Pool</th>\n",
       "      <th>Credit_Score</th>\n",
       "      <th>Claims_History</th>\n",
       "      <th>Home_Use</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Proximity_to_Water</th>\n",
       "      <th>Local_Weather_Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76</td>\n",
       "      <td>2164</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Vacation Home</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-3</td>\n",
       "      <td>507</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000002</td>\n",
       "      <td>Urban</td>\n",
       "      <td>45</td>\n",
       "      <td>1365</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>New</td>\n",
       "      <td>True</td>\n",
       "      <td>444</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>732</td>\n",
       "      <td>High</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000003</td>\n",
       "      <td>Rural</td>\n",
       "      <td>45</td>\n",
       "      <td>2697</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>475</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>1221</td>\n",
       "      <td>Low</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000004</td>\n",
       "      <td>Urban</td>\n",
       "      <td>99</td>\n",
       "      <td>1199</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>New</td>\n",
       "      <td>False</td>\n",
       "      <td>510</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>False</td>\n",
       "      <td>HO-6</td>\n",
       "      <td>1326</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Far</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000005</td>\n",
       "      <td>Urban</td>\n",
       "      <td>21</td>\n",
       "      <td>3351</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>563</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Primary Residence</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-1</td>\n",
       "      <td>344</td>\n",
       "      <td>High</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID Location  Home_Age  Home_Size_sqft  Security_System  \\\n",
       "0      1000001    Rural        76            2164            False   \n",
       "1      1000002    Urban        45            1365             True   \n",
       "2      1000003    Rural        45            2697            False   \n",
       "3      1000004    Urban        99            1199            False   \n",
       "4      1000005    Urban        21            3351            False   \n",
       "\n",
       "   Fire_Safety Roof_Condition  Swimming_Pool  Credit_Score Claims_History  \\\n",
       "0        False            Old          False           719         Medium   \n",
       "1         True            New           True           444           High   \n",
       "2        False            Old           True           475           High   \n",
       "3        False            New          False           510           High   \n",
       "4         True            Old           True           563         Medium   \n",
       "\n",
       "            Home_Use   Pets Policy_Type  Deductible Crime_Rate  \\\n",
       "0      Vacation Home   True        HO-3         507        Low   \n",
       "1             Rental   True        HO-5         732       High   \n",
       "2             Rental   True        HO-5        1221        Low   \n",
       "3             Rental  False        HO-6        1326     Medium   \n",
       "4  Primary Residence   True        HO-1         344       High   \n",
       "\n",
       "  Proximity_to_Water Local_Weather_Conditions  \n",
       "0           Moderate                   Normal  \n",
       "1              Close                   Severe  \n",
       "2              Close                   Severe  \n",
       "3                Far                   Normal  \n",
       "4           Moderate                   Normal  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Home_Age</th>\n",
       "      <th>Home_Size_sqft</th>\n",
       "      <th>Credit_Score</th>\n",
       "      <th>Deductible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000250e+06</td>\n",
       "      <td>50.330000</td>\n",
       "      <td>2440.158000</td>\n",
       "      <td>577.73800</td>\n",
       "      <td>1021.93800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.444818e+02</td>\n",
       "      <td>28.605763</td>\n",
       "      <td>893.736262</td>\n",
       "      <td>109.91225</td>\n",
       "      <td>559.38343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>367.00000</td>\n",
       "      <td>106.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000126e+06</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1708.750000</td>\n",
       "      <td>476.75000</td>\n",
       "      <td>524.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000250e+06</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2518.000000</td>\n",
       "      <td>582.50000</td>\n",
       "      <td>1028.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000375e+06</td>\n",
       "      <td>74.250000</td>\n",
       "      <td>3170.250000</td>\n",
       "      <td>678.00000</td>\n",
       "      <td>1492.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000500e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3986.000000</td>\n",
       "      <td>808.00000</td>\n",
       "      <td>1998.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Customer_ID    Home_Age  Home_Size_sqft  Credit_Score  Deductible\n",
       "count  5.000000e+02  500.000000      500.000000     500.00000   500.00000\n",
       "mean   1.000250e+06   50.330000     2440.158000     577.73800  1021.93800\n",
       "std    1.444818e+02   28.605763      893.736262     109.91225   559.38343\n",
       "min    1.000001e+06    1.000000      802.000000     367.00000   106.00000\n",
       "25%    1.000126e+06   26.000000     1708.750000     476.75000   524.50000\n",
       "50%    1.000250e+06   51.000000     2518.000000     582.50000  1028.50000\n",
       "75%    1.000375e+06   74.250000     3170.250000     678.00000  1492.25000\n",
       "max    1.000500e+06  100.000000     3986.000000     808.00000  1998.00000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate summary statistics for numerical columns\n",
    "summary_stats_numerical = df.describe()\n",
    "\n",
    "summary_stats_numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Location</th>\n",
       "      <th>Security_System</th>\n",
       "      <th>Fire_Safety</th>\n",
       "      <th>Roof_Condition</th>\n",
       "      <th>Swimming_Pool</th>\n",
       "      <th>Claims_History</th>\n",
       "      <th>Home_Use</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Proximity_to_Water</th>\n",
       "      <th>Local_Weather_Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburban</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fair</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary Residence</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vacation Home</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rental</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HO-2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Far</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Severe</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Location  Security_System  Fire_Safety  Roof_Condition  \\\n",
       "Rural                 192.0              0.0          0.0             0.0   \n",
       "Suburban              135.0              0.0          0.0             0.0   \n",
       "Urban                 173.0              0.0          0.0             0.0   \n",
       "True                    0.0            251.0        243.0             0.0   \n",
       "False                   0.0            249.0        257.0             0.0   \n",
       "New                     0.0              0.0          0.0           184.0   \n",
       "Fair                    0.0              0.0          0.0           162.0   \n",
       "Old                     0.0              0.0          0.0           154.0   \n",
       "High                    0.0              0.0          0.0             0.0   \n",
       "Low                     0.0              0.0          0.0             0.0   \n",
       "Medium                  0.0              0.0          0.0             0.0   \n",
       "Primary Residence       0.0              0.0          0.0             0.0   \n",
       "Vacation Home           0.0              0.0          0.0             0.0   \n",
       "Rental                  0.0              0.0          0.0             0.0   \n",
       "HO-1                    0.0              0.0          0.0             0.0   \n",
       "HO-4                    0.0              0.0          0.0             0.0   \n",
       "HO-6                    0.0              0.0          0.0             0.0   \n",
       "HO-3                    0.0              0.0          0.0             0.0   \n",
       "HO-5                    0.0              0.0          0.0             0.0   \n",
       "HO-2                    0.0              0.0          0.0             0.0   \n",
       "Far                     0.0              0.0          0.0             0.0   \n",
       "Moderate                0.0              0.0          0.0             0.0   \n",
       "Close                   0.0              0.0          0.0             0.0   \n",
       "Severe                  0.0              0.0          0.0             0.0   \n",
       "Normal                  0.0              0.0          0.0             0.0   \n",
       "\n",
       "                   Swimming_Pool  Claims_History  Home_Use   Pets  \\\n",
       "Rural                        0.0             0.0       0.0    0.0   \n",
       "Suburban                     0.0             0.0       0.0    0.0   \n",
       "Urban                        0.0             0.0       0.0    0.0   \n",
       "True                       263.0             0.0       0.0  248.0   \n",
       "False                      237.0             0.0       0.0  252.0   \n",
       "New                          0.0             0.0       0.0    0.0   \n",
       "Fair                         0.0             0.0       0.0    0.0   \n",
       "Old                          0.0             0.0       0.0    0.0   \n",
       "High                         0.0           182.0       0.0    0.0   \n",
       "Low                          0.0           166.0       0.0    0.0   \n",
