housingproject.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "5d2bd3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "69df9568",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(r'data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fec871",
   "metadata": {},
   "source": [
    "<h3>first rows of dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "a8ded9f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>price</th>\n",
       "      <th>date added</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600 Yard Bungalow For Sale In DHA Phase 6</td>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>14 hours ago</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "      <td>https://www.zameen.com/Property/d_h_a_dha_phas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "      <td>https://www.zameen.com/Property/bahria_town_pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>2.12 Crore</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Sports City, Bahria Town Karachi, Karac...</td>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>Bedrooms: 4,Bathrooms: 4,Kitchens: 2</td>\n",
       "      <td>https://www.zameen.com/Property/bahria_town_ka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>5 hours ago</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "      <td>https://www.zameen.com/Property/bahria_town_ka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Buying A Flat In Clifton - Block 9?</td>\n",
       "      <td>4 Crore</td>\n",
       "      <td>6 hours ago</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "      <td>https://www.zameen.com/Property/clifton_clifto...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title       price  \\\n",
       "0          600 Yard Bungalow For Sale In DHA Phase 6  11.5 Crore   \n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...  1.45 Crore   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...  2.12 Crore   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   1.5 Crore   \n",
       "4                Buying A Flat In Clifton - Block 9?     4 Crore   \n",
       "\n",
       "     date added   type  bedrooms  bathrooms         area  \\\n",
       "0  14 hours ago  House         5          6  600 Sq. Yd.   \n",
       "1   5 hours ago  House         0          0          NaN   \n",
       "2   5 hours ago  House         0          0          NaN   \n",
       "3   5 hours ago  House         0          0          NaN   \n",
       "4   6 hours ago   Flat         3          3  200 Sq. Yd.   \n",
       "\n",
       "                              location  \\\n",
       "0          DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town Karachi, Karachi, Sindh   \n",
       "2  Bahria Town Karachi, Karachi, Sindh   \n",
       "3  Bahria Town Karachi, Karachi, Sindh   \n",
       "4              Clifton, Karachi, Sindh   \n",
       "\n",
       "                                   complete location  \\\n",
       "0           DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "2  Bahria Sports City, Bahria Town Karachi, Karac...   \n",
       "3  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "4         Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "4                                 Apartment for sale   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...   \n",
       "1               Bedrooms: 3,Bathrooms: 3,Kitchens: 2   \n",
       "2               Bedrooms: 4,Bathrooms: 4,Kitchens: 2   \n",
       "3               Bedrooms: 3,Bathrooms: 3,Kitchens: 2   \n",
       "4  Flooring,Electricity Backup,Broadband Internet...   \n",
       "\n",
       "                                                 url  \n",
       "0  https://www.zameen.com/Property/d_h_a_dha_phas...  \n",
       "1  https://www.zameen.com/Property/bahria_town_pr...  \n",
       "2  https://www.zameen.com/Property/bahria_town_ka...  \n",
       "3  https://www.zameen.com/Property/bahria_town_ka...  \n",
       "4  https://www.zameen.com/Property/clifton_clifto...  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "753dec38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8414, 12)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "85451311",
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8414.000000</td>\n",
       "      <td>8414.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.169242</td>\n",
       "      <td>3.365106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.843161</td>\n",
       "      <td>1.999298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          bedrooms    bathrooms\n",
       "count  8414.000000  8414.000000\n",
       "mean      3.169242     3.365106\n",
       "std       1.843161     1.999298\n",
       "min       0.000000     0.000000\n",
       "25%       2.000000     2.000000\n",
       "50%       3.000000     3.000000\n",
       "75%       4.000000     5.000000\n",
       "max      11.000000    10.000000"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4abc67cd",
   "metadata": {},
   "source": [
    "<h3>dropping the unnecessary columns<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "635b7a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.12 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Sports City, Bahria Town Karachi, Karac...</td>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>Bedrooms: 4,Bathrooms: 4,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms         area  \\\n",
       "0  11.5 Crore  House         5          6  600 Sq. Yd.   \n",
       "1  1.45 Crore  House         0          0          NaN   \n",
       "2  2.12 Crore  House         0          0          NaN   \n",
       "3   1.5 Crore  House         0          0          NaN   \n",
       "4     4 Crore   Flat         3          3  200 Sq. Yd.   \n",
       "\n",
       "                              location  \\\n",
       "0          DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town Karachi, Karachi, Sindh   \n",
       "2  Bahria Town Karachi, Karachi, Sindh   \n",
       "3  Bahria Town Karachi, Karachi, Sindh   \n",
       "4              Clifton, Karachi, Sindh   \n",
       "\n",
       "                                   complete location  \\\n",
       "0           DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "2  Bahria Sports City, Bahria Town Karachi, Karac...   \n",
       "3  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "4         Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "4                                 Apartment for sale   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "1               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "2               Bedrooms: 4,Bathrooms: 4,Kitchens: 2  \n",
       "3               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "4  Flooring,Electricity Backup,Broadband Internet...  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(labels = ['title', 'date added','url'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537f7f7c",
   "metadata": {},
   "source": [
    "<h3>since we want to predict the price of houses and flats so we will filter our dataset in that way<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "fb1bdd8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.12 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Sports City, Bahria Town Karachi, Karac...</td>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>Bedrooms: 4,Bathrooms: 4,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms         area  \\\n",
       "0  11.5 Crore  House         5          6  600 Sq. Yd.   \n",
       "1  1.45 Crore  House         0          0          NaN   \n",
       "2  2.12 Crore  House         0          0          NaN   \n",
       "3   1.5 Crore  House         0          0          NaN   \n",
       "4     4 Crore   Flat         3          3  200 Sq. Yd.   \n",
       "\n",
       "                              location  \\\n",
       "0          DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town Karachi, Karachi, Sindh   \n",
       "2  Bahria Town Karachi, Karachi, Sindh   \n",
       "3  Bahria Town Karachi, Karachi, Sindh   \n",
       "4              Clifton, Karachi, Sindh   \n",
       "\n",
       "                                   complete location  \\\n",
       "0           DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "2  Bahria Sports City, Bahria Town Karachi, Karac...   \n",
       "3  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "4         Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "4                                 Apartment for sale   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "1               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "2               Bedrooms: 4,Bathrooms: 4,Kitchens: 2  \n",
       "3               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "4  Flooring,Electricity Backup,Broadband Internet...  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['type'].isin(['House','Flat'])]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b7e15c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8355, 9)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aceaa95a",
   "metadata": {},
   "source": [
    "<h3> we notice that area has missing values in some rows and and bathroom and bedrooms has \"0\" value in some rows which is not possible for a house or flat to have zero bedrooms and bathrooms and for this purpose we will split our data containing zero values of the bathroom and bedroom and clean that dataset further.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "90a6916d",
   "metadata": {},
   "outputs": [],
   "source": [
    "uncleaned_data = df[(df['bedrooms'] == 0) | (df['bathrooms'] == 0)]\n",
    "\n",
    "# data containing all the non zero values of bedroom and bathroom\n",
    "df = df[(df['bedrooms'] != 0) & (df['bathrooms'] != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9cdb17d6",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>500 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>500 Square Yards Bungalow Available For Sale I...</td>\n",
       "      <td>Flooring,Electricity Backup,Bedrooms: 7,Bathro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 8, Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton Block 8 Near 3 Talwar</td>\n",
       "      <td>Built in year: 2022,Parking Spaces: 1,Flooring...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>flat for sale Clifton Platinium residency 3 be...</td>\n",
       "      <td>Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms         area  \\\n",
       "0  11.5 Crore  House         5          6  600 Sq. Yd.   \n",
       "4     4 Crore   Flat         3          3  200 Sq. Yd.   \n",
       "5     9 Crore  House         7          6  500 Sq. Yd.   \n",
       "8     6 Crore   Flat         3          4  200 Sq. Yd.   \n",
       "9   3.7 Crore   Flat         3          3  244 Sq. Yd.   \n",
       "\n",
       "                      location                           complete location  \\\n",
       "0  DHA Defence, Karachi, Sindh    DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "4      Clifton, Karachi, Sindh  Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "5  DHA Defence, Karachi, Sindh    DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "8      Clifton, Karachi, Sindh  Clifton - Block 8, Clifton, Karachi, Sindh   \n",
       "9      Clifton, Karachi, Sindh                     Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "4                                 Apartment for sale   \n",
       "5  500 Square Yards Bungalow Available For Sale I...   \n",
       "8                      Clifton Block 8 Near 3 Talwar   \n",
       "9  flat for sale Clifton Platinium residency 3 be...   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "4  Flooring,Electricity Backup,Broadband Internet...  \n",
       "5  Flooring,Electricity Backup,Bedrooms: 7,Bathro...  \n",
       "8  Built in year: 2022,Parking Spaces: 1,Flooring...  \n",
       "9  Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "61a7ccdf",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.12 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Sports City, Bahria Town Karachi, Karac...</td>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>Bedrooms: 4,Bathrooms: 4,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 11-B, Bahria Town - Pre...</td>\n",
       "      <td>3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.55 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 27, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms area  \\\n",
       "1  1.45 Crore  House         0          0  NaN   \n",
       "2  2.12 Crore  House         0          0  NaN   \n",
       "3   1.5 Crore  House         0          0  NaN   \n",
       "6  1.45 Crore  House         0          0  NaN   \n",
       "7  1.55 Crore  House         0          0  NaN   \n",
       "\n",
       "                              location  \\\n",
       "1  Bahria Town Karachi, Karachi, Sindh   \n",
       "2  Bahria Town Karachi, Karachi, Sindh   \n",
       "3  Bahria Town Karachi, Karachi, Sindh   \n",
       "6  Bahria Town Karachi, Karachi, Sindh   \n",
       "7  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                   complete location  \\\n",
       "1  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "2  Bahria Sports City, Bahria Town Karachi, Karac...   \n",
       "3  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "6  Bahria Town - Precinct 11-B, Bahria Town - Pre...   \n",
       "7  Bahria Town - Precinct 27, Bahria Town Karachi...   \n",
       "\n",
       "                                         description  \\\n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "6  3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...   \n",
       "7  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "\n",
       "                               keywords  \n",
       "1  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "2  Bedrooms: 4,Bathrooms: 4,Kitchens: 2  \n",
       "3  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "6  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "7  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncleaned_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "c75123ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7307, 9)\n",
      "(1048, 9)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(uncleaned_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6935527f",
   "metadata": {},
   "source": [
    "<h3> we notice that in our uncleaned data the keywords column contain the bedrooms and bathrooms value but the data is missing in the bedroom and bathroom column. so first we remove the data that contains null value of keyword and then proceed further</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "53e229be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking how many null values are present in keywords column\n",
    "uncleaned_data['keywords'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "9c377272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing the null values\n",
    "uncleaned_data = uncleaned_data.dropna(subset=['keywords'])\n",
    "uncleaned_data['keywords'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec148fd2",
   "metadata": {},
   "source": [
    "<h3>notice that some of the entries in keywords are like this: (Bedrooms: 3,Bathrooms: 3,Kitchens: 2) so we will extract the values of bathrooms and bedroom and convert it in int form and then store it in bedrooms and bathrooms column.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "aa617d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                object\n",
       "type                 object\n",
       "bedrooms              int64\n",
       "bathrooms             int64\n",
       "area                 object\n",
       "location             object\n",
       "complete location    object\n",
       "description          object\n",
       "keywords             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncleaned_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "8d4c4580",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "2c894be8",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.12 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Sports City, Bahria Town Karachi, Karac...</td>\n",
       "      <td>4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...</td>\n",
       "      <td>Bedrooms: 4,Bathrooms: 4,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 11-B, Bahria Town - Pre...</td>\n",
       "      <td>3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.55 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 27, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms area  \\\n",
       "1  1.45 Crore  House       3.0        3.0  NaN   \n",
       "2  2.12 Crore  House       4.0        4.0  NaN   \n",
       "3   1.5 Crore  House       3.0        3.0  NaN   \n",
       "6  1.45 Crore  House       3.0        3.0  NaN   \n",
       "7  1.55 Crore  House       3.0        3.0  NaN   \n",
       "\n",
       "                              location  \\\n",
       "1  Bahria Town Karachi, Karachi, Sindh   \n",
       "2  Bahria Town Karachi, Karachi, Sindh   \n",
       "3  Bahria Town Karachi, Karachi, Sindh   \n",
       "6  Bahria Town Karachi, Karachi, Sindh   \n",
       "7  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                   complete location  \\\n",
       "1  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "2  Bahria Sports City, Bahria Town Karachi, Karac...   \n",
       "3  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "6  Bahria Town - Precinct 11-B, Bahria Town - Pre...   \n",
       "7  Bahria Town - Precinct 27, Bahria Town Karachi...   \n",
       "\n",
       "                                         description  \\\n",
       "1  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "2  4 BEDS LUXURY SPORTS CITY VILLA FOR RENT BAHRI...   \n",
       "3  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "6  3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...   \n",
       "7  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "\n",
       "                               keywords  \n",
       "1  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "2  Bedrooms: 4,Bathrooms: 4,Kitchens: 2  \n",
       "3  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "6  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "7  Bedrooms: 3,Bathrooms: 3,Kitchens: 2  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to extract the integer value from a string\n",
    "def extract_integer(text):\n",
    "    try:\n",
    "        value = int(re.search(r'\\d+', text).group())\n",
    "        return value\n",
    "    except AttributeError:\n",
    "        return None\n",
    "\n",
    "# Extract bedrooms and bathrooms values from \"keywords\" column\n",
    "uncleaned_data['bedrooms'] = uncleaned_data['keywords'].apply(lambda x: extract_integer(x.split(',')[0]) if 'Bedrooms: ' in x else None)\n",
    "uncleaned_data['bathrooms'] = uncleaned_data['keywords'].apply(lambda x: extract_integer(x.split(',')[1]) if 'Bathrooms: ' in x else None)\n",
    "\n",
    "# Print the modified DataFrame\n",
    "uncleaned_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f64593fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "853\n",
      "841\n"
     ]
    }
   ],
   "source": [
    "print(uncleaned_data['bathrooms'].isna().sum())\n",
    "print(uncleaned_data['bedrooms'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf37b1a",
   "metadata": {},
   "source": [
    "<h3>we notice that there are still missing values in bedroom and bathrooms column so we will drop these rows.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "bdcf120c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "uncleaned_data.dropna(subset=['bathrooms', 'bedrooms'], inplace=True)\n",
    "print(uncleaned_data['bathrooms'].isna().sum())\n",
    "print(uncleaned_data['bedrooms'].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "4c92eec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 9)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncleaned_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e71ad93",
   "metadata": {},
   "source": [
    "<h3>joining the now cleaned data again with our original df<h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "9cb6283f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7402, 9)\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>500 Sq. Yd.</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>500 Square Yards Bungalow Available For Sale I...</td>\n",
       "      <td>Flooring,Electricity Backup,Bedrooms: 7,Bathro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 8, Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton Block 8 Near 3 Talwar</td>\n",
       "      <td>Built in year: 2022,Parking Spaces: 1,Flooring...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>244 Sq. Yd.</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>flat for sale Clifton Platinium residency 3 be...</td>\n",
       "      <td>Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms         area  \\\n",
       "0  11.5 Crore  House       5.0        6.0  600 Sq. Yd.   \n",
       "1     4 Crore   Flat       3.0        3.0  200 Sq. Yd.   \n",
       "2     9 Crore  House       7.0        6.0  500 Sq. Yd.   \n",
       "3     6 Crore   Flat       3.0        4.0  200 Sq. Yd.   \n",
       "4   3.7 Crore   Flat       3.0        3.0  244 Sq. Yd.   \n",
       "\n",
       "                      location                           complete location  \\\n",
       "0  DHA Defence, Karachi, Sindh    DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1      Clifton, Karachi, Sindh  Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "2  DHA Defence, Karachi, Sindh    DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "3      Clifton, Karachi, Sindh  Clifton - Block 8, Clifton, Karachi, Sindh   \n",
       "4      Clifton, Karachi, Sindh                     Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1                                 Apartment for sale   \n",
       "2  500 Square Yards Bungalow Available For Sale I...   \n",
       "3                      Clifton Block 8 Near 3 Talwar   \n",
       "4  flat for sale Clifton Platinium residency 3 be...   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "1  Flooring,Electricity Backup,Broadband Internet...  \n",
       "2  Flooring,Electricity Backup,Bedrooms: 7,Bathro...  \n",
       "3  Built in year: 2022,Parking Spaces: 1,Flooring...  \n",
       "4  Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, uncleaned_data], ignore_index=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b8a320",
   "metadata": {},
   "source": [
    "<h3>we notice that there are few row with null value in their area. so we separate the rows with the value of area as null to further analyse it and clean it</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "3e087a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['area'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "fd104d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df with all null area value\n",
    "df_with_null_area = df[df['area'].isnull()]\n",
    "\n",
    "# df without any null area value\n",
    "df = df[~df['area'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "8216e30b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df_with_null_area['area'].isna().sum())\n",
    "print(df['area'].isna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "7de9fb9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7273, 9)\n",
      "(129, 9)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df_with_null_area.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "210674d1",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>13 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 8, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Monza lnc.</td>\n",
       "      <td>Built in year: 2019,Parking Spaces: 3,Flooring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>65 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Studio for sale brand new 1st floor 2bed loung...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>3.3 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...</td>\n",
       "      <td>dha phase 5 galaxy skyline 3 bedrooms drawing ...</td>\n",
       "      <td>Parking Spaces: 2,Flooring,Electricity Backup,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>30 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>GFS Mega Twin Tower, Bahria Town Karachi, Kara...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Eme...</td>\n",
       "      <td>Built in year: 3,Parking Spaces,Lobby in Build...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>36 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Dominion Twin Towers, Bahria Town Karachi, Kar...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Ame...</td>\n",
       "      <td>Built in year: 3,Parking Spaces: 2,Lobby in Bu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price   type  bedrooms  bathrooms area  \\\n",
       "285    13 Crore  House       6.0        6.0  NaN   \n",
       "1647    65 Lakh   Flat       2.0        2.0  NaN   \n",
       "1784  3.3 Crore   Flat       4.0        4.0  NaN   \n",
       "2604    30 Lakh   Flat       1.0        1.0  NaN   \n",
       "2608    36 Lakh   Flat       1.0        1.0  NaN   \n",
       "\n",
       "                                 location  \\\n",
       "285           DHA Defence, Karachi, Sindh   \n",
       "1647          DHA Defence, Karachi, Sindh   \n",
       "1784          DHA Defence, Karachi, Sindh   \n",
       "2604  Bahria Town Karachi, Karachi, Sindh   \n",
       "2608  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                      complete location  \\\n",
       "285            DHA Phase 8, DHA Defence, Karachi, Sindh   \n",
       "1647           DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "1784  DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...   \n",
       "2604  GFS Mega Twin Tower, Bahria Town Karachi, Kara...   \n",
       "2608  Dominion Twin Towers, Bahria Town Karachi, Kar...   \n",
       "\n",
       "                                            description  \\\n",
       "285                                         Monza lnc.    \n",
       "1647  Studio for sale brand new 1st floor 2bed loung...   \n",
       "1784  dha phase 5 galaxy skyline 3 bedrooms drawing ...   \n",
       "2604  Bahria Town Karachi comes with world-class Eme...   \n",
       "2608  Bahria Town Karachi comes with world-class Ame...   \n",
       "\n",
       "                                               keywords  \n",
       "285      Built in year: 2019,Parking Spaces: 3,Flooring  \n",
       "1647                                                NaN  \n",
       "1784  Parking Spaces: 2,Flooring,Electricity Backup,...  \n",
       "2604  Built in year: 3,Parking Spaces,Lobby in Build...  \n",
       "2608  Built in year: 3,Parking Spaces: 2,Lobby in Bu...  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_null_area.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84a47421",
   "metadata": {},
   "source": [
    "<h3>we notice that some information about area is stored in \"description\" column around the word \"SQ YARD\" so we create a regular expression to extract the value and store it in \"area\" column</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "40265ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>13 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 8, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Monza lnc.</td>\n",
       "      <td>Built in year: 2019,Parking Spaces: 3,Flooring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>65 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Studio for sale brand new 1st floor 2bed loung...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>3.3 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...</td>\n",
       "      <td>dha phase 5 galaxy skyline 3 bedrooms drawing ...</td>\n",
       "      <td>Parking Spaces: 2,Flooring,Electricity Backup,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>30 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>GFS Mega Twin Tower, Bahria Town Karachi, Kara...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Eme...</td>\n",
       "      <td>Built in year: 3,Parking Spaces,Lobby in Build...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>36 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Dominion Twin Towers, Bahria Town Karachi, Kar...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Ame...</td>\n",
       "      <td>Built in year: 3,Parking Spaces: 2,Lobby in Bu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price   type  bedrooms  bathrooms  area  \\\n",
       "285    13 Crore  House       6.0        6.0   NaN   \n",
       "1647    65 Lakh   Flat       2.0        2.0   NaN   \n",
       "1784  3.3 Crore   Flat       4.0        4.0   NaN   \n",
       "2604    30 Lakh   Flat       1.0        1.0   NaN   \n",
       "2608    36 Lakh   Flat       1.0        1.0   NaN   \n",
       "\n",
       "                                 location  \\\n",
       "285           DHA Defence, Karachi, Sindh   \n",
       "1647          DHA Defence, Karachi, Sindh   \n",
       "1784          DHA Defence, Karachi, Sindh   \n",
       "2604  Bahria Town Karachi, Karachi, Sindh   \n",
       "2608  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                      complete location  \\\n",
       "285            DHA Phase 8, DHA Defence, Karachi, Sindh   \n",
       "1647           DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "1784  DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...   \n",
       "2604  GFS Mega Twin Tower, Bahria Town Karachi, Kara...   \n",
       "2608  Dominion Twin Towers, Bahria Town Karachi, Kar...   \n",
       "\n",
       "                                            description  \\\n",
       "285                                         Monza lnc.    \n",
       "1647  Studio for sale brand new 1st floor 2bed loung...   \n",
       "1784  dha phase 5 galaxy skyline 3 bedrooms drawing ...   \n",
       "2604  Bahria Town Karachi comes with world-class Eme...   \n",
       "2608  Bahria Town Karachi comes with world-class Ame...   \n",
       "\n",
       "                                               keywords  \n",
       "285      Built in year: 2019,Parking Spaces: 3,Flooring  \n",
       "1647                                                NaN  \n",
       "1784  Parking Spaces: 2,Flooring,Electricity Backup,...  \n",
       "2604  Built in year: 3,Parking Spaces,Lobby in Build...  \n",
       "2608  Built in year: 3,Parking Spaces: 2,Lobby in Bu...  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pattern = r'(\\d+) sq yards|(\\d+) SQ YARDS|(\\d+) Sq Yards|(\\d+) yards|(\\d+) YARDS |(\\d+)SQ YARDS'\n",
    "pattern = r'(\\d+)\\s*(?:sq yards?|SQ YARDS?|Sq Yards?|yards?|YARDS?)\\s*(?:,\\s*(\\d+))?'\n",
    "\n",
    "# Extract the area values from the descriptions using the regular expression pattern\n",
    "df_with_null_area['area'] = df_with_null_area['description'].str.extract(pattern, flags=re.IGNORECASE).replace(pd.NA, np.nan).astype(float).max(axis=1)\n",
    "\n",
    "# Print the updated DataFrame\n",
    "df_with_null_area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "bccfe5be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93\n",
      "[  nan  300.  125.  235.  152.  350.   75. 1000.]\n"
     ]
    }
   ],
   "source": [
    "print(df_with_null_area['area'].isna().sum())\n",
    "print(df_with_null_area['area'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "146331f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>13 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 8, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Monza lnc.</td>\n",
       "      <td>Built in year: 2019,Parking Spaces: 3,Flooring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>65 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Studio for sale brand new 1st floor 2bed loung...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>3.3 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...</td>\n",
       "      <td>dha phase 5 galaxy skyline 3 bedrooms drawing ...</td>\n",
       "      <td>Parking Spaces: 2,Flooring,Electricity Backup,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>30 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>GFS Mega Twin Tower, Bahria Town Karachi, Kara...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Eme...</td>\n",
       "      <td>Built in year: 3,Parking Spaces,Lobby in Build...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>36 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Dominion Twin Towers, Bahria Town Karachi, Kar...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Ame...</td>\n",
       "      <td>Built in year: 3,Parking Spaces: 2,Lobby in Bu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price   type  bedrooms  bathrooms  area  \\\n",
       "285    13 Crore  House       6.0        6.0   NaN   \n",
       "1647    65 Lakh   Flat       2.0        2.0   NaN   \n",
       "1784  3.3 Crore   Flat       4.0        4.0   NaN   \n",
       "2604    30 Lakh   Flat       1.0        1.0   NaN   \n",
       "2608    36 Lakh   Flat       1.0        1.0   NaN   \n",
       "\n",
       "                                 location  \\\n",
       "285           DHA Defence, Karachi, Sindh   \n",
       "1647          DHA Defence, Karachi, Sindh   \n",
       "1784          DHA Defence, Karachi, Sindh   \n",
       "2604  Bahria Town Karachi, Karachi, Sindh   \n",
       "2608  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                      complete location  \\\n",
       "285            DHA Phase 8, DHA Defence, Karachi, Sindh   \n",
       "1647           DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "1784  DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...   \n",
       "2604  GFS Mega Twin Tower, Bahria Town Karachi, Kara...   \n",
       "2608  Dominion Twin Towers, Bahria Town Karachi, Kar...   \n",
       "\n",
       "                                            description  \\\n",
       "285                                         Monza lnc.    \n",
       "1647  Studio for sale brand new 1st floor 2bed loung...   \n",
       "1784  dha phase 5 galaxy skyline 3 bedrooms drawing ...   \n",
       "2604  Bahria Town Karachi comes with world-class Eme...   \n",
       "2608  Bahria Town Karachi comes with world-class Ame...   \n",
       "\n",
       "                                               keywords  \n",
       "285      Built in year: 2019,Parking Spaces: 3,Flooring  \n",
       "1647                                                NaN  \n",
       "1784  Parking Spaces: 2,Flooring,Electricity Backup,...  \n",
       "2604  Built in year: 3,Parking Spaces,Lobby in Build...  \n",
       "2608  Built in year: 3,Parking Spaces: 2,Lobby in Bu...  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_null_area.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1930f336",
   "metadata": {},
   "source": [
    "<h3>there are still missing values present in area and we notice that some information of area is still present in \"description\" column in sq feet value, so we extract that value through regular expression, convert it in square yards and store it in \"area\" column</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "359772ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern_sqft = r'(\\d+)\\s*(?:SQ FEET?|Sq Feet?|sq feet?|sqfeet?|SQFEET?|feet)\\s*(?:,\\s*(\\d+))?'\n",
    "\n",
    "# Extract the area values in square feet from the descriptions using the regular expression pattern\n",
    "def extract_and_convert_area(description):\n",
    "    match = re.search(pattern_sqft, description, flags=re.IGNORECASE)\n",
    "    if match:\n",
    "        area_sqft = float(match.group(1))\n",
    "        area_yards = area_sqft * 0.111111\n",
    "        return area_yards\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "mask = df_with_null_area['area'].isnull()\n",
    "\n",
    "# Apply the function to the 'description' column and assign the converted values to the 'area' column\n",
    "df_with_null_area.loc[mask, 'area'] = df_with_null_area.loc[mask, 'description'].apply(extract_and_convert_area)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "1f7648ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
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       "      <th>285</th>\n",
       "      <td>13 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 8, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Monza lnc.</td>\n",
       "      <td>Built in year: 2019,Parking Spaces: 3,Flooring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>65 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Studio for sale brand new 1st floor 2bed loung...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>3.3 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...</td>\n",
       "      <td>dha phase 5 galaxy skyline 3 bedrooms drawing ...</td>\n",
       "      <td>Parking Spaces: 2,Flooring,Electricity Backup,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>30 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>GFS Mega Twin Tower, Bahria Town Karachi, Kara...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Eme...</td>\n",
       "      <td>Built in year: 3,Parking Spaces,Lobby in Build...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>36 Lakh</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Dominion Twin Towers, Bahria Town Karachi, Kar...</td>\n",
       "      <td>Bahria Town Karachi comes with world-class Ame...</td>\n",
       "      <td>Built in year: 3,Parking Spaces: 2,Lobby in Bu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          price   type  bedrooms  bathrooms  area  \\\n",
       "285    13 Crore  House       6.0        6.0   NaN   \n",
       "1647    65 Lakh   Flat       2.0        2.0   NaN   \n",
       "1784  3.3 Crore   Flat       4.0        4.0   NaN   \n",
       "2604    30 Lakh   Flat       1.0        1.0   NaN   \n",
       "2608    36 Lakh   Flat       1.0        1.0   NaN   \n",
       "\n",
       "                                 location  \\\n",
       "285           DHA Defence, Karachi, Sindh   \n",
       "1647          DHA Defence, Karachi, Sindh   \n",
       "1784          DHA Defence, Karachi, Sindh   \n",
       "2604  Bahria Town Karachi, Karachi, Sindh   \n",
       "2608  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                      complete location  \\\n",