       "Medium                       0.0           152.0       0.0    0.0   \n",
       "Primary Residence            0.0             0.0     176.0    0.0   \n",
       "Vacation Home                0.0             0.0     168.0    0.0   \n",
       "Rental                       0.0             0.0     156.0    0.0   \n",
       "HO-1                         0.0             0.0       0.0    0.0   \n",
       "HO-4                         0.0             0.0       0.0    0.0   \n",
       "HO-6                         0.0             0.0       0.0    0.0   \n",
       "HO-3                         0.0             0.0       0.0    0.0   \n",
       "HO-5                         0.0             0.0       0.0    0.0   \n",
       "HO-2                         0.0             0.0       0.0    0.0   \n",
       "Far                          0.0             0.0       0.0    0.0   \n",
       "Moderate                     0.0             0.0       0.0    0.0   \n",
       "Close                        0.0             0.0       0.0    0.0   \n",
       "Severe                       0.0             0.0       0.0    0.0   \n",
       "Normal                       0.0             0.0       0.0    0.0   \n",
       "\n",
       "                   Policy_Type  Crime_Rate  Proximity_to_Water  \\\n",
       "Rural                      0.0         0.0                 0.0   \n",
       "Suburban                   0.0         0.0                 0.0   \n",
       "Urban                      0.0         0.0                 0.0   \n",
       "True                       0.0         0.0                 0.0   \n",
       "False                      0.0         0.0                 0.0   \n",
       "New                        0.0         0.0                 0.0   \n",
       "Fair                       0.0         0.0                 0.0   \n",
       "Old                        0.0         0.0                 0.0   \n",
       "High                       0.0       151.0                 0.0   \n",
       "Low                        0.0       177.0                 0.0   \n",
       "Medium                     0.0       172.0                 0.0   \n",
       "Primary Residence          0.0         0.0                 0.0   \n",
       "Vacation Home              0.0         0.0                 0.0   \n",
       "Rental                     0.0         0.0                 0.0   \n",
       "HO-1                      88.0         0.0                 0.0   \n",
       "HO-4                      86.0         0.0                 0.0   \n",
       "HO-6                      85.0         0.0                 0.0   \n",
       "HO-3                      84.0         0.0                 0.0   \n",
       "HO-5                      82.0         0.0                 0.0   \n",
       "HO-2                      75.0         0.0                 0.0   \n",
       "Far                        0.0         0.0               176.0   \n",
       "Moderate                   0.0         0.0               167.0   \n",
       "Close                      0.0         0.0               157.0   \n",
       "Severe                     0.0         0.0                 0.0   \n",
       "Normal                     0.0         0.0                 0.0   \n",
       "\n",
       "                   Local_Weather_Conditions  \n",
       "Rural                                   0.0  \n",
       "Suburban                                0.0  \n",
       "Urban                                   0.0  \n",
       "True                                    0.0  \n",
       "False                                   0.0  \n",
       "New                                     0.0  \n",
       "Fair                                    0.0  \n",
       "Old                                     0.0  \n",
       "High                                    0.0  \n",
       "Low                                     0.0  \n",
       "Medium                                  0.0  \n",
       "Primary Residence                       0.0  \n",
       "Vacation Home                           0.0  \n",
       "Rental                                  0.0  \n",
       "HO-1                                    0.0  \n",
       "HO-4                                    0.0  \n",
       "HO-6                                    0.0  \n",
       "HO-3                                    0.0  \n",
       "HO-5                                    0.0  \n",
       "HO-2                                    0.0  \n",
       "Far                                     0.0  \n",
       "Moderate                                0.0  \n",
       "Close                                   0.0  \n",
       "Severe                                258.0  \n",
       "Normal                                242.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate frequency distribution for categorical columns\n",
    "summary_stats_categorical = df.select_dtypes(include=['object', 'bool']).apply(pd.Series.value_counts)\n",
    "\n",
    "summary_stats_categorical.fillna(0, inplace=True)  # Fill NaN values with 0\n",
    "summary_stats_categorical\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                   Policy_Type  Home_Use  Location\n",
       " HO-1                      88.0       0.0       0.0\n",
       " HO-2                      75.0       0.0       0.0\n",
       " HO-3                      84.0       0.0       0.0\n",
       " HO-4                      86.0       0.0       0.0\n",
       " HO-5                      82.0       0.0       0.0\n",
       " HO-6                      85.0       0.0       0.0\n",
       " Primary Residence          0.0     176.0       0.0\n",
       " Rental                     0.0     156.0       0.0\n",
       " Rural                      0.0       0.0     192.0\n",
       " Suburban                   0.0       0.0     135.0\n",
       " Urban                      0.0       0.0     173.0\n",
       " Vacation Home              0.0     168.0       0.0,\n",
       "        Fire_Safety  Roof_Condition  Security_System\n",
       " False        257.0             0.0            249.0\n",
       " True         243.0             0.0            251.0\n",
       " New            0.0           184.0              0.0\n",
       " Fair           0.0           162.0              0.0\n",
       " Old            0.0           154.0              0.0,\n",
       "         Claims_History  Crime_Rate  Local_Weather_Conditions\n",
       " High             182.0       151.0                       0.0\n",
       " Low              166.0       177.0                       0.0\n",
       " Medium           152.0       172.0                       0.0\n",
       " Normal             0.0         0.0                     242.0\n",
       " Severe             0.0         0.0                     258.0,\n",
       "           Swimming_Pool  Proximity_to_Water\n",
       " True              263.0                 0.0\n",
       " False             237.0                 0.0\n",
       " Far                 0.0               176.0\n",
       " Moderate            0.0               167.0\n",
       " Close               0.0               157.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display frequency distribution for a subset of categorical columns\n",
    "subset1 = df[['Policy_Type', 'Home_Use', 'Location']].apply(pd.Series.value_counts).fillna(0)\n",
    "subset2 = df[['Fire_Safety', 'Roof_Condition', 'Security_System']].apply(pd.Series.value_counts).fillna(0)\n",
    "subset3 = df[['Claims_History', 'Crime_Rate', 'Local_Weather_Conditions']].apply(pd.Series.value_counts).fillna(0)\n",
    "subset4 = df[['Swimming_Pool', 'Proximity_to_Water']].apply(pd.Series.value_counts).fillna(0)\n",
    "\n",
    "subset1, subset2, subset3, subset4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_ID                 0\n",
       "Location                    0\n",
       "Home_Age                    0\n",
       "Home_Size_sqft              0\n",
       "Security_System             0\n",
       "Fire_Safety                 0\n",
       "Roof_Condition              0\n",
       "Swimming_Pool               0\n",
       "Credit_Score                0\n",
       "Claims_History              0\n",
       "Home_Use                    0\n",
       "Pets                        0\n",
       "Policy_Type                 0\n",
       "Deductible                  0\n",
       "Crime_Rate                  0\n",
       "Proximity_to_Water          0\n",
       "Local_Weather_Conditions    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the dataset\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Generate a list of numerical columns for plotting\n",
    "numerical_columns = ['Home_Age', 'Home_Size_sqft', 'Credit_Score']\n",
    "\n",
    "# Loop through numerical columns to generate boxplots\n",
    "for i, col in enumerate(numerical_columns, 1):\n",
    "    plt.subplot(2, 2, i)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f'Boxplot of {col}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation matrix for numerical columns\n",
    "correlation_matrix = df[numerical_columns].corr()\n",
    "\n",
    "# Generate a heatmap for the correlation matrix\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n",
    "plt.title(\"Correlation Matrix of Numerical Variables\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Local_Weather_Conditions</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Severe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claims_History</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>44</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>123</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local_Weather_Conditions  Normal  Severe\n",
       "Claims_History                          \n",
       "High                          44     138\n",
       "Low                          123      43\n",
       "Medium                        75      77"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a contingency table for 'Claims History' and 'Local Weather Conditions'\n",
    "contingency_table = pd.crosstab(df['Claims_History'], df['Local_Weather_Conditions'])\n",