       "285            DHA Phase 8, DHA Defence, Karachi, Sindh   \n",
       "1647           DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "1784  DHA Phase 5 Extension, DHA Phase 5, DHA Defenc...   \n",
       "2604  GFS Mega Twin Tower, Bahria Town Karachi, Kara...   \n",
       "2608  Dominion Twin Towers, Bahria Town Karachi, Kar...   \n",
       "\n",
       "                                            description  \\\n",
       "285                                         Monza lnc.    \n",
       "1647  Studio for sale brand new 1st floor 2bed loung...   \n",
       "1784  dha phase 5 galaxy skyline 3 bedrooms drawing ...   \n",
       "2604  Bahria Town Karachi comes with world-class Eme...   \n",
       "2608  Bahria Town Karachi comes with world-class Ame...   \n",
       "\n",
       "                                               keywords  \n",
       "285      Built in year: 2019,Parking Spaces: 3,Flooring  \n",
       "1647                                                NaN  \n",
       "1784  Parking Spaces: 2,Flooring,Electricity Backup,...  \n",
       "2604  Built in year: 3,Parking Spaces,Lobby in Build...  \n",
       "2608  Built in year: 3,Parking Spaces: 2,Lobby in Bu...  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_null_area.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "70a3f599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n",
      "[       nan  300.       125.       235.       152.       122.2221\n",
      "   61.11105  105.55545  350.        75.      1000.     ]\n"
     ]
    }
   ],
   "source": [
    "print(df_with_null_area['area'].isna().sum())\n",
    "print(df_with_null_area['area'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf58a476",
   "metadata": {},
   "source": [
    "<h3>we remove the remaining rows with area as null values</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "51b0932d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "df_with_null_area.dropna(subset=['area'], inplace=True)\n",
    "print(df_with_null_area['area'].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "ae655606",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68, 9)\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7120</th>\n",
       "      <td>7.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 4, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>It's a 300 Sq Yards independent bungalow in Ph...</td>\n",
       "      <td>Built in year: 2020,Parking Spaces: 3,Double G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7307</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Ali Block, Bahria Town - Precinc...</td>\n",
       "      <td>3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7309</th>\n",
       "      <td>1.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 31, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7310</th>\n",
       "      <td>1.45 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 11-B, Bahria Town - Pre...</td>\n",
       "      <td>3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7311</th>\n",
       "      <td>1.55 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Bahria Town Karachi, Karachi, Sindh</td>\n",
       "      <td>Bahria Town - Precinct 27, Bahria Town Karachi...</td>\n",
       "      <td>3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...</td>\n",
       "      <td>Bedrooms: 3,Bathrooms: 3,Kitchens: 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price   type  bedrooms  bathrooms   area  \\\n",
       "7120   7.5 Crore  House       5.0        5.0  300.0   \n",
       "7307  1.45 Crore  House       3.0        3.0  125.0   \n",
       "7309   1.5 Crore  House       3.0        3.0  235.0   \n",
       "7310  1.45 Crore  House       3.0        3.0  152.0   \n",
       "7311  1.55 Crore  House       3.0        3.0  235.0   \n",
       "\n",
       "                                 location  \\\n",
       "7120          DHA Defence, Karachi, Sindh   \n",
       "7307  Bahria Town Karachi, Karachi, Sindh   \n",
       "7309  Bahria Town Karachi, Karachi, Sindh   \n",
       "7310  Bahria Town Karachi, Karachi, Sindh   \n",
       "7311  Bahria Town Karachi, Karachi, Sindh   \n",
       "\n",
       "                                      complete location  \\\n",
       "7120           DHA Phase 4, DHA Defence, Karachi, Sindh   \n",
       "7307  Bahria Town - Ali Block, Bahria Town - Precinc...   \n",
       "7309  Bahria Town - Precinct 31, Bahria Town Karachi...   \n",
       "7310  Bahria Town - Precinct 11-B, Bahria Town - Pre...   \n",
       "7311  Bahria Town - Precinct 27, Bahria Town Karachi...   \n",
       "\n",
       "                                            description  \\\n",
       "7120  It's a 300 Sq Yards independent bungalow in Ph...   \n",
       "7307  3 BEDS LUXURY 125 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "7309  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "7310  3 BEDS LUXURY 152SQ YARDS VILLA FOR SALE LOCAT...   \n",
       "7311  3 BEDS LUXURY 235 SQ YARDS VILLA FOR SALE LOCA...   \n",
       "\n",
       "                                               keywords  \n",
       "7120  Built in year: 2020,Parking Spaces: 3,Double G...  \n",
       "7307               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "7309               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "7310               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  \n",
       "7311               Bedrooms: 3,Bathrooms: 3,Kitchens: 2  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_with_null_area.shape)\n",
    "df_with_null_area.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea8fe08",
   "metadata": {},
   "source": [
    "<h3>we notice that df_with_null_area has area datatype as float and df has area datatype as object</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "9b5fb57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                 object\n",
       "type                  object\n",
       "bedrooms             float64\n",
       "bathrooms            float64\n",
       "area                 float64\n",
       "location              object\n",
       "complete location     object\n",
       "description           object\n",
       "keywords              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_null_area.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "26754d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                 object\n",
       "type                  object\n",
       "bedrooms             float64\n",
       "bathrooms            float64\n",
       "area                  object\n",
       "location              object\n",
       "complete location     object\n",
       "description           object\n",
       "keywords              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3db205",
   "metadata": {},
   "source": [
    "<h4>converting df area column into int</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "1e9e826f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Iqbal Siddiqui\\AppData\\Local\\Temp\\ipykernel_11076\\2746772528.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['area'] = df['area'].str.replace(' Sq. Yd.', '')\n"
     ]
    }
   ],
   "source": [
    "df['area'] = df['area'].str.replace(' Sq. Yd.', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "f25f225f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['area'] = df['area'].str.replace(',', '')\n",
    "\n",
    "df['area'] = df['area'].astype(int)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053fae53",
   "metadata": {},
   "source": [
    "<h4>converting df_with_null_area 'area' column into int</h4>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "64fc7b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_null_area['area'] = df_with_null_area['area'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c26439",
   "metadata": {},
   "source": [
    "<h3>merging the original dataframe and data_with_null_area dataframe</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "725fc064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7341, 9)\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA Defence, Karachi, Sindh</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>500 Square Yards Bungalow Available For Sale I...</td>\n",
       "      <td>Flooring,Electricity Backup,Bedrooms: 7,Bathro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton - Block 8, Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton Block 8 Near 3 Talwar</td>\n",
       "      <td>Built in year: 2022,Parking Spaces: 1,Flooring...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>flat for sale Clifton Platinium residency 3 be...</td>\n",
       "      <td>Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms  area                     location  \\\n",
       "0  11.5 Crore  House       5.0        6.0   600  DHA Defence, Karachi, Sindh   \n",
       "1     4 Crore   Flat       3.0        3.0   200      Clifton, Karachi, Sindh   \n",
       "2     9 Crore  House       7.0        6.0   500  DHA Defence, Karachi, Sindh   \n",
       "3     6 Crore   Flat       3.0        4.0   200      Clifton, Karachi, Sindh   \n",
       "4   3.7 Crore   Flat       3.0        3.0   244      Clifton, Karachi, Sindh   \n",
       "\n",
       "                            complete location  \\\n",
       "0    DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1  Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "2    DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "3  Clifton - Block 8, Clifton, Karachi, Sindh   \n",
       "4                     Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1                                 Apartment for sale   \n",
       "2  500 Square Yards Bungalow Available For Sale I...   \n",
       "3                      Clifton Block 8 Near 3 Talwar   \n",
       "4  flat for sale Clifton Platinium residency 3 be...   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "1  Flooring,Electricity Backup,Broadband Internet...  \n",
       "2  Flooring,Electricity Backup,Bedrooms: 7,Bathro...  \n",
       "3  Built in year: 2022,Parking Spaces: 1,Flooring...  \n",
       "4  Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, df_with_null_area], ignore_index=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e490912f",
   "metadata": {},
   "source": [
    "<h3>we will be using location too while creating the application but it has missing values so we will use the information provided in \"complete location to fill the location column\"</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "69b91af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "355"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "be109003",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>complete location</th>\n",
       "      <th>description</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "      <td>DHA Phase 6, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>Chance Deal 600 Yard Bungalow For Sale</td>\n",
       "      <td>Built in year: 1,Parking Spaces: 5,Flooring,Ot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>Clifton - Block 9, Clifton, Karachi, Sindh</td>\n",
       "      <td>Apartment for sale</td>\n",
       "      <td>Flooring,Electricity Backup,Broadband Internet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "      <td>DHA Phase 7, DHA Defence, Karachi, Sindh</td>\n",
       "      <td>500 Square Yards Bungalow Available For Sale I...</td>\n",
       "      <td>Flooring,Electricity Backup,Bedrooms: 7,Bathro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>Clifton - Block 8, Clifton, Karachi, Sindh</td>\n",
       "      <td>Clifton Block 8 Near 3 Talwar</td>\n",
       "      <td>Built in year: 2022,Parking Spaces: 1,Flooring...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>Clifton, Karachi, Sindh</td>\n",
       "      <td>flat for sale Clifton Platinium residency 3 be...</td>\n",
       "      <td>Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms  area location  \\\n",
       "0  11.5 Crore  House       5.0        6.0   600      DHA   \n",
       "1     4 Crore   Flat       3.0        3.0   200  Clifton   \n",
       "2     9 Crore  House       7.0        6.0   500      DHA   \n",
       "3     6 Crore   Flat       3.0        4.0   200  Clifton   \n",
       "4   3.7 Crore   Flat       3.0        3.0   244  Clifton   \n",
       "\n",
       "                            complete location  \\\n",
       "0    DHA Phase 6, DHA Defence, Karachi, Sindh   \n",
       "1  Clifton - Block 9, Clifton, Karachi, Sindh   \n",
       "2    DHA Phase 7, DHA Defence, Karachi, Sindh   \n",
       "3  Clifton - Block 8, Clifton, Karachi, Sindh   \n",
       "4                     Clifton, Karachi, Sindh   \n",
       "\n",
       "                                         description  \\\n",
       "0             Chance Deal 600 Yard Bungalow For Sale   \n",
       "1                                 Apartment for sale   \n",
       "2  500 Square Yards Bungalow Available For Sale I...   \n",
       "3                      Clifton Block 8 Near 3 Talwar   \n",
       "4  flat for sale Clifton Platinium residency 3 be...   \n",
       "\n",
       "                                            keywords  \n",
       "0  Built in year: 1,Parking Spaces: 5,Flooring,Ot...  \n",
       "1  Flooring,Electricity Backup,Broadband Internet...  \n",
       "2  Flooring,Electricity Backup,Bedrooms: 7,Bathro...  \n",
       "3  Built in year: 2022,Parking Spaces: 1,Flooring...  \n",
       "4  Flooring,Floor: 3,Elevators: 1,Bedrooms: 3,Bat...  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['complete location'].str.contains('dha', case=False, na=False), 'location'] = 'DHA'\n",
    "\n",
    "# Replace the value of \"location\" with 'bahria town' where \"complete location\" contains 'bahria town'\n",
    "df.loc[df['complete location'].str.contains('bahria town', case=False, na=False), 'location'] = 'Bahria Town'\n",
    "\n",
    "# Replace the value of \"location\" with 'clifton' where \"complete location\" contains 'clifton'\n",
    "df.loc[df['complete location'].str.contains('clifton', case=False, na=False), 'location'] = 'Clifton'\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "3e12af97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "(7341, 9)\n"
     ]
    }
   ],
   "source": [
    "print(df['location'].isna().sum())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fca513",
   "metadata": {},
   "source": [
    "<h3>since we dont need the 'keywords', 'description' and 'complete location' columns so we will remove them</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "62d9d903",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9 Crore</td>\n",
       "      <td>House</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7 Crore</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        price   type  bedrooms  bathrooms  area location\n",
       "0  11.5 Crore  House       5.0        6.0   600      DHA\n",
       "1     4 Crore   Flat       3.0        3.0   200  Clifton\n",
       "2     9 Crore  House       7.0        6.0   500      DHA\n",
       "3     6 Crore   Flat       3.0        4.0   200  Clifton\n",
       "4   3.7 Crore   Flat       3.0        3.0   244  Clifton"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['complete location','description','keywords'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "b9b99c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7341, 6)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "fa3f0c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price         object\n",
       "type          object\n",
       "bedrooms     float64\n",
       "bathrooms    float64\n",
       "area           int32\n",
       "location      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ddc298",
   "metadata": {},
   "source": [
    "<h3>converting bedrooms, bathrooms and price datatype as int and float</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "f62dc928",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['bedrooms','bathrooms']] = df[['bedrooms','bathrooms']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "e523d229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price        object\n",
       "type         object\n",
       "bedrooms      int32\n",
       "bathrooms     int32\n",
       "area          int32\n",
       "location     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "16990b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['price'] = df['price'].str.replace(' Crore', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918dedfc",
   "metadata": {},
   "source": [
    "<h4>converting values stored as lakh in \"price\" to \"crore\"</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "4aceb8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['price'].astype(str).str.contains('lakh', case=False), 'price'] = df.loc[df['price'].astype(str).str.contains('lakh', case=False), 'price'].str.replace(r'\\D+', '', regex=True).astype(float) / 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "000d232a",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>House</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  price   type  bedrooms  bathrooms  area location\n",
       "0  11.5  House         5          6   600      DHA\n",
       "1     4   Flat         3          3   200  Clifton\n",
       "2     9  House         7          6   500      DHA\n",
       "3     6   Flat         3          4   200  Clifton\n",
       "4   3.7   Flat         3          3   244  Clifton"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "c88210ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price        float64\n",
      "type          object\n",
      "bedrooms       int32\n",
      "bathrooms      int32\n",
      "area           int32\n",
      "location      object\n",
      "dtype: object\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>House</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price   type  bedrooms  bathrooms  area location\n",
       "0   11.5  House         5          6   600      DHA\n",
       "1    4.0   Flat         3          3   200  Clifton\n",
       "2    9.0  House         7          6   500      DHA\n",
       "3    6.0   Flat         3          4   200  Clifton\n",
       "4    3.7   Flat         3          3   244  Clifton"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'] = df['price'].astype(float)\n",
    "print(df.dtypes)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396e3922",
   "metadata": {},
   "source": [
    "<h3>converting categorical value to numerical value</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "568748cb",
   "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>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>Flat</th>\n",
       "      <th>House</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>House</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price   type  bedrooms  bathrooms  area location  Flat  House\n",
       "0   11.5  House         5          6   600      DHA     0      1\n",
       "1    4.0   Flat         3          3   200  Clifton     1      0\n",
       "2    9.0  House         7          6   500      DHA     0      1\n",
       "3    6.0   Flat         3          4   200  Clifton     1      0\n",