    "\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xMcUx9ceImEexj3Yvx7W2/34G9C3fy9taKygzx1Ncz/YjiuP5KYrrqZbFSeX8P6YI+08DB1P8QAWKY+c1ih/v3EtxHdhVbVz2Jym2/WWKP5yuXcq0FwOfiOJXkpc0M35561iDIrQ/V9YxkqVfHqBl1PALDqnTiYizKX559Kmqa9HqIyKOBk4oT6lLUpvYQiZJK0hErEPRanBF1bVIWrUYyCRpBYiI/ShO8z9PcRpIktrMU5aSJEkVs4VMkiSpYgYySZKkirV09/JVQq9evbJPnz5VlyFJktSqCRMmvJiZvZsbt0oHsj59+jB+/Piqy5AkSWpVRLT42CtPWUqSJFXMQCZJklQxA5kkSVLFVulryCRJqtKCBQuYOXMmb775ZtWlaCXSvXt3tthiC7p27drmeQxkkiQtp5kzZ9KjRw/69OlDRFRdjlYCmclLL73EzJkz2Wabbdo8n6csJUlaTm+++SYbb7yxYUyNIoKNN954mVtNDWSSJL0LhjE1tTzHhIFMkqQVaL311uvQZZ988sn84Ac/aOzfb7/9OO644xr7v/rVr3LhhRcu87puu+02Hnvsscb+UaNGrdB7f86fP58TTzyR7bbbjn79+vH+97+fBx544F0vd8aMGfTv3x+A8ePHc9JJJwEwduxYxo0b1zjdZZddxrXXXvuu17eiGMgkSVqFDR8+vDFoLF68mBdffJHJkyc3jh83bhwjRoxY5uU2DWTvxqJFi94x7LjjjmOjjTZi6tSpTJ48mauvvpoXX3xxhayvQX19PZdccgnwzkD2uc99jqOPPnqFru/dMJBJktTOJk6cyB577MHAgQM5+OCDeeWVVwB46qmn+OAHP8iuu+7K4MGDefrpp5k/fz577703gwcPZsCAAdx+++1LXfaIESMag8bkyZPp378/PXr04JVXXuGtt95iypQpDBo0iAkTJjBy5EiGDBnCfvvtx6xZswC48sor2W233dh111055JBDeP311xk3bhy/+c1vOPXUU6mrq+Ppp58G4KabbmLo0KHsuOOO3HPPPUARtk499VR22203Bg4cyOWXXw4UAWivvfbik5/8JAMGDFii5qeffpoHHniA7373u6yxRhFFtt12W/bff38ALrzwQvr370///v0bW/9mzJjBLrvswvHHH0+/fv3Yd999eeONNwCYMGECu+66K8OGDePHP/5x43rGjh3LRz/6UWbMmMFll13GRRddRF1dHffccw9nn302559//lLfn1GjRnHaaae9Y5snT57M0KFDqaurY+DAgUydOnWZjodmZeYq+xoyZEhKklSVxx577B3D1l133XcMGzBgQI4dOzYzM//7v/87v/zlL2dm5tChQ/OWW27JzMw33ngjX3vttVywYEHOnTs3MzNnz56d2223XS5evLjFZWdmbr311vnMM8/kZZddlpdeeml+85vfzN/97nd577335p577plvv/12Dhs2LF944YXMzLzhhhvyM5/5TGZmvvjii43L+cY3vpGXXHJJZmYec8wxedNNNzWOGzlyZJ5yyimZmfm73/0u995778zMvPzyy/M73/lOZma++eabOWTIkJw2bVreeeeduc466+S0adPeUe/tt9+eBx10ULPbMn78+Ozfv3/Onz8/582bl3379s2///3vOX369OzSpUs+/PDDmZl56KGH5nXXXfeO/fu1r30t+/Xrl5mZd955Z+6///6ZmXnWWWfl97///cb11Pa39P60tM1f/OIX8xe/+EVmZr711lv5+uuvv2M7mjs2gPHZQqbxtheSJLWjuXPnMmfOHEaOHAnAMcccw6GHHsq8efN49tlnOfjgg4Hi3lVQ3NvszDPP5O6772aNNdbg2Wef5fnnn+e9731vi+toaCUbN24cp5xyCs8++yzjxo2jZ8+eDB8+nCeeeIJHH32UffbZByhatTbddFMAHn30Ub75zW8yZ84c5s+fz3777dfiej7+8Y8DMGTIEGbMmAHAH//4RyZNmsTNN9/cuL1Tp05lrbXWYujQoct06weAe++9l4MPPph11123cZ333HMPBx54INtssw11dXVL1NB0/37605/m97//fZvX19L7s7RtHjZsGOeeey4zZ87k4x//ODvssMMybWNzDGSSJFWgaDB5p+uvv57Zs2czYcIEunbtSp8+fVq9hULDdWSPPPII/fv3Z8stt+SCCy5g/fXX59hjjyUz6devH/fdd9875h09ejS33XYbu+66K1dffTVjx45tcT3dunUDoEuXLixcuLBxO374wx++I8iNHTu2MVQ11a9fP/7xj3+wePHixlOWDVraL7Xrb6jhjTfeIDPb9ZeuzW3zJz/5SXbffXd+97vfsd9++/HTn/6UD3zgA+9qPQay0oxlTPBSa/pMn151CZJWAj179mTDDTfknnvuYc899+S6665j5MiRrL/++myxxRbcdtttHHTQQbz11lssWrSIuXPnsskmm9C1a1fuvPNOnnnmmVbXMWLECC644AK23XZbunTpwkYbbcScOXOYPHkyV155Jeuvvz6zZ8/mvvvuY9iwYSxYsIAnn3ySfv36MW/ePDbddFMWLFjA9ddfz+abbw5Ajx49mDdvXqvr3m+//bj00kv5wAc+QNeuXXnyyScbl9GS7bbbjvr6es466yzOOeccIoKpU6fy2GOP8f73v5/Ro0dz+umnk5nceuutXHfddS0ua4MNNqBnz57ce++9vO997+P6669vdroePXrw6quvvmN4S+/P0kybNo1tt92Wk046iWnTpjFp0qR3Hci8qF+SpBXo9ddfZ4sttmh8XXjhhVxzzTWceuqpDBw4kIkTJ/Ktb30LgOuuu45LLrmEgQMHMnz4cP79739z1FFHMX78eOrr67n++uvZeeedW13ngAEDePHFF9ljjz2WGNazZ0969erFWmutxc0338xpp53GrrvuSl1dXeMPAb7zne+w++67s88++yyxriOOOILvf//7DBo0qPGi/uYcd9xx9O3bl8GDB9O/f39OPPHExpakpfnpT3/Kv//9b7bffnsGDBjA8ccfz2abbcbgwYMZPXo0Q4cOZffdd+e4445j0KBBS13Wz3/+c77whS8wbNgw1l577WanOeCAA7j11lsbL+qv1dL705IxY8bQv39/6urqePzxx1fIrzVjaU2DK7v6+vpcUfdEsYVMK5otZNLqb8qUKeyyyy5Vl6GVUHPHRkRMyMz65qa3hUySJKliBjJJkqSKGcgkSZIqZiCTJEmqmIFMkiSpYgYySZKkirVbIIuIqyLihYh4tGbYmIiYWL5mRMTEcnifiHijZtxl7VWXJEmdTUTw1a9+tbH//PPP5+yzz+7QGkaNGkXTW1XdfvvtHHTQQY393/ve99h+++0b+3/7299y4IEHLvO6Jk6cyB133NHYX/sg8ZVVe96p/2rgR8C1DQMy8/CG7oi4AJhbM/3TmVnXjvVIklS5bbaZsUKXN316n1an6datG7fccgtnnHEGvXr1WuZ1LFy4kDXXXPGRYfjw4ZxwwgmN/ffddx/rr78+L7zwAptssgnjxo1jxIgRy7zciRMnMn78eD7ykY+skDoXLVpEly5dVsiyWtJuLWSZeTfwcnPjonjo1GHAr9pr/ZIkqbDmmmtywgkncNFFF71j3DPPPMPee+/NwIED2XvvvfnnP/8JFM+4POWUU9hrr7047bTTGD16NJ///OfZa6+92Hbbbbnrrrs49thj2WWXXRg9enTj8j7/+c9TX19Pv379OOuss5ZaV+/evenZsydPPfUUAM8++yyHHHJI41MExo0bx/Dhw5k9ezaHHHIIu+22G7vttht/+9vfAHjwwQcZPnw4gwYNanyI+ttvv823vvUtxowZQ11dHWPGjAHgscceY9SoUWy77bZccskljTX84he/YOjQodTV1XHiiSeyaNEiANZbbz2+9a1vsfvuuzf7DNAVrapryPYEns/MqTXDtomIhyPirojYs6UZI+KEiBgfEeNnz57d/pVKkrQa+MIXvsD111/P3Llzlxj+xS9+kaOPPppJkyZx1FFHcdJJJzWOe/LJJ/nzn//MBRdcAMArr7zCX//6Vy666CIOOOAATj75ZCZPnswjjzzCxIkTATj33HMZP348kyZN4q677mLSpElLravhwehPPPEEO+ywA3vssQfjxo1j4cKFTJo0id12240vf/nLnHzyyTz00EP8+te/5rjjjgNg55135u677+bhhx/mnHPO4cwzz2SttdbinHPO4fDDD2fixIkcfnhxcu7xxx/nD3/4Aw8++CDf/va3WbBgAVOmTGHMmDH87W9/Y+LEiXTp0qXxWZivvfYa/fv354EHHuB973vfCnkPlqaqh4sfyZKtY7OArTLzpYgYAtwWEf0y8x1PAc3MK4AroHh0UodUK0nSKm799dfn6KOP5pJLLlnieY/33Xcft9xyCwCf/vSn+frXv9447tBDD13iVN0BBxxARDBgwADe8573MGDAAAD69evHjBkzqKur48Ybb+SKK65g4cKFzJo1i8cee4yBAwe2WNeIESMYN24cixYtYtiwYQwdOpRzzjmHhx9+mJ122onu3bvz5z//mccee6xxnldffZV58+Yxd+5cjjnmGKZOnUpEsGDBghbXs//++9OtWze6devGJptswvPPP89f/vIXJkyYwG677QbAG2+8wSabbAJAly5dOOSQQ5ZlF78rHR7IImJN4OPAkIZhmfkW8FbZPSEingZ2BFbMgyolSRJf+cpXGDx4MJ/5zGdanKa4qqiw7rrrLjGuW7duAKyxxhqN3Q39CxcuZPr06Zx//vk89NBDbLjhhowePZo333xzqTUNHz6cH/7whyxatIjjjz+eHj168OabbzJ27NjG68cWL17Mfffd944Hh3/pS19ir7324tZbb2XGjBmMGjWqxfXU1tulSxcWLlxIZnLMMcfwve997x3Td+/evd2vG6tVxSnLDwKPZ+bMhgER0TsiupTd2wI7ANMqqE2SpNXWRhttxGGHHcbPfvazxmHDhw/nhhtuAOD6669/V6fnXn31VdZdd1169uzJ888/z+9///tW5+nbty/PPfcc99xzD4MGDQKgrq6Oyy67jOHDhwOw77778qMf/ahxnobTo3PnzmXzzTcH4Oqrr24c36NHD+bNm9fquvfee29uvvlmXnjhBQBefvllnnnmmTZt64rWnre9+BVwH7BTRMyMiM+Wo47gnRfzvx+YFBH/AG4GPpeZzf4gQJIkLb+vfvWrvPjii439l1xyCT//+c8ZOHAg1113HRdffPFyL3vXXXdl0KBB9OvXj2OPPbZNv5CMCHbffXd69epF165dARg2bBjTpk1rDGSXXHIJ48ePZ+DAgfTt25fLLivujvX1r3+dM844gxEjRjRejA+w11578dhjjy1xUX9z+vbty3e/+1323XdfBg4cyD777MOsWbOWe/vfjchcdS/Dqq+vz6b3NFleM7bZZoUsR2rQZ/r0qkuQ1M6mTJnCLrvsUnUZWgk1d2xExITMrG9ueu/UL0mSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJK3mzj33XPr168fAgQOpq6vjgQceqLSeOXPmsPHGG9Nw66377ruPiGDmzOKe8XPnzmWjjTZi8eLFy7zcn/zkJ439Y8eO5aMf/eiKK7wdVfUsS0mSOqUVfd/L1u55eN999/G///u//P3vf6dbt268+OKLvP322yu0hloLFy5kzTWXHi822GAD3vve9zJlyhT69u3LuHHjGDRoEOPGjeOwww7j/vvvZ/fdd2eNNZat3aghkP3Xf/3Xu9mERm3ZlhXFFjJJklZjs2bNolevXo3PcuzVqxebbbYZABMmTGDkyJEMGTKE/fbbj1mzZjFlyhSGDh3aOP+MGTMaHw7e3PQAo0aN4swzz2TkyJFcfPHFLU5Xq+Gh4gDjxo3j5JNPXqJ/+PDhLFq0iFNPPZXddtuNgQMHcvnllwMwf/589t57bwYPHsyAAQO4/fbbATj99NN5+umnqaur49RTT22c9hOf+AQ777wzRx11VGOrXFu3paPYQiZ1Ij6RQiuaT6RY+e27776cc8457Ljjjnzwgx/k8MMPZ+TIkSxYsIAvfelL3H777fTu3ZsxY8bwjW98g6uuuoq3336badOmse222zJmzBgOO+ywpU4PRevUXXfdxYIFCxg5cmSL0zUYPnw4d999N8cddxzTpk3j0EMPbQxc48aN44wzzuBnP/sZPXv25KGHHuKtt95ixIgR7Lvvvmy55ZbceuutrL/++rz44ovsscceHHjggZx33nk8+uijjc+6HDt2LA8//DCTJ09ms802Y8SIEfztb39j9913b9O2dCQDmSRJq7H11luPCRMmcM8993DnnXdy+OGHc95551FfX8+jjz7KPvvsA8CiRYvYdNNNATjssMO48cYbOf300xkzZgxjxozhiSeeaHF6gMMPPxyg1ekajBgxgvPOO4/p06fTp08funfvTmYyf/58JkyYwNChQ/nJT37CpEmTuPnmm4Hi2rKpU6eyxRZbcOaZZ3L33Xezxhpr8Oyzz/L88883u/1Dhw5liy22AIqHls+YMYMNNtigTdvSkQxkkiSt5rp06cKoUaMYNWoUAwYM4JprrmHIkCH069eP++677x3TH3744Rx66KF8/OMfJyLYYYcdeOSRR1qcHmDdddcFIDOXOl2DHXbYgVdeeYXf/va3DBs2DIAhQ4bw85//nG222Yb11luPzOSHP/wh++233xLzXn311cyePZsJEybQtWtX+vTpw5tvvtnsehpO1Tbsh4ULF7ZaY8O2dCSvIZMkaTX2xBNPMHXq1Mb+iRMnsvXWW7PTTjsxe/bsxlCyYMECJk+eDMB2221Hly5d+M53vtPYWrS06Wu1dTqAYcOGcfHFFzcGsmHDhvGDH/yA4cOHA7Dffvtx6aWXsmDBAgCefPJJXnvtNebOncsmm2xC165dufPOO3nmmWcA6NGjB/PmzWt1nyxLjR3FFjJJklZj8+fP50tf+hJz5sxhzTXXZPvtt+eKK65grbXW4uabb+akk05i7ty5LFy4kK985Sv069cPKFrJTj31VKaX1wm2Nn2Dtk4HxWnLO+64g/r6eqAIZNOmTWsMZMcddxwzZsxg8ODBZCa9e/fmtttu46ijjuKAAw6gvr6euro6dt55ZwA23nhjRowYQf/+/fnwhz/M/vvv3+w+WZYaO0o0/NpgVVRfX5/jx49fIcvyYmetaCvjxc4e51rRVsbjvCNNmTKFXXbZpeoytBJq7tiIiAmZWd/c9J6ylCRJqpiBTJIkqWIGMkmSpIoZyCRJehdW5Wux1T6W55gwkEmStJy6d+/OSy+9ZChTo8zkpZdeonv37ss0n7e9kCRpOW2xxRbMnDmT2bNnV12KViLdu3dvfDpAWxnIJElaTl27dmUbbyejFcBTlpIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVLF2e5ZlRFwFfBR4ITP7l8POBo4HGp7CemZm3lGOOwP4LLAIOCkz/9BetUmSVl8zfLakVrA+06e3+zras4XsauBDzQy/KDPryldDGOsLHAH0K+f5SUR0acfaJEmSVhrtFsgy827g5TZO/jHghsx8KzOnA08BQ9urNkmSpJVJFdeQfTEiJkXEVRGxYTlsc+BfNdPMLIdJkiSt9jo6kF0KbAfUAbOAC8rh0cy02dwCIuKEiBgfEeNnz57d3CSSJEmrlA4NZJn5fGYuyszFwJX857TkTGDLmkm3AJ5rYRlXZGZ9Ztb37t27fQuWJEnqAB0ayCJi05reg4FHy+7fAEdERLeI2AbYAXiwI2uTJEmqSnve9uJXwCigV0TMBM4CRkVEHcXpyBnAiQCZOTkibgQeAxYCX8jMRe1VmyRJ0sqk3QJZZh7ZzOCfLWX6c4Fz26seSZKklZV36pckSaqYgUySJKliBjJJkqSKGcgkSZIqZiCTJEmqmIFMkiSpYgYySZKkihnIJEmSKmYgkyRJqpiBTJIkqWIGMkmSpIoZyCRJkipmIJMkSaqYgUySJKliBjJJkqSKGcgkSZIqZiCTJEmqmIFMkiSpYgYySZKkihnIJEmSKmYgkyRJqpiBTJIkqWIGMkmSpIoZyCRJkipmIJMkSaqYgUySJKliBjJJkqSKGcgkSZIqZiCTJEmqmIFMkiSpYgYySZKkihnIJEmSKmYgkyRJqpiBTJIkqWLtFsgi4qqIeCEiHq0Z9v2IeDwiJkXErRGxQTm8T0S8ERETy9dl7VWXJEnSyqY9W8iuBj7UZNifgP6ZORB4EjijZtzTmVlXvj7XjnVJkiStVNotkGXm3cDLTYb9MTMXlr33A1u01/olSZJWFVVeQ3Ys8Pua/m0i4uGIuCsi9mxppog4ISLGR8T42bNnt3+VkiRJ7aySQBYR3wAWAteXg2YBW2XmIOAU4JcRsX5z82bmFZlZn5n1vXv37piCJUmS2lGHB7KIOAb4KHBUZiZAZr6VmS+V3ROAp4EdO7o2SZKkKnRoIIuIDwGnAQdm5us1w3tHRJeye1tgB2BaR9YmSZJUlTXba8ER8StgFNArImYCZ1H8qrIb8KeIALi//EXl+4FzImIhsAj4XGa+3OyCJS23vbiz6hK0mpledQHSaqLdAllmHtnM4J+1MO2vgV+3Vy2SJEkrM+/UL0mSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUsTYFsojo396FSJIkdVZtbSG7LCIejIj/iogN2rMgSZKkzqZNgSwz3wccBWwJjI+IX0bEPu1amSRJUifR5mvIMnMq8E3gNGAkcElEPB4RH2+v4iRJkjqDtl5DNjAiLgKmAB8ADsjMXcrui9qxPkmSpNXemm2c7kfAlcCZmflGw8DMfC4ivtkulUmSJHUSbQ1kHwHeyMxFABGxBtA9M1/PzOvarTpJkqROoK3XkP0ZWLumf51ymCRJkt6ltgay7pk5v6Gn7F6nfUqSJEnqXNoayF6LiMENPRExBHhjKdNLkiSpjdp6DdlXgJsi4rmyf1Pg8HapSJIkqZNpUyDLzIciYmdgJyCAxzNzQbtWJkmS1Em0tYUMYDegTznPoIggM69tl6okSZI6kbbeGPY64HzgfRTBbDegvpV5roqIFyLi0ZphG0XEnyJiavnvhjXjzoiIpyLiiYjYb7m2RpIkaRXU1hayeqBvZuYyLPtqihvK1rainQ78JTPPi4jTy/7TIqIvcATQD9gM+HNE7Nhw3zNJkqTVWVt/Zfko8N5lWXBm3g283GTwx4Bryu5rgINqht+QmW9l5nTgKWDosqxPkiRpVdXWFrJewGMR8SDwVsPAzDxwGdf3nsycVc47KyI2KYdvDtxfM93Mctg7RMQJwAkAW2211TKuXpIkaeXT1kB2dnsWQfHLzaaaPT2amVcAVwDU19cvyylUSZKklVJbb3txV0RsDeyQmX+OiHWALsuxvucjYtOydWxT4IVy+Exgy5rptgCee8fckiRJq6G2/sryeOBm4PJy0ObAbcuxvt8Ax5TdxwC31ww/IiK6RcQ2wA7Ag8uxfEmSpFVOW09ZfoHiIvsHADJzas31X82KiF8Bo4BeETETOAs4D7gxIj4L/BM4tFze5Ii4EXgMWAh8wV9YSpKkzqKtgeytzHw7orjUKyLWpIVrvBpk5pEtjNq7henPBc5tYz2SJEmrjbbe9uKuiDgTWDsi9gFuAn7bfmVJkiR1Hm0NZKcDs4FHgBOBO4BvtldRkiRJnUlbf2W5GLiyfEmSJGkFalMgi4jpNHPNWGZuu8IrkiRJ6mSW5VmWDbpT/DpyoxVfjiRJUufTpmvIMvOlmtezmfkD4APtW5okSVLn0NZTloNretegaDHr0S4VSZIkdTJtPWV5QU33QmAGcNgKr0aSJKkTauuvLPdq70IkSZI6q7aesjxlaeMz88IVU44kSVLnsyy/styN4iHgAAcAdwP/ao+iJEmSOpO2BrJewODMnAcQEWcDN2Xmce1VmCRJUmfR1kcnbQW8XdP/NtBnhVcjSZLUCbW1hew64MGIuJXijv0HA9e2W1WSJEmdSFt/ZXluRPwe2LMc9JnMfLj9ypIkSeo82nrKEmAd4NXMvBiYGRHbtFNNkiRJnUqbAllEnAWcBpxRDuoK/KK9ipIkSepM2tpCdjBwIPAaQGY+h49OkiRJWiHaGsjezsykuKCfiFi3/UqSJEnqXNoayG6MiMuBDSLieODPwJXtV5YkSVLn0eqvLCMigDHAzsCrwE7AtzLzT+1cmyRJUqfQaiDLzIyI2zJzCGAIkyRJWsHaesry/ojYrV0rkSRJ6qTaeqf+vYDPRcQMil9aBkXj2cD2KkySJKmzWGogi4itMvOfwIc7qB5JkqROp7UWstuAwZn5TET8OjMP6YCaJEmSOpXWriGLmu5t27MQSZKkzqq1QJYtdEuSJGkFae2U5a4R8SpFS9naZTf856L+9du1OkmSpE5gqYEsM7t0VCGSJEmdVVvvQyZJkqR2YiCTJEmqmIFMkiSpYm29U/8KExE7UTysvMG2wLeADYDjgdnl8DMz846OrU6SJKnjdXggy8wngDqAiOgCPAvcCnwGuCgzz+/omiRJkqpU9SnLvYGnM/OZiuuQJEmqTNWB7AjgVzX9X4yISRFxVURsWFVRkiRJHanDT1k2iIi1gAOBM8pBlwLfoXgiwHeAC4Bjm5nvBOAEgK222qpDapUkrTr24s6qS9BqZnoHrKPKFrIPA3/PzOcBMvP5zFyUmYuBK4Ghzc2UmVdkZn1m1vfu3bsDy5UkSWofVQayI6k5XRkRm9aMOxh4tMMrkiRJqkAlpywjYh1gH+DEmsH/ExF1FKcsZzQZJ0mStNqqJJBl5uvAxk2GfbqKWiRJkqpW9a8sJUmSOj0DmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxdasYqURMQOYBywCFmZmfURsBIwB+gAzgMMy85Uq6pMkSepIVbaQ7ZWZdZlZX/afDvwlM3cA/lL2S5IkrfZWplOWHwOuKbuvAQ6qrhRJkqSOU1UgS+CPETEhIk4oh70nM2cBlP9u0tyMEXFCRIyPiPGzZ8/uoHIlSZLaTyXXkAEjMvO5iNgE+FNEPN7WGTPzCuAKgPr6+myvAiVJkjpKJS1kmflc+e8LwK3AUOD5iNgUoPz3hSpqkyRJ6mgdHsgiYt2I6NHQDewLPAr8BjimnOwY4PaOrk2SJKkKVZyyfA9wa0Q0rP+Xmfn/IuIh4MaI+CzwT+DQCmqTJEnqcB0eyDJzGrBrM8NfAvbu6HokSZKqtjLd9kKSJKlTMpBJkiRVzEAmSZJUMQOZJElSxQxkkiRJFavqTv0rnb24s+oStJqZXnUBkqRVhi1kkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsU6PJBFxJYRcWdETImIyRHx5XL42RHxbERMLF8f6ejaJEmSqrBmBetcCHw1M/8eET2ACRHxp3LcRZl5fgU1SZIkVabDA1lmzgJmld3zImIKsHlH1yFJkrSyqPQasojoAwwCHigHfTEiJkXEVRGxYQvznBAR4yNi/OzZszuqVEmSpHZTWSCLiPWAXwNfycxXgUuB7YA6iha0C5qbLzOvyMz6zKzv3bt3R5UrSZLUbioJZBHRlSKMXZ+ZtwBk5vOZuSgzFwNXAkOrqE2SJKmjVfErywB+BkzJzAtrhm9aM9nBwKMdXZskSVIVqviV5Qjg08AjETGxHHYmcGRE1AEJzABOrKA2SZKkDlfFryzvBaKZUXd0dC2SJEkrA+/UL0mSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUMQOZJElSxQxkkiRJFTOQSZIkVcxAJkmSVDEDmSRJUsUMZJIkSRUzkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUsZUukEXEhyLiiYh4KiJOr7oeSZKk9rZSBbKI6AL8GPgw0Bc4MiL6VluVJElS+1qpAhkwFHgqM6dl5tvADcDHKq5JkiSpXa1sgWxz4F81/TPLYZIkSautNasuoIloZlguMUHECcAJZe/8iHii3atSrV7Ai1UXsSqI5o5mrSo8ztvI43yV5nHeRivwON+6pRErWyCbCWxZ078F8FztBJl5BXBFRxal/4iI8ZlZX3UdUnvyOFdn4HG+clnZTlk+BOwQEdtExFrAEcBvKq5JkiSpXa1ULWSZuTAivgj8AegCXJWZkysuS5IkqV2tVIEMIDPvAO6oug61yNPF6gw8ztUZeJyvRCIzW59KkiRJ7WZlu4ZMkiSp0zGQCYCImN+kf3RE/Kjs/lxEHN3K/I3TSyu7pse7tKqIiIyI62r614yI2RHxv8u4nLERUV923xERG6zgUrWMVrpryLTyyczLqq5BkgTAa0D/iFg7M98A9gGefTcLzMyPrJDK9K7YQqZWRcTZEfG1snu3iJgUEfdFxPcj4tGaSTeLiP8XEVMj4n8qKldaLhFRFxH3l8f3rRGxYURsEhETyvG7lq0TW5X9T0fEOtVWrU7q98D+ZfeRwK8aRkTEuhFxVUQ8FBEPR8THyuFrR8QN5fE9Bli7Zp4ZEdErIvrUfqdHxNci4uyye2xEXBQRd0fElPL/glvK7/vvdsA2r/YMZGqwdkRMbHgB57Qw3c+Bz2XmMGBRk3F1wOHAAODwiNgSadVxLXBaZg4EHgHOyswXgO4RsT6wJzAe2DMitgZeyMzXqytXndgNwBER0R0YCDxQM+4bwF8zczdgL+D7EbEu8Hng9fL4PhcYshzrfTsz3w9cBtwOfAHoD4yOiI2Xe2sEeMpS//FGZtY19ETEaGCJOziX1xj0yMxx5aBfAh+tmeQvmTm3nPYxikdE1D6bVFopRURPYIPMvKscdA1wU9k9DhgBvB/4P8CHKB7zdk9H1ykBZOakiOhD0TrW9DZR+wIHNpzVALoDW1Ecv5fUzD9pOVbdcKP2R4DJmTkLICKmUTxl56XlWKZKBjIti9ae5vVWTfciPL60eriHonVsa4pWgdMonrG7TBdRSyvYb4DzgVFAbetUAIdk5hLPeY7iYYyt3edqIUueOeveZHzDd/xilvy+X4zf9++apyzVZpn5CjAvIvYoBx1RZT3SilK27L4SEXuWgz4NNLSW3Q18CpiamYuBl4GPAH/r8EKl/7gKOCczH2ky/A/Al6JMYBExqBx+N3BUOaw/xanOpp4HNomIjSOiG0ueAVE7M9FqWX0WuDIiXgPGAnOrLUdaLutExMya/guBY4DLygv1pwGfAcjMGeX/bXeX094LbFH+gSJVIjNnAhc3M+o7wA+ASWUom0ERrC4Ffl6eqpwIPNjMMhdExDkU16RNBx5vj9rVPO/Ur2USEetl5vyy+3Rg08z8csVlSZK0SrOFTMtq/4g4g+LYeQYYXW05kiSt+mwhkyRJqpgX9UuSJFXMQCZJklQxA5kkSVLFDGSSVgoR8d7yWXtPR8RjEXFHROzY9Pl6Lcy7WUTcvILrGRUR/9tk2NUR8Ymy+6cR0Xcp84+OiM1WZE2SVl/+ylJS5cr7Jd0KXJOZR5TD6oD30IbHb2Xmc8An2rPGZtZ5XCuTjAYeBZ5r6zIjoktmNn1GrKROwBYySSuDvYAFmXlZw4DMnJiZSzwvsmwtuyci/l6+htcMf7TsHh0Rt0XEbyNiekR8MSJOiYiHI+L+iNionO6ksiVuUkTcsKwFR8TYiKiPiC5ly9mjEfFIRJxctqLVA9dHxMSIWDsi9i5reCQirirvhE5EzIiIb0XEvcDpEfH3mnXsEBETln13SlrV2EImaWXQH2hL8HgB2Ccz34yIHYBfUQSf5pY3iOJZfE8Bp2XmoIi4CDia4k7mpwPbZOZbEbFBC+vbMyIm1vRvxTufYVkHbJ6Z/QEiYoPMnBMRXwS+lpnjI6I7cDWwd2Y+GRHXAp8v6wB4MzPfV87/wYioy8yJFE8LuLrVvSJplWcLmaRVSVeKR3c9AtwEtHQN152ZOS8zZ1M83uu35fBHgD5l9ySKFqxPUTxUuTn3ZGZdw4vigc5NTQO2jYgfRsSHgFebmWYnYHpmPln2XwO8v2b8mJrunwKfiYguwOHAL1uoTdJqxEAmaWUwGRjShulOpngA8q4ULWNrtTDdWzXdi2v6F/OfMwP7Az8u1zshIpbrjEH5TMtdKZ7t+gWKQNVUtLKY12q6fw18mOL5gxMy86XlqUvSqsVAJmll8FegW0Qc3zAgInaLiJFNpusJzMrMxcCngS7Ls7KIWAPYMjPvBL4ObACst5zL6gWskZm/Bv4bGFyOmgf0KLsfB/pExPZl/6eBu5pbXma+CfyB8mHQy1OTpFWPgUxS5bJ4htvBwD7lbS8mA2fzzl8o/gQ4JiLuB3ZkyZalZdEF+EV56vNh4KLMnLOcy9ocGFtea3Y1cEY5/GrgsnJ4UFwPdlO5zsXAZU0XVON6IIE/LmdNklYxPstSklYyEfE1oGdm/nfVtUjqGP7KUpJWIhFxK7Ad8IGqa5HUcWwhkyRJqpjXkEmSJFXMQCZJklQxA5kkSVLFDGSSJEkVM5BJkiRVzEAmSZJUsf8PyALiUjttFjAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a bar plot for the contingency table\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=contingency_table.index, y=contingency_table['Normal'], label=\"Normal Weather\", color='b')\n",
    "sns.barplot(x=contingency_table.index, y=contingency_table['Severe'], label=\"Severe Weather\", color='r', bottom=contingency_table['Normal'])\n",
    "\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Relationship Between Claims History and Local Weather Conditions')\n",
    "plt.legend(title=\"Local Weather Conditions\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86.7067709398365,\n",
       " 1.4854701135975832e-19,\n",
       " 2,\n",
       " array([[88.088, 93.912],\n",
       "        [80.344, 85.656],\n",
       "        [73.568, 78.432]]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Perform the Chi-squared test\n",
    "chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n",
    "\n",
    "chi2, p_value, dof, expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Home_Age</th>\n",
       "      <th>Home_Size_sqft</th>\n",
       "      <th>Security_System</th>\n",
       "      <th>Fire_Safety</th>\n",
       "      <th>Roof_Condition</th>\n",
       "      <th>Swimming_Pool</th>\n",
       "      <th>Credit_Score</th>\n",
       "      <th>Claims_History</th>\n",
       "      <th>Home_Use</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Policy_Type</th>\n",
       "      <th>Deductible</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Proximity_to_Water</th>\n",
       "      <th>Local_Weather_Conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76</td>\n",
       "      <td>2164</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>False</td>\n",
       "      <td>719</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Vacation Home</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-3</td>\n",
       "      <td>507</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000002</td>\n",
       "      <td>Urban</td>\n",
       "      <td>45</td>\n",
       "      <td>1365</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>New</td>\n",
       "      <td>True</td>\n",
       "      <td>444</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>732</td>\n",
       "      <td>High</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000003</td>\n",
       "      <td>Rural</td>\n",
       "      <td>45</td>\n",
       "      <td>2697</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>475</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-5</td>\n",
       "      <td>1221</td>\n",
       "      <td>Low</td>\n",
       "      <td>Close</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000004</td>\n",
       "      <td>Urban</td>\n",
       "      <td>99</td>\n",
       "      <td>1199</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>New</td>\n",
       "      <td>False</td>\n",
       "      <td>510</td>\n",
       "      <td>High</td>\n",
       "      <td>Rental</td>\n",
       "      <td>False</td>\n",
       "      <td>HO-6</td>\n",
       "      <td>1326</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Far</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000005</td>\n",
       "      <td>Urban</td>\n",
       "      <td>21</td>\n",
       "      <td>3351</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Old</td>\n",
       "      <td>True</td>\n",
       "      <td>563</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Primary Residence</td>\n",
       "      <td>True</td>\n",
       "      <td>HO-1</td>\n",
       "      <td>344</td>\n",
       "      <td>High</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_ID Location  Home_Age  Home_Size_sqft  Security_System  \\\n",
       "0      1000001    Rural        76            2164            False   \n",
       "1      1000002    Urban        45            1365             True   \n",
       "2      1000003    Rural        45            2697            False   \n",
       "3      1000004    Urban        99            1199            False   \n",
       "4      1000005    Urban        21            3351            False   \n",
       "\n",
       "   Fire_Safety Roof_Condition  Swimming_Pool  Credit_Score Claims_History  \\\n",
       "0        False            Old          False           719         Medium   \n",
       "1         True            New           True           444           High   \n",
       "2        False            Old           True           475           High   \n",
       "3        False            New          False           510           High   \n",
       "4         True            Old           True           563         Medium   \n",
       "\n",
       "            Home_Use   Pets Policy_Type  Deductible Crime_Rate  \\\n",
       "0      Vacation Home   True        HO-3         507        Low   \n",
       "1             Rental   True        HO-5         732       High   \n",
       "2             Rental   True        HO-5        1221        Low   \n",
       "3             Rental  False        HO-6        1326     Medium   \n",
       "4  Primary Residence   True        HO-1         344       High   \n",
       "\n",
       "  Proximity_to_Water Local_Weather_Conditions  \n",
       "0           Moderate                   Normal  \n",
       "1              Close                   Severe  \n",
       "2              Close                   Severe  \n",
       "3                Far                   Normal  \n",
       "4           Moderate                   Normal  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86.7067709398365,\n",
       " 1.4854701135975832e-19,\n",
       " 2,\n",
       " array([[88.088, 93.912],\n",
       "        [80.344, 85.656],\n",
       "        [73.568, 78.432]]),\n",
       " Local_Weather_Conditions  Normal  Severe\n",
       " Claims_History                          \n",
       " High                          44     138\n",
       " Low                          123      43\n",
       " Medium                        75      77)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Create a contingency table\n",
    "contingency_table = pd.crosstab(data['Claims_History'], data['Local_Weather_Conditions'])\n",
    "\n",
    "# Perform the Chi-square test\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table)\n",
    "\n",
    "chi2, p, dof, expected, contingency_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the stacked bar chart with a blue gradient and labeled counts\n",
    "plt.figure(figsize=(10, 6))\n",
    "bar_plot = sns.countplot(x='Claims_History', \n",
    "                         order=['Low', 'Medium', 'High'], \n",
    "                         hue='Local_Weather_Conditions', \n",
    "                         data=data,\n",
    "                         palette='Blues')\n",
    "\n",
    "# Adding count labels to each bar\n",