       "4    3.7   Flat         3          3   244  Clifton     1      0"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_type = pd.get_dummies(df['type'])\n",
    "\n",
    "df = pd.concat([df, df_type], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f3cafd7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.0"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "2a5af766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>Flat</th>\n",
       "      <th>House</th>\n",
       "      <th>Bahria Town</th>\n",
       "      <th>Clifton</th>\n",
       "      <th>DHA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.5</td>\n",
       "      <td>House</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>DHA</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
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       "      <td>Clifton</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>House</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>500</td>\n",
       "      <td>DHA</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>Clifton</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price   type  bedrooms  bathrooms  area location  Flat  House  Bahria Town  \\\n",
       "0   11.5  House         5          6   600      DHA     0      1            0   \n",
       "1    4.0   Flat         3          3   200  Clifton     1      0            0   \n",
       "2    9.0  House         7          6   500      DHA     0      1            0   \n",
       "3    6.0   Flat         3          4   200  Clifton     1      0            0   \n",
       "4    3.7   Flat         3          3   244  Clifton     1      0            0   \n",
       "\n",
       "   Clifton  DHA  \n",
       "0        0    1  \n",
       "1        1    0  \n",
       "2        0    1  \n",
       "3        1    0  \n",
       "4        1    0  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_location = pd.get_dummies(df['location'])\n",
    "\n",
    "df = pd.concat([df, df_location], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "9b81c327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7341, 11)\n",
      "price          float64\n",
      "type            object\n",
      "bedrooms         int32\n",
      "bathrooms        int32\n",
      "area             int32\n",
      "location        object\n",
      "Flat             uint8\n",
      "House            uint8\n",
      "Bahria Town      uint8\n",
      "Clifton          uint8\n",
      "DHA              uint8\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11de7dda",
   "metadata": {},
   "source": [
    "<h3>removing type and location column from the dataset as we dont need them anymore</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "41b8b9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['type','location'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "73856065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>Flat</th>\n",
       "      <th>House</th>\n",
       "      <th>Bahria Town</th>\n",
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       "      <th>DHA</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>11.5</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>200</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  bedrooms  bathrooms  area  Flat  House  Bahria Town  Clifton  DHA\n",
       "0   11.5         5          6   600     0      1            0        0    1\n",
       "1    4.0         3          3   200     1      0            0        1    0\n",
       "2    9.0         7          6   500     0      1            0        0    1\n",
       "3    6.0         3          4   200     1      0            0        1    0\n",
       "4    3.7         3          3   244     1      0            0        1    0"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee55618a",
   "metadata": {},
   "source": [
    "<h3>renaming columns to appropriate column names</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "d3f5c663",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = {\n",
    "    'price': 'price(crore)',\n",
    "    'area': 'area(sq. yard)'\n",
    "}\n",
    "df.rename(columns=column_mapping, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce8fdb1",
   "metadata": {},
   "source": [
    "<h3>A final look on our clean dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "1f674e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows and columns:  (7341, 9) \n",
      "\n",
      "price(crore)      float64\n",
      "bedrooms            int32\n",
      "bathrooms           int32\n",
      "area(sq. yard)      int32\n",
      "Flat                uint8\n",
      "House               uint8\n",
      "Bahria Town         uint8\n",
      "Clifton             uint8\n",
      "DHA                 uint8\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "      <th>price(crore)</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area(sq. yard)</th>\n",
       "      <th>Flat</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price(crore)  bedrooms  bathrooms  area(sq. yard)  Flat  House  \\\n",
       "0          11.5         5          6             600     0      1   \n",
       "1           4.0         3          3             200     1      0   \n",
       "2           9.0         7          6             500     0      1   \n",
       "3           6.0         3          4             200     1      0   \n",
       "4           3.7         3          3             244     1      0   \n",
       "\n",
       "   Bahria Town  Clifton  DHA  \n",
       "0            0        0    1  \n",
       "1            0        1    0  \n",
       "2            0        0    1  \n",
       "3            0        1    0  \n",
       "4            0        1    0  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('rows and columns: ', df.shape,'\\n')\n",
    "print(df.dtypes)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "c1082784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.0"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price(crore)'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ca98c17b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>price(crore)</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area(sq. yard)</th>\n",
       "      <th>Flat</th>\n",
       "      <th>House</th>\n",
       "      <th>Bahria Town</th>\n",
       "      <th>Clifton</th>\n",
       "      <th>DHA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.250616</td>\n",
       "      <td>3.851383</td>\n",
       "      <td>3.840757</td>\n",
       "      <td>299.965264</td>\n",
       "      <td>0.472688</td>\n",
       "      <td>0.527312</td>\n",
       "      <td>0.418608</td>\n",
       "      <td>0.082005</td>\n",
       "      <td>0.499387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.073313</td>\n",
       "      <td>23.606830</td>\n",
       "      <td>1.647757</td>\n",
       "      <td>262.817985</td>\n",
       "      <td>0.499287</td>\n",
       "      <td>0.499287</td>\n",
       "      <td>0.493364</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.500034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.550000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price(crore)     bedrooms    bathrooms  area(sq. yard)         Flat  \\\n",
       "count   7341.000000  7341.000000  7341.000000     7341.000000  7341.000000   \n",
       "mean       7.250616     3.851383     3.840757      299.965264     0.472688   \n",
       "std       11.073313    23.606830     1.647757      262.817985     0.499287   \n",
       "min        0.250000     1.000000     1.000000       33.000000     0.000000   \n",
       "25%        1.550000     2.000000     3.000000      125.000000     0.000000   \n",
       "50%        3.300000     3.000000     3.000000      222.000000     0.000000   \n",
       "75%        8.750000     5.000000     5.000000      400.000000     1.000000   \n",
       "max       99.000000  2022.000000    10.000000     3600.000000     1.000000   \n",
       "\n",
       "             House  Bahria Town      Clifton          DHA  \n",
       "count  7341.000000  7341.000000  7341.000000  7341.000000  \n",
       "mean      0.527312     0.418608     0.082005     0.499387  \n",
       "std       0.499287     0.493364     0.274391     0.500034  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000  \n",
       "50%       1.000000     0.000000     0.000000     0.000000  \n",
       "75%       1.000000     1.000000     0.000000     1.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d58513f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c9ad0948",
   "metadata": {},
   "source": [
    "<h3>removing outliers from price and area columns</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "c4c7b5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6668, 9)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1a = df['price(crore)'].quantile(0.25)\n",
    "Q3a = df['price(crore)'].quantile(0.75)\n",
    "IQR1 = Q3a - Q1a\n",
    "\n",
    "# Define the lower and upper bounds for outliers of price\n",
    "lower_bound1 = Q1a - 1.5 * IQR1\n",
    "upper_bound1 = Q3a + 1.5 * IQR1\n",
    "\n",
    "\n",
    "Q1b = df['area(sq. yard)'].quantile(0.25)\n",
    "Q3b = df['area(sq. yard)'].quantile(0.75)\n",
    "IQR2 = Q3b - Q1b\n",
    "\n",
    "# Define the lower and upper bounds for outliers for area\n",
    "lower_bound2 = Q1b - 1.5 * IQR2\n",
    "upper_bound2 = Q3b + 1.5 * IQR2\n",
    "\n",
    "\n",
    "#removing outliers of area and price\n",
    "df = df[((df['area(sq. yard)'] >= lower_bound2) & (df['area(sq. yard)'] <= upper_bound2)) & ((df['price(crore)'] >= lower_bound1) & (df['price(crore)'] <= upper_bound1))]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "249e767d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.5"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price(crore)'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7d501f",
   "metadata": {},
   "source": [
    "<h3>plotting pearson correlation btw area and price to indicate relationship strength between these attributes</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "1ee99d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dbNu2jbPOOqvDv+mKiooWfeD69euX2N+YjlyDzmBf592fuZV0LlIAknQKNpuNu+66i4cffpiVK1cCe/w5tm/fTkFBQYvfW7lyJd999x1z585l5syZie2dEekCkJWVhcvlatX3IisrK+nv9ka+OJ3OhBDYVjIzMzEMg927dycJQUIISktLW30wtoVp06bxzDPPsGTJEr766it+//vfA9bb/8KFC9m6dSter7fbI1gGDBjQ6rw1XJNHH3201X7m5uYCe3yF7rvvvqT95eXlpKWlNfteS9fX4/Ewa9YsZs2axa5duxLaoNNOO421a9cmFrSSkpJm3925cyeqqrZLa9gaRxxxBBkZGfz3v/9l9uzZ+7wXMzMz+eqrrxBCJLUtKyvDMIx23dtN9zV899VXX01oY9vKBx98wM6dO/noo48SWh+gxfQV7SEzM7PVawDNf8s9lf2ZW0nnIkVNSbtp6SEEe1TbDW9k06dPR9M0/va3v7V6rIYHb1Pn3qeeeqpZ2729wbWmfTj11FPZtGkTmZmZCY1V409HExTuDw0OvC+88ELS9v/85z8EAoHEfmifVqXh2IqicMcdd6CqKj/84Q8By0H6ww8/ZOHChfzwhz/cZ3j+gXpbbokpU6aQlpbG6tWrW7xmRxxxBHa7HbDun6b3zltvvcWOHTs6dO7c3FwuvvhizjvvPNatW0cwGOSQQw6hf//+zJ8/HyFEom0gEOA///lPIjJsf7HZbNxyyy2sXbuWe+65p8U2ZWVlfP7554B1revq6nj99deT2jz33HOJ/R1lxowZ6LrOpk2bWr0GrdFZv+mmTJs2jdWrV7N8+fKk7c899xyKonD88cfv8xg9gf2ZW0nnIjVAknYzY8YMBgwYwGmnncbIkSMxTZNvv/2WBx98EK/Xy29/+1vACmP9v//7P+655x5CoVAiLHj16tWUl5cza9YsRo4cydChQ7n11lsRQpCRkcEbb7zBwoULm5137NixAPzlL39h5syZ2Gw2DjnkEHw+H2PHjuWll17i5ZdfZsiQITidTsaOHct1113Hf/7zH374wx9y/fXXM27cOEzTpLi4mAULFnDjjTdy9NFHH9D5O/HEE5kxYwa33HILtbW1TJkyJREFNmHCBC688MKkMbc0rtbIyclhzJgxLFiwgOOPPz6xMJ9wwglUVlZSWVnJQw89tM8+Npxjzpw5nHTSSWiaxrhx4xKCR1fi9Xp59NFHmTlzJpWVlZx99tnk5OSwe/duvvvuO3bv3p0Qqk899VTmzp3LyJEjGTduHMuWLeOBBx7Yq9m1KUcffTSnnnoq48aNIz09nTVr1vD8888nCTb3338/F1xwAaeeeipXXHEFkUiEBx54gOrqav70pz912thvvvlm1qxZw1133cWSJUs4//zzKSgooKamhk8++YSnn36aWbNmMWXKFC666CIef/xxZs6cSVFREWPHjuWzzz7jvvvu4+STT94vM+fgwYP5wx/+wO23387mzZv58Y9/THp6Ort27WLJkiUJrVlLTJ48mfT0dK688kruuusubDYb//rXv/juu++atW3PfXb99dfz3HPPccopp/CHP/yBQYMG8dZbb/HEE09w1VVXMWLEiA6PtyVaC5mfOnVqM/N1e9ifuZV0Mt3qgi3plbz88svi/PPPF8OHDxder1fYbDYxcOBAceGFF4rVq1c3a//cc8+JI488UjidTuH1esWECRPEs88+m9i/evVqceKJJwqfzyfS09PFOeecI4qLi5tFWQghxG233Sb69esnVFVNilwqKioS06dPFz6fTwBJUVd1dXXi97//vTjkkEOE3W4XqampYuzYseL6668XpaWliXaAuOaaa9o8Dw1RYPuiaRSYEEKEQiFxyy23iEGDBgmbzSby8/PFVVddJaqqqpLa7W1crXH99dcLQPzxj39M2j58+HABiO+//z5pe0tRYJFIRFx++eUiOztbKIqSFH3X2jw1jdppiYYInQceeGCf4/j444/FKaecIjIyMoTNZhP9+/cXp5xySlJ0TlVVlbjssstETk6OcLvd4thjjxWffvqpmDp1alL03N4ie2699VZxxBFHiPT0dOFwOMSQIUPE9ddfL8rLy5Pavf766+Loo48WTqdTeDweMW3aNPH5558ntWmIiNq9e3fS9oYIoZYiGFviv//9rzjllFNEdna20HVdpKeni+OPP148+eSTIhKJJNpVVFSIK6+8UuTn5wtd18WgQYPEbbfdJsLhcNLxWrtmDf1qGiXZeMzHH3+8SElJEQ6HQwwaNEicffbZYtGiRc3G3JjFixeLSZMmCbfbLbKzs8Xll18uli9fLoCk3/7e7rOW7qetW7eK888/X2RmZgqbzSYOOeQQ8cADDyRF5+3tHmvpmdKUhnultU/D76TpsVqby5Z+X22dW0nXogjRSKcrkUgkEolE0geQPkASiUQikUj6HFIAkkgkEolE0ueQApBEIpFIJJI+hxSAJBKJRCKR9DmkACSRSCQSiaTPIQUgiUQikUgkfQ6ZCBGrHtXOnTvx+XztLoUgkUgkEomkexBC4Pf76devX7vrqEkBCKuWTGu1qiQSiUQikfRstm3b1q4M8CAFIAB8Ph9gTWBKSko390YikUgkEklbqK2tpaCgILGOtwcpALGneF9KSooUgCQSiUQi6WV0xH1FOkFLJBKJRCLpc0gBSCKRSCQSSZ9DCkASiUQikUj6HFIAkkgkEolE0ueQApBEIpFIJJI+hxSAJBKJRCKR9DmkACSRSCQSiaTPIQUgiUQikUgkfQ4pAEkkEolEIulzyEzQEolEIpF0IoZhsnxbFRWBKJkeO4cXpKPrUt/Q05ACkEQikUgkncT7a3Yx9/MiiioCxOImNk1lcKaHi6cMZtqhud3dPUkjulUkNQyD3//+9xQWFuJyuRgyZAh/+MMfME0z0UYIwd13302/fv1wuVwcd9xxrFq1Kuk4kUiEa6+9lqysLDweD6effjrbt28/0MORSCSSLsEwTJZsqeCdlSUs2VKBYZj7/tIBwDQF2yqDrC2tZVtlENMU3d2lbsM0BS8vLebuN1axprQWr0Ojf7oLn1NnfZmf2e+s5f01u7q8Dx25Hn31OnarBmjOnDk8+eSTzJs3j9GjR/P1119zySWXkJqaym9/+1sA7r//fh566CHmzp3LiBEjuPfeeznxxBNZt25dovrrddddxxtvvMFLL71EZmYmN954I6eeeirLli1D07TuHKJEIpHsFz1Vo7CxzM97K3exaXcdYSOOU9cYmu1lxphchuW0vzJ3b2ZjmZ93VpTy3BdF1IZjeO06/rCBTdPwOW147BrFVSHmLS5i6vDsLjGHdfR69OXrqAghuk3UO/XUU8nNzeWZZ55JbPvpT3+K2+3m+eefRwhBv379uO6667jlllsAS9uTm5vLnDlzuOKKK6ipqSE7O5vnn3+en/3sZwDs3LmTgoIC3n77bWbMmLHPftTW1pKamkpNTY2sBi+RSHoM76/Zxex31uIPx8j02HHZNULROBWBKD6njdtOGtktQtDGMj/Pfl5EZSBKfqoTt10nGDUoqQmT4bFzyZTBB/3i2UDDXGwqq2PlzhocuopNU4kYlrCal+LEZdfwh2P4wwYPnjueowozu6QP7b0eB8N13J/1u1tNYMceeyzvv/8+69evB+C7777js88+4+STTwZgy5YtlJaWMn369MR3HA4HU6dOZfHixQAsW7aMWCyW1KZfv36MGTMm0UbSMoqi8Prrr/eY40gkkj0Yhsncz4vwh2MMTHfhc9rQVRWf08bAdBf+cIx5i4sOuDnMNAXvrdxFZSDK8BwvPqcNTVXwOW0Mz/FSGYiyYNWuPmFGaTwXGR4bQoBdV9FUBZdNJRY3qQxEEULgsmvE4iYVgWiX9aE910Nex24WgG655RbOO+88Ro4cic1mY8KECVx33XWcd955AJSWlgKQm5v8hpObm5vYV1pait1uJz09vdU2TYlEItTW1iZ9uprS0lKuvfZahgwZgsPhoKCggNNOO43333+/y8/dWdx9990cdthhzbaXlJRw0kknHfD+tMU/rCUeeeQRDjnkEFwuFwUFBVx//fWEw+HE/tmzZ3PkkUfi8/nIycnhzDPPZN26dV05FImkGcu3VVFUESDTY0dVkx/VqqqS6bGzpTzA8m1VB7RfO6pDbNpdR36qE0VRkvYpikJ+qpONZXXsqA4d0H51B43nwu3Q0VSFWNwSGBRFwa6rhGJxooZJKBrHplnXrav60J7rIa9jNwtAL7/8Mi+88ALz589n+fLlzJs3jz//+c/MmzcvqV3TiyOEaLatKXtrM3v2bFJTUxOfgoKC/RvIPigqKmLixIl88MEH3H///axYsYJ3332X448/nmuuuabDx43FYu3a3lXk5eXhcDgO6Dlhj3/YY489xtKlS8nLy+PEE0/E7/e3+p1//etf3Hrrrdx1112sWbOGZ555hpdffpnbbrst0ebjjz/mmmuu4csvv2ThwoUYhsH06dMJBAIHYlgSCQAVgSixuInL3rIfY1dpFPZFIGoQNuK47S27kLrsGhEjTiBqHNB+dQeN5yI/xUWay0YoGqfBs0RTrLWo4ToVZnk4vCB9H0fteB9aorXrIa9jNwtAN998M7feeis///nPGTt2LBdeeCHXX389s2fPBqyFFWimySkrK0tohfLy8ohGo1RVVbXapim33XYbNTU1ic+2bds6e2hJXH311SiKwpIlSzj77LMZMWIEo0eP5oYbbuDLL79MtCsuLuaMM87A6/WSkpLCueeey65de6IGGjQw//znPxOapAZB78knn+SMM87A4/Fw7733AvDGG28wceJEnE4nQ4YMYdasWRhG6zfzLbfcwogRI3C73QwZMoQ77rgjIUzNnTuXWbNm8d1336EoCoqiMHfuXKC5CWzFihX86Ec/wuVykZmZya9+9Svq6uoS+y+++GLOPPNM/vznP5Ofn09mZibXXHNNuwQ3IQSPPPIIt99+O2eddRZjxoxh3rx5BINB5s+f3+r3vvjiC6ZMmcL555/P4MGDmT59Oueddx5ff/11os27777LxRdfzOjRoxk/fjzPPvssxcXFLFu2rM39k0j2l0yPHZumEorGW9zfVRqFfeGx6zh1jWArC2MoGseha3haWVgPJhrPhaoqHDYwDbuuUhMyiBgmhikwTMEuf4QUp42Zkwd3ugN0R6+HvI7dLAAFg8Fmql1N0xJh8IWFheTl5bFw4cLE/mg0yscff8zkyZMBmDhxIjabLalNSUkJK1euTLRpisPhICUlJenTVVRWVvLuu+9yzTXX4PF4mu1PS0sDrAX9zDPPpLKyko8//piFCxeyadOmhGN3Axs3buSVV17hP//5D99++21i+1133cUZZ5zBihUruPTSS3nvvff4xS9+wW9+8xtWr17NU089xdy5c/njH//Yal99Ph9z585l9erV/OUvf+Hvf/87Dz/8MAA/+9nPuPHGGxk9ejQlJSWUlJQ06xtY1/THP/4x6enpLF26lH//+98sWrSIX//610ntPvzwQzZt2sSHH37IvHnzmDt3bkKgAkvYGzx4cKt9bYt/WEsce+yxLFu2jCVLlgCwefNm3n77bU455ZRWv1NTUwNARkZGq20kks7m8IJ0Bmd6qAhEk1KDAJhm12kU9kX/NBdDs72U1IRpGkMjhKCkJsywHC/901wHtF/dQdO5KMzy8oPhWWR67ERicWpCMRTg0Dwft3aRw3pHr4e8jt0cBn/aaafxxz/+kYEDBzJ69Gi++eYbHnroIS699FLA0ixcd9113HfffQwfPpzhw4dz33334Xa7Of/88wFITU3lsssu48YbbyQzM5OMjAxuuukmxo4dywknnNCdwwMsgUUIwciRI/fabtGiRXz//fds2bIlYZJ7/vnnGT16NEuXLuXII48ELAHw+eefJzs7O+n7559/fmLeAC688EJuvfVWZs6cCcCQIUO45557+N3vfsddd93VYh9+//vfJ/49ePBgbrzxRl5++WV+97vf4XK58Hq96Lqe0My1xL/+9S9CoRDPPfdcQuB77LHHOO2005gzZ05CK5eens5jjz2GpmmMHDmSU045hffff59f/vKXAGRlZTF06NBWz7M3/7CtW7e2+r2f//zn7N69m2OPPRYhBIZhcNVVV3Hrrbe22F4IwQ033MCxxx7LmDFjWj2uRNLZ6LrKxVMGM/udtRRXhZpFgXWVRmFfqKrCjDG57KwJsaHM8iFp6FdD9ND00bmo6t7dFA4GWpqLggw3GW47a0v92HSVn0zoz+nj+nXZdero9ZDXsZsFoEcffZQ77riDq6++mrKyMvr168cVV1zBnXfemWjzu9/9jlAoxNVXX01VVRVHH300CxYsSOQAAnj44YfRdZ1zzz2XUCjEtGnTmDt3bo/IAdQgWe/LZ2nNmjUUFBQk+SONGjWKtLQ01qxZkxCABg0a1Ez4ATjiiCOS/l62bBlLly5N0vjE43HC4TDBYBC3293sGK+++iqPPPIIGzdupK6uDsMw2q0dW7NmDePHj0/Sdk2ZMgXTNFm3bl1CYBk9enTS9cnPz2fFihWJv3/961830xq1RHv9wz766CP++Mc/8sQTT3D00UezceNGfvvb35Kfn88dd9zRrP2vf/1rvv/+ez777LN99kUi6WwaNAYNeYAqA1FsmsohuT5mTu6+PEDDcnxcMmVwIn/MrtowDl1jbP9Upo8++PPHNKa1ufjhiOwDNhcdvR59/Tp2qwDk8/l45JFHeOSRR1ptoygKd999N3fffXerbZxOJ48++iiPPvpo53dyPxk+fDiKorBmzRrOPPPMVtu1tnA33d6SGa2l7aZpMmvWLM4666xmbZ1OZ7NtX375JT//+c+ZNWsWM2bMIDU1lZdeeokHH3yw1T63ZxyQLKzYbLZm+5qq+fdGY/+w/Pz8xPa9+X4B3HHHHVx44YVcfvnlAIwdO5ZAIMCvfvUrbr/99iST7LXXXsv//vc/PvnkEwYMGNDmvkkkncm0Q3OZOjy7x9WWGpbjY8hxXnZUhwhEDTx2nf5proNaY9AaPWEuOtqHntD37uLg9W7qIWRkZDBjxgwef/xxfvOb3zQTVKqrq0lLS2PUqFEUFxezbdu2hBZo9erV1NTUcOihh7b7vIcffjjr1q1j2LBhbWr/+eefM2jQIG6//fbEtqamJLvdTjzeskNmA6NGjWLevHkEAoHEWD///HNUVWXEiBHtHEXrNPYPmzBhArDHP2zOnDmtfq81vzMhREJbJ4Tg2muv5bXXXuOjjz6isLCw0/otkXQEXVc7PXleZ6CqCgUZzbXJfZGeMBcd7UNP6Ht3IMvTHgCeeOIJ4vE4Rx11FP/5z3/YsGEDa9as4a9//SuTJk0C4IQTTmDcuHFccMEFLF++nCVLlnDRRRcxderUZuattnDnnXfy3HPPcffdd7Nq1SrWrFnDyy+/nOTn05hhw4ZRXFzMSy+9xKZNm/jrX//Ka6+9ltRm8ODBbNmyhW+//Zby8nIikUiz41xwwQU4nU5mzpzJypUr+fDDD7n22mu58MIL96qZacpjjz3GtGnTWt3f2D/stddeY+XKlVx88cVJ/mEAF110UVKI+2mnncbf/vY3XnrpJbZs2cLChQu54447OP300xMmuWuuuSaRnsHn81FaWkppaSmh0MGbD0MikUj6HEIiampqBCBqamq67Bw7d+4U11xzjRg0aJCw2+2if//+4vTTTxcffvhhos3WrVvF6aefLjwej/D5fOKcc84RpaWlif133XWXGD9+fLNjA+K1115rtv3dd98VkydPFi6XS6SkpIijjjpKPP30061+7+abbxaZmZnC6/WKn/3sZ+Lhhx8Wqampif3hcFj89Kc/FWlpaQIQzz77bIvH+f7778Xxxx8vnE6nyMjIEL/85S+F3+9P7J85c6Y444wzkvr629/+VkydOjVprIMGDWppKhOYpinuuusukZeXJxwOh/jhD38oVqxYkdRm6tSpYubMmYm/Y7GYuPvuu8XQoUOF0+kUBQUF4uqrrxZVVVVJ89LSp2G8EolEIukZ7M/63a21wHoKshaYRCKRSCS9j15bC0wikUgkEomkO5ACkEQikUgkkj6HFIAkEolEIpH0OaQAJJFIJBKJpM8hBSCJRCKRSCR9DikASSQSiUQi6XPITNASiaRHYJqiT6bjl0gk3YMUgCQSSbezscyfKMgYNuI4dY2h2V5mjDn4CzJKJJLuQQpAEomkW9lY5ufZz4uoDETJT3XitrsIRg1W7qxhZ02IS6YMlkKQRCLpdKQPkEQi6TZMU/Deyl1UBqIMz/Hic9rQVAWf08bwHC+VgSgLVu3CNPt8wnqJRNLJSAFIIpF0GzuqQ2zaXUd+qhNFSfb3URSF/FQnG8vq2FEtC9FKJJLORQpAEomk2whEDcJGHLe9ZWu8y64RMeIEosYB7plEIjnYkQKQRCLpNjx2HaeuEWxFwAlF4zh0DU8rApJEIpF0FCkASSSSbqN/mouh2V5KasIIkeznI4SgpCbMsBwv/dNc3dRDiURysCIFIIlE0m2oqsKMMblkeOxsKKvDH45hmCb+cIwNZXVkeOxMH50r8wFJJJJORwpAEomkWxmW4+OSKYMZ0y+V6mCMovIA1cEYY/unyhB4iUTSZUjDukQi6XaG5fgYcpxXZoKWSCQHDCkASSSSHoGqKhRkuLu7GxKJpI8gTWASiUQikUj6HFIAkkgkEolE0ueQApBEIpFIJJI+hxSAJBKJRCKR9DmkE7REIklgmkJGYkkkkj6BFIAkEgkAG8v8vLdyF5t21xE24jh1jaHZXmaMyZW5eCQSyUGHFIAkEgkby/w8+3kRlYEo+alO3HYXwajByp017KwJyYSEEonkoEP6AEkkfRzTFLy3cheVgSjDc7z4nDY0VcHntDE8x0tlIMqCVbswTbHvg0kkEkkvQWqAJJI+zo7qEJt215Gf6kRRLH8fIQT+sEE0buJ16GzY5WdHdUgmKuwmpG+WRNL5SAFIIunjBKIGYSOO225VXK8MRNhUFqAyGMUwTdR6oWhNSa0UgLoB6ZslkXQNUgCSSA4y2qst8Nh1nLpGMGoQi5t8u62aUDSO16lj03QCEYPqYIy3VpQwJNvTrkVXai72j474ZvW0Oe/K/vS0sXYlLY0VaNf4O2u+DpZ571YBaPDgwWzdurXZ9quvvprHH38cIQSzZs3i6aefpqqqiqOPPprHH3+c0aNHJ9pGIhFuuukmXnzxRUKhENOmTeOJJ55gwIABB3IoEkmPoCPagv5pLoZme1mxo4aaYJRQNE6Gx46iKAghiBomgzLdRGJxFqzaxZAsb5sedlJzsX809