    "for p in bar_plot.patches:\n",
    "    bar_plot.annotate(format(p.get_height(), '.0f'), \n",
    "                      (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                      ha = 'center', va = 'center', \n",
    "                      xytext = (0, 5), \n",
    "                      textcoords = 'offset points')\n",
    "\n",
    "# Adding plot title and labels\n",
    "plt.title('Number of Homes by Claims History and Local Weather Conditions')\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Number of Homes')\n",
    "plt.legend(title='Local Weather Conditions')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6fjywR+0Ky2aJ0yl+wd1OUUNzOPAJil81rwH/QZFIQZHAbVKWXwtc09yNKz/451N09q0re5aiP9NfKfoYtcT9jH4L/AWYXD6+X65rLPB5iuaqORRNdqesQfyzKZqrzqFoQvhX4COZOavJF66ZL1EkCZMp9sVvKfr6rY2vU2zj36No+vkrRc0DFB2d/0rRH2oM8LNs+j5Ct1N0hB9P0Rn7qroJZZLwKEXy8UBDLy7newPYn+IYe7g8Zu+hqHl4voGXNHpsr6PeFMnqHIrmlNnAj1po2ftQbNubFLV7X87MF8tpF1Ako3Mj4vjMvAf4DvAHilqpQRSfu9W5jqL/YKPNlRT9k+6m6Cs3lSLpbKpJbLnMfBc4luKzMYeiX+kfV/Oy/SmSqNrHPJr+vJxEcSw8TdGP6Ctl+Y8pulLMojgHruk9w+6jqGGpPZeML5c5LjPfLrfzVopz243l52MixcVUsPr99/vyeXZEPLq6gDLzZYrbU3yLIsl7maJWttnfwZl5KcUPtPNqlnEmK2pJv0/Rh3MC8ATFZ7K5N/Ndk/P6VcDu5XF8WwPT1yWOk4Ap5fvxBVY0z3cIsXI3AUlacxFxNUWNxHlVx7KhK2uSfkPR+X9Z1fFIahnebFDSOoni/l7HUtzzSa2obGb9MvArkzFpw2KTpaS1FhEXUTT1/LCmaU6tIIqbQ8+luDLvx5UGI6nF2WQpSZJUMWvIJEmSKmZCJkmSVLH1ulN/v379cuDAgVWHIUmStFrjxo2blZn9G5q2XidkAwcOZOzYsVWHIUmStFoR0ejfUtlkKUmSVDETMkmSpIqZkEmSJFVsve5DJklSlRYvXsy0adNYuHBh1aGoHenevTsDBgygS5cuzX6NCZkkSWtp2rRp9OrVi4EDBxIRVYejdiAzmT17NtOmTWPHHXds9utsspQkaS0tXLiQzTff3GRMy0UEm2+++RrXmpqQSZK0DkzGVN/aHBMmZJIktaBNNtmkTZf91a9+lR//+MfLx4844ghOO+205ePnnHMOl1566Rqv67bbbuPJJ59cPj5q1KgWvffnm2++yRlnnMGgQYMYPHgwBx98MA8//PA6L3fKlCkMGTIEgLFjx3LWWWcBMHr0aB566KHl811xxRX8+te/Xuf1tRQTMkmS1mP777//8kRj2bJlzJo1i0mTJi2f/tBDD3HAAQes8XLrJ2TrYunSpauUnXbaafTt25fnnnuOSZMmce211zJr1qwWWV+dESNGcPnllwOrJmRf+MIX+MxnPtOi61sXJmSSJLWy8ePH8773vY9hw4ZxzDHHMGfOHACef/55PvjBD7LHHnuw11578cILL/Dmm2/ygQ98gL322ouhQ4dy++23N7nsAw44YHmiMWnSJIYMGUKvXr2YM2cOixYt4qmnnmLPPfdk3LhxHHLIIey9994cccQRTJ8+HYBf/vKX7LPPPuyxxx58/OMf5+233+ahhx7ijjvu4Nxzz2X48OG88MILAPz+979n5MiR7LrrrjzwwANAkWyde+657LPPPgwbNoxf/OIXQJEAvf/97+dTn/oUQ4cOXSnmF154gYcffpjvf//7bLRRkYrstNNOHHnkkQBceumlDBkyhCFDhiyv/ZsyZQq77bYbn//85xk8eDCHH34477zzDgDjxo1jjz32YL/99uOnP/3p8vWMHj2aj3zkI0yZMoUrrriCyy67jOHDh/PAAw9wwQUX8KMf/ajJ92fUqFF8/etfX2WbJ02axMiRIxk+fDjDhg3jueeeW6PjoUGZud4+9t5775QkqSpPPvnkKmU9e/ZcpWzo0KE5evTozMz8zne+k1/+8pczM3PkyJH5xz/+MTMz33nnnXzrrbdy8eLFOW/evMzMnDlzZg4aNCiXLVvW6LIzM3fYYYecOnVqXnHFFfnzn/88zzvvvLzrrrvywQcfzIMOOijffffd3G+//fL111/PzMwbb7wxP/vZz2Zm5qxZs5Yv59vf/nZefvnlmZl58skn5+9///vl0w455JA8++yzMzPzrrvuyg984AOZmfmLX/wiL7rooszMXLhwYe699945efLkvPfee3PjjTfOyZMnrxLv7bffnkcffXSD2zJ27NgcMmRIvvnmm7lgwYLcfffd89FHH80XX3wxO3XqlI899lhmZh533HF5/fXXr7J/v/a1r+XgwYMzM/Pee+/NI488MjMzzz///PzhD3+4fD214429P41t85lnnpm/+c1vMjNz0aJF+fbbb6+yHQ0dG8DYbCSn8bYXkiS1onnz5jF37lwOOeQQAE4++WSOO+44FixYwCuvvMIxxxwDFPeuguLeZt/61re4//772WijjXjllVeYMWMGW221VaPrqKsle+ihhzj77LN55ZVXeOihh+jTpw/7778/zzzzDBMnTuSwww4DilqtrbfeGoCJEydy3nnnMXfuXN58802OOOKIRtdz7LHHArD33nszZcoUAP7yl78wYcIEbrnlluXb+9xzz9G1a1dGjhy5Rrd+AHjwwQc55phj6Nmz5/J1PvDAA3z0ox9lxx13ZPjw4SvFUH//nnTSSdx9993NXl9j709T27zffvtx8cUXM23aNI499lh22WWXNdrGhpiQSZJUgaLCZFU33HADM2fOZNy4cXTp0oWBAweu9hYKdf3InnjiCYYMGcJ2223HJZdcQu/evTn11FPJTAYPHsyYMWNWee0pp5zCbbfdxh577MG1117L6NGjG11Pt27dAOjUqRNLlixZvh3/9V//tUoiN3r06OVJVX2DBw/m8ccfZ9myZcubLOs0tl9q118XwzvvvENmtuqVrg1t86c+9Sn23Xdf7rrrLo444gh+9atfceihh67TekzIKjLijCurDqHDGfuL06sOQVIH1KdPHzbbbDMeeOABDjroIK6//noOOeQQevfuzYABA7jttts4+uijWbRoEUuXLmXevHlsscUWdOnShXvvvZepU6eudh0HHHAAl1xyCTvttBOdOnWib9++zJ07l0mTJvHLX/6S3r17M3PmTMaMGcN+++3H4sWLefbZZxk8eDALFixg6623ZvHixdxwww1su+22APTq1YsFCxasdt1HHHEEP//5zzn00EPp0qULzz777PJlNGbQoEGMGDGC888/nwsvvJCI4LnnnuPJJ5/k4IMP5pRTTuEb3/gGmcmtt97K9ddf3+iyNt10U/r06cODDz7IgQceyA033NDgfL169WL+/PmrlDf2/jRl8uTJ7LTTTpx11llMnjyZCRMmrHNCZqd+SZJa0Ntvv82AAQOWPy699FKuu+46zj33XIYNG8b48eP57ne/C8D111/P5ZdfzrBhw9h///157bXXOPHEExk7diwjRozghhtu4L3vfe9q1zl06FBmzZrF+973vpXK+vTpQ79+/ejatSu33HILX//619ljjz0YPnz48gsBLrroIvbdd18OO+ywldb1iU98gh/+8Ifsueeeyzv1N+S0005j9913Z6+99mLIkCGcccYZy2uSmvKrX/2K1157jZ133pmhQ4fy+c9/nm222Ya99tqLU045hZEjR7Lvvvty2mmnseeeeza5rGuuuYYvfvGL7LfffvTo0aPBeY466ihuvfXW5Z36azX2/jTmpptuYsiQIQwfPpynn366Ra7WjKaqBtu7ESNGZEveE6UtWUPW9qwhk9TSnnrqKXbbbbeqw1A71NCxERHjMnNEQ/NbQyZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqlirJWQRcXVEvB4RE2vKboqI8eVjSkSML8sHRsQ7NdOuaK24JElSy4kIzjnnnOXjP/rRj7jgggvaNIZRo0axvt4Gq05r3qn/WuD/Ab+uK8jME+qGI+ISYF7N/C9k5vBWjEeSpA1ajDijRZeXY3+x2nm6devGH//4R775zW/Sr1+/NV7HkiVL6NzZPw5qtT2QmfdHxMCGpkXxp1PHA+v2PwOSJKlSnTt35vTTT+eyyy7j4osvXmna1KlTOfXUU5k5cyb9+/fnmmuuYfvtt+eUU06hb9++PPbYY+y1117Mnj2bHj168PTTTzN16lSuueYarrvuOsaMGcO+++7LtddeC8C//Mu/8Mgjj/DOO+/wz//8z3zve9+rYItbR1V9yA4CZmTmczVlO0bEYxFxX0Qc1NgLI+L0iBgbEWNnzpzZ+pFKkqQmffGLX+SGG25g3rx5K5WfeeaZfOYzn2HChAmceOKJnHXWWcunPfvss/z1r3/lkksuAWDOnDn87W9/47LLLuOoo47iq1/9KpMmTeKJJ55g/PjxAFx88cWMHTuWCRMmcN999zFhwoQ228bWVlVC9kngdzXj04HtM3NP4GzgtxHRu6EXZuaVmTkiM0f079+/DUKVJElN6d27N5/5zGe4/PLLVyofM2YMn/rUpwA46aSTePDBB5dPO+644+jUqdPy8aOOOoqIYOjQoWy55ZYMHTqUjTbaiMGDBzNlyhQAbr75Zvbaay/23HNPJk2axJNPPtn6G9dG2jwhi4jOwLHATXVlmbkoM2eXw+OAF4Bd2zo2SZK0dr7yla9w1VVX8dZbbzU6T9FjqdCzZ8+VpnXr1g2AjTbaaPlw3fiSJUt48cUX+dGPfsQ999zDhAkTOPLII1m4cGELb0V1qqgh+yDwdGZOqyuIiP4R0akc3gnYBZhcQWySJGkt9O3bl+OPP56rrrpqedn+++/PjTfeCMANN9zAgQceuNbLnz9/Pj179qRPnz7MmDGDu+++e51jbk9a87YXvwPGAO+JiGkR8bly0idYubkS4GBgQkQ8DtwCfCEz32it2CRJUss755xzmDVr1vLxyy+/nGuuuYZhw4Zx/fXX85Of/GStl73HHnuw5557MnjwYE499VQOOOCAlgi53YjMrDqGtTZixIhcX+87MuKMK6sOocMZ+4vTqw5B0gbmqaeeYrfddqs6DLVDDR0bETEuM0c0NL936pckSaqYCZkkSVLFTMgkSZIqZkImSZJUMRMySZKkipmQSZIkVcyETJIkrbWLL76YwYMHM2zYMIYPH87DDz9cdUjrpc5VByBJklpGS9/jcnX3bxwzZgx33nknjz76KN26dWPWrFm8++67LRpDnSVLltC584abtlhDJkmS1sr06dPp16/f8v+e7NevH9tssw3jxo3jkEMOYe+99+aII45g+vTpPPXUU4wcOXL5a6dMmcKwYcMAGpwfYNSoUXzrW9/ikEMO4Sc/+Umj820INtxUU1Ll/EeKtuc/UqgtHX744Vx44YXsuuuufPCDH+SEE05g//3350tf+hK33347/fv356abbuLb3/42V199Ne+++y6TJ09mp5124qabbuL4449n8eLFjc4PMHfuXO677z4WL17MIYcc0uh86zsTMkmStFY22WQTxo0bxwMPPMC9997LCSecwHnnncfEiRM57LDDAFi6dClbb701AMcffzw333wz3/jGN7jpppu46aabeOaZZxqdH+CEE04AWO186zsTMkmStNY6derEqFGjGDVqFEOHDuWnP/0pgwcPZsyYMavMe8IJJ3Dcccdx7LHHEhHssssuPPHEE43OD9CzZ08AMrPJ+dZ39iGTJElr5ZlnnuG5555bPj5+/Hh22203Zs6cuTxxWrx4MZMmTQJg0KBBdOrUiYsuumh5zdd73vOeRuev1dz51lfWkEmSpLXy5ptv8qUvfYm5c+fSuXNndt55Z6688kpOP/10zjrrLObNm8eSJUv4yle+wuDBg4Giluzcc8/lxRdfBKBr167ccsstjc5fp7nzra8iM6uOYa2NGDEix44dW3UYa8XOzm3Pzs5tz+O87Xmct62nnnqK3Xbbreow1A41dGxExLjMHNHQ/DZZSpIkVcyETJIkqWImZJIkSRUzIZMkaR2sz32x1TrW5pgwIZMkaS11796d2bNnm5Rpucxk9uzZdO/efY1e520vJElaSwMGDGDatGnMnDmz6lDUjnTv3p0BAwas0WtMyCRJWktdunRhxx13rDoMbQBsspQkSaqYCZkkSVLFTMgkSZIqZkImSZJUMRMySZKkipmQSZIkVcyETJIkqWImZJIkSRUzIZMkSaqYCZkkSVLFTMgkSZIq1mr/ZRkRVwMfAV7PzCFl2QXA54G6f2H9Vmb+uZz2TeBzwFLgrMz8n9aKTZKkljLijCurDqHDGfuL06sOocW1Zg3ZtcCHGii/LDOHl4+6ZGx34BPA4PI1P4uITq0YmyRJUrvRaglZZt4PvNHM2T8G3JiZizLzReB5YGRrxSZJktSeVNGH7MyImBARV0fEZmXZtsDLNfNMK8skSZI2eK3Wh6wRPwcuArJ8vgQ4FYgG5s2GFhARpwOnA2y//fatE6WkDiWXLeOpW/+Nrj03ZecPnckrj9zOvKmPQwSdu/di4KhT6Npz06rDlLQBa9OELDNn1A1HxC+BO8vRacB2NbMOAF5tZBlXAlcCjBgxosGkTZLWxOsT76H7pluxbPFCALba43C23edj5bS/Mf3Ru9jhoBOrDFHSBq5NmywjYuua0WOAieXwHcAnIqJbROwI7AL8oy1jk9QxvfvmHOa99AT93nvg8rJOXXssH166eFEVYUnqYFrzthe/A0YB/SJiGnA+MCoihlM0R04BzgDIzEkRcTPwJLAE+GJmLm2t2CSpzstjbmbbfT++vHasziv/uI3Zz/2dTl17sOtHzq4oOkkdRaslZJn5yQaKr2pi/ouBi1srHkmqb+7UCXTp0Yue/XdgwavPrDRt25FHs+3Io5n+2N3MnHQv24z4aEVRSuoIvFO/pA7rrRkvMHfq4zzx228x+Z5fMf+Vp3nxbyv/buy780jmvPhYRRFK6ija+ipLSWo3th15DNuOPAaABa8+w4wJ/8uOh36OhfNm0L3PlgDMm/o43TfdqsowJXUAJmSSVM8rD9/KwnkziAi6btKX7b3CUlIrMyGTJKDXNu+h1zbvAWDQ4V+oOBpJHY19yCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFWu1hCwiro6I1yNiYk3ZDyPi6YiYEBG3RsSmZfnAiHgnIsaXjytaKy5JkqT2pjVryK4FPlSv7H+BIZk5DHgW+GbNtBcyc3j5+EIrxiVJktSutFpClpn3A2/UK/tLZi4pR/8ODGit9UuSJK0vquxDdipwd834jhHxWETcFxEHNfaiiDg9IsZGxNiZM2e2fpSSJEmtrJKELCK+DSwBbiiLpgPbZ+aewNnAbyOid0OvzcwrM3NEZo7o379/2wQsSZLUito8IYuIk4GPACdmZgJk5qLMnF0OjwNeAHZt69gkSZKq0KYJWUR8CPg68NHMfLumvH9EdCqHdwJ2ASa3ZWySJElV6dxaC46I3wGjgH4RMQ04n+Kqym7A/0YEwN/LKyoPBi6MiCXAUuALmflGgwuWtN4YN25c1SFI0nqh1RKyzPxkA8VXNTLvH4A/tFYskiRJ7Zl36pckSarYahOyiPhyRPSOwlUR8WhEHN4WwUmSJHUEzakhOzUz5wOHA/2BzwL/3qpRSZIkdSDNSciifP4wcE1mPl5TJkmSpHXUnIRsXET8hSIh+5+I6AUsa92wJEmSOo7mXGX5OWA4MDkz346IzSmaLbUBmjL6Oua99ASde/Ri8HHnA/DKI7czb+rjEEHn7r0YOOoUuvbclFy2lCn3/Zq3Z70EuYy+u7yPrff8p4q3QJJU38K5rzH5nl8uH180fxbbjDiKt2ZMZuG8GQAsXfQOnbr1YPePf6eqMDu05iRkCexOcXf9C4GeQPfWDErV2fw9+7HFkPfz4r3XLC/bao/D2XafjwHw+sS/Mf3Ru9jhoBOZM3kcuXQJg487n2VL3mXSzRfQd+d96NarX1XhS5Ia0H3TrZYnWrlsGRNu+DqbDtyTLYd+cPk8L4/5PZ269qgqxA6vOU2WPwP2A+ruK7YA+GmrRaRK9dp6Vzp123ilstoP6NLFi1aatmzJInLZUpYteZfo1IlOXfwwS1J7tuDVp+nWuz/dem2+vCwzmTN5HH133qfCyDq25tSQ7ZuZe0XEYwCZOSciurZyXGpnXvnHbcx+7u906tqDXT9yNgCb7bQ3c6c8zoTf/CvLlrzLgP2Oo3P3nhVHKklqyhvPP0LfQSsnXm++9hxdevSie58tK4pKzakhW1z+z2RC8b+T2Km/w9l25NEMO/Hf6bvzSGZOuheAt15/kdhoI4Z9+j8Z8smLmTHhryyaP7PiSCVJjVm2dAlzpz7OZjvtvVL5G88/Qt+dR1YUlaB5CdnlwK3AFhFxMfAg8G+tGpXarb47j2TOi48B8Mbz/6D3gMHERp3o0qM3m2w5iLdmTq04QklSY+a/PJGN+21Pl417Ly/LZUuZO+UxNttpRIWRabUJWWbeAPwr8ANgOnB0Zv6+tQNT+1F3BQ7AvKmP033TrQDouklfFrz6NJnJ0sWLeOv1F5dPkyS1P0VN2MrNlfNfeYrum25F1002qygqQfP/XHwG8EA5f4+I2CszH229sFSVyff8igWvPsOShW8y4Yavs83eRzHvpYksnDeDiKDrJn3Z/qATAeg/eBRTRl/Hk7d8D7K4QnPjzQdUvAWSpIYsW/Iu8195ih0O/vRK5XNeGLtKnzK1vdUmZBFxEXAK8AJlP7Ly+dDWC0tV2ekDp61S1u+9BzY4b6cu3Rl02BmtHZIkqQVs1Lkrw0++dJXygaNOaftgtIrm1JAdDwzKzHdbOxhJkqSOqDmd+icCm7ZyHJIkSR1Wc2rIfgA8FhETgeV3Bc3Mj7ZaVJIkSR1IcxKy64D/AJ7A+49JkiS1uOYkZLMy8/JWj0SSJKmDak5CNi4ifgDcwcpNlt72QpIkqQU0JyHbs3x+X02Zt72QJElqIatNyDLz/W0RiCRJUke12tteRESfiLg0IsaWj0siok9bBCdJktQRNOc+ZFcDCyhuEHs8MB+4pjWDkiRJ6kia04dsUGZ+vGb8exExvpXikSRJ6nCaU0P2TkQs/zPDiDgAeKf1QpIkSepYmlND9i/AdWW/sQDeoPizcUmSJLWA5lxlOR7YIyJ6l+PzWzsoSZKkjqTRhCwizm6kHIDMvLSVYpIkSepQmupD1qvm8bV6471aPzRJkqSOodEassz8Xt1wRBxdOy5JkqSW05yrLKH4qyRJkiS1guYmZJIkSWolTXXqf4IVNWM7R8SEuklAZuaw1g5OkiSpI2jqthcfWZcFR8TV5TJez8whZVlf4CZgIDAFOD4z55TTvgl8DlgKnJWZ/7Mu65ckSVpfNNpkmZlTm3o0Y9nXAh+qV/YN4J7M3AW4pxwnInYHPgEMLl/zs4jotBbbI0mStN5ptT5kmXk/xV39a30MuK4cvg44uqb8xsxclJkvAs8DI1srNkmSpPakrTv1b5mZ0wHK5y3K8m2Bl2vmm1aWrSIiTo+IsRExdubMma0arCRJUltoNCGLiHvK5/9ogziigbIGb7WRmVdm5ojMHNG/f/9WDkuSJKn1NdWpf+uIOAT4aETcSL2kKTMfXYv1zYiIrTNzekRsDbxelk8DtquZbwDw6losX5Ikab3TVEL2XYpO9wOA+v9bmcCha7G+O4CTgX8vn2+vKf9tRFwKbAPsAvxjLZYvSZK03mnqr5NuAW6JiO9k5kVruuCI+B0wCugXEdOA8ykSsZsj4nPAS8Bx5bomRcTNwJPAEuCLmbl0TdcpSZK0PmqqhgyAzLwoIj4KHFwWjc7MO5vxuk82MukDjcx/MXDx6pYrSZK0oVntVZYR8QPgyxS1V08CXy7LJEmS1AJWW0MGHAkMz8xlABFxHfAY8M3WDEySJKmjaO59yDatGe7TCnFIkiR1WM2pIfsB8FhE3Etx64uDsXZMkiSpxTSnU//vImI0sA9FQvb1zHyttQOTJEnqKJpTQ1b3N0d3tHIskiRJHVJb/5elJEmS6jEhkyRJqliTCVlEbBQRE9sqGEmSpI6oyYSsvPfY4xGxfRvFI0mS1OE0p1P/1sCkiPgH8FZdYWZ+tNWikiRJ6kCak5B9r9WjkCRJ6sCacx+y+yJiB2CXzPxrRGwMdGr90CRJkjqG5vy5+OeBW4BflEXbAre1YkySJEkdSnNue/FF4ABgPkBmPgds0ZpBSZIkdSTNScgWZea7dSMR0RnI1gtJkiSpY2lOQnZfRHwL6BERhwG/B/7UumFJkiR1HM1JyL4BzASeAM4A/gyc15pBSZIkdSTNucpyWURcBzxM0VT5TGbaZClJktRCVpuQRcSRwBXAC0AAO0bEGZl5d2sHJ0mS1BE058awlwDvz8znASJiEHAXYEImSZLUAprTh+z1umSsNBl4vZXikSRJ6nAarSGLiGPLwUkR8WfgZoo+ZMcBj7RBbJIkSR1CU02WR9UMzwAOKYdnApu1WkSSJEkdTKMJWWZ+ti0DkSRJ6qiac5XljsCXgIG182fmR1svLEmSpI6jOVdZ3gZcRXF3/mWtGo0kSVIH1JyEbGFmXt7qkUiSJHVQzUnIfhIR5wN/ARbVFWbmo60WlSRJUgfSnIRsKHAScCgrmiyzHJckSdI6ak5CdgywU2a+29rBSJIkdUTNuVP/48CmrRyHJElSh9WcGrItgacj4hFW7kPmbS8kSZJaQHMSsvNbcoUR8R7gppqinYDvUtTCfZ7inwAAvpWZf27JdUuSJLVHq03IMvO+llxhZj4DDAeIiE7AK8CtwGeByzLzRy25PkmSpPauOXfqX0BxVSVAV6AL8FZm9m6B9X8AeCEzp0ZECyxOkiRp/bPaTv2Z2Ssze5eP7sDHgf/XQuv/BPC7mvEzI2JCRFwdEf6BuSRJ6hCa04dsJZl5W0R8Y11XHBFdgY8C3yyLfg5cRFEbdxFwCXBqA687HTgdYPvtt1/XMCRJWifjxo2rOgRtAJrTZHlszehGwAhWNGGui38CHs3MGQB1z+U6fwnc2dCLMvNK4EqAESNGtEQckiRJlWpODdlRNcNLgCnAx1pg3Z+kprkyIrbOzOnl6DHAxBZYhyRJUrvXnKssP9vSK42IjYHDgDNqiv8zIoZT1L5NqTdNkiRpg9VoQhYR323idZmZF63tSjPzbWDzemUnre3yJEmS1mdN1ZC91UBZT+BzFMnUWidkkiRJWqHRhCwzL6kbjohewJcpbt56I8UVkJIkSWoBTfYhi4i+wNnAicB1wF6ZOactApMkSeoomupD9kPgWIpbTAzNzDfbLCpJkqQOpKk79Z8DbAOcB7waEfPLx4KImN824UmSJG34mupDttq/VZIkSdK6M+mSJEmqmAmZJElSxdb4z8Ulab03ZTTMewk694DBxxVl0/4Oc6fCRp2gW2/Y4RDo3A3eeh2mPlC+MGHrvWGzHauKXNIGyoRMUsez+XtgiyHw4r0rynoPgG1HQmwE0x6G18bDgH2hR1/Y7ZiifPHb8OQtsOkOxbgktRDPKJI6nl5bQ6duK5f1HrAiyeq5BSwu/6xko84rypctAaLNwpTUcVhDJkn1zX4GNhu0Yvyt12HKffDuAhj4fmvHJLU4EzJJqjX90SLh6rvzirKeWxR9zd6ZU/Q/67NdUXMmSS3En3mSVGf2s0Vn/x0PhWigabLHZkUi9o7/ICepZZmQSRLAvJeLjvyDjli59mvRfMhl5fACWDQPuvWqJERJGy7r3CV1PJPvgQWvwpKFMOEG2GbvIhlbthSe+3MxT88tYIeD4M3X4LXHV/Qb2/5A6Ny9stAlbZhMyCR1PDt9YNWyfu9teN7Ndy0ektSKbLKUJEmqmAmZJElSxUzIJEmSKmZCJkmSVDETMkmSpIqZkEmSJFXMhEySJKliJmSSJEkVMyGTJEmqmAmZJElSxUzIJEmSKmZCJkmSVDETMkmSpIqZkEmSJFXMhEySJKliJmSSJEkVMyGTJEmqWOcqVhoRU4AFwFJgSWaOiIi+wE3AQGAKcHxmzqkiPkmSpLZUZQ3Z+zNzeGaOKMe/AdyTmbsA95TjkiRJG7z21GT5MeC6cvg64OjqQpEkSWo7VSVkCfwlIsZFxOll2ZaZOR2gfN6ioRdGxOkRMTYixs6cObONwpUkSWo9lfQhAw7IzFcjYgvgfyPi6ea+MDOvBK4EGDFiRLZWgJIkSW2lkhqyzHy1fH4duBUYCcyIiK0ByufXq4hNkiSprbV5QhYRPSOiV90wcDgwEbgDOLmc7WTg9raOTZIkqQpVNFluCdwaEXXr/21m/ndEPALcHBGfA14CjqsgNkmSpDbX5glZZk4G9migfDbwgbaOR5IkqWrt6bYXkiRJHZIJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKVXWn/g5v3LhxVYcgSZLaCRMySZI2dAvnwuR7Vowvmg/bjICli2DW09C5R1G+7T7QZ/tKQuzoTMgkSdrQdd8Udv94MZzLYMINsOlAmP0MbDEUtlrl9qBqY/YhkySpI1nwKnTrDd16VR2JalhDJklSR/LG89B30IrxmZPgjedg434wYD/o3K262Dowa8gkSeooli2FuVNhs52K8f67w5BPwG4fhy4bw7Qx1cbXgZmQSZLUUcx/uagJ67JxMd5lY4iNIAL67QZvzaw2vg7MhEySpI7ijeeh784rxhe/vWJ47ovQY7O2j0mAfcgkSeoYli2B+a/ADgevKJv2d3h7dlFD1nWTlaepTZmQSZLUEWzUGYafvHLZjodWE4tWYZOlJElSxUzIJEmSKmZCJkmSVDETMkmSpIqZkEmSJFXMhEySJKliJmSSJEkVMyGTJEmqmAmZJElSxUzIJEmSKmZCJkmSVDETMkmSpIqZkEmSJFXMhEySJKliJmSSJEkVMyGTJEmqmAmZJElSxUzIJEmSKmZCpqblMnjyD/D8f69c/trjMO5KWLKwmrgkSdqAtHlCFhHbRcS9EfFUREyKiC+X5RdExCsRMb58fLitY1MDXp8I3TdduezdN2HBK9B1k0pCkiRpQ1NFDdkS4JzM3A14H/DFiNi9nHZZZg4vH3+uIDbVevdNmPcS9HvvyuUvj4Ft960mJkmSNkCd23qFmTkdmF4OL4iIp4Bt2zoONUNd4rVs8YqyuVOga0/YePPKwpIkaUNTaR+yiBgI7Ak8XBadGRETIuLqiNiskdecHhFjI2LszJkz2yrUjmfuVOjSA3r2X1G2bAlMfwy2GVFdXJIkbYAqS8giYhPgD8BXMnM+8HNgEDCcogbtkoZel5lXZuaIzBzRv3//hmZRS3hrRpGUPfFbmHwPzH8FXvwbvLsAnrylKH/3raLD/+K3q45WkqT1Wps3WQJERBeKZOyGzPwjQGbOqJn+S+DOKmJTaduRxQNgwaswYwIMOnzleZ74Lex2LHTu3vbxSZK0AaniKssArgKeysxLa8q3rpntGGBiW8cmSZJUhSpqyA4ATgKeiIjxZdm3gE9GxHAggSnAGRXEpob02qZ41Df0U20fiyRJG6AqrrJ8EIgGJnmbC0mS1CF5p35JkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFTMhkyRJqpgJmSRJUsVMyCRJkipmQiZJklQxEzJJkqSKtbuELCI+FBHPRMTzEfGNquORJElqbe0qIYuITsBPgX8Cdgc+GRG7VxuVJElS62pXCRkwEng+Mydn5rvAjcDHKo5JkiSpVbW3hGxb4OWa8WllmSRJ0garc9UB1BMNlOVKM0ScDpxejr4ZEc+0elSq1Q+YVXUQayPiyqpD0PrD41wdgcd529uhsQntLSGbBmxXMz4AeLV2hsy8Elhv34n1XUSMzcwRVcchtSaPc3UEHuftS3trsnwE2CUidoyIrsAngDsqjkmSJKlVtasassxcEhFnAv8DdAKuzsxJFYclSZLUqtpVQgaQmX8G/lx1HGqUzcXqCDzO1RF4nLcjkZmrn0uSJEmtpr31IZMkSepwTMgkSZIqZkImSZJUsXbXqV/tU0RsS3FDu+XHTGbeX11EUsuKiO6ZubDqOKTWFhGbUdzzs/Z8/mh1EQlMyNQMEfEfwAnAk8DSsjgBEzJtSCZGxAzgAYpj+/8yc17FMUktKiIuAk4BXmDFP+EkcGhVMangVZZarfLvqYZl5qKqY5FaU0RsDxwEHAB8GJibmcMrDUpqQeX5fGhmvlt1LFqZfcjUHJOBLlUHIbWmiBhAkYgdBOwJTAJuqjQoqeVNBDatOgityhoyNSoi/ouiKntbYA/gHmB5LVlmnlVRaFKLi4hlFH/f9m+ZeXvV8UitISJGALdTJGa15/OPVhaUABMyNSEiTm5qemZe11axSK0tIvYADgQOBrYHngPuy8yrKg1MakERMQn4BfAEsKyuPDPvqywoASZkkrRcRGxCkZQdBHwayMwcWGlQUguKiPsy85Cq49CqTMi0WhHxBCuuxqkzDxgLfD8zZ7d9VFLLioixQDfgIeBB4P7MnFptVFLLiohLKZoq72DlJktve1ExEzKtVkT8J8XtLn5bFn0CCIqk7MDMPKqq2KSWEhH9M3Nm1XFIrSki7m2gODPT215UzIRMqxUR/5eZBzRUFhFPZObQqmKTWkpE9AHOp+hDBnAfcKH3IpPUFrwxrJpjk4jYNzMfBoiIkcAm5bQl1YUltairKa48O74cPwm4Bji2soikFhYR322oPDMvbOtYtDITMjXHacDVZYfnAOYDp0VET+AHlUYmtZxBmfnxmvHvRcT4qoKRWslbNcPdgY8AT1UUi2rYZKlmK5t0IjPnVh2L1NIiYgxwbmY+WI4fAPwoM/erNjKp9UREN+COzDyi6lg6OmvI1KiI+HRm/iYizq5XDkBmXlpJYFLr+ALw6/KHB8AcoMl78UkbgI2BnaoOQiZkalrP8rlXpVFIbSAzHwf2iIje5fj8iPgKMKHSwKQWVO82Rp2A/oD9x9oBmywlqRER8VJmbl91HFJLiYgdakaXADMy04uz2gFryNSoiLi8qen+l6U6gKg6AKklRETvzJwPLKg3qXdEkJlvVBGXVjAhU1PG1Qx/j+IeTVJHYhOCNhS/pbiichzFcV37YyOxH1nlbLJUs0TEY5m5Z9VxSC0tIhbQcOIVQI/M9IerpFbniUbNZeauDVJmetGKNngRsVdT0/0vy+qZkEmStOG7pHzuDowAHqeoBR4GPAwcWFFcKpmQqVH1mnI2joj5dZMo/oy2dzWRSZLWRGa+HyAibgROz8wnyvEhwNeqjE0FEzI1yqYcSdrgvLcuGQPIzIkRMbzCeFQyIZMkqeN4KiJ+BfyGogXk0/hflu2CV1lKktRBRER34F+Ag8ui+4GfZ+bC6qISmJBJktShREQPYPvMfKbqWLTCRlUHIEmS2kZEfBQYD/x3OT48Iu6oNCgBJmSSJHUk5wMjgbkAmTkeGFhdOKpjQiZJUsexJDPnVR2EVuVVlpIkdRwTI+JTQKeI2AU4C3io4piENWSSJHUkXwIGA4uA3wHzga9UGZAKXmUpSZJUMZssJUnawK3uSsrM/GhbxaKGmZBJkrTh2w94maKZ8mGK/yRWO2KTpSRJG7iI6AQcBnwSGAbcBfwuMydVGpiWs1O/JEkbuMxcmpn/nZknA+8DngdGR8SXKg5NJZssJUnqACKiG3AkRS3ZQOBy4I9VxqQVbLKUJGkDFxHXAUOAu4EbM3NixSGpHhMySZI2cBGxDHirHK394g8gM7N320elWiZkkiRJFbNTvyRJUsVMyCRJkipmQiapXYiIrSLixoh4ISKejIg/R8SuETEwIprsgBwR20TELS0cz6iIuLNe2bUR8c/l8K8iYvcmXn9KRGzTkjFJ2nB52wtJlYuIAG4FrsvMT5Rlw4EtKe4u3qTMfBX459aMsYF1nraaWU4BJgKvNneZEdEpM5euS1yS1k/WkElqD94PLM7MK+oKMnN8Zj5QO1NZW/ZARDxaPvavKZ9YDp8SEbdFxJ8i4sWIODMizo6IxyLi7xHRt5zvrLImbkJE3LimAUfE6IgYERGdypqziRHxRER8taxFGwHcEBHjI6JHRHygjOGJiLi6vCcUETElIr4bEQ8C34iIR2vWsUtEjFvz3SlpfWMNmaT2YAjQnMTjdeCwzFwYEbtQ/C/fiEaWtyfQneKO5F/PzD0j4jLgM8CPgW8AO2bmoojYtJH1HRQR42vGtwfurDfPcGDbzBwCEBGbZubciDgT+Fpmjo2I7sC1wAcy89mI+DXwL2UcAAsz88Dy9R+MiOGZOR74bPk6SRs4a8gkrU+6AL+MiCeA3wON9eG6NzMXZOZMYB7wp7L8CYo7lANMoKjB+jSwpJHlPJCZw+sewB0NzDMZ2Cki/isiPgTMb2Ce9wAvZuaz5fh1wME102+qGf4V8NnyvwdPAH7bSGySNiAmZJLag0nA3s2Y76vADGAPipqxro3Mt6hmeFnN+DJWtAwcCfy0XO+4iFirFoPMnFPGMxr4IkVCVV+sZjFv1Qz/Afgn4CPAuMycvTZxSVq/mJBJag/+BnSLiM/XFUTEPhFxSL35+gDTM3MZcBLQaW1WFhEbAdtl5r3AvwKbApus5bL6ARtl5h+A7wB7lZMWAL3K4aeBgRGxczl+EnBfQ8vLzIXA/wA/B65Zm5gkrX9MyCRVLou/DDkGOKy87cUk4AJWvULxZ8DJEfF3YFdWrllaE52A35RNn48Bl2Xm3LVc1rbA6LKv2bXAN8vya4EryvKg6A/2+3Kdy4Ar6i+oxg0Uf2/zl7WMSdJ6xr9OkqR2JiK+BvTJzO9UHYuktuFVlpLUjkTErcAg4NCqY5HUdqwhkyRJqph9yCRJkipmQiZJklQxEzJJkqSKmZBJkiRVzIRMkiSpYiZkkiRJFfv/AYDUiKfIv9CnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjusting the plot to stack the bars and using specified colors\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Custom colors\n",
    "colors = ['#003366', '#336699']\n",
    "\n",
    "# Creating the stacked bar chart\n",
    "stacked_bar_plot = data.groupby(['Claims_History', 'Local_Weather_Conditions']).size().unstack().plot(kind='bar', \n",
    "                                                                                                      stacked=True, \n",
    "                                                                                                      color=colors, \n",
    "                                                                                                      figsize=(10, 6))\n",
    "\n",
    "# Adding count labels to each bar\n",
    "for bar in stacked_bar_plot.patches:\n",
    "    stacked_bar_plot.annotate(format(bar.get_height(), '.0f'), \n",
    "                              (bar.get_x() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2), \n",
    "                              ha='center', va='center', \n",
    "                              xytext=(0, 5), \n",
    "                              textcoords='offset points')\n",
    "\n",
    "# Adding plot title and labels\n",
    "plt.title('Stacked Number of Homes by Claims History and Local Weather Conditions')\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Number of Homes')\n",
    "plt.legend(title='Local Weather Conditions', labels=['Normal', 'Severe'])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjusting the plot with white labels and ordering the x-axis as Low, Medium, High\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Custom colors and order\n",
    "colors = ['#003366', '#336699']\n",
    "order = ['Low', 'Medium', 'High']\n",
    "\n",
    "# Creating the stacked bar chart with the specified order\n",
    "stacked_bar_plot = data.groupby(['Claims_History', 'Local_Weather_Conditions']).size().reindex(order, level='Claims_History').unstack().plot(kind='bar', \n",
    "                                                                                                                                         stacked=True, \n",
    "                                                                                                                                         color=colors, \n",
    "                                                                                                                                         figsize=(10, 6))\n",
    "\n",
    "# Adding count labels in white to each bar\n",
    "for bar in stacked_bar_plot.patches:\n",
    "    height = bar.get_height()\n",
    "    if height > 0:  # Only add labels to non-zero bars\n",
    "        stacked_bar_plot.annotate(format(height, '.0f'), \n",
    "                                  (bar.get_x() + bar.get_width() / 2, bar.get_y() + height / 2), \n",
    "                                  ha='center', va='center', \n",
    "                                  color='white',\n",
    "                                  xytext=(0, 5), \n",
    "                                  textcoords='offset points')\n",
    "\n",
    "# Adding plot title and labels\n",
    "plt.title('Stacked Number of Homes by Claims History and Local Weather Conditions')\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Number of Homes')\n",