c1qME/6nDbLLFlW1+x69LQ578r+9LSxdiUtjTXNbQMB1aFYm8bfWfN1MM17tzpBL126lJKSksRn4cKFAJxzzjkA3H///Tz00EM89thjLF26lLy8PE488UT8fn/iGNdddx2vvfYaL730Ep999hl1dXWceuqpxOPxbhmTRNJdNGgLVu6sIc1tY0iWlzS3jZU7a3j28yI2lvlb/J6qKswYk4tDV9laGcSuqwggYsSpDERx2XWG5fjol+ZiY1kdO6pDXdYXyR5a8s1qQFEU8lOdSdejp815V/anp421K2lprCBYuHoXC9fsAsQ+x99Z83WwzXu3CkDZ2dnk5eUlPm+++SZDhw5l6tSpCCF45JFHuP322znrrLMYM2YM8+bNIxgMMn/+fABqamp45plnePDBBznhhBOYMGECL7zwAitWrGDRokXdOTSJ5ICyv5Fcw3J8nDI+H69TJ24KqoJRwjGTnBQnhxWkkeGx47JrRIw4gajRpX2RWOzxzWpZUd/4evS0Oe/K/vS0sXYlLY1VVaC0JoJdU7DrKqW1EVSFVsffWfN1MM57jwmDj0ajvPDCC1x66aUoisKWLVsoLS1l+vTpiTYOh4OpU6eyePFiAJYtW0YsFktq069fP8aMGZNo0xKRSITa2tqkj0TSm2mvtqAlDs1LYXR+CuMGpHJ0YSaThmRyxKB0Mjx2AELROA5dw9PKgtyZfZEk+2a1ROPr0dPmvCv709PG2pW0NFZ/2KAyGMXnsuFz6lQGovjD1j3S0vg7a74OxnnvMQLQ66+/TnV1NRdffDEApaWlAOTm5ia1y83NTewrLS3FbreTnp7eapuWmD17NqmpqYlPQUFBJ45EIjnwtEdb0Br901wMy/FRF4mT6bGT4rIlhcWX1IQZluNNOF92ZV8ke3yzSmrCCJH8Vt30evS0Oe/K/vS0sXYlLY01GjcxTBObpmLTVAzTJBo3E/tddo1wLM6m3XWsLa1l0+46QjFjv+frYJz3HiMAPfPMM5x00kn069cvaXtTSVMI0WxbU/bV5rbbbqOmpibx2bZtW8c7LpH0ANqjLWiNBl+gDI+dDWV1+MMxDNPEH46xoayODI+d6aNz9+kA3Rl9kbTvevS0Oe/K/vS0sXYlLY3VrqnoqkosbhKLm+iqil3bs5SXVIcoqgjw4lfF/PX9Dby4pJitFSF2VgdbPEdb52u/5j0ahUikDSM+sPQIAWjr1q0sWrSIyy+/PLEtLy8PoJkmp6ysLKEVysvLIxqNUlVV1WqblnA4HKSkpCR9JJLeTHu0BXtjWI6PS6YMZky/VKqDMYrKA1QHY4ztn9rmchid1RdJ269HT5vzruxPTxtrV9LSWH1OnQy3HX8ohj9skOGx43NaQkdFXZilRVUYpqBfmpMhWV76pbow4iZLi6qoqEsWQtozXx2a93gcdu2CoqIeKQD1CBH52WefJScnh1NOOSWxrbCwkLy8PBYuXMiECRMAy0/o448/Zs6cOQBMnDgRm83GwoULOffccwEoKSlh5cqV3H///Qd+IBJJN9GgLdhZE2JDmWWnd9k1QtE4JTXhNmtvwFp0hxzn7XCej87si6Rt16OnzXlX9qenjbUraW2seakOtlUFAUFeioO4EATDMZZssZQBRw3OIMVl+e6luGwcVZjOx+vLWVJUyZShmbgdervnq13zLgRUVkJVFZimJQi98AKccQb079/V09ZmFNFUlDvAmKZJYWEh5513Hn/605+S9s2ZM4fZs2fz7LPPMnz4cO677z4++ugj1q1bh89nvflcddVVvPnmm8ydO5eMjAxuuukmKioqWLZsGZqmtakPtbW1pKamUlNTI7VBkl5N4xwdEcNSSQ/L8TJ99IHP0dGT+tJX6Glz3pX96Wlj7UpaGmvjPEARI45hCrZVhhiZ56Ugw9PsGNsqA6wt9VOQ7kbXlA7P1z7nvaYGKirAMCxB6KOP4MEHYcMGuPxy+PvfO3Fm9m/97nYBaMGCBcyYMYN169YxYsSIpH0NiRCfeuqppESIY8aMSbQJh8PcfPPNzJ8/PykRYnscm6UAJDmY6ElZWntSX/oKPW3OZSbozmFfmaBLa8K8vLSYodk+tBbmwDBNtuwOcO6RBeSlOjs/E3QoCOXle0xd330HDzwAS5fu+aKqwsqVcOihHZqDlujVAlBPQApAEolEIjmQdLbwtq0yyMML15PmtuFz2prt94djVAdjXH/iiM6t6ReJwO7dEKx3st6yBR5+GN57L7mdosAFF8B990EnRl7vz/rdI3yAJBKJRCLpK3RFOYkGJ+WVO2vwOvSkSOgGJ+Wx/VM7zzncMCyNT0MevfJyeOwxeOUVy+enMcceC7NnW//vQUgBSCKRSCRdRl8yU7WFjhS4bQsHzDncNPc4OAsBdXXw7LPwz3/u0QI1MHo03HwzTJoE9ZHdPQkpAEkkEomkSziYCmd2Bh0pcNseGtImNMz5rtowDl1jbP/U/XcOF2KPg3M8DrGYpe15/HFrW2MGDIAbboCTTrL8fmw2cDg6fu4uQgpAEolEsp8YhsnybVVUBKJkeuwcXpCOrndPmrVoNM6CtaWU1kTIS3UwfWQedrvW6vZgMMbTizexvTLMgAwnv5o8FLe7uQ9JU0xTsL0qyObyAAAD0p2sLvFTVmsdf0imlxe+KmZbZYCaUBTDBJ9Lpy4cY3tVkCMLM3DZtaT5au88tjam/WnfoLGqqAnz35U7qA0aFGS6Wp2XhvY7KwM89P56Kuqi9E938fDZ48lIcSVpwGpDMTaW+clPdRIIBHh9xW6C0Thuu8aZY7OTykk09dNpqyZtSJaXk8bCwtUKteEYh+T5+PGh1jhbm999zktdnWXiikZBCOJvvY3x0EM4dmxPPnl6Olx9Nfz852C3g6ZBZiakplo+QD0M6QSNdIKWSCQd5/01u5j7eRFFFQFicatEweBMDxdPGcy0Q1tPyNoVPP9FEf/4dAu7/WHiQqApCtk+J2MHpLJie02z7dk+Oyu21xJpVErBoamcfcQA/viTsa2eZ2OZn/lfFfPl5gpqgjHqIgbBqIEpQFMVNFVBVxUMUxCOmTReZDQF7LqVvdhpU7HrGoMzPRw2MI1vi6vbPI+tjfXyHxRy4aTBHWrfoLF6ddk2iiqCSf1uaV4a2j/96SZqQs0zJA/LdvOTCQUJDVgkFmdbVYjiigBho/nS67apHD8yh2unDWdk3p61qK2atI1lfp74YCOfbqygLhJDCGuuh+d4mTwsq8X5HZjp5rMN5S3Py4Q8y8E5VF/f66uv8N87G9/6NUn9jtrs7DrrPAp+9xvwei2tT3q69VG79kVARoHtJ1IAkkgkHeH9NbuY/c5a/OEYmR57wueiIhDF57Rx20kjD5gQ9PwXRTzw3joi9fWaHLpCxBDUhoz6hc1KitewvToYSyzwKtYLuhBgAqoC5x01sEUhaGOZn0cWbeC7bdVoilWbqrQ2QkMRcLsGuqoQjO19abFrMCLPh2nCjuowwXrNRr805z7nsbWxBqMGDl3j5hmHJAlBbWk/aWgmz35exCfrd7OtquWCno3npcGX53/f7cAfjrfYHqzMzedMHIDbrlNWG+bfy7azt5lx6AqLbjguoQFq7jOkE4waCb+eBp+hjWV+7nlzNV8XVWEK8Ng1FMUSQCOGaW1z6PRvNL/bKoMEovFENfmGeYmGwuRG6/jNUXmcOWEArF1r5fL55JOkvsYVlc/HT+X5I08jmpnNFT8cyuQjhkFGhqX9OQDsz/rdI0phSCQSSW/DMEzmfl6EPxyjIM2JXVOJGiZ2TaUgzYk/HGPe4iIMw2z2XdMUbKsMsra0lm2VQUxz/95Do9E4//h0CxEjTobbhtuuoakqLpuKWf+OKwQ4dQVNVbGrJGtlGrQ2moKugCng1WXbCQZjzfr97spS1pf6sWsKWV47FXVRhABdsbQ7cRMiTbQbCs0Xm2gczLjAY9cQpolhCuKmideho6sqPqeNgemuZvPY2ljddo0Mt42IEeeZz7YQjcbb3P4fn27mre9K2F0bZke98KNgCTwKez4N81IXiPLeyl2UVof2KvyAVb1dR6CpCl5d7FX4AWvuNOKJ+W7sM+Rz2tBUBZ/TxvAcL5WBKAtW7cIwTN75voSVO6yIrDSXjqNeu+Zz6hhxgWEKhGnisWvoqorHrhGJxTGFdW2cuooNQb+on1GRSmzBAB9++B3xm2+BM89sJvx8P/JI5lzzAP/vJ1fg6pdHGXae3mZipGceMOFnf5E+QBKJRNIBlm+roqgigNeuUVobIRQzMYVAVRRcNhWvXWNLeYDl26o4qjAz8b2ucAxesLaU3f4wbruO2sjkEGpkfjKBcEzgdkAgmiyUmfWmD7CiidS4IGKYPL14E9edMDLRbkd1iBU7aogLgc9loypoEI0LVMX6nhAQF4Km8lxri76lhVIIGyZ2VSFimPjDBikuW31fVDI99qR5bG2sDe0bNC0L1pZy6rj+bWq/qzbM55vKicZNGmamwWWlQTMGlhAUMUwe/GAdNcE4a3f5W7skSby7Zhc/mVDAf1fualP7y55bxjvXHceO6hCbdlsRXU0LfCuKkvAZWr6tiqVFVUSMOC67luQbFDUEKKAI636oi8RJcalUBWPETEv4MUyBs66W7GgdqhB4IkHO/+p/nPL9+2jxZNPehgEjeGvGBRQNPASAsMNFrTcNxYBNVZFm93tPRgpAEolE0gEqAlFCUYOQohA3BQ5dRVUUTAGBaJywYYIQVASiie90VQh0aU2EuBA49ORFMt5EEonXr+RNtwssIUjBUnkoirVxe2U4qV0gatRXAxfYNJWYaYkLibPWf6+txBEYpmlpkDSIxUkcswGXXaMyEE3MY2tjbcChKwSjgtKaSJvb10UE/rCRKPLZmrtuw/C2V4ax6SrBSMuV0ZtSF7baBSN71xY1UOa3xhqIGoSNOG57y7l7XHaNXbVhKgJR/FHL58fWxDHaFCJxTUT9fAMJvy9fNEhqsJZUOzgUkxnfLOLMJW/ijSSHtAcGDOKxw09n22HHoGsaEbsTvzeVmM2K7nKpZtJ16g1IAUgikUg6QLrbRiwuMIWJz7kn8ZymgMum4g8bqIpCen3kUFeGQOelOtAUy3/Dbd+zvWlJhAYtT9PtpmjQcogkjceADGdSO49dx23XAcVypK3XqCRknnZa8hyaZY5R6k1nikLimA2EonFsmqUJ2ttYG4gYljYrL9XRrvY+p060XigQtCwENQxvQIaTmmAct0OnqgXn56Z466u1ux0aIWPf7XN8Vkc9dh2nrhGMGi1mdw5FrVpcmR47PrsNRYGYKXA0LpSrKAnJTUFBr5/flHiUfrVlOI0YqjD50dqvOf/L18jyVyadI5KRheOG69h05HF8t2AjdnTi6dlE7cn3RtPr1BuQPkASiUTSAfJ8Tuy6itGK/45hChw2lTyftVC01Zyxo7plB9y9MX1kHtk+pxWJ1UiD4rKpiYVcBZw26y+PPfnRr2AJHw1+LgJwaAq/mjw0qV3/NBej81OIxOKUVIfQVYFNtb5jmqJeiwS2FqSHprOkKdZC73VoOHWVaL0Wzefc815umiYVgSiFWR4OL0jf61gb2gejBjkpTqaPzGtz+9wUJ1OGZpHjdSQWxQYhsHGYkAAcusqNPzqEodleRua2TVv343oH7jPHti0Z4LMzjwD2ZHcuqQnTNF6pIbvzsBwvhxekc+TgdBy65dzc2KfMritQf01dNpU0zSS9ajdDI1V4jChHFq/gqf93L79Z+EyS8BOwO3nlB2ejvPsenHMOo4bk4xlcwBpHBmE9Wchp6Tr1BqQAJJFIJC1gGCZLtlTwzsoSlmypaObMHI6bDMp049BVakJGfaSN5TtTEzJw6ioDMzyE67UKe8wZLSveXXaNiBEnEG2bWaUxdrvG5T8oxKFrVAZjBKNx4qZJKGZaGgAsASdsWI7G0XhLJjBoPMKR+T6czuS+friujI/X76YiEGV3XZSNu4OIesHJEBAXoKngsO97aUl32xCKQiAaR1FVbKrloF0XMTBME384RnFViBSnjZmTByfyAbU21mA0TmUwhlPXuOzYwkQem7a0v/wHQzhlfD7ZKU76p7uS5kSw56MqcPbEAXg9dmaMySUvzYXPuXeHX59Tx0CxTH2azl7SFAGQn2InL90L7MnunOGxs6GsDn84lpibDWV1iezOuq5y0rh8xvS3oqCqQwbhmEnUiFtO2JqCA5PMYA3e0h3o4QC5Wzfw57cf5k/vPcaQyh2J88dUjf83dhq/unA29muuwp6ZBrm56EML+fm00ficNoqrQkl9aek69QakCUwikUia0JbcPh67zsAMD+luO+tK/VSHYoSiVrRPptfOIbk+fE4bnnqBp63mDE8rAtK+aAj7bsh1E4xapp2CDBdj+u/JAxSMWr4+HrvlAFwZiNJYHtIUGJbjYWi2LykhX+OQ/35pTvwhg5pQjJgpEsJBvaiFaVranUAkTiCa7PdiaaI0dE1lR1UIm6YyfkAq4wv25AGqDESxaSqH5PqYObl5HqDWxpqf6uKyY5vnAWpr+0umDKZfqqvlPEC6ytkT9+QBasi63C/V1aY8QA1Zma874RD++dlmygOxZu3zU+x88X8nJh+jjdmdh+X4uOPUUc3yADk1mJQGP8xysHZHGGPLDs757FUmr1/a7PwfDDuK544+E6Nff84+chBnnjAO0tISHuEN16Hht7Gv69TTkXmAkHmAJBLJHtqa28c0BX/7aBMrd9YwNMtDqT9MKGpF4eT5nGwqDzC2fypXTh2KqipJ7Rv7AIFlzthQVpfUvr00ZAquCkb4priKiCHol+ZsMRO0aQqWb61kWG4KZtxgWXENNeEYqU4bEwemomgaReWBREI+wzC5ZO5S1pf5GZjuQlVVRL22KxQxKKuLMjTbzYWTBlNeFyUnxUHR7jpWl/jJcuus3RUgZJhkuu2M6edlU3mQTK+DKcOyyPY5+kwm6MYZnEur6rhk3teU+aPk+Ow8O/OIhOZnb9d3X5mgTVOwtTLA10VVxKuqGGmLMibHi15difnoYyj/fgWlSbFSc/IUlp1zCVtyBpPtszNl4gjsedmtJjHsSZnPZSLE/UQKQJLeiCwy2fm0tNA3YJomxVUhDsn18c+ZR6LrarOorqbFJ5tGdbW3fVtpb2j9tsogDy9cT5rb1qI2yh+OUR2Mcf2JIyjIcLNkSwU3vvIdPqfeant/2ODBc8dzVGFmu48v6WQCASuDczRq/fvZZ+GZZ5oXKx01yipWOnmypeVJTbWSGOq9xzi0P+t37xmlRCJJIItMdg0NuX0yPfYWc8Y0zUnT3uKTXVGssiOh9Q3OtSt31uB16M20USU1Ycb2T6V/muUPUxGIEoubuFrRrjQNVW9r+HZH/J0keyESsQSfYHDfxUqvvx5OPtnS8vh8kJVlFS3tQ0gBSCLpZXRVLhlJ+xd6sISaIcd526yNa2/7vdHR0PoG59qdNSE2lNW1qI2aPjo38Z1Mjx2bphKKxvE5m5s6moZAd7W/k6QJhmEVK62ttcLW3nsPHn4YioqS26WlWcVKzzvPKlbq8ViCTw+s1H4gkHefRNKLaLzgDcv2UBeJUxWMYtdUhmV72Lg70OFcMpL2L/QNqKrSLlNOe9u3RntC65uerz3aqMML0hmc6WF9mR+PXWtmGqwIRDkk15cIgW6vhknSQUwTKiuhqsoSfJYsgQcegO+/T27ndMLFF8Pll1vaHpfLEnxcfXv+pQAkkfQiGhY8l03l663VVAWjGHETXVNJd9vJT3W0uuBJ9k17F/ruZn9NTW3VRum6ysVTBjP7nbUUV4WaOYc3DYFur4ZJ0k6EgOpqS/iJx2H9eqtY6UcfJbdTVTj7bPj1ryE319L0ZGZaFdslUgCSSHoTgahBeV2EikCUSCyO12nD5tSJxQW7/WFq6yOXpG9Fx2jvQt/ddIapqa3aqPaGQHeFv5ME8Pstc1csBiUl8Ne/wmuvJWdsBJg2DW68EYYOtXx7MjNBBvkkIQUgiaQX4bJplNdFCUQMclMcCdOCQ1ewe+zsqo0ghNVO0jF6U66TA21qmnZoLlOHZ7c5BLoz/Z36PKGQ5eAcDkNNDTz9NDz/vOX43JgJE6zIrokTrarsmZlWdJci57wpUgCSSHoR1iNMoLRadMnaJx91+8e0Q3P5wdCsduWY6Q66w9Sk62q7qn13lr9TnyUatTQ+dXWWsPPCC/DUU5YQ1JjCQrjpJkvzo2mQnm59WsnlI5ECkETSqwjG4mR5HVQoUBmI4nXqVlXuuEld2MDr1Mn0OAjG2lZ1WtJyPqXN5XXN0gxsLQ/1yDQD0tR0kBKPW+HrNTVWlNcbb8Bf/gI7dya3y86G3/wGzjrLMnWlpVm5fLSeJaz3RKQAJJH0Ijx2nSyvgyyvndKaCJVByxymqSo5KU7yUhyAIsOL20hL+ZTSXDbK6iLETdFr0gxIU9NBhBBWVFeDg/Mnn1gOzuvWJbfzeOCXv4SZM8Httvx7srJ6VRLD7kbOlETSi2js8zFxUBp1kTjRuIldU/E6NDbuDsjw4jbSUj6lQCTGZ5vKCUXjTB2RlXAs3ldenZ6QlVuamg4Camstc5dhwIoVVkj7V18lt7HZrDw+V11laXq8XkvwsdtbPqakVaQAJJH0Ihr7fGzcHSA/1Uma20YoGmfj7oAML94LjYUUt03j3RWlzRIIgoKKVRB0c3mQDM8eR/PW8urIrNyS/SYYtBycIxHYutVKYvjOO83bnXoqXHcdFBRYOXyys60cP5IOIQUgiaSXsS+fjyFZXrZVBqUppBFNhZS4KdhWGWJkXnJR0mjcxDAFKW4blYEo/rBBimtPeHnTvDoyK7dkv4hGLcEnELD8fZ54Al56ydIANWbSJMvBecwYK5dPVpZlApPsF1IAkkh6Ia35fGwur+NvH206qLUR7TU3tSSk7KgKUhmIsG4XeBw2MuozO9s1FV1TUVCIm5Z5sTGN8+p0tAyFpGeYDLsVw9jj4Ly3YqUjR1qCz7HHWiaurCwrk7OkU5ACkETSS2nq89EXtBHtNTe1JqSkue2kumzUhQ027a4j3Z2Ooij4nDrpbjs7q0M4bSp2bU8IcdO8OvtThqIv06dNhqZpOThXVVnmrn//2ypWWl6e3K5/f/jtb+G00yzBJyND5vLpAqQAJJEcBPQFbURHBLzWhBRffbqAHdVBKusiCVOXoigMzfawvSpYn1hXYJhmi3l1ZMXz9tMXhPRWqamxtD6xGCxYAA891HKx0quugvPPt3x7MjKsXD5S8OkSpAAkkRwEHOzaiI4KeK0JKYqiMDTHQ004SkVdlKpgFLdjT8mL8QVp5PgcVAdjlPkjLebV6asVzztqvuoLQnqLBAKWn080Cl9/bUV2ffttchuHY0+x0tRUmcvnAHFw/TIlkj7Kwa6N6KiAtzchJcPj4JBcH2uFn1A0TlF5oJkz+d4W+r5Y8Xx/zFcHu5DejEjEEnyCQdiwwcrl8+GHyW1U1Upg+JvfQF6elcsnM1Pm8jlAyFmWdAt93gmyE2g8h7WhGA5N7XJtRHuvWzQab3M5ib0duyUBTwiBP2wQjZtoikI4Fm8m4OWnOMnyOlhdUsOQTDeBaJyQYeK2aeSlOAjFTE4Z249Tx+cTisWTzmsYJiU1oUTNq/wUZ9JYG6ckWLWjmo276whF47jsGsOyveSlufnRyBy+3lrZrG5WMBjj6cWb2F4ZZkCGk19NHorbbcMwzGZ1tgDeWl3MH99YTyAcJy/VwfOXHkm/jJYFjnDY4OXlxeyoCtM/3cnPDh+I06m3Or/V/jC3/XdFov3sM8aS5ksOrTZNweebynlxSTG1wQgxEyKxOE6bhj8UTTJfNT2PTTG58l/L2VYVJhY3+emE/i3222XXKKkJ89XmClburNlnnbH23F8NfaoJRSmqCKAoCtlexz6P3x4S4w6E8NXVkK/EUMt27SlWaiY71POjH8ENN8Dw4ZZjc2bmXnP5tOe31NJ91BnjPNie24oQTUvIHlh27NjBLbfcwjvvvEMoFGLEiBE888wzTJw4EbAecrNmzeLpp5+mqqqKo48+mscff5zRo0cnjhGJRLjpppt48cUXCYVCTJs2jSeeeIIBAwa0qQ+1tbWkpqZSU1NDiqyW2+X0aSfITqLpHDo0lfK6KCgwoSCtmTZiQ1kdY/uncuXUoR1+YLX3uj3/RRH/+HQLu/1h4kKgKQrZPieX/6CQCycNbtext1UGeXjhetLcNnxOG5WBCJvKAlQGoximiRDg0FX+7+RDOXZ4dtIxv9lWxbfbqqkNxQDQVNBUFYeuMaZ/CnecOqpZ/99fsytRDDUWN7FpKoMzPVw8pXkx1FP++gmrdvqbjX9gupNBmd5mx1BV+HJTJZFGEWYOTeWYoRmYJkntfU6dtSV+zGZHhyyPja/vmJ607cEF63hu8VbqIjFMAaoCXoeNU8fn0y/V1Wx+3165s8W+HzEojVevmpKYx3dXlPL2ylKKygOEYvGkSnQOTWFAhoufHl7ACYfmsnD1nuv40doygrHmvU9xalwyZUjSttU7a/h2WzW6alW629ucQ9vvr4b74LONu1m/q45Q1EBVFbwOneE5vlaP3x42lvl57/sSSrdsR62tITUSZPrnbzBq0euo0SbFSg87DG6+GfPwiZTGVPy+VNwp3r0KE+35LbXn3m33GHvgc3t/1u9uFYCqqqqYMGECxx9/PFdddRU5OTls2rSJwYMHM3ToUADmzJnDH//4R+bOncuIESO49957+eSTT1i3bh2++nDAq666ijfeeIO5c+eSmZnJjTfeSGVlJcuWLUNrgw1VCkAHjuZOkDrBqJFwMD2onSA7idbmcMOuOkpqw+SnOhme421WFHN/5ra91+35L4p44L11RIw4bruOQ1eIGIJg1MCha9w845DEg7stxx6S5eVvH22q1wzY+G57DaFoHK9TR1cVdvujOGwqRwxK59JjCwESxzTiJl9uriAUiwMKqmKFu6NYZrC7ThuVtDC8v2YXs99Ziz8cI9NjT8xjRSCKz2njtpNGJtqf/bfP+XprdavzZlNheK4vcYwt5QFC9QKBiuXbKgQJAcdlUynM8uCya+yuDVNcFd7rdWksBD24YB1PfrwJwxTYVQVNhbgJ0bhAUaAwy83UETmJ+f1/y7ZTHW7dJHrEoDT+9NNxPPt5Edurgny3rZrqUHJ7BRBYgtbADDfjB6QRjZvkpzr599fF+CMtiW4WjYWgzbv9fLBuNwAD0124HXqrcw5tv78a7q21pbVsKqsjFhfYdRVhClAsQTjdY292/PawcVctLy5YQbSsnHy7YOJnbzP+rZdwBpoIloWFcOONcMIJFAXivFcaZ50/vk9hoj2/pfbcu+0aYw9+bu/P+t2tZWLnzJlDQUEBzz77LEcddRSDBw9m2rRpCeFHCMEjjzzC7bffzllnncWYMWOYN28ewWCQ+fPnA1BTU8MzzzzDgw8+yAknnMCECRN44YUXWLFiBYsWLerO4Uma0NQJ0ue0oakKPqeN4TleKgNRFqzahWl2q1KyR7O3OZwwMI38FMt0URWIUlQeoDoYY2z/1P16QLX3ukWjcf7x6RYiRpwMtw23XUNTVdx2jQy3jYgR55nPthCNxtt8bIAZY3JJd9tZsqWK2lCMNLeNaMxkV20Ep03jyEHpVAVjvLeylHdXWlmeh2Z5WFfqxxSQ43OQ4bbhtGl4nTojc71EjDjzFhdhGNZCbRgmcz8vwh+OMTDdhc9pQ1dVfE4bA9Nd+MOxRPtqf3ivwg9AzASnrqKrKm6bmhB+oEETZQkqDYRiJm6biqYolNXsXfgBKA/E2FnpJxw2eG7xVgxT4NYV7LqKpqrYdBVNAVPA9soQLl1BUxVsxPcq/AB8vbWa/369lcpAlFyvjZpGwo+qWMKPooBef/ziiiBl/hDDc7yoiL0KPwC14Ti1wQg1wSiLN1VimoLCTDcpLnurcw5tv7/CYYP3Vu6i3B+hrCZMLC5Iceq47Rruep8tp67gDyUfvz2YNbUsXrSM+M4STljzGRfOuoKjX/l7kvATSs3AvPtuePNNOOUUNtlT+PvWON9WRklz2xiS5SXNbWPlzhqe/byIjWV7vtue31J77t12jfEgfm53qwD0v//9jyOOOIJzzjmHnJwcJkyYwN///vfE/i1btlBaWsr06XvUvA6Hg6lTp7J48WIAli1bRiwWS2rTr18/xowZk2jTlEgkQm1tbdJH0vW0xwlS0jL7msPhuV6yPHbOO3og104bzvUnjuDKqUP36+2svddtwdpSdvvDuO06qpr8iFFVFbddp6w2zIK1pe069rAcHyeNzUPXVEwBm3cH2F4dIhAxiMbjbC4P4rKpfL+9hhU7ashPdVLqD1MdiuGyayiKgk1Xcdk1YnGBYUKmx86W8gDLt1UBsHxbFUUVATI99hb73rj9bf9d0ab521IRAKCkiUAjmvy/gZKaMP6IQbiN69SF/1zKy8uLqYvEsKtKUr+FABTrQR+JC1bWm7sWrato07H/820J+alOtlaGE/1ssNIo9cdXFAUFS4sVisZRFIU3vi9p0/H/3zc72FwewIib9Et14nEk+681nXNo+/318vJiNu2uQ1cVasIGLruWMDEpCth1lbAhSHHpScdvE6EQFBeza+1m7J98zDWP/Y6pz/wZX0VZoknU6ebL0y7g8VufoPS0s6F/f8yBg3h3S6DNwkR7fkvtuXfbw8H83O5WAWjz5s387W9/Y/jw4bz33ntceeWV/OY3v+G5554DoLS0FIDc3GSVXW5ubmJfaWkpdrud9PT0Vts0Zfbs2aSmpiY+BQUFnT00SQvscWRt2RHXZdeIGM0dWSV7aMscRuMmKS4bI/NSKMhwd8jnxzQFxRUBPlpXxsfry6gIRHDZWjYnN71upTUR4kLg0Fs+r0NXiAtBaU2k3fdEts9BpsfS4ngcOv3TXQzJ9pDqsrPbH2bdrjrK6yIEowZuu2VGiZsCm6YgBBhxgWmaGPUlLyxhyKQiEAWgIhAlFjdxteJc2rj9jn2YpxqI1Gt9wk3evE0BphA0fXEOG1b/2kp5IMaOqjCmIEmbBCAQ9UKK9XdtxPKDqg3H2nTsuoh1baLxePOdSqNzJM6353ttQddUpo/OxWlTSfO07ADc9Bq19f7aURW2yp4IYd0DTX4HmqJgCoGuKUnH3yuxGOzcCdu2wddfk3rNFfz86Vlkb9+SaBLXdFafcAav/umfrPjJRZRm5FCbPwBSU9stTLTnt9See7c9HMzP7W6NAjNNkyOOOIL77rsPgAkTJrBq1Sr+9re/cdFFFyXaNb1RhBDNtjVlb21uu+02brjhhsTftbW1Ugg6APTVvCmdyYGYw41lfuZ/VcyXmyuoCcYwTJNwzGS3P8LEQelkeBx7PWdeqgNNsfwU3C2saRHDcuLMS3W0ezwum0ZFIEbUMJMWEU0Fu8fOrtoIEU0l0+sgGLXe+jXVihAz4mCYJqZpLdjl/ggep4ZNs96OwdII2TSVUDSOz9n8/TAUjSfa90938v2OfWuPHTbrOM4mUTiWANS8vVO3ynG0lSyPjf7pTlTF8vlp/FUFBUXZE4CUUq9hSXHa2F23byHI67CuTYpzz4UUYo/vT9O/nfWLr9ehEYjuWwgqSHcyIteHXdfaNOfQ9vurf7qT8rookVgcTVWImQJHIyEoLgSqomDERdLxWyQe31O6orjYKlb61ls0DdbffNRUlp81k9rcAUS8PnbbPJjheEKz1d50Fe35LbXn3m0PB/Nzu1s1QPn5+YwaNSpp26GHHkpxcTEAeXl5AM00OWVlZQmtUF5eHtFolKqqqlbbNMXhcJCSkpL0kXQ9DXlTSmrCNPW9b8ibMizHe1DlTelsunoON5b5eWTRBhau3kUwYpDps5OX4kJRFLaUB/hiUzmVgT1RLS2dc/rIPLJ9ToJRA7NJ6K9pmgSjBjkpTqaPzGv3eKzlS6A0MxwlvoVdg0GZbkpqwuT5nHjsWn24fLzeb8UyhYViBjurw+SmOBLh5ocXpDM400NFINpi3ysCUQqzPBxekM7sM8a2aU4LM62ilfmpbavanea24XPotLCGtcjzlx7Jzw4fiNdhI2qKpH4r9ZKJiRWtNaafZQo94ZDMNh37p4flU1ITZnS+F4fWMPt7PpBQBOHQVVIdNoQQnDYuv03Hf/oXE9s159D2++tnhw9kaLYXwxSkOi1tYINpSQiIGiZOXaE2ZCQdPwkhoLIStmyBzZvhnnvgpJPgrbeSmm0eNpb/3vEXPr7q/ygvHEltXn9CKemU+KNJ929jYaIlmgoT7fkttXce28rB/NzuVgFoypQprFu3Lmnb+vXrGTRoEACFhYXk5eWxcOHCxP5oNMrHH3/M5MmTAZg4cSI2my2pTUlJCStXrky0kfQMGvKmZHjsbCirwx+2tAv+cIwNZXVJZQYkLdOVc2iagndXlrK+1I9dU8hJceKy6TjtGgXpLmyaSklthFU7a4jFWz+n3a5x+Q8KcegalcEYwWicuGkSjMapDMZw6hqXHVuIvd4noz3jCcbiZHkdeJw6lYEoESOOKQQRI05lIIrXqZPlc3JkYQYZHjsbdwdwOzQQEI8LDFOgKlaItWGCqigMbGQm1HWVi6cMxue0UVwVSupPcVWIFKeNmZMHo+sqaT4nRwxK2+uc2tR6k5ZpEoyatCUVy25/FMMU5LRBYMry2OiX4cPp1Llo8iB0VSFoCKKGSdw0iRkm8fpw+AEZLkKGVdojhkaac+9v7EcMSuOMIwaR4bGzpTLMyHxfwv+n3rUIXYW4AJumcPbEAWSnONlQVoeJgs+x98EOTHeSk+Zp15xD2+8vp1NnxphcsnwOclKd6JpCbdggGI0TjBgIQb0PUPLxE9TWWoJPcbFVr+uEE+D55y0zWAOHHMKuhx7l9d/ey+KcQ9iRmoM/NZ3amGjx/m2vMNGe31J757GtHMzP7W4Ng1+6dCmTJ09m1qxZnHvuuSxZsoRf/vKXPP3001xwwQWAFSk2e/Zsnn32WYYPH859993HRx991CwM/s0332Tu3LlkZGRw0003UVFRIcPgeyiN80lEDOuNZ1iON6nMgGTvdMUcbqsMcu9bq9lYVofPqePQk387NcEYZf4wDl1lZH4K6W77Xs/ZUu6SnBQnlx279zxAextPQz4gsPweKoNR4qaJpqpkeOzkpTgAhetPHEHEiPPK0m28s7KUiBGnLmwQNwWqqmDTVNLcNkbkeklx2rn+xBFJ2YdbyqVSmOVh5uTmuVRaC4Uflu0mP9WdOIaiWFqHQMQgbCQ/dhXAY9cwhABh1Spz2jR8ThtrS2rbmQeoCH/YSAgpPpfOqeP6JfIANZ7ft1a0LQ9Qw7X5fnsVWytCGPWaFFWxSllcOGkQN04/pNl1/LCVPEAD0518csu0pG3tmXNo+/21tzxAI3J9zY8fDFrFSevq4D//gUcftTI6N6ZfP6tY6emng8/HRtPBexuq2/R7bBpS3pZ0Fe35LbV3HttKT31u99o8QABvvvkmt912Gxs2bKCwsJAbbriBX/7yl4n9DYkQn3rqqaREiGPGjEm0CYfD3HzzzcyfPz8pEWJb/XqkAHTgOdgyinYHnT2Ha0tr+eNbq9lZHSLD40Bt4kNnCkFlIEKGx86lxw5hTL/UA5YJunGbhnxAw7I91EXiROMmdk3F69DYuDuQlPBxdUkN97+7lrwUFw5NoS5iJDJB56c6MYGi8gDXThvOyLzk3357sum2lk258THqwgavf7ON0toIKQ6dXf4IEcPEoavkpzhQNY1yfxifU+f0wwYwbkBquzNBbyzz89Z3O/lgbRm1YYMUp86PRuZwyvh+rZb2aGsm6Ibvaip8vqGcndXJmaZbu44NmaBLa6Pkpdh5+hcTyUnztDiP7c1g3KmZoKNRS9Cpq4P337dKV2zenHyg1NQ9xUrT0iArC1yuFse9t99GR4QJmQm6ZXq1ANQTkAKQRLJvDVDEiOMPGwzL9vL7U0d1W72m9rxBN80g3RR/OEZ1MNZMA9QVdPX89uRkdT2aeNzS+NTUWMVK//xn+Oab5DYOB1x0EfzqV5CdbZWt8Hr367Q9UZjojezP+t373LYlEkmX0D/Nxdj+qWzZHcAfimH3qokoq4a6W5qqMG5A9xb4HJbj45IpgxNv0Ltqwy1Wa4eeVbC0K+e3z1Za3x+EgKoqy8l5/XpL4/PBB8ltVBV+8hO49loYONASfDrpJVlVlYOj6GsvRgpAEokEsB7IPx6Tx9pSP99tq2ZXbZhUtw1QqAnGMIVgfEEaM8bkdfsiOizHx5Dj9l6tHZILlm4oq2tRY3SgHDi7cn77XKX1/aWmxgpr37HDKlb6//5f82Klxx9vFSs99FDIyLBMXvtIvyLpXUgBSCKRJBiW4+O6E4Yn8gBV1FlJ01JddiYNyeC8owf2GDNKW9+g26Mx6mq6an4b55dp0CY1+Eb5nHqz/DJ9lmDQ8vMpL4e//x3mzYNwk4SW48fDzTfD0UdDerr1Ubs1YFrSRUgfIKQPkETSFNMUbK8KsrncKuNQmOWhIL1jWaV7Cj3J56Kz57dxdFxJTYSqoFUEVtdU0t128lP3RMf1SQ1QJGIJPVVVMH8+/O1vUF2d3GbwYEvjM2OGJfRkZEAboog7Sk+6H3sz0gdIIpF0KqqqMDDTw8DMlqN1eiM9yeeis+e3f5qLNJeNhWt2YdetApg2p04sLijzh9leFWT6qNxemaxuvzAMy9RVVWUVI33kEcvs1ZisLLjmGjjnHMvHJzMTbM0d5juTxlFg+6oGL+k6pAAkkUgkBwMNygPRkKe5Pg10vZK/T6n6TXOPg/Onn1qRXWvWJLdxu+Gyy+CSSyA31xKE7O0rE9ERmkfruQhGDVburGFnTUhG6x1ApAAkkUgkvZwd1SGqgzGOHJyeSBAZiBhoqkpuqou8FAfVwVjfcIKuqbHMXd9/bwk+ixcn79d1+NnP4OqroaDACmt3tq1Myf4io/V6FlIAkkgkkl5OgxP0kCwvA9LdzZyg40JQVB44uJ2gAwHLwXnTJsvU9eabzducdBJcfz2MGGFpfDwH1sQro/V6FlIAkkgkkl5O04rdKa5kH5ZQxOi1Fbv3SThsaXy2b7ecm198MbleF1gRXTfdBBMnWoKPr3tMTO2tBi/pWg7CX4NEIpH0LXpSwscDRixmCT5lZVY4+9//bpWxaMyIEZbg86MfWc7NqandmsunqaDalKbV4CVdi5xliUTSZbQW6tsdIcDtPWdvClPuSQkfuxzTtCK7ysutYqV//WvrxUrPOMPy8UlL6xG5fA6UoNqb7t3uRApAEomkS2gt1Hdkvo+1Jf4DGgLc3rDj3him3JDw8d2VpazYUUMwGsdt1xjXP61H97vNCGHl7qmogAULWi9WeuWV8ItfWJFdXZzLp70cCEG1N9673YUUgCSSg5iGN0F/OEZdxMDt0AhG4ngdOj6nrcveDFsL9f1ySwWvfbuD/BQnw3O9ByQEuL1hx70+TLk+Cl5Y/+GgyHXr91san6++ggcegOXLk/fb7Vax0iuusCK7MjOtaK8eSFdmJu/19+4BpmfeIRKJZL9peBP8ZlsVxRVBasMxYnGBTVNIcdoYmOlmQkF6p78Zthbq63XoGIaJPxwj22tPmAC6MgS4vWHHvTlMufHi1z/dlagGv6qklpLacO9c/EIhy7y1ahU89BAsWpS8X1GsYqW/+Y3l75OZeUBy+ewvba1l1x56873bXUgBSCI5CGlYDIsrg+z2hwlFDSKGSdQwiZsKNlVhe1WQiGF2+ptha6G+/rBBVShGpsdOVTCGP2wkopW6KgS4vWHHvTVM+aBb/KJRS+OzaRM89hi8+mrzYqXHHQc33giHHWZFdh2gXD6dRWdnJu+t9253IgUgieQgo2ExrKiLYhgmMcNMZAFOceqE6/+OxQVG3KSiLtqpi2Nrob7RuIlhmqS6bNSEYkTjyQtaV4QAtzfsuLeGKR80i188vqdK+9NPw9y5zYuVjhtnRXZNnWoJPu4ePJ4DSG+9d7sTKQBJJAcZDYthilOnqCKA3aZRHTJw6CqqqmDXVUIxkxSXjapgjAHp7k5dHFsL9bVrKrqqEorG0VUVu5YclROMGBhxQWlNuNMiV9obdtxbw5R7/eInhFW6oqRkT7HSqqrkNoMHW0kMTzvNiuzyerulqz2V3nrvdidyJiSSg4yGxTDFacMwTRy6hikEar1mQFMUosJEUSAeN9FUhWDU6LTFsbVQX59TJ91lY3NFgCFZHnzOPY+firoIS4oqsakKLy/dhsvWOZEr7Q077q35dHr14ldba+Xy+d//4OGHrYSGjcnMtIqVnn8+5OVZkV6SZvTWe7c76YG/BolEsj80LIamKdBVNSH8mAI0BeL1fwsBmqoSN0WnLo57C/XVdZUUpw1dVamLGLjsGiXVIZYWWW/7Ywan0y/N3WmRK+0NO+6t+XR65eIXDFoOzh9+aNXsWrUqeX9DsdLLLoOBA61cPt2YxLCn01vv3e5EEQdFjOT+UVtbS2pqKjU1NaSkpHR3dySS/cI0BX/7aBMrdtRQE4xS5g8TMUwC0ThOXSVsmHjsGg6bRo7PQarLzrgBqVw5dWinPhwb5yOJGJYGYliOl0PyGuUBihkUVYQw4iZHFaaT6d3jyCqEYENZHWP773/fWutLa2HH7W3fE2gaAt108esxUWCRiOXgvHSpJfh8/nnyfl2Hc8+Fa6+FYcOsXD49IIlhb6E33rv7w/6s31IAQgpAkoOPxlFgZbVhghGDumicqGFi01R8Dg23QycnxcnADHeXLY77ygS9aXcdLy4ppl+qq1n9KgB/OEZ1MMb1J47Yb/+kgzkTdAM9evEzDMvBeeVK+Mtf4I03mrf58Y/hhhtg/HhL8OmhuXx6Or3x3u0o+7N+y7tLIjkIaZxs7ZttVRRXBomZAk1R0DUFt0OnIMPN4QPTu3RxbC3Ut2F7IGqgqQoeR8uPos503m1v2HFnhykfCLoiv8x+Y5qWQ/PGjZZz8/z5zYuVHnUU3HwzTJliRXbZmgvDkrbTG+/d7kAKQBLJQUrjxfBAZ4JuK73aebeH0qMWv5oa2LYNnn3WCmtvqVjpjTfCySdbkV0OR/f0U9InkU8VieQgpkcthi3Qk513+5IZoTNoPF/eWIR+oWrUf79iFSstK0tunJdnFSv92c+sf7t6kHO2pM8gBSCJRNJt9NTIlY1lft5dYRUVDcQMPDadsf1T+fHYvO73pelkOkPQa/A92rqjAr2ynAnfL+bEd18gbWdxcsOUFPjVr6zIrgEDZC4fSbciBSCJRNKtdGVxyI6wsczPI4s2sH6Xn7i5J0ZkS0WAtbv8XHfC8INGCOqMyuEby/zM+3gD0V27Oap4Bce+Ppe8DU1C2u12q0L7r38NQ4dagpBE0s1IAUgikXQ7PcV51zQF878s5rtt1dh1FZ/Thk1TiMUF/nCM77ZV8+JXxdx+yqhebw7rjMrhphHn489Wk7V0KWe8/yKDly9O3q8obJlyAoX3/h513FiZy0fSo5ACkEQi6RH0BH+lbVVBvtxSiaooZHrsCZ8kh65g99jZVRvhi82VbKsKMijT06193R/2p3iqaQp2VAUJ7S4ntmoNEx58mMO+WoTapFjptnFH8unpF7N60CiuGH04Bem9y9wlfcAOfqQAJJFIJPVsKQ9QHYqS7XW0WFQ01W2joi7ClvJArxaAOlo8dWOZnw+WbKJ69QaOXvAKkz55A3sskvT93YUj+Prcy9l6+LHUeVOoqgoTMJpUcu/h9CUfsL6MFIAkEomkEYoAQWv5YQ+OvLEdKZ66aWsZr7+1lFELXuPC9/+Nq6426TvVWfl8c/YlrJ96EuHUdEzdRjAc63VpDPqSD1hfp/fclRKJRNLFDMnykOq2URuM4UzRmoXl1wRjpLlsDMnqvdofaGf+pWgUs3QXO//0Ny595Z9kVO5Kauv3pPDyUafz3bQzGTZ+BGZ9Lp/uTmPQEfqSD5hElsIA+m4pjNKqOi6Z9zVl/ig5PjvPzjyCvL3Y6dtqE99Z6efCfy5ld12UNJfOH88cw6AsHwIIxeKJ7wJJx8tPcbKzJsTm8gAAhVke8n1Ovt1RTUUgSqbHzuEF6ei6Sll1gF+9sIzS2ih5KXaevOBwglHBwnW7qKqLsNsfQQFqwgYD0+wUldfx4YYaGhTxqQoM7e8jFDPY7Y9iCoHPYS14hgmhUJTKqNVWV2BgmkZBdjo/OSKPd74pZX1ZEJ9L4+dHFTAyJ5Xvd1YTMQT90pxMH5mH3a61Oo/hsMHLy4vZXhnC49Q4fkQuGV4HuV5Hs7GqqsK3O8q57d+rqAxE6Z/m5OkLJ5KV4mbdzhoeen8d5f4oQ3M83HnyaFK8DgzDZPm2qqTjmKZgwdpSdlaHsWkKA9Jd2DSNHK+Nfy/bzvaqMAMynNz4o0PweuxEo3HeXlXCx+t2gwJThmWS53PyzbZKXlm6nUAkTrrHxmVTBjMsL5VhmW7++O4aNuwKIBAcWZjOmH6prNtZwlOflSfGfsdpg8j1pVNZF+bvn2ymKhjDoav0S3VQXhWkJLxnnh65YCgT+hXw/prt/OmdjcTi4LbBi1ccQdFug9eXbeSDjXsS651/WBpHjxxEOBrmD/9bR8AAFRiUpnP00Bx0LcgLS6oT7W86MZdTxo9k7c5q5ry7ntpQjP6pDjJTnazY7icaieJvlIDao4LDoRGLC0wgw63z53PGETIUPli5hueW7unL7Wfk8uNDRrG5rIZbXv2e8oCBTVM49/Bcpo0q4IM1G5n7VVWi/Z/PK+Ts8aMorarj4rlL2VkdwefQOOfIfhTmpLJq01aebtT3J2cewo8PHZa4l3ZUhclLtTM8K4X3vt/EC8srE22fvngk00cOJRiM8fTiTWyvDNM/zUltMML63UH0WIBPiiMIrPk6Z3QKpTE7BV4bqeFa+n/5CdPeep7c4o1J93FId/DKuBNYc/LprPIOZlfUJNPrJBwzAYGqQLrHwWEDnMxfsouIAW67ystXHkFdRE26PwGWb6tiVUklf/+oiEAsTpbHzvOXHkleWnPneNMULN9WxbKtO3lkYTGxOLhsCv+5+iiGZWewdGtFosDuhIFp6KpKdSiGEAZ/XbiR3YEYaS6dO08/lCFZloAWDMW4662VvLeyDIHArQt2B/csj4fkugjHwKapnHBoDpqmckiejx8fav3e3169gaufW59o/8RFIzh51HCi0TgL1pZSWhMhL9WReD58u62Uc55YRkyATYF/Xz2RwwryMAyTr4sr2VhWh1PXOGJQOgMzPZRW13HhP5dSHoiR5bHx/KVH4rXb+cM7q9leaf1+f//jQ6kMxXj5683M/2onUcMkx2fnxV8ezYDM1te22rpI0nHuPGkUKV5Hq31v6Rmj6y3Xa2tP247Qa2uB3X333cyaNStpW25uLqWlpYD1BjFr1iyefvppqqqqOProo3n88ccZPXp0on0kEuGmm27ixRdfJBQKMW3aNJ544gkGDBjQ5n70RQFo0n0LKamNNtuen2Lni/87sdn2tobLHnHPAsoDsWbfVxUYmZdCltdOltdBmtsGAqpDMcKGVaOqKhC1/o7GEQpoikLcNGnQQts0lcGZHtaU1LR4ju5EwRqjTVPJTXFy+Q8KuXDS4GbtHlywjucWb8UfjiWEMV2FHJ8Tm6YSMeKAdZwcn4PVJTWEYs1/ohoQb6Efo/v5yHA7KKoIEItbdb/smkJt2MAfNojGTYSwAnFUwGhyaE2Bkfk+dlaHqQr2rDmWNCfVaaMuEiPehqe4Q1OJxNvgiyMEqeE6jtixmiu++g9HbV+dtDuuqLw58gf848gz2ZhZQMjubOVAeycvxWHVpXNahojVJf4W27ltKj8ek5947ggExRVBlhVXt3psR73WxsT6beoqxFoZukNXGJzhZsPuAGY7VkMVq0Zrjs/BzppIq+0GZbjZ7Q8TF1Ypmmyfk62VwVbbTxyYxsayOiKGiaKA16FTHYoSa+kH3w7SXDrf3jWj2faZ//yKT9aXJxl3FWBotptYnGZ9P3Z4FsUVwaRnzOBMDxdPGcy0Q3OTjv3+ml3M/byoTW07Sq8WgF599VUWLVqU2KZpGtnZ2QDMmTOHP/7xj8ydO5cRI0Zw77338sknn7Bu3Tp8PmvRveqqq3jjjTeYO3cumZmZ3HjjjVRWVrJs2TI0rfW38Mb0NQGoNeGngaZCUPNwWZ1g1GhWZbo14acBRYFR+SnkpzpYV2q9LR85OB2nTeeLTeWU1obRVIX+aU4ihmBHVYi4EHgcGgXpliPm2hI/PdWdUldAUxVMwGXTuHnGIUlC0IML1vHkx5sw6lerBt1Zw3g0BXxOnX5pLiJGnE27W39I7g27pjAsx4vLrrGjKsiu2mji7b7hx97n1b4HEboCcdG2a6qw93beSJCRZVv41ZLXOGHjV6hNWn9UeDhPHXUWK/JHUOfY/4i9gnQnO6vD+xTgnLrC5T8YyrrSWhZvriAQ2bc0YNuL0LO/6CrYVAXDFG06R7rbhkNXiBiiTS8WPoeGy64RNwUVnfiy11QImvnPr/h4fflevpHcd384hilIPJMbkpZWBKL4nDZuO2lkQrB5f80uZr+zFn84RqbHvte2+0OvLoaq6zp5eXnNtgsheOSRR7j99ts566yzAJg3bx65ubnMnz+fK664gpqaGp555hmef/55TjjhBABeeOEFCgoKWLRoETNmNJd2+zqlVXV7FX4ASmqjlFbVkZfubXO4rFMT+9TKCAF1wQjrQzFsKiiqSklN2PKtCFumEAWoq9dWAOiqQswQ1IZiZLrUHiv8gKVN0RUFhwqRWJxnPtvCzyYWYLdrhMMGzy3eihEXaCp7BBJB4q2zYRGoDRmEw6EO9yMaF7h0FUUIqhpdExNLyFJorvmR9F7acy0FLQtBzliYwsqdXLLsf5y56iPsZnLx2e/yhvPEMWfzxaDx1Do8nZbLZ2dVuEVNZlPChqAuGGJdqb9Nwg90nfADVn1X3aYSMtrWl2gwhjvNiWqG990Y0EUcm2bDNPb+rG4v1SGD7RW1DMhMobYuwif7EH4AbIqJptpw6oIA1v0Wicbx2DVUVcXnVPHYNYqrQsxbXMTU4ZYCY+7nRfjDMQamu1BVy+TVUtvONIe1l+47cz0bNmygX79+FBYW8vOf/5zNmzcDsGXLFkpLS5k+fXqircPhYOrUqSxebCXbWrZsGbFYLKlNv379GDNmTKJNS0QiEWpra5M+fYVL5n3drnZtDZc9/x9L2nTcnTURqkMxHHYdn1NnV22EndVhTBMcNg27TcMfjhMyTHRNwaZZt2hdJM66DmpEDjRxAU6bRlltmAVrLXPuy8uLqYvEaPitq9BsPgHipiAQNaho23OyVbZWBqkKGkTjgqZnaYu5RNI3sBsxBlbt5PpP/8W/59/CuSsWJQk/W9L7cduPf83Mc+5mwYhJ1Dq9nZrIsD1WnVe/KaE61D5tSFe5KbdXtgrU/7+tv+uqermnMtT5UlzDs/oP76xuk9awNmL1IRYXGMJ6dsVMkjRZqqqS6bGzpTzA8m1VLN9WRVFFgEyPPSH8tNa2O+lWDdDRRx/Nc889x4gRI9i1axf33nsvkydPZtWqVQk/oNzcZBVZbm4uW7duBaC0tBS73U56enqzNg3fb4nZs2c38z3qK5T52/ZG0dCureGylUGjxf1NMYS1yKuKJdzETJNY3HKa1OofrHEhEMJq0/CsNYXYbxv4gUAgQCjomkLYEJTW+wbsqApjCrBpEIvvWUOaPoDM+vnZXxklYpjE6hPTyVgVSVM0M05WXTVnrXqfS5a9QU4geSHa7UnjnxNP599jT6Tak4KhtM2doCsJx0wctr51N3eFEqvhWb29sm3SWEMqAFOYUO8/KATN/Mlcdo3KQJSKgLV2xOImrlaCQZq27S66VQA66aSTEv8eO3YskyZNYujQocybN49jjjkGaP6WLIRo8c25PW1uu+02brjhhsTftbW1FBQUdGQIvY4cn71NNuUcnx1oe7hshlunrg2q6YSfjBCWU5yqomjWwy1e746mKQqKomCKPSpKS2CCeNvkrG5DwRLajLgl0OWlWiHB/dOdqArEzeQX6KZ3qVo/P/vy1dgXDl3FVv/mJRU+kgYUYZIerGXG+i/41dLXKKzambS/zu7iX+N/zPOHn0pJShZxVUPd35uxk3Da1D4Xeq7S+UJQhtta9gdkOGHLvttr9XOuKirUCz9gOdU3JhSNY9Ms7Q5YgRyhaByfs7mhqWnb7qLbTWCN8Xg8jB07lg0bNiT8gppqcsrKyhJaoby8PKLRKFVVVa22aQmHw0FKSkrSp6/w7Mwj2tWuf5qLodnehK9OYxryfAzL8TL/8qPadNx+qQ7SXDYiUcvPJzfFQb80J2q930w0Fsfn1HDpKkZc1GuHwOvQOCS7e8sktBVNgXAsTk6KFRIP8LPDB+J12GhIiGtCs/kE62HjsetkdiywJsGgDDfpbh27pjRbu7S+tYZIAIQgJVzHjHWLefbVWcxe8HiS8BNVdV4ZewI/+cWf+fPUi9ielktc7XqtT3vOcPaEfNJczV/C9kZXyW3t1Rw0ZI1q6+86vV4uyHB1/hLd8Ky+86RRbdIOpzisPtg0BV2xnl021XKObsA0TSoCUQqzPBxekM7hBekMzvRQEYhiNimR0rRtd9KjBKBIJMKaNWvIz8+nsLCQvLw8Fi5cmNgfjUb5+OOPmTx5MgATJ07EZrMltSkpKWHlypWJNpJk8tK95KfsXerOT7En8gGpqsKMMblkeOxsKKvDH45hmCb+cIwNZXVkeOxMH53LgMwUsjx7fzgpCnjdDkbkeYmZEDVM8lOdFGZ7SXXa6s02Ao9TJ93rQACGKbBpCikuGzG0nnXDNkFXLKEmEhc4bRqXHVuYyAfkdOpcNHkQuqYQNy0nyrhJUthtg2CS4tJJ9XVc2LNrCiHDRCgK6Y2uiUq9ia0HvM1LOg9d2buZ0x0NMan4ex793/08+d8/Mb50Q2KficKCYcfws/Nnc8f0q9mQPYiYlvw7bk9oeHvpl+5sk0Du1BU8bieH5PnwONomNtm68GGh6QpG3GzzOexum5XSQ22bAGcoGrG4iehkITTNpSfyAaV4HfxwRNY+vxMTKnHTJGyYmFhaaoddIxCNJ9aC4qoQKU4bMycPRtdVdF3l4imD8TltFFeFktaNpm27k24Ng7/ppps47bTTGDhwIGVlZdx77718/PHHrFixgkGDBjFnzhxmz57Ns88+y/Dhw7nvvvv46KOPmoXBv/nmm8ydO5eMjAxuuukmKioqZBj8PtifPEARwzJ7DcvxMn30/ucBihhxIo3zANU7+6gt5AEqzPKwemfPzgOUl+rksmPbngfIpkK2z4lNV4nEGuUBSnGwemcn5AHSFWpDMg/Qwcje8gA5jCjDd2/ll0tf49S1n6GJ5DfxJQNG8fgx57KkYEyLuXyaml8cukpkLzW97JpCtB3SdXvzADU8dwC2VgQ6Nw9QppsNZW3LA+S2q1ayR2HlAcr1OdjRjjxAOSlOiirangfI59SpCvaMPEA5KU6mDGueB6gwy8PMyW3LA9Ra247Sa/MA/fznP+eTTz6hvLyc7OxsjjnmGO655x5GjRoF7EmE+NRTTyUlQhwzZkziGOFwmJtvvpn58+cnJUJsj09PXxSAQGaClpmgOzcT9G2vreDbbdWEY3FSnRoel51d5QEqG/ltHT/EzZRDB2LXSMoEneHW2VYeobFb5imjvJx7zKFsKqtqlgn6i411/GvxOopr9zy+Ds3UOPOoYaS56HAm6AFpTq6YNgQVnU2lO/nzoj0m+F9PymT4wHweXLCeymCcdJfWIzNBL1y+jo8/WsNlX/+Pc1YsxNUklHpj9kBemPpzVh15HCGnh+IyP7X110gD/nPNRDI9KZT7w/zv+x3UBA0GZrr41eShuN02PttUzC/+viJxvBd+OZZjhw4kGIzx1OebWFvix2FTGZHjJcVhJxQJ8pcPi3pFJugHP1jHtooQqS6dFFuUZ5dUJMY5/1fjOGbwAIoq6li4uozacExmgpaZoHs/fVUAkkg6i/W7ann8w01U1EXIT3WiqyrLi6uoDsVId9s4fGA6TpvWLHkmtD3RZgPtbd9niMehpASefhoefxwqK5N2V2fm8uIPzuHzY04ilpqGwDJDh2JxDFNw+MA04iZcf+KIpArwEklPplcnQpRIJL2b9aV+7nlzDZt21+G2a+z2RwhG4ygKDEx3URWMUVQR5IhB6QzP8SaSZw7JsrSNbUm0OSTLi6oqbU7M2dC+TyAEVFTAv/4FDz0ExcVJu+Npabw39acsO/V8Pq5WSPc6iMTiVAZihGJxTGFixAVrS/2ku+1JFeAlkoMZKQBJJJIOs7HMz+MfbmTT7jrS3DY8Dp26iEFVMIpD14i4BF6nTmUgij9skOKyJZJn7qi2sl23JdHmjuoQBRnuNifmbGh/0FNbC2+8AX/6E6xcmbzP5YKLL2bX5Vfz/roIqq6h19XiD8eoDESJxQV2XUUIDTCprItQFzYo90egeXL+HktbTfMSSVOkACSRSDpEgzamIhDBZVfxOHRURUFXrSSXcdOkMhglL8VB3DSJ1qc0aEie2aBpaEuizYa2bU3MedBrMYJB+OQTuO8++PTT5H2aBmefDf/3fzBqFHmqxpDqTazYUU2aS2d9WR2mKXDbrcd/yIjjtms4dQ1NU/huWzWTh2b1CiGirUWaJZKWkAKQRCLpEA3amH6pLnbVhNhVW+/CLKwoEq0+EVowGkdTVez1idMakmd66hfgtiTabGjb1sScDe0POiIR+O47mD0b/vvfPVnp6qk5diprLvsN2qRJTBiai66rqMCMMbnsrAlRXhexivEqEI2bGHETTVXRVAW3Q2d4jodNuwO9QoPW3BfMRTBqsHJnDTtrQn3XF0zSZg7Sp4REIulqGrQxdSGD4soQkfq6YwIrnN6uqzh0FX/YoCDDjc+pJ5Jnju2fmogEHJrtZeXOGrwOPcms1VLbhsScjdsLIfCHDSJGnB3VIY4anJlof9BgGLBhA/z5z/DCCxBNjuyqHT2Of0z9BW/ljyWwU8f2+hoGZxZz8RQr3HhYjo9LpgzmhS+3srUyiBEXRA2zPseWTn6qNa8pLp2i8kCP16BJXzBJZyAFIIlE0iE8dp0dVSHWlNRimMKqbo8Veh4XEIqZxOKCDI+dwZlu6iJ7IrWmj85NLEwN2okNZZZvj8uuEYrGW2zbkJizob3LprKzOszuugiBiIHLpjM0K8Lm8rqD4+3fNGHHDnj0UXjqKcvnpzHDhrF65tXcrB1KudDJ9NhJq5+/9WV+Zr+zFqCREFRISXUYp02tzxGl4tA1fE5LmPSHY71CgyZ9wSSdQU9OrCuRSHow2W47ReVBYqbApSs47So2TUFV92QmFkIwKMtNTShGdTDG2P6pzUwTDdqJMf1SqQ7GKCoPtNq2cfv8VCffFFdTVGHljRqc6WHCwFRKasM8+3kRG8taTqzXK2iI7Przn+Goo+CBB5KFn5wc+NOfMBZ/yey8yZQLnYHpLnxOG7qq4nPaGJjuwh+OMW9xEUZ9AsOCdDfjBqQRjQv6pbnI9jlJcdkSmrSG0jY9XYO2xxesZUHNZdeIGPEer8mSdC89W8yXSCQ9lkXrdxEx4thUiAsFTVGw6wpxU6AqAiEEqqJw3Ihsjh6StdcInWE5PoYc1zzhXWvmiyFZXjLcdgZmuumf5krSYgghercJxO+Hf/8b5syB9euT93m9cNVVcP31kJfH8qJKiioCZHrsqGry+6yqWsUmt5QHWL6tiqMKM5tp0Palceup9HlfMEmnIO8OiUTSIUprIggEPqeNqCEwTBNRX+3eYdOwawp1EYNY3CqDsi9UVWmzuWJHtZUxfGi2t9kC2GtNIOEwLFwI994LS5Yk77PZ4Be/sCK7hg61JhmoCESJxU1crWQed9k1KgNRKgJ7fIYaNGgN0VO7asM4dI2x/VOblbbpqbTkC9ZAS75jEklLSAFIIpF0iLxUB5qiYAqrXlHcFAgECgqaqhCKxdEUhbxUR6ef+6AKh4/FLIHnnnvgvfea7z/9dLjzTpgwwSo+1YhMjx1bfbSdz9ncoyEUjWPTLE1QY9qrcetpHCyaLEn3IgUgiUSShGkKtlcFk2qyFaS7my0m00fm8YBvPaW1IZy6gq6pNHj/mKZJMGqQn+pi+si2ZdVrT0K7vZlAhBCU1YYJx0xqQzFMUySySPeoBT8eh7VrrVw+r7xiRXo15thj4a674LjjQG/5UX14QTqDMz2sL/PjsWtJZjDTNKkIRDkk15eot9WY9mjceiIHgyZL0r1IAUgikSTYWOZn/lfFfLm5gppgDKFAmsvOMYUZnH/MwKRFxW7XuPwHhTzw3joqgzHcdh2HrhAxBMGogVPXuOzYwr0Whm183vYktGvNBFIZiLKxzM/WiiA+p86LXxWzdEsVI/N9rC3x94yEeULAtm3w4IPwj39YSQ0bM2qUpfE56yzL9LUXdF3l4imDmf3OWoqrQmR67AlNSEUgSorTxszJgzu1+GRPordrsiTdiyyGiiyGKpGAJYQ8smgD322rRlMgxW1DQaE6GMMUgvEFaVx3wvBmAsPzXxTxj0+3sNsfJi4EmqKQk+LksmMLuXDS4DadtyPFTZt+LxSLs3xr8wKsG8rqKKkJk5/iZHiut3uLp1ZWwmOPwV//akV5NWbAALjlFrjsMquMRTt4f80u5n5eRFFFgFjcxKapFGZ5mDnZygMkkRysyGKoEslBTGumm8406Zim4N2Vpawv9WPXFDK9joRWJTdFpSIQZf0uP++uKOH0w1SCsXjinBdOGszPJhawYG0ppTUR8lIdTB+Z1ybNz/4ktGtsAtlY5md1SS3+sMGQLA/DcrxkeBwIITAME384RrbXntAWHfCEecEgzJtnRXZt3Zq8Ly0Nfvtb65Pe3FTVFqYdmsvU4dks31bFLn+YyroIWW4nPqeOYZgHrQZIItkfpAAkkfRgWjMNdbZJZ0d1iBU7aogLga8+L0wDlsCgU1EX5b/f7mTFzlo0VWl2zlPH9e/QefcnoV2DCeTrrZU89fEmMj0O8hodyx82qArFyPTYqQrGEgVZ23r8/SYahf/9D/7wB1ixInmf02lpe267Dfq3f+6aotdn3X5lyfYkTdDgTE8iI7REItmDFIAkkh5Ka7WOvtxSwWvf7mhk0tn/GkiBqEEwagACm9ZcW2DEBTWhGAEVCk0PKW47cdNkxY7q/aq71BnRXKqqkOKy4bBp5KQkC1LRuIlhmqS6bNSEYomCrO05foeIx60ipXfdZRUtTe4wnHMO3H03jBzZaad8f80uZr+zFn84luQL1DQjtEQiseiQAPTqq6/yyiuvUFxcTLRJTZrly5d3Ssckkr5Ma6Yhr0PvEpOOx67XZ9VViMVNHPoe85UQgvJAhFjcRFM0Nu0OsLk8gK6ppLttBCLxDpuROiuhXWvHsWsqumqFieuNCrK29/htRghL03P33fD6682KlXLCCVa4+9FHJ3L5dAaGYTL38yL84RgD012JaDCfU8Vj1yiuCjFvcRFTh2dLc1gfoMdFPPZQ2v1L+Otf/8oll1xCTk4O33zzDUcddRSZmZls3ryZk046qSv6KJH0OVozDbVk0mmgqUmnPfRPczG2fyqaouAPxWgcGxGJxakNxoibAkW1cv6ke+w4bRq7/RHK/GGWF1e1+5wN5x2a7aWkJkw8HmdHVZANZX52VAWJx+NtLs3Q+DiN++5z6qS7bFQEoqS7bficewSdTi/9sHUrXHEFHHkkvPZasvBz+OHw5ptWnp9jjulU4Qdg+baqNmeElhzcbCzz87ePNvHwwvX89f0NPLxwPX/7aFPvLg3TRbT7teeJJ57g6aef5rzzzmPevHn87ne/Y8iQIdx5551UVlZ2RR8lkj5Ha6ahrjLpqKrCj8fksbbUz3fbqtlVGybVbQMUymojROu1Qv1TnThslnbIoSvYPXYq6qJsqwzij8TaPc6GhHZLiip4cel2IkYcIQSKouDQNcb0T2lTQru9JcbTdZWU+hpZdRGj8xPmVVZaNbsef7x5sdIhQ+COO6wszq3k8ukMOpIRWnLw0ZrZfH/M4wcz7dYAFRcXM3nyZABcLhd+vyVVXnjhhbz44oud2zuJpI/S2KTTmK406QzL8XHdCcM5cVQuHofl9FxRF8Fh03DaNPJSHbgdzctOOGwqwWicunDH/Gi2VgTZUh4kYliZo22aiqYoRIw4W8qDbK0I7vsgtF5UddKQTG49aSTHDMlsU7HVNhMKwSOPwJgxMHt2svCTnW0VMF25Ei6+uEuFH0jOCN1iV1vJCC05eGhqNvc5bWiqZR4fnuOlMhBlwapdmGafz3yToN2/yry8PCoqKhg0aBCDBg3iyy+/ZPz48WzZsgWZUkgi6RxaS/TXYNLZXBFgSJanRZPO/tRAGpbj4/enjErKBB03Tea8s464aSa0M43PGYnFcds1vI72L/INvisRI86heT5icZHIJWTTlHb7ruwtMd7xh+R0jl+EYViZm//wB1i3LnmfxwPXXGPl88nIaP+xO8j+ZISWHBzsb0RlX6TdT6wf/ehHvPHGGxx++OFcdtllXH/99bz66qt8/fXXnHXWWV3RR4mkz9EWk46mKJTUhNBUqwK7P2yQ6XXst0lHVRUGZnoYmOkBYFtlkIGZbrZXBakMRPE6dWyaSixuUhc20DWVggx3i07M+6Kp74qjiYzTtJp5W/vf0gN+v0s/CAHvv2+ZtL78MnlfQ7HSu++GgQM7fo4O0tczQksOsvp4B4h2C0BPP/00pmn5HVx55ZVkZGTw2Wefcdppp3HllVd2egclkr5Ka7WOJg3JxOfUWbBqF19urtyT7yXLwzkjczrFxt84isRl0zisII2IYWIYJlWhGIGIgaaqZPsc6JrK4QPTW9Q67SsapbHvihCCqGEmNEB2XcVl16ioi7B+l58Ul637Ilq++QZ+/3t4++3m+844w4rsGjv2wPapCQ0h7g0ZoSsDUWyayiG5PpkRug/QWRGVfQlZCgNZCkPSs2kqRISiceZ9UURFXZQUp57ICl0bNsj07n9ph5aSL6a5bJTVRTDiAp9Tb6J1sjNz0mBcdi1J0Nm428+rX+9g0+464sIk3WVnWI4vKVnjki0V3PjKd9h1hUhMEIrFMYVAVRRcNg1FEdSGDMYXpOF16ge0hpdpCkq/X4v7vj+Q+v9eRYk3eXOeMsUqZPrDH3bo2F0VpmwYJsu3VVERiJLpsXN4QbrU/PQBTFPwt482sXJnTVLqDLBM1RvK6hjbP5Urpw49qELiD3gpjE8//ZSnnnqKTZs28eqrr9K/f3+ef/55CgsLOfbYYztySIlE0gqNTTcND7nKQJQRuckPubz6h9x7K3dhG68SalSuAmi24AJJvj6DMt0Ulwd4dvFWygMRnLqKqoJd09hdF0FXIcVpY1dtGFMIPHadcQNSGZmfwsLVyQKTQLB6Zy21oRiqqmBTFcqdEXb7I0nRKIcXpJPjc7C6pBaHruKwaWiKSlwIakJRgrE4PoeNoVlePE79gEW0bFpbRO2d9zDqf/NxRMLJO0eNsjQ+P/lJh8LZ21v4tb3outpmc6Hk4GFvZvNOi3g8yGi3APSf//yHCy+8kAsuuIBvvvmGSCQCgN/v57777uPtllTEEomkU9iXo6PLpvLWip18v70aTVNwaCq6phI14lTVFzX12HW8To2S6jDbqoLURQzipiBuWiYow4SmamFVAZtmlb9w2zXsukqWz8H6Mj/LtlZh01RSXDopThuVdRG+2FJB1DDxOWyIuEmdKSgPRPHYo4RiyYkTB2W6WVvqJxYX6JpAVcGIm4RjVkdyU+z4XAeohlcoRPl9D5D3l4cZ6q9O2lWVnsPH5/6KMXfewLB+HXMmlmHKkq6kNbP52P6pTB/d9VrT3ka7BaB7772XJ598kosuuoiXXnopsX3y5Mn84Q9/6NTOSSSSZPbm6FgZiLBul79eO+TDbdf5dlsVm3cHMEyBTbMiq4y4IGyYtCca1hQQMQRG3MDt0KkNxagMRFmz048pBGkuHZdDR1MUKgJRQpE4KAqBqEGK04ZDh7gJdRGDrZVBlm2tZEd1v/qjK0wamsH60jqqQzFCpkAB/j975x0myVle+1/lzj15ZnNeraSVUEQBUECBaC4GLAwYIwG2bLCNQDLRSQZLFlyC4QK2DEgYEMHkJCEBCiggCcVdrXZXm8Pk6Rwqf/ePr7t3esLuzO5skvo8z4Kmu6a6qrpq6tT7nvccQ1fojFsoinr4M7yCAL7xDcT1/0rXju1Nb9mJFI+98Soee8OVPFsI6N80xvK+tlkTr0MJfm2hhZlif5OQLTRj1gRo06ZNXDBFzzuVSpHL5eZim1pooYVpMJ3QUQjB1uEyJdsnHTUYKzk8NpYhU3YJhAAhEEKh7AQEh6D6CwTSG0hXCYXAD0OEUBgreySDkO6khR/Iz1AQ+IGQ3jQKaIpCzJCtuW2jZYqOh6oo2H7Amr40J/alGMjbVLwAzw/ZPlomHTMoHO4Mr1/+Ej72MXjyScbfInzT4ulXX8Hv/+y9OClZ8ZmneAdNvFpjyi3MFXaM5Ljivx4hb3skTJ1/f+PJrJnX0SA6hzzx+ALBrAnQvHnz2LJlC0uXLm16/f7772f58uVztV0ttNDCFJjOH6ho+4yVHapeSK7ssn2khDeulWVpSo2sHPo2uIFAIUQAbgC6KtBVFT+EXMUHoaAgq0ahAM/ZR1JUBXRNTqSUbJ956WgToVvQLv9oF6oeu7NVqm6AdrgyvB59FD78Yfjtb5teDhWVjRe+igevej/FeYua3jsU4tUaU25hLnDSP95Oxdv3QOD4Pn/xjafQgA9cfsIRGRB4vmDWowFXX30173vf+3j44YdRFIX+/n6+9a1vcd111/Ge97zncGxjCy20UENd6NgRN3luuETR9vDDkGzFZajgkKu42EEIioIxjhs4gSCYQttzsNBUBbVGvoIQQiHQNQXXl5898XPq9Y5QgOuD54ckLH3/GV6xWoZXVEcgGC05FKoeYRgeWobX1q3wp38qM7kmkJ/qRZfwmRu+xfc/cNMk8gOHRrymc/eei3W38MLARPIzHgHw6Ts3ccsDO1q5XzPErK+0D37wg+TzeS6++GJs2+aCCy7Asiyuu+46/uZv/uZwbGMLLRxxHEtpyhO3ZXlXYpLQ0fND3CBAUSAV0SnawSRndgHMxR4otXWJGs0RSAKkqQq+EBhaM9FSqA1LiX2vV7ygNmW2H8NHVcXSNfrzDsOl0cYeaIrC6r7k7CdaRkfh+uvhv/8basMbDZxxBtx0E9bLLyF6z1a2TqiwwaE7bU9XvZuLdbfw/MeOkdy05KeOENg5lGtpyWaIWRGgIAi4//77ufbaa/nYxz7Ghg0bCMOQk046iUQicbi2sYUWjigO95jyXG3LX1+0okGMNg8V+f22MTRNQVGUxnR2/c/fXJp9CcAdJ6Kut7t8P0RRJUGZuHyd/CjINpgfhKwfKLC8Jznt5Mrq3gSqikyKD8fRt9n+Ta9U4NOfRnz6Myj5XPN7y5fLkfa3vAUUBRUO2yhxa0y5hUPBFf/1yIyW+8OeAj3tyZaWbAaYFQHSNI1XvOIVPPvss3R0dHDWWWcdru1q4TjBsVQpmQsczJjybI/BTI3qNg8V+OLdWxkrOcxLR1jWGafqBVNuy/bRMqaugqLgeFJ0HAQ1cjJunXNFhIJx5AdAQ1Z1DE1Fm7DvjWWUeutMEqbhwr4qzMTJlYiu8q2HdxGEghcvaWN31qboeCQjBif3JdmeqR74Kdf34StfkQSnv7+JNxVT7Tz5Z+9h3kc/wMoFzZlddUJ2x/pB1u3NU3Fl1tmpC9qmJcEzPQeOxJhyywjx+Yn8DMOGPR8cP2hpyWaAWbfATjnlFLZt28ayZcvmdENuvPFGPvrRj/K+972Pz33uc4AsC19//fXcfPPNZLNZzjnnHL74xS9y8sknN37PcRyuu+46vv3tb1OtVrnkkkv40pe+xMKFC+d0+1qYjGOpUjIXOJgx5dkeg988O9SIKmhEWHTGufIlzVEFmweLfPznz7J1pETM1BgtubTHpO5lVU9i0rbI7CcdhMD1AnxfTOnnM9eor98ToPqCMAwYP7BVb5cp45bVVYVQQF/aalpXfXJly3CR2x7ezR3rByk7Pg9vy+CHAkWR1aMnd+U4aX5q+okpIeDHP4aPfhQ2bmx6y41Eefz1b+fuP34Xu1yNjkf2ctVLjKnPV9nnk60+wbRhz7M9Bw7nmPJMz69jBc+3B6jDiXREZ7jkHXA5Q6elJZshZn2E/u3f/o3rrruOj3/845x55pnE4/Gm9w8mSuLRRx/l5ptv5tRTT216/ZOf/CSf+cxnuPXWW1m9ejWf+MQnuOyyy9i0aRPJpPzDcs011/Czn/2M73znO3R2dnLttdfy2te+lsceewxN02a9LS3MDM9HQ7fZjinP9hj85tkhbrx9I0Xbawqr3Dxc5Mbb5Y36khN72TJc5It3b2HrSIm2mEHc0vECwUjRpuT4nLaobdK2THRUjlsaRds/pJH32ULXlNqx2if2EchJC0UBXVMRIqTiCealLC5f0zdpHfVjunOsTNnxyVW9xuSagqwilRyfx3ZmWdXrT37Kvf9+mcT+4INNL/uazobL38BDf/53VDq7iQCras7ZU5Ha+ve6oD1KzJQO1M8MFBgo2E3f68FeB4djTHmm59exgufbA9ThxveufjEXffqBAy531sLUwQ8IvMAw67roK1/5Sp566ile97rXsXDhQtrb22lvb6etrY329tm7o5ZKJd72trfx3//9302/L4Tgc5/7HB/72Md4wxvewNq1a/n6179OpVLhtttuAyCfz/PVr36VT3/601x66aWcfvrpfPOb32TdunX8+te/nvW2tDAzTKyUJCMGmipdelf1JMiUXe58ZohwNk57xwD2jSlP/VwQNbVGaXm2x8D3Q259YAdF22Nxe5RkxEBXVZIRg8XtUYq2x60PbGfbUJFvPrSLvbkKEUMhbumoioKlq3TETaquz9aREhFDbSpzywT3GKqq4PgBzhRGh6oCpqYw/rHA0sDSZVRFzFCxxr2pIgnHTKFCbTsNjAl/WYSQuh8/BFVROHF+elJbZvwxXdYhj4kQchvq5EqgEDFU/FCwY7SEWa8WbNwoQ0lf9rJJ5Ofpsy7mP7/wI37z/o9T6exuvD6R1E7chgN9r8fSdTCT8+vrD+7A9/cvoj1SqBPH9f152mIGy7sStMUM1vfnW1NM02BpdxuxiRfWBKjAkt62lpZshph1Bejuu++e9r0nnnhi1hvw3ve+l9e85jVceumlfOITn2i8vn37dgYHB7n88ssbr1mWxYUXXsiDDz7I1VdfzWOPPYbneU3LzJ8/n7Vr1/Lggw/yile8YsrPdBynEeEBMkythZnj+WroNps05dkeg8d3Z9kxVqYzbqKqzX/EVFUlYWo8vSfPh3+0jr3ZKrqmUHYDTN0jHTUb601EdDJll5Gi01Tm3puroqBw/vJOnukvMFiwGy0nVQFdBT8ETQUFhaBWGlJVVbakVAhC0USadJVZVZCcQJCIajWvH1nt8cU4DZKApKWxdkGaVMSYdH6MP6b92UqjlaYodfG0QlgzdNQUKcTevG4Ly7/9Rfif/wFvQnvgpS9lx4f+mf/Mt7O8K8FU9eCJ3juz+V6BY+Y6OND51Rk32TZS4o4NA6zsSR7VdlPLEfvgseHjr5p2FF4Drn3FCa3Ii1lg1gTowgsvbPo5n8/zrW99i6985Ss89dRTXHPNNTNe13e+8x0ef/xxHn300UnvDQ4OAtDb21yy7e3tZefOnY1lTNOcVHnq7e1t/P5UuPHGG7n++utnvJ0tNONYMHSbrXZgJsvPZkx583BxVsdgrOziBSFRc/JtuOoG5G15TAEipkrCNCg6FfpzNoaqErPkpWpoKiXbZyBvc/6KrkaZu/6dnNCXojthccczg+SrHoamoGsqQRDi13pJ4+/VfiCT1xUgEM0EKIRZxWXAuBF5IatB+CGdCRND1/D8gJef0ENH0mLHaHnS+TH+vCrVhNxqbTvU2rYLAX4oSHg2Vz/wXS753C+gWmneiJNOgptugte8Bi1bJXLX5hmR2onbIIRMu3eDEFNTSUb0Sd/r0b4O6tjf+QWAArmqxzce2klnwjqq7abn6wPUkcKGj7/qgE7QLcwMB62S+u1vf8vXvvY1fvjDH7JkyRLe+MY38tWvfnXGv797927e9773ceeddxKJRKZdbuIFIoSY9NpEHGiZj3zkI3zgAx9o/FwoFFi0aLLpWQtTYzaVksOB2WoHZrr8bMaUZ3sMOuMmhqZSdQOSkX1P6EIIMmUHxwswNZXOhEnB9lFUWJCOsDNTrd0Iopi6RtnxqbgBnQmrqcw9fnssQyMR0Sk6vhyJh5oZT+0zx22nocmqShhK3Y5WG9EKhKy4BLOQUStKs+YnEAJVVeiIW1iGiu2FREx92vOj6ZiaOqpCw2xRVn5ADQOuevwXXH3/d+mo5Js3YMEC6fNz1VVQq4LM1nunvg39uQqDeYdMxcUPQ3RVpSNm0pe2mrb9aF4H4zHd+VXfjoGctBLoTFgs70ocVb3esfAAdbxjaXcbj/zD5QdesIX9YlYaoD179vCJT3yC5cuX85a3vIX29nY8z+MHP/gBn/jEJzj99NNnvK7HHnuM4eFhzjzzTHRdR9d17r33Xj7/+c+j63qj8jOxkjM8PNx4r6+vD9d1yWaz0y4zFSzLIpVKNf1rYeaYzr0X9t1UDpcIb7bagfry6/bm0VWFVMRAVxXW7Z16+fqY8tr5aXIVjx2jZXIVj1MWpJtuFLM9BmcsamdpZ5yxsksY7itfu35I1Q3wgpC4pdOdtGiPGZRsn6ipMz8dQVUVshWPwXyV0ZLLiu4E7714RdNNa/z2JCyNBW1RdFUGn4a11pZRm74K6+0vBdJRnVRExzI0OhMmnTF5I9dVqQ+KGsqMbXfipoofhKiKgqYqeIHA0lUSlkbJ9umImyQsbdrzY/w+LO+KETd1glCgq1K79Lpn7+W3N1/NR+68uZn8pNNwww2wZQu8610N8gPTO2cXbY/nhkuTvHcWtEVpixk8uiPLUKFKxFBpj5lEDJWhQpVHd2RpixksaIse1etgIqY7v4QQjJVsKq5PV8JkRVfiqOv1Wo7YLRwrmDEBevWrX81JJ53Ehg0b+MIXvkB/fz9f+MIXDvqDL7nkEtatW8eTTz7Z+HfWWWfxtre9jSeffJLly5fT19fHXXfd1fgd13W59957Of/88wE488wzMQyjaZmBgQHWr1/fWKaFucdsbypzhdmKTuvL78pUyFdcnt6b5w87Mzy9N0++4rIrU5nyj//KniR/fdEK3n/Zav72klW8/7LV/NWFK6asFs30GOi6ypUvWUoyYrArW20sP1p2yFU9gtqY9yPbs1TdAEVRyJRdQiSB8fyQihtg6ioru+ONysjU21MmFTVIR3VCIaj6IaBgGRphKC3zI4ZCzNDIVWVFydQU4paGG0LU1ElEDISioioqM7GQ0VRoi5noqkLJ8fGCEE1TiZoaw0UHy1DpS1lsGSlPe36M34dtY1VOmp9E01RO2/ok3/3K+/jsTz7Foty+ByLPMNn153/J7sefIfzQh2GaSvJMSW0D9dNhYhW59nP91aN1HUyF6c6vTNlltOwR1TVOX9zetC1TaZqOBI4l4tjCCxszpth33nknf/d3f8df//Vfs2rVqkP+4GQyydq1a5tei8fjdHZ2Nl6/5ppruOGGG1i1ahWrVq3ihhtuIBaL8da3vhWAdDrNu971Lq699lo6Ozvp6Ojguuuu45RTTuHSSy895G1sYXocCUO3iZitdmBvrsoTu7OMFG38QJCI6BiajheEjJQcNFXh8V3ZKbUGMxlTnu0xqI8g131ahgsOZddHUxV6UxZ9qShl12es7KKpcvJrIFfFrW37ks4489siDBYdbnlgx6Sb98qeJC9f09NYvx9IHY4XhIQixPXB1FXSUbkuLwjZOVbBqREk1xecPD/FZSf1sn2kzL3PjTBcsFFVBYOpfYUUYElnjM64yXDRAaQTdTpq0J20EEJBQdAZtwDlgOfH+GNa+cPjXPeD/8uZG5s1goGi8POTLuSzL7+KTHsP3T/czIUn5HjrOYv3u96ZeO/szVXJVT3OXtrOQN4hW3EpOT66qtKbitCXsshWvMY5czSug+kw8fzKlF0EkLR0zl3ewbKuyW79R6Pd1HLEbuFYwYwJ0O9+9zu+9rWvcdZZZ7FmzRre/va38+Y3v/lwbhsf/OAHqVarvOc972kYId55550NDyCAz372s+i6zhVXXNEwQrz11ltbHkBHAIfT0G0qzFagWrQ9do1VCMKQzoTVIE2WrmHGVcZKDrszFYr2gc3FxmOioPrqC5YzUPvcAx2DS07s5cJV3fxhV4ZvP7yLgXwVVVHoz9v05ypU3JBACPwwRAMUVWFJZ5S189PMb4uiqipinH/N0o5447NHiw6/eXaIuKVx7vIONFW2pAbzVXwBL17awfkrOzljYTtDJafhuDxYsMlWvCbX4DAUvCNbYdtoibGiS3vcIGpqZEsuP3pyD9mSx8KOCO+5YBWr5qUIQ9FwH26PGfSlIth+SNTQUJAu0TM9P1Y6OVZ87V/g299GCZunXR5YcSafuuQqtvWtwNQ1CEOGig6/XDfAcNHhmktXTUs6ZkJq6+fY8q4EC9tjk86xQIhJAu4jfR3sD/Xzq/5dBGHIrzcM0xE3p1z+aLWbjiXi2MILF4qYzt50GlQqFb7zne/wta99jUceeYQgCPjMZz7DO9/5ziZicjyhUCiQTqfJ5/MtPdAxjN2ZCp+9azMgphWogsL7L1vNoo4Yj2wf49rvPUUyok8pUi3aHkXb59NXvIgXL+uc0TbMlXlbfV/aYgajJZd7Nw1T8QLqbjdBKEfQNRV6khbpqEl7zGRlT4KOuCnJXabC8q44oyWXqhewc6yMHwpevLSDzsQ+l+U6YTplQZq/unDFQd+YD7dxXZjNUfqHfyLx1f9Gdeym94ZXncxNl76b3/adCCiSWCly36peiIIUAr/+9IX89UWz38d6fMSmwSJ3PTPE8u4Y