    "plt.legend(title='Local Weather Conditions', labels=['Normal', 'Severe'])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FPgEWhI+HwnVlANcRNFetJmiy670b8a8kaK7qS9CE8EfgHHdfUeQLd8/NBEnCAoJ98SpBX7+SuItgG/9nQdPPpwQ1DxB0dP6UoD/UZOAZL/o6Qu8QdISfRtAZ+8WcCWGS8DVB8jGhoBeH860CTiQ4xqaEx+xnBDUPPxTwkkKP7T1UkyBZXU3QnLISeLSUln0cwbatJ6jdu9Xdfwyn9SdIRteYWXd3/wz4E/BPglqppgSfu+KMIOg/WGhzJUH/pI8I+sotJEg6i2oSy+XuvwIXEnw2VhP0K32rmJedSJBExT7WUvTnpSfBsTCXoB/RbWH5kwRdKVYQnAN395phnxPUsMSeS6aFy5zq7hvD7Xyb4Nz2Wvj5mEnwZyoofv+9ET6vNLOviwvI3X8muDzFvQRJ3s8EtbJxfwe7++MEP9D6xSzjJnbWkj5E0IdzOjCD4DMZ78V8d+e8/iJwbHgcjy1g+p7E0RPIDN+PG9jZPJ8ULG83ARGR3WdmLxHUSPSLOpZ9XViT9ApB5/8dUccjIqVDFxsUkT1iwfW9LiS45pMkUNjMeivwgpIxkX2LmixFpMTMbCBBU88jMU1zkgAWXBx6DcE/856MNBgRKXVqshQRERGJmGrIRERERCKmhExEREQkYnt1p/66det6kyZNog5DREREpFhTp05d4e71Cpq2VydkTZo0ISMjI+owRERERIplZoXelkpNliIiIiIRU0ImIiIiEjElZCIiIiIR26v7kIlI6dm6dSuLFi1i8+bNUYci5UjVqlVp2LAhlSpVijoUkX2aEjIRAWDRokXUqFGDJk2aYGZRhyPlgLuzcuVKFi1axGGHHRZ1OCL7NDVZiggAmzdv5sADD1QyJrnMjAMPPFC1piJlQAmZiORSMib56ZgQKRtKyESkUPvvv3+ZLvv222/nySefzB3v0qUL1157be543759efzxx3d7XWPHjmX27Nm54507dy7VaxiuX7+e66+/nqZNm5KamkrHjh2ZMmXKHi83MzOT5s2bA5CRkcEtt9wCwPjx45k0aVLufEOGDOHll1/e4/WJSHSUkIlIuXHiiSfmJho7duxgxYoVzJo1K3f6pEmT6NChw24vN39Ctie2b9++S9m1115LnTp1mDdvHrNmzWL48OGsWLGiVNaXIz09ncGDBwO7JmQ33HADV155ZamuT0TKlhIyEdkt06ZN44QTTqBly5ZccMEFrF69GoAffviB008/nVatWtGmTRvmz5/P+vXrOe2002jTpg0tWrTgnXfeKXLZHTp0yE00Zs2aRfPmzalRowarV69my5YtzJkzh9atWzN16lQ6depE27Zt6dKlC0uWLAHg+eef57jjjqNVq1ZcdNFFbNy4kUmTJvHuu+9y5513kpaWxvz58wF44403aNeuHUcddRQTJkwAgmTrzjvv5LjjjqNly5Y899xzQJAAnXLKKVx++eW0aNEiT8zz589nypQpPPTQQ1SoEJxSDz/8cLp27QrA448/TvPmzWnevHlu7V9mZibNmjXjuuuuIzU1lTPPPJNNmzYBMHXqVFq1akX79u15+umnc9czfvx4zjnnHDIzMxkyZAhPPPEEaWlpTJgwgf79+/Poo48W+f507tyZu+66a5dtnjVrFu3atSMtLY2WLVsyb9683ToeRKSUuPte+2jbtq2LSOmYPXv2LmXVq1ffpaxFixY+fvx4d3f/05/+5Lfeequ7u7dr187feustd3fftGmTb9iwwbdu3epr1651d/esrCxv2rSp79ixo9Blu7s3btzYFy5c6EOGDPFnn33W+/Xr5x988IFPnDjRTz75ZP/111+9ffv2vnz5cnd3f+211/yqq65yd/cVK1bkLue+++7zwYMHu7t7r169/I033sid1qlTJ7/jjjvc3f2DDz7w0047zd3dn3vuOR84cKC7u2/evNnbtm3rCxYs8HHjxvl+++3nCxYs2CXed955x88///wCtyUjI8ObN2/u69ev9+zsbD/22GP966+/9h9//NFTUlL8m2++cXf3Sy65xEeOHLnL/v3DH/7gqamp7u4+btw479q1q7u7P/DAA/7II4/krid2vLD3p7Btvummm/yVV15xd/ctW7b4xo0bd9mOgo4NEdl9QIYXktPoshciEre1a9eyZs0aOnXqBECvXr245JJLyM7OZvHixVxwwQVAcO0qCK5tdu+99/LFF19QoUIFFi9ezLJly/jNb35T6DpyaskmTZrEHXfcweLFi5k0aRK1atXixBNP5LvvvmPmzJmcccYZQFCrVb9+fQBmzpxJv379WLNmDevXr6dLly6FrufCCy8EoG3btmRmZgLwySefMH36dN58883c7Z03bx6VK1emXbt2u33ph4kTJ3LBBRdQvXr13HVOmDCBbt26cdhhh5GWlpYnhvz7t2fPnnz00Udxr6+w96eobW7fvj2DBg1i0aJFXHjhhRx55JG7tY0iUjqUkInIHgt++O1q1KhRZGVlMXXqVCpVqkSTJk2KvYRCTj+yGTNm0Lx5cw499FAee+wxatasydVXX427k5qayuTJk3d5be/evRk7diytWrVi+PDhjB8/vtD1VKlSBYCUlBS2bduWux1///vfd0nkxo8fn5tU5Zeamsq3337Ljh07cpsscxS2X2LXnxPDpk2bcPeE/quxoG2+/PLLOf744/nggw/o0qULL7zwAqeeemrCYhCRgikhE5G41apViwMOOIAJEyZw8sknM3LkSDp16kTNmjVp2LAhY8eO5fzzz2fLli1s376dtWvXctBBB1GpUiXGjRvHwoULi11Hhw4deOyxxzj88MNJSUmhTp06rFmzhlmzZvH8889Ts2ZNsrKymDx5Mu3bt2fr1q18//33pKamkp2dTf369dm6dSujRo2iQYMGANSoUYPs7Oxi192lSxeeffZZTj31VCpVqsT333+fu4zCNG3alPT0dB544AEGDBiAmTFv3jxmz55Nx44d6d27N3fffTfuzttvv83IkSMLXVbt2rWpVasWEydO5KSTTmLUqFEFzlejRg3WrVu3S3lh709RFixYwOGHH84tt9zCggULmD59uhKy3WTp10cdQtLxjOeiDqHUqVO/iBRq48aNNGzYMPfx+OOPM2LECO68805atmzJtGnTuP/++wEYOXIkgwcPpmXLlpx44oksXbqUK664goyMDNLT0xk1ahTHHHNMsets0aIFK1as4IQTTshTVqtWLerWrUvlypV58803ueuuu2jVqhVpaWm5fwQYOHAgxx9/PGeccUaedV166aU88sgjtG7dOrdTf0GuvfZajj32WNq0aUPz5s25/vrrc2uSivLCCy+wdOlSjjjiCFq0aMF1113HIYccQps2bejduzft2rXj+OOP59prr6V169ZFLmvYsGHceOONtG/fnmrVqhU4z7nnnsvbb7+d26k/VmHvT2HGjBlD8+bNSUtLY+7cufq3pkhErKgq9fIuPT3dS/NaQiLJbM6cOTRr1izqMKQc0rFRNNWQlb29tYbMzKa6e3pB01RDJiIiIhIxJWQiIiIiEVNCJiIiIhIxJWQiIiIiEVNCJiIiIhIxJWQiIiIiEUtYQmZmL5nZcjObGVM2xsymhY9MM5sWljcxs00x04YkKi4RkYKYGX379s0df/TRR+nfv3+ZxtC5c2d0KR+R5JTIK/UPB/4BvJxT4O49cobN7DFgbcz88909LYHxiMheIv36oaW6vIzn+hQ7T5UqVXjrrbe45557qFu37m6vY9u2bVSsqJufiEjJJOzs4e5fmFmTgqZZcLO27oDuzyEi5ULFihXp06cPTzzxBIMGDcozbeHChVx99dVkZWVRr149hg0bRqNGjejduzd16tThm2++oU2bNqxcuZJq1aoxd+5cFi5cyLBhwxgxYgSTJ0/m+OOPZ/jw4QD8/ve/56uvvmLTpk1cfPHFPPjggxFssYiUJ1H1ITsZWObu82LKDjOzb8zsczM7ubAXmlkfM8sws4ysrKzERyoiSePGG29k1KhRrF27Nk/5TTfdxJVXXsn06dO54ooruOWWW3Knff/993z66ac89thjAKxevZr//Oc/PPHEE5x77rncfvvtzJo1ixkzZjBt2jQABg0aREZGBtOnT+fzzz9n+vTpZbaNIlI+RZWQXQaMjhlfAjRy99bAHcCrZlazoBe6+1B3T3f39Hr16pVBqCKSLGrWrMmVV17J4MGD85RPnjyZyy+/HICePXsyceLE3GmXXHIJKSkpuePnnnsuZkaLFi04+OCDadGiBRUqVCA1NZXMzEwAXn/9ddq0aUPr1q2ZNWsWs2fPTvzGiUi5VuYJmZlVBC4ExuSUufsWd18ZDk8F5gNHlXVsIiK33XYbL774Ihs2bCh0nqDXRaB69ep5plWpUgWAChUq5A7njG/bto0ff/yRRx99lM8++4zp06fTtWtXNm/eXMpbISJ7myhqyE4H5rr7opwCM6tnZinh8OHAkcCCCGITkSRXp04dunfvzosvvphbduKJJ/Laa68BMGrUKE466aQSL3/dunVUr16dWrVqsWzZMj766KM9jllE9n6JvOzFaGAycLSZLTKza8JJl5K3uRKgIzDdzL4F3gRucPdViYpNRKQoffv2ZcWKFbnjgwcPZtiwYbRs2ZKRI0fy1FNPlXjZrVq1onXr1qSmpnL11VfToUOH0ghZRPZy5u5Rx1Bi6enprmv2iJSOOXPm0KxZs6jDkHJIx0bRLP36qENIOp7xXNQhlIiZTXX39IKm6Ur9IiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISMSVkIiKhQYMGkZqaSsuWLUlLS2PKlClRhyQiSaJi1AGIiORX2td1iueaRZMnT+b999/n66+/pkqVKqxYsYJff/21VOPIsW3bNipW1OlXRHZSDZmICLBkyRLq1q2be//JunXrcsghhzB16lQ6depE27Zt6dKlC0uWLGHOnDm0a9cu97WZmZm0bNkSoMD5ATp37sy9995Lp06deOqppwqdT0SSk36iRURXdi57e+uVnaVsnHnmmQwYMICjjjqK008/nR49enDiiSdy8803884771CvXj3GjBnDfffdx0svvcSvv/7KggULOPzwwxkzZgzdu3dn69athc4PsGbNGj7//HO2bt1Kp06dCp1PRJKPEjIREWD//fdn6tSpTJgwgXHjxtGjRw/69evHzJkzOeOMMwDYvn079evXB6B79+68/vrr3H333YwZM4YxY8bw3XffFTo/QI8ePQCKnU9Eko8SMhGRUEpKCp07d6Zz5860aNGCp59+mtTUVCZPnrzLvD169OCSSy7hwgsvxMw48sgjmTFjRqHzA1SvXh0Ady9yPhFJPupDJiJCUGs1b9683PFp06bRrFkzsrKychOnrVu3MmvWLACaNm1KSkoKAwcOzK35OvroowudP1a884lI8lANmYgIsH79em6++WbWrFlDxYoVOeKIIxg6dCh9+vThlltuYe3atWzbto3bbruN1NRUIKglu/POO/nxxx8BqFy5Mm+++Wah8+eIdz4RSR7m7lHHUGLp6emekZERdRglok79ZU+d+os2Z84cmjVrFnUYUg7p2Ciazudlb289n5vZVHdPL2iamixFREREIqaETERERCRiSshEREREIqaETERy7c19SiUxdEyIlA0lZCICQNWqVVm5cqW+gCWXu7Ny5UqqVq0adSgi+zxd9kJEAGjYsCGLFi0iKysr6lCkHKlatSoNGzaMOgyRfZ4SMhEBoFKlShx22GFRhyEikpTUZCkiIiISMSVkIiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISMSVkIiIiIhFTQiYiIiISsYTdy9LMXgLOAZa7e/OwrD9wHZBz9+J73f3DcNo9wDXAduAWd/84UbGJSNmw9OujDiHpeMZzUYcgIiWQyBqy4cBZBZQ/4e5p4SMnGTsWuBRIDV/zjJmlJDA2ERERkXIjYQmZu38BrIpz9vOA19x9i7v/CPwAtEtUbCIiIiLlSRR9yG4ys+lm9pKZHRCWNQB+jplnUVgmIiIiss8r64TsWaApkAYsAR4Ly62Aeb2gBZhZHzPLMLOMrKysgmaRUlShgvH1qPt474kbARhwQze+Hf0nvhnVj4//cSv169aKOEIREZG9X5kmZO6+zN23u/sO4Hl2NksuAg6NmbUh8Eshyxjq7ununl6vXr3EBizcetlpzPlxae74IyM/odVlA2l9xUO8P2E691/XNcLoRERE9g1lmpCZWf2Y0QuAmeHwu8ClZlbFzA4DjgS+LMvYZFcNDqpN1w4teGHsxNyy7A2bc4erV6uCF1iPKSIiIrsjkZe9GA10Buqa2SLgAaCzmaURNEdmAtcDuPssM3sdmA1sA2509+2Jik3i82Tf7vxx8D+pUb1qnvKH/u88rjz7BNZu2MQp1z8eUXQiIiL7jkT+y/Iyd6/v7pXcvaG7v+juPd29hbu3dPdu7r4kZv5B7t7U3Y92948SFZfEp+tJLVi+Kpuv5/60y7R+z7xDo3PuYdRHX3JT91MiiE5ERGTfoiv1S4E6tGpKt46t+PHdQbw26FpOPe4YRg64Os88r/7rSy46rXVEEYqIiOw7EtZkKXu3e58ey71PjwWgU9uj+MPvzqDn/S9xxKEH8cPPywHo1qkVczOXFrEUERERiYcSMtktf7n5Ao5ufDA7djgLl6zihodHRR2SiIjIXk8JmRTr86nf8/nU7wG4+I+6T56IiEhpUx8yERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJWMISMjN7ycyWm9nMmLJHzGyumU03s7fNrHZY3sTMNpnZtPAxJFFxiYiIiJQ3iawhGw6cla/s30Bzd28JfA/cEzNtvrunhY8bEhiXiIiISLmSsITM3b8AVuUr+8Tdt4Wj/wMaJmr9IiIiInuLKPuQXQ18FDN+mJl9Y2afm9nJhb3IzPqYWYaZZWRlZSU+ShEREZEEiyQhM7P7gG3AqLBoCdDI3VsDdwCvmlnNgl7r7kPdPd3d0+vVq1c2AYuIiIgkUJknZGbWCzgHuMLdHcDdt7j7ynB4KjAfOKqsYxMRERGJQpkmZGZ2FnAX0M3dN8aU1zOzlHD4cOBIYEFZxiYiIiISlYqJWrCZjQY6A3XNbBHwAMG/KqsA/zYzgP+F/6jsCAwws23AduAGd19V4IL3EW3bto06BBERESknEpaQuftlBRS/WMi8/wT+mahYRERERMozXalfREREJGLFJmRmdquZ1bTAi2b2tZmdWRbBiYiIiCSDeGrIrnb3dcCZQD3gKuAvCY1KREREJInEk5BZ+Hw2MMzdv40pExEREZE9FE9CNtXMPiFIyD42sxrAjsSGJSIiIpI84vmX5TVAGrDA3Tea2YEEzZYiInu9oxofzJg/X5c7fniDutz/3Hu0b3E4Rzc+GIDaNaqxJnsTra94KKowRXbbi/dfyTkntWD56mxa9BgAwIAbunFep1bs2OEsX51N7/7DWbJiLRVTKvDCn66kzTGNqJhSgZc/+B9/Gf6viLcgucSTkDlwLMHV9QcA1YGqiQxKRKSsfL9wWW6iVaGCsfjDv/L2uG94avRnufM8etvFrF2/KaoQRUpk+HuT+ceYcbw8YGcdyiMjP+H+Ie8CcHOPU7j/uq78/uFXueT0tlSpXJGWlw6gWpVKzH6jP6M//oqFS1ZGFX7SiafJ8hmgPZBzXbFs4OmERSQiEpHTjjuG+Yuz+Glp3utSdz+9LaM//iqiqERKZsI381i1bmOesuwNm3OHq1erQnADw6DmpXrVKqSkVKBa1cr8unU76zboR0hZiqeG7Hh3b2Nm3wC4+2ozq5zguEREytylXY7bJfE6ufWRLFuVzQ8/L48oKpHS9dD/nceVZ5/A2g2bOOX6xwF489OpnNepFUv+9Tf2q1qZ2x9/g9X5kjlJrHhqyLaG95l0CO47iTr1i8g+plLFFLp1bMUbn07NU35Zl+MY/fGXEUUlUvr6PfMOjc65h1EffclN3U8BoF3zw9i+fQeHnPVHDut2H31/dzqHNagbcaTJJZ6EbDDwNnCQmQ0CJgJ/TmhUIiJl7LcdmvP13J9Yvio7tywlpQIXntKaMf/OiDAykcR49V9fctFprQG4vEs7/jV5Ftu27yBrdTb//XY+6c0aRxxhcik2IXP3UcAfgYeBJcD57v5GogMTESlLlxXQXHl6u2bMzVzK4uVroglKpJQdcehBucPdOrVibuZSAH5atopT048BYL+qlTmh+WG506RsxHtz8WXAhHD+ambWxt2/TlxYIiJlp1qVSpzRrhnXD3olT/mlZ6Yz+hN15pe906uDrqFz26OpW3t/fv7gLzww9D3O7tCcoxsfzI4dzsIlq7jh4VEAPP36eIY90IuZYx7ADIa9N5kZPyyOeAuSS7EJmZkNBHoD8wn7kYXPpyYuLBGRsrNpy1bqnt53l/KrHhwRQTQipePy+17cpeyld/5b4LwbNm2h+91DEx2SFCGeGrLuQFN3/zXRwYiIiIgko3g69c8Eaic4DhEREZGkFU8N2cPAN2Y2E9iSU+ju3RIWlYiIiEgSiSchGwH8FZiBrj8mIiIiUuriSchWuPvghEciIiIikqTiScimmtnDwLvkbbLUZS9ERERESkE8CVnr8PmEmDJd9kJERESklBSbkLn7KWURiIiIiEiyKvayF2ZWy8weN7OM8PGYmdUqi+BEREREkkE81yF7CcgmuEBsd2AdMCyRQYmIiIgkk3j6kDV194tixh80s2kJikdEREQk6cRTQ7bJzE7KGTGzDsCmxIUkIiIiklziqSH7PTAi7DdmwCqCm42LiIiISCmI51+W04BWZlYzHF+X6KBEREREkkmhCZmZ3VFIOQDu/niCYhIRERFJKkX1IasR8/hDvvEaiQ9NREREJDkUWkPm7g/mDJvZ+bHjIiIiIlJ64vmXJQS3ShIRERGRBIg3IRMRERGRBCmqU/8MdtaMHWFm03MmAe7uLRMdnIiIiEgyKOqyF+fsyYLN7KVwGcvdvXlYVgcYAzQBMoHu7r46nHYPcA2wHbjF3T/ek/WLiIiI7C0KbbJ094VFPeJY9nDgrHxldwOfufuRwGfhOGZ2LHApkBq+5hkzSynB9oiIiIjsdRLWh8zdvyC4qn+s84AR4fAI4PyY8tfcfYu7/wj8ALRLVGwiIiIi5UlZd+o/2N2XAITPB4XlDYCfY+ZbFJbtwsz6mFmGmWVkZWUlNFgRERGRslBoQmZmn4XPfy2DOKyAsgIvteHuQ9093d3T69Wrl+CwRERERBKvqE799c2sE9DNzF4jX9Lk7l+XYH3LzKy+uy8xs/rA8rB8EXBozHwNgV9KsHwRERGRvU5RCdn9BJ3uGwL571vpwKklWN+7QC/gL+HzOzHlr5rZ48AhwJHAlyVYvoiIiMhep6hbJ70JvGlmf3L3gbu7YDMbDXQG6prZIuABgkTsdTO7BvgJuCRc1ywzex2YDWwDbnT37bu7ThEREZG9UVE1ZAC4+0Az6wZ0DIvGu/v7cbzuskImnVbI/IOAQcUtV0RERGRfU+y/LM3sYeBWgtqr2cCtYZmIiIiIlIJia8iArkCau+8AMLMRwDfAPYkMTERERCRZxHsdstoxw7USEIeIiIhI0oqnhuxh4BszG0dw6YuOqHZMREREpNTE06l/tJmNB44jSMjucveliQ5MREREJFnEU0OWc5ujdxMci4iIiEhSKut7WYqIiIhIPkrIRERERCJWZEJmZhXMbGZZBSMiIiKSjIpMyMJrj31rZo3KKB4RERGRpBNPp/76wCwz+xLYkFPo7t0SFpWIiIhIEoknIXsw4VGIiIiIJLF4rkP2uZk1Bo5090/NbD8gJfGhiYiIiCSHeG4ufh3wJvBcWNQAGJvAmERERESSSjyXvbgR6ACsA3D3ecBBiQxKREREJJnEk5Btcfdfc0bMrCLgiQtJREREJLnEk5B9bmb3AtXM7AzgDeC9xIYlIiIikjziScjuBrKAGcD1wIdAv0QGJSIiIpJM4vmX5Q4zGwFMIWiq/M7d1WQpIiIiUkqKTcjMrCswBJgPGHCYmV3v7h8lOjgRERGRZBDPhWEfA05x9x8AzKwp8AGghExERESkFMTTh2x5TjIWWgAsT1A8IiIiIkmn0BoyM7swHJxlZh8CrxP0IbsE+KoMYhMRERFJCkU1WZ4bM7wM6BQOZwEHJCwiERERkSRTaELm7leVZSAiIiIiySqef1keBtwMNImd3927JS4sERERkeQRz78sxwIvElydf0dCoxERERFJQvEkZJvdfXDCIxERERFJUvEkZE+Z2QPAJ8CWnEJ3/zphUYmIiIgkkXgSshZAT+BUdjZZejguIiIiInsonoTsAuBwd/810cGIiIiIJKN4rtT/LVA7wXGIiIiIJK14asgOBuaa2Vfk7UOmy16IiIiIlIJ4ErIHSnOFZnY0MCam6HDgfoJauOsI7gQAcK+7f1ia6xYREREpj4pNyNz989Jcobt/B6QBmFkKsBh4G7gKeMLdHy3N9YmIiIiUd/FcqT+b4F+VAJWBSsAGd69ZCus/DZjv7gvNrBQWJyIiIrL3KbZTv7vXcPea4aMqcBHwj1Ja/6XA6Jjxm8xsupm9ZGa6gbmIiIgkhXj6kOXh7mPN7O49XbGZVQa6AfeERc8CAwlq4wYCjwFXF/C6PkAfgEaNGu1pGCKSQG3bto06BBGRvUI8TZYXxoxWANLZ2YS5J34LfO3uywBynsN1Pg+8X9CL3H0oMBQgPT29NOIQERERiVQ8NWTnxgxvAzKB80ph3ZcR01xpZvXdfUk4egEwsxTWISIiIlLuxfMvy6tKe6Vmth9wBnB9TPHfzCyNoPYtM980ERERkX1WoQmZmd1fxOvc3QeWdKXuvhE4MF9Zz5IuT0RERGRvVlQN2YYCyqoD1xAkUyVOyERERERkp0ITMnd/LGfYzGoAtxJcvPU1gn9AioiIiEgpKLIPmZnVAe4ArgBGAG3cfXVZBCYiIiKSLIrqQ/YIcCHBJSZauPv6MotKREREJIkUdaX+vsAhQD/gFzNbFz6yzWxd2YQnIiIisu8rqg9ZsbdVEhEREZE9p6RLREREJGJKyEREREQitts3F5d92/1XduKkFo1Ynb2JHgPeBOCWi46nY8vGbN22nUVZ63hwxOes3/QrqU3qce/vTgbAMIa+P5Xx0zIjjF5ERGTvpIRM8nhv8neMGTeTAVedkls2ZfYinn77S7bvcG6+sB1X/TaNv7/1JT8sXsWVf36b7TucA2tWY/SfLmbC9IVs36F7vouIiOwONVlKHt/MW8q6jVvylE2Zszg3yZqxYDkH1a4OwJat23PLq1SqiKNETEREpCRUQya7pVuHo/l3xvzc8dQm9bi/Vyfq16nB/cPGqXZMRESkBJSQSdyu/m1rtm/fwUdTfsgtm5WZRY8H36TJb2rzYO/OTJr5M79u2x5hlCIiInsfNVlKXLqecCQntWxEvxf/U+D0zKVr2PTrNpo2OKCMIxMREdn7KSGTYrVPbUivLmnc8fTHbNm6s/brkANrkFLBAPhNnf1pfHAtflmRHVWYIiIiey01WUoeg645lbZHH0Lt/avywV8uZ+h7U+l9VhqVKqbw9G1nAzBzwXIefnUiaUf8hl5ntWLb9h24w19encjaDVuKWYOIiIjkp4RM8rivgCbJd/77XYHzfjhlHh9OmZfokERERPZ5arIUERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiZgSMhEREZGIKSETERERiVjFKFZqZplANrAd2Obu6WZWBxgDNAEyge7uvjqK+ERERETKUpQ1ZKe4e5q7p4fjdwOfufuRwGfhuIiIiMg+rzw1WZ4HjAiHRwDnRxeKiIiISNmJKiFz4BMzm2pmfcKyg919CUD4fFBBLzSzPmaWYWYZWVlZZRSuiIiISOJE0ocM6ODuv5jZQcC/zWxuvC9096HAUID09HRPVIAiIiIiZSWSGjJ3/yV8Xg68DbQDlplZfYDweXkUsYmIiIiUtTJPyMysupnVyBkGzgRmAu8CvcLZegHvlHVsIiIiIlGIosnyYOBtM8tZ/6vu/i8z+wp43cyuAX4CLokgNhEREZEyV+YJmbsvAFoVUL4SOK2s4xERERGJWnm67IWIiIhIUlJCJiIiIhIxJWQiIiIiEVNCJiIiIhIxJWQiIiIiEYvqSv0iIiL7hLZt20YdguwDlJCJSFJrfHAt/nzdzivuNKhbk+fey6BGtSqcf9IxrF6/CYBnxn7Ff2f+HFWYIrKPU0ImIklt4bK1XPHQWwBUMOPDv17BuG8y6Xbi0bz62Qxe+ff0iCMUkWSgPmQiIqHjjjmExVnrWLpqfdShiEiSUQ2ZiEioy3FH8PFX83PHu3dOpesJRzJn4QqeeHMy2Rt/jTA6EdmXqYZMRASomFKBjq0a8+nUBQC8+flszu/3Gpc/9E9WrN3I7Re3jzhCEdmXKSETEQE6ND+UuT+tYFV20Il/VfYmdrjjDm9PnENqk3oRRygi+zIlZCIi5DRX/pA7fmDNarnDp6QdxvxfVkcRlogkCfUhE5GkV6VSCu2aNWDQK1/klt160QkcdeiBuDtLVq7PM01EpLQpIRORpLdl63ZO7/tynrL7h42LKBoRSUZqshQRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMRERGJmBIyERERkYgpIRMREUkCFcwYdd+FPHFjlzzlvzujJRnP9aFW9SoRRSYQQUJmZoea2Tgzm2Nms8zs1rC8v5ktNrNp4ePsso5NRERkX3XZac35cemaPGUHH1Cd45s1YMnK7GiCklxR1JBtA/q6ezPgBOBGMzs2nPaEu6eFjw8jiE1ERGSfc1Dt6nRo0YixE+fmKb/jkvYMfmsK7hEFJrkqlvUK3X0JsCQczjazOUCDso5DREQkWfTt3p7B/5xC9aqVcss6tmzM8jUbmLdoVYSRSY5I+5CZWROgNTAlLLrJzKab2UtmdkAhr+ljZhlmlpGVlVVWoYqIiOyVTmrRiFXZm5j704rcsiqVUrj67NYMeTcjwsgkVpnXkOUws/2BfwK3ufs6M3sWGAh4+PwYcHX+17n7UGAoQHp6uipZRUREitCq6cF0bNWYDs0bUblSCvtXq8zAq0/lkANrMPpPFwNw0AHVGdXvIno9/DYr122KOOLkFElCZmaVCJKxUe7+FoC7L4uZ/jzwfhSxiYiI7EueHvsVT4/9CoC2R9Xnd2e05I/P/TvPPO8Ouoyef36LtRu2RBGiEM2/LA14EZjj7o/HlNePme0CYGZZxyYiIiIShShqyDoAPYEZZjYtLLsXuMzM0giaLDOB6yOITUREZJ819fslTP1+yS7l3e4bHUE0EiuKf1lOBKyASbrMhYiIiCQlXalfREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGJKyEREREQipoRMREREJGLlLiEzs7PM7Dsz+8HM7o46HhEREZFEK1cJmZmlAE8DvwWOBS4zs2OjjUpEREQkscpVQga0A35w9wXu/ivwGnBexDGJiIiIJFR5S8gaAD/HjC8Ky0RERET2WebuUceQy8wuAbq4+7XheE+gnbvfHDNPH6BPOHo08F2ZB5rc6gIrog5CJMF0nEsy0HFe9hq7e72CJlQs60iKsQg4NGa8IfBL7AzuPhQYWpZByU5mluHu6VHHIZJIOs4lGeg4L1/KW5PlV8CRZnaYmVUGLgXejTgmERERkYQqVzVk7r7NzG4CPgZSgJfcfVbEYYmIiIgkVLlKyADc/UPgw6jjkEKpuViSgY5zSQY6zsuRctWpX0RERCQZlbc+ZCIiIiJJRwmZ5GFm66OOQaQkzMzNbGTMeEUzyzKz93dzOePNLD0c/tDMapdyqCIJlf88bma9zewf4fANZnZlMa/PnV/KTrnrQyYiUkIbgOZmVs3dNwFnAIv3ZIHufnapRCZSTrj7kKhjkIKphkyKZWZpZvY/M5tuZm+b2QFmdpCZTQ2ntwprJxqF4/PNbL9oo5Yk9RHQNRy+DBidM8HMqpvZS2b2lZl9Y2bnheXVzOy18PgeA1SLeU2mmdU1syZmNjOm/A9m1j8cHm9mT5jZF2Y2x8yOM7O3zGyemT1UBtssEjcz629mfwiHjwuP+8lm9kjsMQ4cYmb/Co/jv0UUblJRQibxeBm4y91bAjOAB9x9OVDVzGoCJwMZwMlm1hhY7u4bowtXkthrwKVmVhVoCUyJmXYf8B93Pw44BXjEzKoDvwc2hsf3IKBtCdb7q7t3BIYA7wA3As2B3mZ2YIm3RqRkqpnZtJwHMKCQ+YYBN7h7e2B7vmlpQA+gBdDDzA5FEkpNllIkM6sF1Hb3z8OiEcAb4fAkoAPQEfgzcBZgwISyjlMEwN2nm1kTgtqx/JfPORPollM7AFQFGhEcv4NjXj+9BKvOuYD1DGCWuy8BMLMFBHcfWVmCZYqU1CZ3T8sZMbPeQJ4r8od9I2u4+6Sw6FXgnJhZPnP3teG8s4HG5L3XtJQyJWSyJyYQ1I41JqgVuAtwYLc6UYuUsneBR4HOQGztlAEXuXue+9+aGQTHbVG2kbdFoWq+6VvC5x0xwznjOs9KeWTFTI89jrej4zjh1GQpRQp/Ia02s5PDop5ATm3ZF8DvgHnuvgNYBZwN/LfMAxXZ6SVggLvPyFf+MXCzhRmYmbUOy78ArgjLmhM0dea3DDjIzA40syrkrUkQ2eu4+2og28xOCIsujTIeUcYru9rPzBbFjD8O9AKGhB31FwBXAbh7Zvjd9kU470SgYfhBF4mEuy8Cnipg0kDgSWB6mJRlEiRWzwLDwqbKacCXBSxzq5kNIOiT9iMwNxGxi5Sxa4DnzWwDMB5YG204yU1X6hcREUlCZra/u68Ph+8G6rv7rRGHlbRUQyYiIpKcuprZPQS5wEKgd7ThJDfVkImIiIhETJ36RURERCKmhExEREQkYkrIRERERCKmhExEygUz+014T8n5ZjbbzD40s6Py30eykNceYmZvlnI8nc3s/Xxlw83s4nD4BTM7tojX9zazQ0ozJhHZd+lfliISufC6YG8DI9z90rAsDTiYOG7X4u6/ABcnMsYC1nltMbP0BmYCv8S7TDNLcff89xQUkSSgGjIRKQ9OAba6+5CcAnef5u557osa1pZNMLOvw8eJMeUzw+HeZjbWzN4zsx/N7CYzu8PMvjGz/5lZnXC+W8KauOlm9truBmxm480s3cxSwpqzmWY2w8xuD2vR0oFR4Q2eq5nZaWEMM8zspfCK/5hZppndb2YTgbvN7OuYdRxpZlN3f3eKyN5GNWQiUh40B+JJPJYDZ7j7ZjM7EhhNvpsmxyyvNcE9J38A7nL31mb2BHAlwRX77wYOc/ct4Y2WC3KymU2LGW/ErvdqTQMauHtzCG7a7O5rzOwm4A/unmFmVYHhwGnu/r2ZvQz8PowDYLO7nxS+/nQzS3P3aQR3xRhe7F4Rkb2eashEZG9SieBWLzOAN4DC+nCNc/dsd88iuB3Me2H5DKBJODydoAbrdwQ3Dy/IBHdPy3kQ3Lg8vwXA4Wb2dzM7C1hXwDxHAz+6+/fh+AigY8z0MTHDLwBXmVkK0AN4tZDYRGQfooRMRMqDWUDbOOa7neBG360IasYqFzLflpjhHTHjO9jZMtAVeDpc71QzK1GLQXjv1lYE9wK8kSChys+KWcyGmOF/Ar8luM/mVHdfWZK4RGTvooRMRMqD/wBVzOy6nAIzO87MOuWbrxawxN13AD2BlJKszMwqAIe6+zjgj0BtYP8SLqsuUMHd/wn8CWgTTsoGaoTDc4EmZnZEON4T+Lyg5bn7ZuBjwpuelyQmEdn7KCETkch5cA+3C4AzwstezAL6s+s/FJ8BepnZ/4CjyFuztDtSgFfCps9vgCfcfU0Jl9UAGB/2NRsO3BOWDweGhOVG0B/sjXCdO4Ah+RcUYxTgwCcljElE9jK6l6WISDljZn8Aarn7n6KORUTKhv5lKSJSjpjZ20BT4NSoYxGRsqMaMhEREZGIqQ+ZiIiISMSUkImIiIhETAmZiIiISMSUkImIiIhETAmZiIiISMSUkImIiIhE7P8BFueSZQbJvrUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjusting colors to make 'Severe' the darker of the two\n",
    "colors = ['#336699', '#003366']  # Reversing the color order\n",
    "\n",
    "# Creating the stacked bar chart with the adjusted colors\n",
    "plt.figure(figsize=(10, 6))\n",
    "stacked_bar_plot = data.groupby(['Claims_History', 'Local_Weather_Conditions']).size().reindex(order, level='Claims_History').unstack().plot(kind='bar', \n",
    "                                                                                                                                         stacked=True, \n",
    "                                                                                                                                         color=colors, \n",
    "                                                                                                                                         figsize=(10, 6))\n",
    "\n",
    "# Adding count labels in white to each bar\n",
    "for bar in stacked_bar_plot.patches:\n",
    "    height = bar.get_height()\n",
    "    if height > 0:  # Only add labels to non-zero bars\n",
    "        stacked_bar_plot.annotate(format(height, '.0f'), \n",
    "                                  (bar.get_x() + bar.get_width() / 2, bar.get_y() + height / 2), \n",
    "                                  ha='center', va='center', \n",
    "                                  color='white',\n",
    "                                  xytext=(0, 5), \n",
    "                                  textcoords='offset points')\n",
    "\n",
    "# Adjusting x-axis labels to be horizontal\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Adding plot title and labels\n",
    "plt.title('Stacked Number of Homes by Claims History and Local Weather Conditions')\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Number of Homes')\n",
    "plt.legend(title='Local Weather Conditions', labels=['Normal', 'Severe'])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'data' is your DataFrame with the same structure as before\n",
    "\n",
    "# Custom colors and order\n",
    "colors = ['#336699', '#003366']  # Severe is darker\n",
    "order = ['Low', 'Medium', 'High']\n",
    "\n",
    "# Creating the figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Grouping and plotting the data\n",
    "stacked_bar_plot = data.groupby(['Claims_History', 'Local_Weather_Conditions']).size().reindex(order, level='Claims_History').unstack().plot(kind='bar', \n",
    "                                                                                                                                         stacked=True, \n",
    "                                                                                                                                         color=colors, \n",
    "                                                                                                                                         figsize=(10, 6))\n",
    "\n",
    "# Adding count labels in white to each bar\n",
    "for bar in stacked_bar_plot.patches:\n",
    "    height = bar.get_height()\n",
    "    if height > 0:  # Only add labels to non-zero bars\n",
    "        stacked_bar_plot.annotate(format(height, '.0f'), \n",
    "                                  (bar.get_x() + bar.get_width() / 2, bar.get_y() + height / 2), \n",
    "                                  ha='center', va='center', \n",
    "                                  color='white',\n",
    "                                  xytext=(0, 5), \n",
    "                                  textcoords='offset points')\n",
    "\n",
    "# Adjusting x-axis labels to be horizontal\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Adding plot title and labels\n",
    "plt.title('Stacked Number of Policies by Claims History and Local Weather Conditions')\n",
    "plt.xlabel('Claims History')\n",
    "plt.ylabel('Number of Policies')\n",
    "plt.legend(title='Local Weather Conditions', labels=['Normal', 'Severe'])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4164295161004717"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def cramer_v(chi2, n, shape):\n",
    "    \"\"\"Calculate Cramer's V statistic.\"\"\"\n",
    "    return np.sqrt(chi2 / (n * min(shape[0] - 1, shape[1] - 1)))\n",
    "\n",
    "# We already have the chi2 value and the shape of the contingency table from the previous analysis\n",
    "n = data.shape[0]  # total number of observations\n",
    "shape = contingency_table.shape  # shape of the contingency table\n",
    "\n",
    "# Calculate Cramer's V\n",
    "cramers_v = cramer_v(chi2, n, shape)\n",
    "cramers_v\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the result of Cramér's V in a simple bar chart for visual representation\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.bar('Cramér\\'s V', cramers_v, color='skyblue')\n",
    "plt.title('Cramér\\'s V for Association between Claims History and Local Weather Conditions')\n",
    "plt.ylabel('Cramér\\'s V Value')\n",
    "plt.ylim(0, 1)\n",
    "plt.text('Cramér\\'s V', cramers_v, f'{cramers_v:.2f}', ha='center', va='bottom')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}