6Zg1abmi7ZGreI1z7EjgUNySw1Dw5Xu2sr4/3zRyDnN3XhwKWk7QLRwqDuX+PWvaH4vFeOc738k73/lONm3axFe/+lX+/d//nQ9/+MNcdtll/PSnP53tKltoYUaoC1Tv2jCEqSkko0ajpTVUqLI7W+Gyk3ob2oFErSrkeCEJS0z64+94ITFTTkzNBHPpfl2vNESNCLsykriotXEtMT52IZT+PBFdbXKC9sOQzUNFbC9gdW+SRKCzeaiIHwie3J1jVU+CmKU3KheHOlp8WJ2/HYfRj/87if/4DKlSsyeXu2QZhX+6nn/U17BxoAiOj6mr+8JeFQVTV3H9ECcIeXpvbtb7OD4+wg2kKP2ZgQLnr+hkefe+ttHRSGw/VNJ5rLebDocjdgstzBSHlJB3wgkn8MlPfpI9e/bw7W9/e662qYUWpscMBaoASctgcUcMXZOCYscPCIXA8QMyZRddV1nYHqVk+2wcLLA7U5l2GmauXX/rkzBDBZs92Sq6qtARM0lHdQxVQatdmaoKRdtHQMMJestwkU0DBfxAsLJbbksgpIg6YWn056v8bssov986ykPbxnh0R5aqFxx0QOJhczwOQ7jlFrzlK+j6t38hMo785OJp/t8f/x3X/eu3efzcy6h4QWMkf2LafN2tOhSCiju7fazHR2weLpKM6Cxsj9KdNKm4Pr/dOMwz/bmjJmyeK7fkWYvAW2jhBYI5afxqmsbrX/96Xv/618/F6lpoYUrMVqC6oC3K6YvacbwQLwgYLrp4YYihqvQkTbxA4AaCbz+8CycI9/t0vT8BNkDC0nlsZ4Y/7GznrCUdBzRlDIUgFdV5fGcW2w2IWbKlEwZCGhYK0Gur8IOQYtXD1lVCIdiTreL4AYs6YqSisrVnaipBKBgs2wSBQKjIGA1VZbhQZahQpScZoVD1CEMxqxv44TCuC3/+C/y//3vMjc8yvjlZNSP8+KIr+N9L3kJOtfAGykQ2DBE1NLQavQ2EQB+3HXW3alVRiJkz17NMjI+oOyh3JSJEDY0dYxWe3lPAUBWipnFE9Slz7ZZ8LOmUWmjhWEHLaKGF4wazFajWy//PDhbYNFjFD0OoCYwH8jYChSWWTnvcbAReTtfSmc68LVN22DpcZrTsUKh6/Ne923h0cXZGpoyjJYehgk3VDxoFLdsLqLvcqwqI2uTVrmwFRVEIQ4EALE1lXmofIUlYOkEgsL2QVETD8eVyYSiwvYDRskeh6nPbw7t4dPv027e/4z4nxnV/+AOVv7uG2EMPMF6W66saPznzVXz54rczFm9DdRSiRkgoBOv35jl/RRfbRyvgeLh+iDZOA+T6UgNkaSqnLmibcXtqf/ERcctgcXuMbNXl5Sf2ce7yziNKGA4H6Wy1m1pooRktAtTCcYOJ7sv16kcdVcefdqJFQcHQVBSko3Cu6mPoKks6Yg2B9P6erqdyfs6UHZ7cnaNa8+dJRw064sa0JGqijmZ+W5SIrnD3phFKTkDNhBnY15uuN5X8EAxVoKlymUAInhko0B436YhblBwfTZNBoRU3lKGoXsBY2cP2AqKGSiKiYWrqrHU7c+L8vW2bTGn//veZeAu+64TzuPHid7KnYz7JiEFUVwmEoOwGKAhGSw7Lu+MMFmyKOzxKjk8QhDJeIwwJQ0HC0jlhXopXrJ15e+pA8RFRSyNTkVqyI00c5totuS7yHiu7TdN+LbTwQkaLALUwIxzJaY3xnxUzNARQ9QKihsby7jjP9BdmFGtQbyMEoeAVJ/dScgLcIMTxA9btzmH7gm2j0pivvq7pnq4nRioAbB0uU3UD2mMG2YpHTyrCvLT87IkkarqWxgl9KZ7eW2Co6CCEdGAWSJIzXlJjqDKmwtBUUhGdshuwN1vl50/tZV5bjL60haYo9KUiDBRsFGC0JG/wbTGD9piJ4wcYusqqdILNQyW+94fdvP70BSQtY7/f52zjJJowMgL//M/wla9MCivds/ZMvvPmv+PmahcImUnm+iGWLgNaNUOjbPt4QcC8tgjXXLqK2x7exb2bRhguOlRcH0NV6EtaXHBCz359gKbC/uIjQBI7Q5NBokcacxk3M17k7QUhhqaytDPOlS9Z2vAOaqGFFyJaBKiFA+Jwjz9P91mjJYfRkjTX60qYdCUs2qJSgDuTiZbxbQRVVUlF5U1utOQQAOmYQabsUrT9pmrSVE/XE6dpEpbOaNnB1FWyFY+oqbOiex+xmcqUcaqWRskJiJoasVrlJhAy8HOinNgPZQhoKATZitdIg3arAdlqkQ0DRTQFOhMW89NR5qcjPDdcIlrLBXODED9UMTWVbMVlpGjzTH+ezUNFOmLWfr/Pg5okKpfhU5+CT38aSqWm9Y0sWs79f/khdrz4QgZyFZSnBhCKgqqAH4Yy/kKTsaohkhhFDY2VPUn+4TUnceX5FbaMlBgruXQmTFZ2y5bobAl5PT5i83CRuKk1tcHCMGSs7HJCb5IzFrXvZy2HB4dEOsehLvIu2tLnqf69bR4ucuPtGwFaJKiFFyxaBKiF/eKwjj9PwObBIl+8ewtjZUcSjJJDxfERKCgKdCVMBgo2miqrNLmKN8lAbXlXgt2ZCmXXZzBvU3V9EpbOSNGWeqHayLQU0Uo9kBtIMlE3V8xWXPxQEDP2tUbCUGDpGhee0M0ftmfYMlKiUPVIRw16UhFWdCeazOYmkqjpTBxLjo8KtEcNyq6DIvYlqo9HoxUWhLjB1McvEDBcdFjTG6c7FWHbaJl4bcS/WPWJmipbR0rsyVYQNW1QtuxiuwGDhSqbh4q89+KVrO6b/H3WJ4nuWD/Iur15Km5AzNQ4dUFbM3Hyfbj5ZvjXf4WhoaZ12L3z+O7r/oLR178ZRdco2n6jyuIG0gAyDAUVVwrbBbIiJzVaGr/fNipH/92Qld1xzlnWcVDEp456fMSNt29kV7baRBDGyi6piME7zl96VFpFczG+Pp3IOxlRiZsau7JVvv7gDi5c1d1qh7XwgkSLALUwLeZ6EmV/2DxU4OM/f5atIyWihkre9gmFYEFblKihkSm7DBYczlzcxpYRKVy98vylTQ7D20ZLfPmerY1KVb7isXmoiKbK1opfqyykIwZBKCjYHqmIgampDTHzWNkhX/XoiFv89Ml+XnlKH0BTBczSVHqTESqdAYs6osxLRycJVSe2KManjNcn2PwgRAgYK3tUa0Sp2fd4iu9kBsfyga0ZzgnlTbTsSEJXqMr1bxoq4QeiQahGSi4KUnAdj+hkyi7/+Ecnsrp3GkMxIf8J+T/7/IqEgB/+ED78YRlKOg6VWJI7/uhKfnPpm9leDpg/UqLshGQrknwJamLmGrHznACFAE2FjphBOmrw0R+uY8dohaonl9dVhd5UhMtO6p1162s8poqPMDSVE3qTvOP8o9siOlS35P2JvFVVtva2j5Z5fHd2xkagLbTwfEKLALUwLQ7HJMpU2DJc5It3S+LSFjPQVZVMRY5rDxUc+lIRErWbc8kJmJeOsHWkjKIorOlLNdYxvlJleypbh4uUHB8/EER0BcuUk1IF28NQFYpugKFr5CoOm4dLlGwf2W6zWN2b4JmBAhuHpNdKEIqmClh/rkrVCxgsOA3dTx1TtSgWtEVpixrc9ewQpq6SjBgYEZ1i1aPi+rjBxJrPwSMQ8MTuHO0xA9sPqTg+Sq2N5PjBpOpSXXxdrPo8tSfHF+/eyt++fOW0Au4F7dHG1NwzAwWMhx9k4fe/QOSJx5rW6+sGd192BU+8/T2oHZ30OB7rN43w0NYMHXGTzoRFwtLIVV3KrvwdBel+rdb2I1v1KTklAhEShpL4KAp4gWAgX+WnT/UfMALjQJgYH3EsiYQPZXz9gCJvUz5YjNUPfgstvMDQIkAvIIwXF0d0lb25Ko/uyLBlKMdv1o0y2zzonigUPIW7NgwQNTVef0off3LWUjxFULJ9YqZGxQ1IWDKKov6He6LI+Y51g4yVHGKmhq4qVDxZ/YmZGrYfkqm49KWsRruqLWY0tZd8P+R7j+5h51iZFV1xgjDkmf4CVS8kYWpkqz5eKLAEaKqMTnBVmYbeGTPYNFgiU3Fpixp0JKxGOysMQ361YQgEvOLk3kYOlxDSlHC0JEffNw+VmN82gxZF7T/l6HaA6wvGyi6HVjubGrqmkIroeGUPISAd1QlCGWg6EYoiPYe8ECquz3C+yu3rBjlzqc22kTKWrrJpoMhYyWF1b7JBhpcM7+LPbr6J5Y/c27Q+oShsuvT/8D+veTddJ67EGFc5bIsaDBdsSraHVfM1qtZKPwpgaAoxQ0VRVPwgoOiG+IGcstNUUSNACromLQNKtsemQakbW37RwVcidV09ZqsgBzu+fiyLvFto4VhAiwC9QDBeXLwrU+a5wSJFJ5hRS2U6DFcBBLYvKDohNz+4m5sf3E26FkHhBQJDU0hFDBZ3xjh9UTtr5iXZOFBstJOCULA7U6U9ppOreoyW3Fq+U4Bfi4CougEVJ0BXpYh3fHtpy3CR7z26m9vXDxKGgo2DRYJQ4Hj7tCWmpuAGIbmqS1jz1VEUmZReqHqkYybn9nXSHjNJRvYJTkuO3L76f/uhx9bhciODTMjeDcne5JR6pPEVib25KrmKx5q+JJsGi+wpVfGCEC8I0ed4mk5VJEEbq3gEoSAVNfBDqEwnHhIydFWtJb5nyi5fe2A7/3VfWGuXCRAKCzsidCcjLLKznH/L5zjprh+jigln0CtewdA/fpyb9xi0xYymymHR9nH8kHRU5p8VnDIilG09pfbPCwSlMEBV5XGp66FCITA1Zd+0HmBoKn4oKDveQUVgPN9xLIu8W2jhWECLAL0AML594QUBGweKlKa7Gc4B8rZPtUZAglDB0BT2ZCtkKy4/enIv81IRVvUmiJlR9mYrDBds+nM1QqEoRE15Y3O9EBEKNFWh6Pgs6oiRsDS2jJRZOz/NttES3310N8MFm6BWVfECOSnlC4GhgFfT2YCMTFBq4+RBKG+2I2WXkhtwQl9qkq9QXRytIBgpOewck2PviYiOoek4fsBwwaHkeLztnCV0Ja1pWxRl12e05DBWdjBqERyOH8ptDw+Fhk5G3NTojFvkKyUEsLQjRhjCQKFK1Zvc7qhrcGq6cLaNlgkERA2NVFTH9QIKdkCmf5Q1P7+ZKx78EYbrNK1j59ITUT79KRa+/tVsfG6E4Y3bSFh6bb1K43jmqx7ZsksoBAlLQwgoOkFdWoSuQtLS0VSVqjfuHJUnB2FNf6SgoCoKQsipsdlGYBwIz4eQzmNZ5N1CC8cCWgToeY7xQuYVXTF+8mQ/5cNIfupwA0FHTMMJZMvI8wXDto0bCLoTZmO0Nx0zEAgcX5CKaIQCHF8QMVTCMMTxpalfxFDpS1lsGSmjqQqjRZubbh8gU3aIGiq5souqymqTH4TYfojrh42Kj0ASH0tXa4RIGujpmkoQhmwaKtCV6GqqWJi1QC4hkJofN2jyDFIVhbaoQcUNeHpPfr+J2jFDa0y19dQcnE0vIKdrhKFKZYJHzsFCAfrSEWxPev4oAmwvJBkxSFoGw7iTNEAIWYUZb7qoawpxU8PQVEzf40//8CP+6oHv0mY3j7Tn5i3irrdfw0OnX8wfn7KQn92zlaf35tg+XGYgZ9OTjDQS7HUF8rUJO1NTiBq6dOd29p2PQQiGrmJost1YP1dDARU3lNU7ZJWr/lVp6swiMMJQsDtbYftoGYDlXXHmp6MM1Nqp48X0R8r2YaY4WEJ2LIu8W2jhaKNFgJ7nGC9kHiw4jJWnuAEeJpTdgJilU/VCIqZG3vbpreV1jffeURQFXZU+N90J6Wpc9aTTrx/KMfG2qAFI4fVw0WHbWBnHD+hNRXB92S4jFPhBiK7JVpnjy5tnfX+12g0jCAWGpqLWXutJWgzk7EY7rI6EpaGpCl4AZdsjMa49JoTUOfWkIizvih9QDC63QUGMU/yYukrUUCk5wZSj77OFUtvmqKEyVHQ5oXazfm6kRNzUaI8ZmJqCM0FwLRRZHRuvDzI1FUOBy5/4NX9559eYlxtu+p1yqp2H3/43PP3qK9iUcZgfN7l93SDZisv8dJR8xWMwX2V4XIJ9xfEbLUVdU2vfh9K07wIIAoGhganvO1bjN1nUfxZSLG3MIAJjy3CR236/i99vz5CruigCIoZKW8ykPW5i6ioRXaMtZjBcdCaJ3g+H7cNMcag+XMeyyLuFFo4mWgToeY7x/jODtVbRkYIfCjRFwROyDRXUKi5esM97xwuk2BkhxckCWcEouwEl26c3adEWM3jny5Zz8rwUP32yn4G8zYK2KHuzVcyakDZiaNheQMHxSUcMoobamHgaryMJg3r1QJEExNRY0hmj6BTYMlJidW+yScy8ujdZEzoXsUydUAi8IKRk+w3zw5ilM1x09tuCqXoBXQkTRZEaG9lGU0lEdAq2j6krOP7BfTeqAklLI2JI0fmenE1n3OKqly4DaGqB9KQs9mTtfaSQfeRHqwnUdU3hop1P8P47v8LKwW1Nn1UxLL71kj9h97veg55OMZCxaY8ZCCBb2WeXsKo3Qdn1qTh+4/jFDJWQGvlUFOn7I2S8x3jyFQiB44dU3QMTQwFETZ3LTpreE2fLcJHP/fo5ntqdQ1UUuhMWVc+nP2fTn7fpTUU4f0Unlq5x/3OjVL2AC1d3zygi5XBjrny4jmWRdwstHC20CNDzHOMt9WOGhqbMRa1hZtBVeZNTFDm6rKmKrNDUxMwgKw0xU8fQFKj6eIEgX/XQVJVFHTH6UhagsHZ+GpD6lHnpiIyN0FS8QJIsQ1PRFPa1voS80eqq3FfHFw2HYUtXSUQMEqYOikxNX92bZHlXohFQOl7MPJC3ufGXGyk7PhUXdFVtMj8s2t4BYwnipk5XwpJmjhOS7Jd3xynbPjszlQZRPJAqSEGKuE/oS5Itu+SqHgXbRwFO7E5w9YUrmtob+6IQBOmoQdULCMOwlismqyHz2yK0b3yGa3/9Fc7d8VTT5/mqxg/PeCWfOf9PCbt7OdlTaa94nLIgzakL0/zw8b1NdgkdcYvTFrWxdbjMUNFmT7bCgnSUmKGTiuoIAVVPiqxVRcHSwQ8EYa1lZwpoi2oHbNcKoC9pTTvqHYaCO9YNsnmoiKnvm3gaK0mNmq6EFKoeO0YrrO5LoCiytTnTiJTDiSPpw9VCCy9EtAjQ8wD70weMt9Rf0RWjM25SzdlHhALFTQ3HD4mbGiKEdESnUPVZ3h0nWXMoTkZ02qMG28ZcVnUnOKEviRcKTE0lYWk8N1xmcWeUvO3ybH+R4WKVhKWTimjETI2RokN7TFZ8yo6cGutOmlS9kO6EiaEpbB2tkK4RsI64ia6qGJpCtuLRnbAYKtis6E7y1hcvQlEVyq5PyZYO0paucd6yTl69to9Hd2ZY0BbF0rXGtNhMYwnq38O6vTlWdccYKGj4oaAjZtKXsnh6bw5dV9AUlWREIxCC4bxN0QmI6Aq2F9IRM3GCkIoXYqoKS7rinL20Q2qU8hW2jVRYMy/J9a89GdPUcN2AOzcOMph3+JOzFtKRMCjaMrusI25w98ZRCrZLd8LiHLXAwps+QfInP5i07XevfRlfvvydrI/1EjU1Lj2xhwtWd9OdsDhjUTtbRmVrJmpEKFS92uSdSnvM5KylJrmKx/axMu84fwm3PrCD54ZLLGqLNOI9Rou2tCZQAmKmwYnzkiQjOnszFfbmD+xRsyNTbqq+jb8eClWPp/fkCEJBMiKn0hwvoOrJzDGBdAYfKtp0pywCIRoRKYWqh6Iojf2JmrKqOFWlL1Oo8v7vP0V/zmZ+W4TPvulFdKRmlko/HY6UD1cLLbxQ0SJAxzkOpA8Yb6m/dbTCCfOSZCveYZ0CAzA1haITYGiy+qNrCm3xCIWqrHqUHL/RatJ1lVTEkILkQLCuP0uu7BGE0BE3yFdd7tk0QtH2KNkBW4bLGJpsfRVtn1zFxdI1QhHihYKKG5KOGSztijNSdGmLmaSjOqVa9IJlQNGWVaL+gl1LXlf4/G+30BYzQECu6jUdzxPnpxgo2LVWhCQoVcefcSyBqiqsmZfkp0/t5YEto4RCing1BVBULF1BVxXyVVnFqae8o3iyfZaKcsbiNhwv4NEdWUDmjQU1H52KG3LS/BRXvWQppqnxjYd28JXfbWekKNuemqLQnYzwutPmE9G1xvnSWSlw+e23sPpn30Hzm4XYDy86mc+94i/ZvHhNbYReiszHSi6/eHqAiK7x6PYspy5K4/ohv982RtkNGjqs9pjJyp4EhibbTif0prjqJcu48faN7M7ZjamkuKWTrdqEQvoADeSrDOQVBnP2jM412wsb1beJ14PjhWwZLjbWDTRab6pSH76XbU2Q1T2QcRxP7M7h+GFjf+KmRnvcnFTpe83n7+OZ/mLj5+eGy5xxw285eX6SX/zdBTPah6kw14nwLbTQQjNaBOg4xkz1ARMt9dfMS86JD9B0SFkaes2Dx/ZC/NCj7ATETY2zl7bRk4iyK1thMC9vziu647zm1Hnccv92frdltGlde/M2cVOjLxWhPWpStiuMFuUoeczU6UlalGoVGy+QN8JURK+1OhTOW9HJCX3Se+iJ3Vl2ZSoUbV+Km/2QuKWzdkGK+W0x+nMV7tog86vOXtrO8q5E0/F8+ZqehofRbGMJtgwX+dETe2vtPQUNWf0oOQEQ4BkanXGTvrRFpuySr7o1sqaysC1Ke9ykUJWttstP6kUAuYrHjtHypO34xkM7+NSvNuH4ATFTx6rpi/rzVf7z3q2s6k3w0vkxXnb7N3nx976CVS03beuO3iXccNFV3LXkTISiYNgeScsgFTVY2B5jcUes4QS9vj/PhoECuzMVclWPeakIRkSXFgNFm6Lt0R4zOW9FJwvaoo1KxfipJJAaJqnJ2vcnSdeAGQzHzW+PsKAtOuX1MFSQjt22FxC3NNJRE02RI/T1kXqQLdT2mEFHzGTHWImKG6Agc8iMiI7nh+zJVfHDfcaNMJn8jMcz/UVe8/n7DpoEzWUifAsttDAZrSvnOMVs9QETLfXnwgl6PExNYUl7lMWdcfbmK2wfrewT9foAAZlKnsd350lZOhef2ENPMsJAvspQwebOZ4boz0/9xF92AzJlh1U1gXLFCwhDgReGVL2AnoSJqcrpppet7OJvXr4S2w+b2oEXn9DD3lyVou1RsD1uXzfAjrEKpy5INxyeB/MOpibnqwcLDgvbY03Hc9NgkasvWD5pbPpA+oswFNyxfpDNg9KQbnFHDMcPGcjbKELghrICEYYhnfEYnXHZlmuLGrTFTc5a0sHrTptPdVzuGTBl29N1A77yu+04fkBHzGiY30UN8PwA2/E4564f8HcP3EYi20w2h1Nd/O/rr+Z3576SUFFZXXXpz9n0pSO8dEUXAwW7yQk6GTGImxq/emYIJwjpihuUHJ+EIgXecUtjsOCgqQqXntTTOE7jp5JGSg73bx4hU/ZY1RNnsOBQ8QJihka7Bf/90N4Dnnv/+MoTAaa8HualoyzvirOuv8Bw0SFp6Y3pu7IbIMIQVZXZbqmIwbKuKM/05wmEoC0qtWleLbS2O2GSihr8+tkhVvYkyJXsaclPHc/0F8kUqgfVDpurRPgWWmhharQI0HGKg9EHTLTUX9qV4CUruxs/bxku8vGfb+APO7LSWNALZ1whWtwRY2lXnAe3jWG7AfuLtio4Pj97qp9lXTEuXN2DTsjt6/eRn6lk2tmqT6li44dSN2R70kK4XhVZ2BGnL2URCtBUlTV9iabfH7/vuzMVinbAiu5EgyAUbZ9MxSVZG83PlN3GqP744zlQsGett9ibq7Jur7ypJmvrU5BiZ8vUcW0fFUHZlQJuy5CtFtsL6UlG2D5aRh2Xe1bHVNtx58ZBRoo2MVNvcv4NgpALNv2e6379VZZnmklF0YrzlQv+lO+d+8f09qSxVA0FaItZaKpKtuyydbTMynHEoo6SExAIGVGxujfFUMEhU3EpOz6aKg0f22MmUaP5T019Kml3psJvNgwTtzQe25VvhMTqmkrUkCP9VW/6s7AnadKVjk57PciJtBSDBYfhosNA3qEjYRKzNHJV6ZTdGzdY2hWj5PjsydkkIjrpiIETCCqei9YQvccxNLVxXf3Dj58+0FcPwPu//xRff+e5M1p2POYiEb6FFlqYHi0CdJxirvUBYSi4fd0A6/cWADkePZv22JYR6ZLs++F+yU/j8wTsyVSJ6ioPbMk0vTfdrw+WPAQKEUNDhpaaOH7IKQvSLOmMEQjBjtHyAfd5qmPnBiF+GGJo8pIoOX5jVB8OTW9Rdn0qrg+1EXDYp0PRFQWl1ogJhWjYFBiaWiMRChXXn/HnDuYdAiGwxnnonLxzA+/9xX/yot0bmpb1dZO7LvkT/vWU1yHa2+Wk3ASbhKipMVwIKTkeMVMnDIV0lK615woVn5LtoWsqEUPjrKXtFG1/nHBYY+fY9N/JPodsF8cLSNRCYr1AMFpy5HqFjFuZiBXdMdb0pRrrnu566IjLFtxDW8dQFRgrSRfrvqRFuuYDlK962F7I8q4EQsApC9JU3KCxH3XRux+GjfOgf4YapZkuNxUONRG+hRZamB4tAnScYq71AbIdlsXxZfBk0Z69M/FQ0UbMYrzMCQTrBwrkZvhZfiAwdDn6rioKhqqimWpjXLnq+DPa56mOnamp6KraJIatj+rDvuMZNTR2ZyqzaoHFTZ2YqQOynWLpWkOHIv38ZM1LVZSaTYGM8NBUVVaJZvE99qUtNEVqftbkd/PXt/83Fzz7YNMyIQq/Ou0SvvGad7Mn2c1YwSbqBuia1vj88fttaCoJy2DTYIFNg0VyVQ/HD2ROmJCEVVXh3k3DnLWsg2Vd+6pvB7IIiBoaoyVZMepNWY3qjaUrdMQMxsouUVPnDWf2sqG/QKHqk4rqnLesCycIyVW8xrr3dz1EDY3TF7Xx5hcvktVDpnaCFkLwuV8/R9ULJkWj1I9HfX/mt0V4brg8aZmJmN8WOeAy+8OhJMLPBM+H2I8WWjgYtAjQcYq51geUXZ+S4yGEQFOYFZGpww+lKd9sULQ9ItrMHGl1VSFqqGQrHm1RHScI6U1FSEb0We3zVMcuGdHpiJkMFaqgKI31wr7jOT8d4WdP9bNtpDwrR94FbVFOWZBm+0iZYtXDTKg1HYpG0ZYi4BCIm/L1ust0d9KiaPucunDm3+Pla/r4qniIP/3xV3nTk3eiTQgrvWfZmXzq5VeinXoqlqnT7gUMFW0KTkBHVJpD1lEPzFzdk6A9ZvDrjcOIULa76p499dNEQRoh/u45qSta1pWY0XdS89VGmaLuZ+oqRs07ylBVLly9z9dICMGOTKVp3TO5Hl68tHPSzX18KzEMxYyvq8++6UWcccNvp/sqGvjsm150wGUOhINNhD8QDtVluoUWjme0CNBxirnWB8RNnYQl9SlBbUR7tmZB9aHi2SBpGagzDAMNQx/Q0FUF2xckoipLOqV2Yzb7PN2x60tb7M5WAEFfzROmPuquqQpDRYf+vD1rR15VVXjl2j42DhZ5aneOoYJNOmbUdCgAssqj1uwBHC9A11R0TaUzMYvvsVDA/OQn+d//+2l0p7nt8nTfSm64+J38fvGpJC2NFZoqQzFLDiBqo/8+20fLdCctgEZg5tvPXcp/3beVMJStNan7kW1SFNnOBLAMHdsLeGJXVpLJonPA76TiBXQlLMYmOGTX3ba7kyZlN2TrSJnVvep+z/G5uB5mc111pKKs7I6xZaQy7fpWdscO2Q/ocGGuXKZbaOF4RYsAHceYS33AgrYoZy9t55n+ArYXYGmqDKqcBSx9XwtpRstrCos7oty/tYKuwP6SIDTADVVMXeOk+XF0VZUalKqH44Wz3ufpjt1lJ/U2fIDqI+Zr56cYK7sM5O3GhJEQsv3THjPYk63wq/VDLL9oekfelT1Jrrl0Fbc9vIvfbxtjrCQrP72pCG0xA1NTG8LrmKmxqCPGGYvbZ7ZPrgv/+Z/wr/8KY2NNF/XOdB+fvPAd/HLNS0FR5HEOBQO5KlVPeiF1xC0UIFORE3IlxycdNRqBmcmIjProipuMld1GbEVAPYtLVgwNTSEUKqMll22jZc5d3nnA7R/vkD2YbxZQ96Qi9KUsCrbP8q44oyV3v+f4XF0PM11PGAouWNXDjrEdTVEedegqXLi6hzAUx1xLqeUy3UILLQJ03GOu9AGqqvCqU+bxh53ZxhSYysyF0Es6ojh+yGjJRRHigMUjVYGFHVEKtk+u4mGZGr4ztTmjrsDLT+xhpOjwqlPmcdqiNk5b0MZQyTmkfV7Zk2TxS2MNt+S+tMXla/rQaxYBE3Uh9QmjTNlh63CZTMXFD2Wq/N5slY64wUtWdiGAQsXj8d0ZHF8wvy3C5Wv6WNmT5B9ecxI7RkvcuWGI3bkyQsDKrgRdKYulXXFsNyRh6SQjxoH3KQzhu9+Fj34UduxoeisfT/P/Lngrd5z7WoRpsVCRE3OGqlBwfHJVj6SlM78tSsTQZaaW4UiiUXZrjtJrMU2N29cPUHX9hlAbJPFRkMSnXgFKR3UipsZAzubyk3t564uXHPA7Gd+OPHNJGyVnn/A4YWlsGSlzxuJ2/vJlk+0HgEl6rPr1sDNT5g87slQ9n7aYie0H7BwroyCrTgc6Z2ZyXe3OVvj99gwL2mK0RxS2jdk4QYilqSzvjJC1BQ9ty7A7W2FJZ3y/x+FgcbD6nZbLdAsttAjQ8wJzpQ9Y2ZPkH197El+6ewu/e26Ugu3h+gcmMwvaIpw8P82OsTKqIv8oj5U9vHDq32yLGrzm1HnMT0d5ePsYthegK1LfoxMSoBCEAk1V0BVBgMaOsTJVN+TBraNsHCjyaHeWV6ztnTQaPhv85tmhcRlZIYam8t1H9nDlS5Y25WhtHCw0JowyZYcnd+eougGJiI4fKIyUHPIVj0/fuZmv3L+dsuPXxrn3TXT1pjbz7pctY35blC/dvYVnBwqN8FcFiBoqJ85L8Z6LV84stPLXv4YPfhCeeKLp5SAa43sXXsGNJ76aciSG5kAk8ElGdapeSCGof58C1/eoeiEL2iLELB2jZiewqDNOruIxVHJY1BGjPWZgezLAVtQIUFjbbrXWAlMAQ9MIQ4iZMltNVRV8P5w2hbx+817Vl2DzUJHnhsvMb5MVsaobsGWkTEfM5JSFabaMloibOqtr7ub7067sHKvUIjeKlGyfUAhMXSNp6SSj9YqTdUCty4Guq+2jZXJVGSViGRonzrea3k+rssW4fbR8WAjQoeh3Wi7TLbTQIkAtTMDKniT/909OY1emzKO1J+iYqbGhP8vvt+bIVxy8UNAZN3jx8i6uveQE8q7MRxotOvxy3QCZkouqgeeFbM+UKdsBKHDu0g5OW9LGn5y2iDHbo+h49KRMdo6VCcMQgULZDUgaaq3NJEXSrhcwVHBY1B7jpL4Uth8esk7hN88OcePtGynaXiOSoeoGbB4ucuPtGwEaJKg+NVZ2fLYOy3H/jppPz3DRwfYDdFWh5Hhye2vER1dptMv681VuvH0jli7H2+vLyOAFGefw9N481/9sQ9NnT8KTT8KHPgR33tn8uqax501/xvvXvI7NigxXtTQFgULFCyg5/qRqngAqbsCOsQqdCZNlXQmSEZ1AiKabn+MG+KGQwbNq8+/XLQ90Vf4bLrmc0JvkjEXtUxLMpZ1xrnzJUpZ0xppu3q4f4nghuzIVLF3F0jXmpSMg4EeP7226wa+Zl+S3G4en1K48smOM7aMVyo5PEIboqpRYFx2fkuNj+zqKAl0Jc060LkrDTXoqHL7EvUPV77RcpltooUWAWpgCqqqwtCvB0nHjzJy5eNrlGzWYPuhLRxo3NoWAUxe0s7In0dBObBkucsvvdzZufEEgZAaYokithKJQ8UJ0VYZWVlwZ1xGEgrLr88TuPCt7EqzqSTR0Cks74rNyZ/b9kFsf2EHR9ljcHm0YBiYjMu9pV7bK1x/cwYWrutF1tdGmeWTHGGNlh0RtOixTdrC9AMeTZoBC7CMEqiLJj6EpBCFYqnS0rrhBo32kqzInLRSCIJQVkdGiza0PbG98dgM7dsA//APcdtvkEb03vhH/Xz/BRx7Ms2u4yLyowTa3jEBpRH5M1cqsr90LBIWqz7LO2CQ7gTAU3L15BKMWbRKEUxtVKgr052064hbvOH8p9z43Mi3BvP5nG1jWJeM0xt+8+3NVLEPjNafMI2Zq/HLdANmK17TMur057twwSCpqcPqitibtSsxQ+faje3D8gFREp+opRE1dRp8oAoGC44VUHZ/BgsOZi9vYMlI+aK3L8q446ZhBoeIRSWmTJsbyFY+2qMHyrrmt/syFfqflMt1CCy0C1MIcY3/aiameWsuOTAovOz69SQtNUyhUPao1EzohIGbKaS9D0xgp2pQcn9MWtTEvHeHxXVk++auNjJbcGbcBHt+dZcdYmc642eSWDKCqKp1xk+2jZR7fneXFyzobk0HPDOTJVz0sQ8P2AgpVn6rnQ83TB2gYCYZCkjZDkyTEDcIGaQipV4dqn6kooIhGQOqW4VLjsxkbg3/7N/jiF6XYeTxe9jL41KfgnHN4fPsYO8b66YybJCydiCEJh0Kzjmu8rkuwz7YgFALHDyfd/PbmqvTnquiaQtTQcP2ay7doJkFhCMu6Yvz1Rau4cFU3V9366LQE89nBImXH5y1nL0TTtNp7Bqt75c37uaEiAshWvEk3+L6U4Ok9eXRt8o19sODg+HKfq26IaUgfJWlwqREKge2HKKoUnJec4JC0LgvbY5y7vJO7NgwxVnJIRo3GBFux6hEKOGd5Jwvb51ZDMxf6nZbLdAstHGUC9OUvf5kvf/nL7KgJOE8++WT+6Z/+iVe96lWAfBK5/vrrufnmm8lms5xzzjl88Ytf5OSTT26sw3EcrrvuOr797W9TrVa55JJL+NKXvsTChQuPxi4d05gLw7OZrGMq7UT9qXW0aJOwDAYLNjFDtjlevLSDezePUPVCzlrSjqLAk7vyDBZtwkCwpDNGwqoZFsZNMmWXrSMlFndE2TxUxPYCVvcmZ9wGGCu7eEFI1NQQQuD6YUP0DbKlUbYd/vex3fx6wxAL2iP8yWmLuPTEXp7tL7A3V8EPZEVKU2QOWtkNJ1VFAgF27fWJFRg5NSR/o6GlAcpuSNl1uO5/HuDT/fdx+m03o5ea86YyS1bQ/6F/5gcLTuGnvxrC/eUddERUhooeA7nmabqJOqzx2zG+hVX1Qr7/+F5WtSss6OmiVK3y2s/fy+6sje0FCJk8IitbyKBSTZVmlFUvIGmppKIGX7t/G9//wy62DOXpTERqhKqK60thc8SQJlNVz2cg77BwwnmSsDQe2DKKrqms6I5L92U/YP1AgaLtoyhgqAol26dQ9VAUpSGarrg+QoCiSEdrTVHwhSTRigqKEIShJHpBGOL4AW0xg0zZ5t7Nwyxsj7G8K87C9ti0+iVVVZrO/z89ezHDRYfNg0WKtl87qgqaqvKi+Unees7iOScRc6XfablMt/BChyLExHr6kcPPfvYzNE1j5cqVAHz961/nU5/6FE888QQnn3wyN910E//2b//GrbfeyurVq/nEJz7Bfffdx6ZNm0gm5cX513/91/zsZz/j1ltvpbOzk2uvvZZMJsNjjz3WeLo8EAqFAul0mnw+Typ18KLaYxlzYXh2KOvYnanwwe8/xc6xsvSRqYmc01GD0xe3o6uwcbDEoo4orh+yZbhEW9Sg5MqRbEvf9106fkDF8QGFbMXlglXdLGiPNp6GhRA8N1zilAVp/urCFZNuQI9sH+Pa7z2FqSk4fkjVC/GCsDHC7wVhY6y57m1kaCqpqE6m7M4o6uNgoYYBb1r3Gz5w/zfpKzVHhAwmOvj0y97OD9a+nFCd2bl9NGFqEIRMebw0BVb0JDhnWQcdcYtM2WXLcImxssNYyUFVFVZ0JSg7PpuHijjjVqICMVNjRU8Cxw8b2WGaCttHylI8r6qYhgoCCjWnca/WwouZGpqqsLAtiuMHjJZcIob0l0rHDM5d3smyrjh3PjPUpF/qSVos6YwByiRN0rMDBdbtzVNxA2KmxqkL2g6bmeDuTIXP3rWZtpgxpX6naHvkKh7vv2z1jKpaLSfoFo5nHMr9+6gSoKnQ0dHBpz71Kd75zncyf/58rrnmGj70oQ8BstrT29vLTTfdxNVXX00+n6e7u5tvfOMbvPnNbwagv7+fRYsW8ctf/pJXvOIVM/rM5zsBmtx60qm4+8wDZyICPdR1fP3B7XzqV5sQQj6hGqqCF4qG2PLcFR0gBFecvZiqF/DdR3dxUl+KJ3fnGS7ajbgLgLLjsztTwfYCaWCYitAet1jZk6AjbgL7vwn4fsibb36IDQMFGUtRy9sKa0Lf8VWS+m1ATHhtzi8aIbhk6yN8+J5bWTW2u+mtghXjS+dewS1n/hGOYU2zguMLCjA/bdGRsFjWFWf7aIWq62Pqai1MV5CreuSrsoqhIqtPwbjWWyqis7w7UUtsFxQqLnvzVVRFpT0mp96ihkau4mH7AULI1mPM1DB02e6x/YCYobO8K45QBIWKVxNLCyxdpTdlETU1chWX/pyNqiict6KDNX3ppvP/HecvIWroR4REhKHgy/dsZX1/vqlFCAcm/y208HzDody/Z5ZBcAQQBAHf+c53KJfLnHfeeWzfvp3BwUEuv/zyxjKWZXHhhRfy4IMy2+ixxx7D87ymZebPn8/atWsby7zQMVEwmYwYaKpCMmKwqidBpuxy5zNDhNOMrM/FOnw/5OdP9eMHgripYukqqqpg6SqpiI7jBzy5K4uhKsRMmbdl6RpVL2RFT5yoqZEpuzh+QNnx2ZurUvUCNE2RPjamzkjR5sndOTJlqZOJmppcfoo2gKoqLO6UbQ7XD6jWyI8QzeTHmObmMde3lNP3buR7t32Ir/7g403kx9V0bj77j3nZ1V/lP8990/OG/IAkMUlToWx7PLkrR8X1aY8ZuH5IXzpCbzJCoUZ+FEBT5fc2XvpTsH0yJYfBfJVsRTpaJy0dU1cbeqay7RKEYSO2w9BUDF0FIRp6oZgpTTWjhk530qLk+FRdn4iukLB0ma3mSUIEsHmohIJoOv9/vWGYBW1R1vSlWNQRO6zEo67f6YibPDdcomh7+GFI0fZ4brjU0u+00MIMcdRF0OvWreO8887Dtm0SiQQ/+tGPOOmkkxoEpre3eRy4t7eXnTt3AjA4OIhpmrS3t09aZnBwcNrPdBwHx3EaPxcKhbnanWMOcyGYPNR1PLYrw55sFUtXqXghMUVpVFEUFCKGylhZugd/5b5tVDyfwbzLtuES5yzv4LRFbWwZKjFUtKXY2Qtoj+oYhkYQChCC9phBtuKxebBIxFAYK3toKuhCbu/4Mn+h6oGA85d3sn5vnuGiPBfG8ze5fXXHnGbMVfVn+dge/v6+r/OqzQ81vR6i8KOTL+IzL3s7e9M9c/Rpxx62ZaQezPYCFrRHyVY8orXW1sbBQtNxFgA1gjq+ArcrW20sY2kKa+aliJkafiDYna1QsEP8UGBqknAbmoqCQtn10VWFiKHhh+D6IZah7Yv5UKDihri1XmjVC7AMDV0T5CoeA3mbBe2xxvn/3FCRP+zMkIoaR6SNNNf6nVYbrIUXIo46ATrhhBN48sknyeVy/OAHP+Ad73gH9957b+P9iTdcIcSk1ybiQMvceOONXH/99Ye24ccJ5kIweSjr2DJc5D/v3SZJhpBC2pIToCpyDFxVFMIwxAthoGBTqSV1u35Ioerx62eHWbsgRYjA9kJpmqgqKKpK2Q7Ilj0ihgwWrboB20bKjSqOArzhPx/kj06bz/x0tKFdcryA3dkqq3qSdKci5KoeqiKrQd64EtB0zeFDJUDdpQzX3H8bb376TvRJYaVncNNFV/Jsz/JD/JRjH3pNQO34IWXHZ2lXghXdcTriFuMTVaQDtWyB1dtg41EvY7uB4Jn+PCf0pbjpjadQcQOe3J3j9nUD9KYiDBelYWXZrXsiKeiqIm0Ial+2H4byi1fkRN++qT6BpqioKlRDQcXb51pe9QI2DBT4r3u3YhnaEQsUnSsX+FYgagsvVBx1AmSaZkMEfdZZZ/Hoo4/yH//xHw3dz+DgIPPmzWssPzw83KgK9fX14bou2Wy2qQo0PDzM+eefP+1nfuQjH+EDH/hA4+dCocCiRYvmdL+OFcyF4Vnc1LE0laFCFVPXMDWVZGSfd8h069gyXORzv36O9XvzAGgaUBPFhkL6zxgaeGFtJBvZVqtrOkAmjD+yPSt1G7U0eBSFou1jqAqmJpPJR2ynSSirq5IAFRyfbz+yi2VdcS5c3U3MjDJcsHl6T577t4zIfdM1TF1WpTx3341tukDYg9UAJZwKf/HID/mLR35EzHea3nu6byU3XnQVDy059OTw4wWdcQNT19iTq5KMGJy5uG3cyLw8l+oTcqauSvIThk25W6oip9Go2Q74IWwbKRHTNdYuaGNeOsoj2zPsGCvjB4JkVCdu6VRrnkxFxydqyDYXSFKGIifVNEVpvK4qiiRENfF+zJAi9EzZ5fGdWYq2T2fcoicVOaKBoofqAt8KRG3hhYxjRgNUhxACx3FYtmwZfX193HXXXY33XNfl3nvvbZCbM888E8MwmpYZGBhg/fr1+yVAlmWRSqWa/j1fUTc8G8jbjRgDIQSFqsdI0WbrSIkV3Yn9Gp5VXTkp88CWMR7eNsZD28Z4dEeWTNlt+Mas7GleRxgK7lg/yObBIglT+r/4tYdrQ933NK8q+xyRdVWOlKuKbFf0pSy02o3n7KXtvHh5B0Ztsi8V0VFUBdPQiFtaE/mpQ1Fkjlgo5OSMF4RkKy5RUyWiq9hegIIgYWl4NX1SHRNFz+OhKbPTARmBxzse+xn3/te7ed+D32kiP7vSvfzN6z7I//nzz7ygyA9ApGaG1Bk3Kdp+bYxcYu28VM3Jeh/JURWFiTIzrdZOVRRQa6TX8ULWDUjSPS8VwfFCshWP9picJowYksCrisAPBGEYYtTERQlLQ6tVmWKmiqnLf1FDw/ECqq4cnZ+XliP+W4aL5KoeSzpj9KUjs9bXHU3MhT6whRaOZxzVCtBHP/pRXvWqV7Fo0SKKxSLf+c53uOeee7jjjjtQFIVrrrmGG264gVWrVrFq1SpuuOEGYrEYb33rWwFIp9O8613v4tprr6Wzs5OOjg6uu+46TjnlFC699NKjuWvHDCYankUNlf6czUjJoVx7+l3R5bBttDTlk96W4SJff2gHKJCK6nh+iKrAcMEmU3boSUZY3BmbJLrcm6uybm+eQAjScQsUlVKmgqDRYUAIqOefShKzT4sBskKkKAqEIaqqkrSMRvlFqd0UK47fCOgcj4np3G4guPOZQZJRgzCUbThTV8nb0oDR8UNsP8TUoF4EmirhG+Rn17d/f1BEyGs23s/f3/c/LMk1a9Iy0RT/8ZK3cNtpr8TTJlfmXgjIVn3mtUVZ05dk3d48W0ZKrO5NSkM+P2RhR5TtoxX8EFQ/RFPBH5eXWzM9aHwPQuzLJts8VAKgP18lRBA1NIaLLm0xA1NXSVg62apG4IW4oaDoBJh6SL7ikbB0bF9g+4KS4xM1NSxDYawcoioKq3sThMBwvsrOsQrtMYOVE6axjodA0VYgagsvdBxVAjQ0NMTb3/52BgYGSKfTnHrqqdxxxx1cdtllAHzwgx+kWq3ynve8p2GEeOeddzY8gAA++9nPous6V1xxRcMI8dZbb52xB9ALAXXB5G0P7+LujcNUvYC4pbOkM0571GDzcJEv/GYL7714Jav79h3b8U+Ipy9qI1txGynoiiIo2gG9KXjH+UuayFMYyifjPdkqZccjZmjELZ2ooeH5gawEIf9pyCf3iKHih/s0FyA1GKEQjWqLFwpipkYxCMmUvVrL48BhrXV4gZDTZa6PG8inflG7YUZNTVYbVIOw6jWZCdZhavImW9cJ7a8Vdt7Op/jIPbdw6uCWptcrusVXzn49N5/zRkrWC/um0hY1OW1RG4Ymp7aWdcXZlak0vHTecPoi+vNVfv7UACXHww32HW8V0DWp3xHjCLFAIEJIRQy2DBf55kO72DFSltodL6Tsymy7mKmzqidBxZGGjJmyg6GptEUNLjmxt8kHKFN2MTSVk+enWNwhfYB2jJaxvZBkROeMxe10xCdP6B3rgaKtQNQWXug4qgToq1/96n7fVxSFf/mXf+Ff/uVfpl0mEonwhS98gS984QtzvHXPLyzvStARM1ncGWNBW5SqGzCYt9mRqeAFUjz88Z9v4B//6ERW96YIQ8EfdmZ4fFeGztof9464RftS2a5wAzkh4wchUWPfabRluMhtD+/id5tH2ZWVuotc1SdhaBiaiqUpVH1pOiiEbHkJRaHsSE8fbdyTqIoUpUZ0jfaYgYLUZLg108LZGhJWa3lTQkhRqxsIYqbOmUva5YSPqrBuT56BQhVThd5UDE+EUguC4LmRCm4QogZy8kzX5PZEdBW/5jK8dmwn1/z6q5y18dGmz/YVlR+f+Uq++LK30R9ta2rZaQpYhoZGSNGd3U7VCWRUh5I72XH6cGIiAdRqL6oKTWLymh8hINucXgCGrnL+ik7aYnKUe3FHjJSl1yI25My6EIKrXrKUf3zViXz38V3szdpYOnzl/p04foiugKKpDQIkQoEdQNTQWNUb55YHdrAnWyFiaiQsHZATXKaucmJfkkUdMYq2x96czWUn9dKdtJqcoN9y9uJpnaCLjse2kRI/fHwvfiCmHLw41gNFW4GoLbzQ0TqzXyDYm6uybbTMiu4EXhCyZaRE1Q1IRHSSEZ2y7rN1pMQX797K6140n40DRR7fleWZ/gLpqMGerMWKHjmhk4rKP5Z+GLJjtNx4QqyLnp/anUNVoC1qkKt4+H5IIRS14M+apkNRQIV0zKTiSX8fPaiNrAsh85RsD0NVSUYMkpZOpuwwUnKoTBE7MROoqsyz8sOQqifwfYGphSQiOumoSaEqJ4RMTWNBe5SzlrSTrXg8uTtHvurK33MDIoaKpmo1Z+EQFIVT/Bx/f/83edF9v0CZ0BvbfP5lfOHSq3jE6iZpaayxDEq2jxfKUtho2cHUVDrjFosMrRFIum2s0lhHPVm+Lvat54mdt6yT05e0I4Tgfx/fw2jRRWXfuDg0kxGYGyPHukN2fT2GprCoLYLthxRtH98JZIVPgbilo6sykb3e1lzSEUPTFJ4bLqGpCsNFpzZaHm2YbD4zUGCgYHPVS5byjvPlVFwYCp4dKHHP5lGcQKADuqLgh1LPoyoK5y1vZ0N/kUzZ5dQFaVxfMFIz0+xNSU+poaLDwvYogwWH0xa18cYzFk6antJ1VeaxTYDjB9z97EijyrlxoMiSjhgrexONStDxECjaCkRt4YWOFgE6zrBnrMBbv/IImYpPR0zntne/mIWdBxZxl10Z3Bn3NR7eMsj2rIsQcjKrO6aRiEaJGiq7MxU+/5vnSEd1hJBBnqEQDBVtCrbH/LYoiiIYKth4fq0KEsL20RJf+u0WHt2eAaTvStX1cINai2qKco2pQcJSAYHjySf5PdlKrUUlCVNbzKQrYfLbTSPkKg6V2o31YFB1Q8LQQVNVdEUhVMH1BZmySxjKzx4ru/SmIqzoTgAymLTi+gRBiOvLbYqZugzc9AK63RJX//Z7vOWRn2AGza2Cp5eu5Rd/9n62rTyFrSMl+iydshsQMzSWd0XZMVql5EgTRif0iJsRSm5Apuw2/GfqGJ8dVoci5MTTpuECbREdxwtqI92SZOqaIm9qNbGvVtNx+UFIwZl9rUiZ+P8KRHWVBW1R0lGD4aJDEAoSls6ijhh+KBgrSQNLL5BkT1MVYpZOT9IkX/VZOz/NWMlhoGDPKNlcVRU+/OoTyVWf5pn+Am4Q4tdEZaYu21RXvmQ5P3x8L/PSEVRVZWVPgpLjkym7JCI6MUu2dp7em2dh+2T92v5Qn5oaKzkkIzoru6Vn0XPDBXbnynREZaCvoSqs7Eke04aErUDUFl7oOOaiMI4GjpcojNOu/xW56uR+fFtU58l/3n/sx++eG+HGX25k02Bh2tZRzFRJR3Xy1aA2Xh7i1haOGCqOLyszE4dCDEXqBcpucFA5WZauYGgKpYO4KR8sFOTEjwC6EpbM/wpCbC9kQXuUM5e0o6sqD20bQwUG8jYV10dRZH6Z6dn8n/t+wNUPfo+kU2la946eJfzHpe/kF0vORNdl/8cPpVljzNIZqqWWB0ewX1WfhpvtR6rI73ZFV5w18+Vk1tN78hRsn1RE5+VrenjNi+aztCM+qV20I1Pm9nUD3P7MILvHKjhBiK4oxCyNRR0x3nz2Ys5b3sl//Pq5WedabRku8sun+rl/yxhFxyNpGbxsVSevOnU+fij4/G+eY3lXQo7IQyNrLFuRQbi2F3DZSb382blLZjzmXY+g+P22MfxQTpb5gax4ZcpOU6VNU2BpZ4yPvuYkLjmxd/qVHgMY7wPk+LLttbIn0QpEbeG4wKHcv1sVoOME05EfgFzV57TrfzUtCdoyXOT2dYM8NzQ9+QHpfFtxZZQEhkrc0rBCyNseBTuY9vc8AZ4z/fsHguMLnKlUx4cRAig6AYYq/WgWtsdQFYVnBwoMFx0e35llWXccPwixDLVhihdXBa974le8+65b6S2MNq1zJNXFT97wl3x+wflUQzkqJvVRgnzVI1P2GC3tC1M9LJli08AXkgTFTA3bCxoTbmptQxRkS01VFNxAEAp4w+nzufjEHpZ2xklHTapuwJ3PDNKXdknGfOKGjlbz7dF1lbOWdDRM+QYKNsu7EqyZl+L7j+9F1xQ64hFiprQc2Jut8pXfbcfxgoMS4q7sSfI3l6zmj8+cbAK4O1OZpG3piJucvbSdou2TrbhU3YCrXrKMJZ3xGR/DvbkqT+zOMly0ZaUrYlAOQ8bKTtPEoKnKgueOTIV/+PF6gGOaBM2VoWILLRxvaBGg4wB7xgrTkp86clWfPWOFSe2w+iRX/1hhkhZkf/DDEMdTsAx1zrOvjiV4ISRMOSKdrbhU3AA/CNmZqeD6YS0vLMQPAl6+9VE+eM+trBze2bSOohXnlpe9mdsvfhNKLI49XEIgfW7q+VFRI6Q4gSQe6dKrLyTh6YqbDBYl0Q2RfwTkVJ6CUtMPpaMG//Z/TiFSMySs2yFkyu6UOp2Xr+lh40CxyU14WVecezYNU7LlJGDJCSjYPqqiENEVsmWHnz/dz4J09KCEuNOZAE6nbVEUhWREZ7Bg86JFbSxqn90UXtHx2JWpEASCzoQM3d2Wtxvkp36daJqCoSi4vmC05HDL/du4cFW3rAYeozhUQ8UWWjge0SJAxwHe+pVHZrzcfR9q9j+qe31sGC7P+nPdUODawZR+OEeyenG48fCODD3JKB0Ji2REJ2bpDORs9uaqdMRN1ux6lk/dfjOn7VjX9HuupnPbWa/lv17yp1RS7SxLRRktObWMM0HM0KjrSk1d3Wd6dBQRCoGmqSQsSUjka1LP4wcCFzmZ9/bzljTIz0TDvIk6nSd25fj8b55jXjrK/LZ9bsIPbBllw0ABQ1MJRShDcGtmhvXIk92ZKiu7kwzk7TkT4h4ubUvJ9qm6QcMFvVD1GpEY9TWJ2k+qoqBr4AUhm4ZKPL47O6WguoUWWjh6aBGg4wCZysx8OKZaru71YbuzU3+oioKugDOFz87zifyAHMsOwhARhtihDNVc2G6hbdnCx37xTc554p6m5UMUfnHqxfznJVcy3NGH64ckTB1NU3EC6VUkR8EFoRdKcnGMuOnWNyMdNWRauhsyXl+dtHSufMlSrr38hMbvjDfMAyhUPdwgxKwRqYrrM1J0OH1RW6OKk4wYtMd03Foqe3vMbBAOTYGooVJxQ0qOz8qeOE/vLfDYzgzpqNQChUJQtH064ianLkyzebg4q9bMXIeFAiRqXlaOF5CwdLwwbJgwKtA0eQdSiA7gBiFjZXfWn9dCCy0cXrQI0HGAjpjeeFo/0HITUff6iJgqhVlUIEIhEKioU1Cd5qfd4x+6CpmKJ8mAEKTyY7znd7dxxeO3TworfWT12Xzq4ivZ1LuciKnRnYhw7rIOLj6xhyAUfO1329mbqxACYyWXsuc3fIeOBYwnD1FTR1UCVvTE8QPQVPj0G09j5bxmctAg0Z7GswNZshUXPwjRNZWYqTFWdjF1BW8iyatZZgeh1BSpTW8pjarTk7vzjJYcNg+VqLo+qqqQsCTZiZkaP3x870GFdM61tiUZMVjcGWNPtkKmRmjqeXH1s6Qe8QL7yKa0ODAP6jNbaKGFw4cWAToOcNu7X8xLP/W7GS03EXU9RKZQZriYm9HnKcg/2tKVefKNe+KT7vEOLwAlCIm7Fd7x0A941+9/RMyzm5bZu+xEMv/0ryQuv4zrbI+yG6AqCsu64iyqGeeFoWD9njz/+1iFbMVFQRC35CXm+gHlWVbh5hpS6Kw0MuGqbkBnwuSlK7rZOlrmlAVplvcmJv1e3JSVnMd2ZhriXyOi4wWCkaJDtuLRlTAxtWaNS1vUxFAVHF/g+gGgoalKLWtUUHV9BLA7U6bqhbRFDTriBtVaFsneXJXhosPZS9tZ3pVohHTuzVV49Snz6IiblBxfellZxpTkZi61LQvaopy+qB3HD/H9kExFOkT7ofxetZobtYIkvH7N8+iE3gRnLGqfk21ooYUW5g4tAnQcYGFniraovl8hdFtUn9IPaLweIrar0NBeTAd1XH8rZmr4oYpwA/za6FL9t4+NesbcQA183vLUHbzvgW/TVck3vbe3rY8H33kNd7/o5axd1M5f9aWmrSCoqsLla3v52dP9OH6IPEqyBeQdffkP6YiOrqnSqDAURHSVE3qTbB0t71cXUw8UzVU9FrdHG4ntlq7QHjMYKTq4fkDCao6fsT1ZJbL9gLzto6syf81QlYY/VMyUepoglMJiRVEQMcGW4RICQdzUGSw4LGyPkYwYuH7IIzsyPLw9g6YoVD3pIL64I8bpi9pnXB06GIy/lsZKLgs7YvQmLR7ZmcX2pD2EhsAX4PnSm6g3EeGqly4/pgXQLbTwQkWLAB0nePKfXzEjH6AwFE0l/+6YycbBApahsLg9wsbhyqTfr0OBRjm/5IZU3RBVlXqRUxem2DmcZ2v2eZQLJASv3vQAH7z36yzNDTS9lY0m+dJL38JPznktLztlET2WxlN7ctz33AgruhPMS0UYqI1nlxybf/zhBoZLLumIiggCVMD29+mnjmbFLGaodMQMAgEVTxpJRg2NjrjJYL7KQN5GAZ4bzPOxV5+IZRlN7aKBgo1lqLTHDLIVj0REx9BUvCCk7Nb0MIGgP2cTMTXM2nubhwoIIG5qKAicQFB1AxxFIR3VCVGZn45ScQMSNe2Q4wVUvQA/FKgKWLpKpuw2SNtTe/KUbJ+S49FW0ws5XsjuTAXHC+nPV7nqJUsPGwmaqC1KRg1OX9TG1pES2bIrBwYCgaEprOxN8IHLTjimR+BbaOGFjJYRIsePESLs3wl6vKGZ7QfszVbZMVqh5HgHZVBYR1/aouL4+/UCOt5w7q6n+cjdt/CiweeaXq/qJl85+4+5+Zw3ULTiWLpCV8JCBZwgZHVfkoiu4XjSH+i+zcON9PhjASpSy1MPnF3UHuHi1b2oKmwbLTNScsiVXcqOP60mLGaovHLtvIbepm4s2BY12DFaIVNxCcIQTVXpiMs215O7c5i6SsySBKjs+Di+oD2moygqYShQVJnXVXIDLF06f69dkOa5oSIRQyNXcal6Muet6vloqsrCtggoCi9e2sH20QrDRZuq65Ov+izrihO3dISQbt7dSatG1tv4qwtXHFYfm4kPGr0Ji8d2ZfnDzgwAZy5t58VLOluVnxZaOMxoGSG+gLCwMzVp1B32WfRnyi7z0hG2jjhs6M/PyvtnOgzmnUNfyTGCE0Z28OF7buHibY81ve4rKt990eX8x/lvYTi5b1w5DEXtZh5iaCoasGmwQN72KVb9Y6oVWO9ehkJqfToTJmcubufuzcMArOlL4vmylVXdz4lR8UJ+8+wQVS+gP1/llWv7pJDe0DirZiZYnwLzwpBHt2cwdZXelIUXCEqOT7biETd1TpqfJh01Gy7MPiEJS0dVoCcZIWFpBLVx91AILF1FU1QcT7bFxkoebXHZ+spWXCxNJeOFmLqKru7z90lEdLIVj4XtUbYMl9ibqx5WX5uptEXnrezivJVdh+0zW2ihhblFiwA9DzDRpyUMBU/uyh1S1ef5hvmFYT7wu2/xhvW/nTTZdsfq8/jUBX/Ots5FkwiNF8oU+Xpb56GtY/jhkU1dnwkUpAjXl0HqKLVppD/szGJo0szyqT05yo6PPQNWnLd9kpbCzrEy920eYVlXjA0DRVb1JEjVRugLtsfTu3OMlV1WdSc4a2k7JSdgqGjzzN48mqowWnJZ2hlvuDC7QYiqwGjRoTcVZSBfqUWQBKQiusz7UmSGmSpC3CAkCGVLyQ8k8XH9kLaYIb2VajA0lZLjo6kKFdef5Bx9ODGxGtRyUW6hheMDLQL0PMB4nxZFUdg6WqTs+qgKk3K7XmhI2SXe+9D3uPKxn2EFXtN7jy44kRsveiePLzxxv+twAtn+qkM7Bo2QBJL8gPzO3UDQP65ypylT5tHuFz99aoDedJSdYxXOXdaBpsr09qih0p+zGchVGSrJJPuqF5Creo009G1mGVWhod9JRQ1SUanzKdoeUVPn0pN6+N/H9mB7IYamYHshmqYQ1OJH/KA+MSYF1QLIVz0MTW2YEdbhBSG6qhKEYlrn6MOBiW3n2Y7qt9BCC0cPLQL0PEDdp6Wep1RyAkTN3feFCstzeMfjP+e9D32PtNPsgr2lYyE3XXQld6085wVzkA4mf8wXMih2rOQyULBpi5lEDJUnduWoegGGphIzNLqSchz9yd05TlvURnvMpD1mMlywURSBO448jnd3Pn9FFxU34Nn+Am4QyAqRL8lQW9QgHTUIQ8FY2WWkJI0MNUWhK25SdgOEkGPmQghKtk930qJo+5y6sG1WztEHi4lt57oD9vr+/GEXY7fQQguHjhYBeh6gbnZYz1NKWNoL5b4+CWoY8MfP3MO1v/sG84vNYaWDiQ4++9K38f1TLiVQm0e2Ffa5+R4Ix/PYwGw2PaLJikrE0FjZnWCwYJOr+CzqiLGwPYrrh6zbmydq6piaQqbssnWkzFlLTFb2JMiUHYp2ILPUwnDKKIoT56U4eX4KQ1PRdZm7Zmoqlq6RjOgUbY/+vM1bXrwYTVW4fd0gu7MVbN9mrORgGVKQrusquqrSmbAOKuZitjhQPMhzwyXufGaI5V2JVjushRaOUbQI0HEO1w14eMcozw7kGSvZjJSOLWHuEYMQXLTtD3z43ltZMzIhrNSM8eVz38TXznodthGZ+teZOTl4oRzflV1RSrZPTypCKmpQdn2e6S9w3vJOupMRhBDszdkMF6okLB1NVRjM2xSqHu0xg55khJ4U+EHIjtHylFEUC9qirOxJsr4/z6p0YlIW2GDB4UUL27hgVTeqqjAvHeFX64d4YneWXZkKRdsnZmosao9yxuL2g465mC0mtp3HQ1Hkdh4JMXYLLbRw8GgRoOMY33hoB5+5axPZGWaFPV9x6sBmPnL3LZy3e0JYqarzjTNew/877wqysfR+16Er+zQ0LchqWNGFmKWzolsSE01V8IKwUdFQFIXupMW20RJDRacWdCp4eHuGroTF4s4Y7zh/CVFDn1YgPNvg0vHxFkXbO6AT9OESKE9sO09E1NQYqvlEtdBCC8cmWgToOMU3HtrBv/3iWeypotpfIFiS7efv7/0fXrvp/knv/eiki/j0y/6MPW19M1qXZaiE9WDQA0BVQRyDk2AzwUxz3DQVKp7PyQvSdNRyrOQ0lvT0ASlw3j5axtJVFAxsz5ej62UXU1d5+ZoeVvce2JdjtsGlM423OJwC5Ylt54mousERFWO30EILs0fr6jwO4boBN9+z5bgmP7oC6ZjBorTFusESwSx2pauc5e8e+A5veeoOjLDZzO++padz00VX8kzvihmvT0WOUTtKOKOpOUtT8BQIZzBWdawNjBmqDEGtuD7100dX5eSYpkLcUGuvK2iKwvbRMumoQXvMoGj7LO2KU7B9esOQLcMyvLQvJduKQwWb9pjJixalGSq4bBoscvEJPTNOb5/L4NLDLVCuZ+yt78+TsPRJrbu60PtIiLFbaKGFg0OLAB2HuHPjIIPF49ucsCNm0NceIxQCQ5WmdwdC3Knw7kd/zF888kMSE8JK1/cs598vuor7l50+621RFWir3eAPuCzg1CIuDJWG0aSiyPeE2BcWa2hyv4SYncbocCJWu1mHAgwN+lIR8raPpiis7ElgewF7c1UcPyQZ0ak4PhsG8nQnLDoTFn+ypoffbhzm6b15hos2CUvHDUJKtVH3UxamaYtZaKo6aw3MXAWXjhcor+yOU3ICshUXU1NZ2R1ny0j5kAXKs23dtdBCC8ceWgToOMRg3jkm/H1UQFEBMXuPmYipMVZy8EOBqWskoyrZkksgJhMFPfD506d+xTUP3DYprHR3uof/+7K389OTLkQoBxc7YBoqthfIpPRQoNX8kybuk6bAss4YXgh9KYtFHTF2Zco8sSuHN64lZqhw0vw0XhDy3FARr7aeo+3LpACOH6CgoGvScLDuCq0pCgXbIxUxiFs6cbN+DEKGCw5nLengT85ayMqeJEs6Y3zzoV1sGSqhINA1jZ5UhBXd8YYP0NHUwNQFylFD5bGdOTIVFz+UPkEdMZO+tDUnAuXZtu5aaKGFYwstAnQcoi9toR6Esd1cwtTkyG9Qi4pQFWYVu5GOGXi1m3FVE/SkY2gioOoJyl6IH4IqBK/e/ADX3TM5rDQTTfL/zvtTvnX6q3H0fRoMo+aALBQIAphJTNcp8xLELJOC7bFtpEzFCzAQBAKCUB5nQ4MT+lK8+axF3PXsEN1xg12ZCmMlF0tXiRCComHqKilLY+2CNAJBruKSKXt0xg0sXSVb8chOEWhraRA1VNwAYqbKyQvS7BytMFZ26YzrFKqyZWVoCis6LbZnXRw/pDNuYOo6K7ribBwqsCdTlVEYmkLUkNujiJC8EzK/Lcr8tgh7s1U8X1Dx5Ii6EAI3EOzNVinHA9qiJqcuSOMEASXbJ1f1eN2p8xs39JU9Sd750qUMFKrEDI22mDnJmPBIaGCmEziXXZ/RksNY2cHxwlp4q44XhAwXbfK2S2fcmhNyNtetuxZaaOHIoUWAjkNcvqaPvqTF7qOY0eUGMFb2DrzgNFi3t9j0846s2/TzObvW8ZF7buG0gc1Nr1d1k6+e/Xr+65w3UrTik9brHUSv6eGdhaafp9Lt+CEUbZ+fPNnPM/15KlOyvQCcgGzZI7U3zxmL29Fq01MdCZNsWWaKTbd+JwBTkyRorOSi1FpCoYCL18xrOCkXqh5j9ggAF6zqJhU1yJQdUgUTy3CpugFeKDAFmLqGrhloesCpC9IMFeS01rw2i1zFoz9XJagFp1bcAFX1WNEVZ/tomUzFper5hCH85Kl+LFNtkKCF7TFOXdDG+v78JPJzJDQw+xM4xwyN0ZJDxZEj/PVts3QNM64yVLBBQMzQDvApM8Ncte5aaKGFI4sWAToOYZoaf3nRSj7+8w24z7PAr9UjO/jQvV/nkq2PNr0eKCrfO+VSPvfStzKUPLyBk+OPqEotRgLYNVZhb7ZywEpXCKzbm8fQFHRNQ1U8PF+Qq3rYfjglPwsEeIEgYam4QchoyaXiBixsj+KHYZObcv2/FaTLcqbs8OTuHFU3YEF7lELVI1vxZIwEcOGqLlRFYetoibGyQyKiY3syFFVVBIYql4voKrYb8MSuHOmYFD57vkpbymBXpswtD+xoiIcPhwZmpiPrBxI4X35yb+3oTPfZ8r3n15XTQgstzBYtAnSc4m3nLOHeTSPcu3l4ThLfjzbmFUb4wO++xRvX/2ZSWOmdq87lpgvewdauRYd1G6ab2FJUUGrttHCGxzoU8NTuHJes6cHUFYYKVapu0HCRnmocPQgFQU2r0h4zKTkVRksOHTETU9unb6r/t0DB0BQ2D5aougEdcRNFUbB0jaihc/KCFPmqR3cywmUn9/C5Xz9Hvuph6VJ/5XgBKCqaJlt9ti/wQ4Ef+hiaKqMuLJ2T5qVpjxmT3I3nUgMz05H1mTgw371xmM64gVLLIpMtMBlmW7Klb1Bn3KTqzaRB2kILLTxf0SJAxyn25qqkogZve/ESto4WeXJ3HhAUneOLDaXsEu/5/f9y5WM/I+I3t8Eem7+GGy++ij8sPPmIbMtU5CdEtvtmCwXZ1lrZm+RPzl7M9T99hmy12kR8JtYnBOD4Ie1xGWcS0VVyFY/upEXC2teuSVgamirLO0IIMhV5k2/kYjk+6ZgkBJausmW4yOtOm89bXryYHaMVclWXvO2hKkrD2yduKpTdAL+m0s5XPXqSFqctamv4AE10Nw5rwaMXr+nm7GXt+zUk3B9mM7I+EwfmvbkqMVOnO2kxkHfIVlxKjo+uqvSkIvSlLEBpefS00MILHK2/AMcp6k6089uiLO5MMFBwaI+ZZCsu20bKjdHrYxWW7/Lnj/2c9/7+e7TZpab3tnYs4JMXvoNfrTrvuA0rNTTwAnD9kEtO7GX7SJmb7tjYIBgwDeESEDFUdmerlN2QQAj6czYPbh3jhL4kEUNjIG+zulcSgq0jZaqeT9yycPyATNnF8UJCIXhkewZVVVCAZwcLXLqml1ev7eOezcOEocALBa4fEjVUQMHxQwxdIWFqVP0QXVNoj+0TmI+f7NpfxWa2ba/ZZGrNxIFZUxR6UhGGijZnLWmj5AS4gcwYS1gaW0bKLY+eFlpooUWAjleMd6I1NRkE6dXUrMcy+VHDgNdvuIdrf/dNFhRGmt4birfzuZe+je+detmksNLjCQqSyCjAvDZpEnjqojRxSyM/xQTYeIRCMJCzURRIRjR01aAjbjKQtxkpOazuTTYyrwC+9+hudo7JaTRFkRUkS1dJR00MTaHs+OQqHr94aoDlXXFeeUofz42U2JWpYHshUUMjEOD6AaauoqkKqqpiaQplRya018XX9cmu0aLD7esH58RkcLaZWjNxYI4YGpee1MPt6wfZMlJmXjpCW8yg6gZsGSm3PHpaaKEFoEWAjluMd6Jd2R2nI2YyXLSx9H0KmmPKhVgILtz+OB++5xZOHNnR9FbJjPLlc97E1876P1TNqcNKjycIZPvLUKFQcdkyXOTUeekDpsirgKEIVE1haUeUshPQm4qwsidOf96mP2fTnbB490uWYZqSIH7wFWsAhWf6c3KKC+iIm3iBoOqGlB2fJR0xHD/kzmeG+KsLV/Dei1ewe6zChoECqiLQVI24pdMeM8iWXbIVj7aojqLsE1zXJ7vWzk/z5K7cIaWgjxc7D+Ztqp7P/Blmas3Ugfn8FV301YJTWx49LbTQwlRoEaDjFOOncLaMlOlLW+Rtl8H8PofkY4X8nDLwHB+55xbO3/V00+uuqvPN01/NF85/8wHDSo83qAqcND/FxqEyww/sYGF7VFboDgChaCgC8lUfU9fIVFx+ub6E7UkB9Z5sFT8UXH3hclb2JNF1lSvOXsgXfmvz6PYMUVOlP29TcaXHj6GptMcC5pn7nJlX96a45tLVfPRH6wiEoD1mEDN1/DBEVRTMGokWtf0o2l5jsuvURWl+9Pjeg05Bn9g6C0LB7kyVqKGxqGOyrcFEP6HZTJ+1PHpaaKGF/aFFgI4DjH9ijhkaAqh6AZaucvrCNu54ZrDhbVJxDt6bZ66xODvA39/3P/zRxt9Neu/HJ13Ip1/2dnbPMKz0UKCrMDE2TQGihoLji8NiKGlqKglTxfc87t2UlTqUaTyA6tsD4AcBCipRQ6PqhWQrLkHg4ws5geaH8Mun9/BMf543nLGQPz9nCSt7krzmlHk8tSvHUN4hEAJTU2mLGSQjOiXHZ9NQkc64RdH22J2p0JUyOW9FJ0/uzuH7AQNVF4RCe9zglAUpnhsuo2sqYyWHiKE3KiduEJKpOJIkCSZ5AO3PAXoqsXPZ8dk+UubRHVlipk5nwmosP52f0Gymz1oePS200MJ0OKoE6MYbb+SHP/whGzduJBqNcv7553PTTTdxwgknNJYRQnD99ddz8803k81mOeecc/jiF7/IySfvmwxyHIfrrruOb3/721SrVS655BK+9KUvsXDhwqOxW3OK8U/MoyWH0ZIDKCiKFMdWHJ9QSMHJsTLV21nO8bcPfoe3PXn7pLDS3y05jX+/6Eqe6Vt5RLalI6JQ9sUkAiSQvju6qqAImddlaDCDOLAZwfZDHtiWm/Hyas3BOhkxiJkaUVNjuOhQdf1JNgdlDzYMFNl8+7N848EdvPuC5SzpjFF2fXwhqz6KIsXPpqaSikhSYnshP3p8L2NlF9uXhMwLQjIlGRUhkNNfA3mHk+en+IsLltOdtBqVk22jJX78+F62DpfZMVohYmi0x0xW9iQaYanZiosfikkmg9OJnVNRgxcva+fezaM8siPDS1Z0ErP0A/oJ7a+6M1M/oRZaaOGFjaNKgO69917e+973cvbZZ+P7Ph/72Me4/PLL2bBhA/G4LId/8pOf5DOf+Qy33norq1ev5hOf+ASXXXYZmzZtIpmUT3rXXHMNP/vZz/jOd75DZ2cn1157La997Wt57LHH0LTjV0w7/ok5aqiMlaW7bcULyFY8glrpQlUmVziOBqKuzbsf/RFXP/yDSWGlz/Qs598vupLfLTvjiG5Txp6+vOOFYGiCVESnYPs4wdHTTQUCVBXiloaqauQrLkXb26/Hkx/CQMHmpjs2sawziu0FGKpKwtQJgbIb4AY2vUkLLxDkqy6bhgqs7EkSM6NsHMwzVLDxAkHEUGUWmpB5YTvGKrh+yJq+FLDvXBwrufQkLXIVl4iuMlK0GS05xE2NqheQr3p0xC1++mQ/rzylb0bj652JCGcvbWfjYJH+nI2uKTPS60xV3Zmpn1ALLbTQgiLEgaSZRw4jIyP09PRw7733csEFFyCEYP78+VxzzTV86EMfAmS1p7e3l5tuuomrr76afD5Pd3c33/jGN3jzm98MQH9/P4sWLeKXv/wlr3jFKw74uYVCgXQ6TT6fJ5VKHdZ9nCl8P+STv9rIhoECK7rjbB4qMVJ0aI8ZbBwsUHKmdhQ+GtADnyvW3cU1999GTznb9N6eVA//94K385NDCCs93IhqIBQFPxD4YmqTwiMBTYHzlnfgBoL1e7JUZlCN6oio5B05st4dN3ECUQuYVVGR8RaapqDXKiOXn9xHdzJCGIb8+Ml+MmUXXYWoqdOVsNBVaa64K1vlhN4kX3vH2aiqwpfv2cr6/jyrehJkK27DeVpToT9voyoK6YhBKmqwujdB1QvpiJuNibCNgwU+/5vnWN6VkB5GE+CHIdtHylxx9iL60pGDqtxMbrHpVFy/UUmazXRaCy20cHzgUO7fx5QGKJ+XSd8dHR0AbN++ncHBQS6//PLGMpZlceGFF/Lggw9y9dVX89hjj+F5XtMy8+fPZ+3atTz44INTEiDHcXCcfTlahUJh0jJHE1uGi3zv0T3cvn4QTYWBnE3e9uhKmJScgKp3jJAfIXjF5of44H1fZ0Vmb9Nb2UiS/3f+m/nG6a/B1SePKx9LcAKImjLW3q+V0uKGQsk78kdZ11SEInBm2Ior+zK93g8EoYDOhEXJlgnvQghJIAToikI0ZmDpsiI6kLfJVz3pm6MqjXagVWtddcZNto+WeXx3lnnpaFP1piMuDRK3DJd4briEH8g4ja6kxcnz03TETYQQTRNhMx1fX9GdOCjNzmz9hFpooYUWjhkCJITgAx/4AC996UtZu3YtAIODgwD09vY2Ldvb28vOnTsby5imSXt7+6Rl6r8/ETfeeCPXX3/9XO/CnKD+FLtzrIymKnQmTEq2T9UNGCu5RAz1mGh3nb17PR+55xbO6N/U9Lqtm3ztrNfxn+e8iUIkcZS2bnYIkf47okYrBdSqVUdGVDW+LparuJy+uIN1e3MUqgf+/DAU0itSgKGr+EHIvHQELxAEQqZhZcoOVS9kccIiGZGXfMWTE1iGqqAoCp6Qpot1RE2NTNllrOySihoN88EwFPTnq2TKLqqqENVV2mNRvECwpi/ZcI2eOBE20/H1gzUnnK2fUAsttNDCMUOA/uZv/oann36a+++/f9J7E/+gCSEmvTYR+1vmIx/5CB/4wAcaPxcKBRYtOrw5UzPB+KfYld0JRksuQQhRQyNiqFTdgIJ9dKe8lYJ6MQAARaZJREFUVo3s5IP3fZ3LtjzS9HqgqPzvKZfy2SMQVno44E4IKQ3FEWSZijSv1FWFFd1JUlGD+akohWrpgL+qa4rUgimwuD1GzvbJVjwSEVlxKTs+biBbXPPb9pGDmFGr/IQCTZXXmDbueqm6AYam0hk3G9WbTYMF1u/Nkym7DUdrAaQiOu1xs1FdqmP8RNjhCE8dj5k4RE83ndZCCy28MHFMEKC//du/5ac//Sn33Xdf0+RWX58ckR4cHGTevHmN14eHhxtVob6+PlzXJZvNNlWBhoeHOf/886f8PMuysCxryveOJsY/xSYsnfaYyUjRpj1moKsqhcBvCJ+PNPoKo7z//m/xpvW/QZtADu5a+WI+ecE7eK57yVHZtkOFQrN7tqpA3NBwPJ/DTYPUGvlRFOhKmMyvOUdfvLqLjUMHJkBJU2Os4mPqKqcuTFFwQrYMl2T+le1TcQNO6E2ysjvOYNFpPBjMS0dIRw0yZRdDhUTUxNRlLSoMQ8bKLif0JjljUTuqqiAQ3L9lDNcP0FQFy1AJQ4HtC/JVH4EyyedooofPXIanTsRMWmzjt6WFFlpo4aj+NRBC8Ld/+7f86Ec/4p577mHZsmVN7y9btoy+vj7uuusuTj/9dABc1+Xee+/lpptuAuDMM8/EMAzuuusurrjiCgAGBgZYv349n/zkJ4/sDh0ixj/FKorCyp4EJceX+U5BSBCKw35DnoiUXeKvf/99rnrsp5PCSh+ffwI3XnQVjy5ae4S3am5h6dIPSAC6IltJbhAS0RUq/uEhnCqS/NSjwUxN5fTF7aiqJCGGodMRM8hUpq/4KUDeDoiaOmv6EmwbqzIvHeH0xWlGig4DeZvOhMV7L16Bqijc8sCOpurLCX0JHtqawQkEnbpCIARVx5dtr4jBO85fiq6r+H7IztEyXiCNEnVNkW07VUFTpI+S7flsHS41Eun35+FzOMwJD3eLrYUWWnj+4agSoPe+973cdttt/OQnPyGZTDY0O+l0mmhUkoBrrrmGG264gVWrVrFq1SpuuOEGYrEYb33rWxvLvutd7+Laa6+ls7OTjo4OrrvuOk455RQuvfTSo7l7s8bEp9iOuMlpi9r4w84MZbt6RMeSTN/j7Y//nL956Lu0Twgr3da+gJsufAe/Wn3sh5WOJxlTQVOkH5CmShISt3Qqjk/BObxUU+qO9m3D/LYIval9MSBF26cnFSEIBflpzIlUBbqSJn914UrOW9HZqKw4vqx2nL+iq6myMrH6koqYvPLkXnaMVRguOuzNVjE0lRN6k7zj/KVccqKssj6+O8tAXsasKAoEYW1aToGIoeEGAX4g2JmpsLI3gaGp+21rHQ5zwsPdYmuhhRaefziqBOjLX/4yABdddFHT67fccgtXXnklAB/84AepVqu85z3vaRgh3nnnnQ0PIIDPfvaz6LrOFVdc0TBCvPXWW487D6B5qQhdCYsNA3lWdidIRQ3aYwaGKp+6DQ0MTaXiBg0h9FxzIkWEvP4ZGVa6sDDc9N5wvJ3PvfStfO+Uy/C1o3fqaEgCoYz7/3r1JqiPsSuS0KhKyH6KKLWbujQNVBVpBOgewTajgiQSY2WXp/bkOH95JyUnYLhoU/UCVvUkGClUcQJBxNBIWjq9KZOqLyi7Pv/6+rWcukC2fg9UWZmu+hKGgsd3Zxkru3TGTc5Y1I6u75Nmj5Vd3CDE0BSihkpYaxcqgKYquIFGruJiewE7xsp0JyJHJXPrcLbYWmihhecfjikfoKOFY8EHqG7g9sTuLJsHi/ihYF46woL2KI9uzzBaclAUMDUNxw8IBXMb4SAEF2x/nA/deysnD29veqtkRPivc97IV89+PZVpRKZHGpoC7XGz4WbseKE0hBT7SIWK/Hl/UICIoUr9ijjw8nMNXZUidzcQRA2NroRZc6kOyJQ9dFWlO2lx7vLOxoQVyHyuXMXj/ZetPuxTTY9sH+N9336CshsQq43Nj4fjh9hewEnzUrzn4pWs6E4cVffllhN0Cy28cPC88QF6oWK8gdvijhg9SYtNg0UG8jY7M2WKVR9NUzFUhbgps8DK7tyNaK8d3MKH77mFl+58qul1T9X41mmv4gvn/ylj8bY5+7y5QCAgX3GZ3xbB8QXDvtNMXgRMbBxN5fIsAKdmt3yknwRUZAUlZmoEjk/J8YibKhFTx9A0TC3ACUKECJu27khrWs5Y1M6K7gRP7MlheyExU21obIQQVByfmKnzslXdXLCq+6iTjVb+VwsttDATtAjQUcZUBm7JiEFXwiJfcbnr2SF0XWVBOkKu6uMEMutpLjxqFuUG+fv7/ofXPXvfpPd+tuZlfOqC/9/efcfZVdeJ/3+ddvvMZHpJJpk0EiAhktBCCxoJRAER14BoDKB+dUHXLPhVXL/7ALdIcVd3LcCyS1tdBNcAPxVBopCAREqapPcySWYymXZ7OeXz++PMvZmZTE2mz+f5eAxk7j3n3M+5Z+6c93zK+/15DhdWdrHnyGA50BjLML00RDiZIWmeXJqtqW7x0Pa6C3Ac3B6lnuYKDQZNBQXFfd22YqczykJMK80jYzsk0ja7j0dojGfYXhfh4qnFpMyhn9Oi6yq3Xz6Vw7/ZzoloCttx8Hs0bEeQyNioqsKciQUsnVsx7MGPJElSX8kAaJh1l8BNURRU9WRV8LTlUJ7npSWRobWnSS19UJQIu8VKN72Cx+nYT7Ju8nk8eNVtfFB51hm9xmBR6NiTk8g4bi0qr4GiWCQybsX1zsHPSKQoCkrbMnjLcdwhTl0j39+2jDsEIZ/O9mNhGiJpdtRFKAx4hmVOS3ZC9CNv7GVvQ4xI0kJR3Grwl88o5c4PT5dzbCRJGlVkADTMekrglrEdUBRCXg1DV0maNsUht8xA7DSGwPyZFF9Y/xJfeXcVoUyyw3M7Smt48KrbWTt1/ohd2ZWb29MucaDpCLe3LGni2A62LtAUt6SDaVnEMr136+iKO7ncdhwyQxg4OUJgKG0lOBx30rbPUIkkTfJ8elvZCQ8XTS1kU20rl80o4bxJBadMUh4qi88u54rpJby6o47d9THy/DpXzyqnplSWl5AkafSRAdAw6ymBm0dzb3KetpUsx8NpmhMZkv2shaE5Nss+WM3KP/0P5Z2KlR7NL+VfrljOS+deNWKLlWZle39oW4JNWzCU7zeIpixMx10ppavusJLP0IlnzF7n9ngNFdsRKKqCega5lvpbSd5yQLFsUqadC+i2H4tg6HEKAx5mlLmlRLYfC9MQTfOnvY1sPxbh/QMtw1LdvKtK65YF18xB9v5IkjTqyABomPWUwC3k1VCBVNsqm8oJPnweld310b4dXAiu2fNnvrn21GKlrb4QP154Mz+f/3HSuqebA4ws2TkzlhBogOUIvLrKtJIg8bTFsXCSfMOgwK8TS9lkbBtDg+46yxTAo4EtBKYt2laEQdLsWyDTPuDRFVDaior2+XyUk/mAVMUtyFoY9GA5cCKaojHmZm6Opi0mFfo5tzKfpGmz9ViYY+HkKdXNLcvpcTn7mTi10rqfRMbqti2SJEkjnQyAhllPCdz+cqSVloRJPGPx+s4GDFUhnulbJfgFR7bz7Tee5IJjOzs8ntI9PLXgBh69ZPQUK81SFcXN+9OWAkBVYFJhAE1VUBX3qzVpkrEcNFVBU1XyfQYpyyFjurWtPIZC0nSwbdBUge2AZYrce2qbYOggbFBUsOy2SdUKaJpbWd1BIIRbabwoYBBOmsTSFrYQ+HR3VZehKJyIm132JhUGdEJenYwtSGZs1LaEggJoiWfI87tB3L4TCUzHoabIzzmV+eiaSp6mdlnd/I87jvP02wc52ORmbDY0lZriILdddjKh4emSldYlSRqLZAA0AkwrCXHtnAr+uOM4R1uTCEewrc4d9lCAPK9GxlJImnavwc/0xlq+tfZplux9t8PjtqKwas5ifnD556jPH75ipSr0e4hJz/b8tFU+1zW3cGeez6Ai30dtSxLLEUwtDdIQTpM0LRwUMrYbWEwuDqC3ba+2rQ470BinIZrGESI3pyj73gqhoGng1TV8ARVNUQj5DWqKAswsC3KgKUlTLM3yS6dw7TmVOI7gtZ311IfTVBR4WTK7gl+sP8zDr+4ibbmBl666E7YFEEvbTCkKUhjwsPdEjMKAjqqqhJMmBX4PCdMmZVpYjoNXUzmrPJ+i4MnadZ2rm+8+HuWBV3YSTZkUBz25AHp3g/s4cEZBkKy0LknSWCQDoGHWfl5F0rTYdjTM0dZUh0CnKdH7hOeyaBN/+6f/YdmW1Widclv+cdoFPHTVbewurRnYxp8Gj64wozRI2hLUNifcBIC6gqMoGCqUF/gxFGiKm6RME79Hp6IgAG0lGHRNJc+rc8GUQuZPKaQo6OGlTcfQVYWzyvNoTmTYfixCSzyDIxxMG0qCXv7uY2cT9OrEMxb14STf+OUHOG1RT/ssOwrkaq4JLAzNoDDPi2U71EXSlOX7EMCiWWV8bE5VrsfjuvMm5s4xk7F58q0DmLbDBL+Bpqo4QmDaFuCQtgTbjoWpyPfRkjRxhKAw4EFTFWZX5uHVNRqiKbYcDWNoKgHvqR/TbHXzcCrD028fJJoymVzox7QFGcvBo6lMLvRzuCXJM+sOsmhmaZfDYX1JGigrrUuSNBbJAGgYdZ5XsetAhCOtqX4dIy8d58vvruIL77+Ev1Ox0s2VZ/HAVbfz7uS5A9nsM2I5gtakxdXnVCCEYH9jnCXnlhP06NQ2xXn3YAv7TsQQCkwry6O6KISqwPHWGPXxDHkBD6V5BlfPLkPR3RVTJ6IpSoMG7+xrpDGRIWBozJuYR8DnwbQFlu3mrQH3hr9qw1GiKRNDVRC4q7EccTJztBDukJdP10maDuFkhqoCH40xk/cONnPBlMIOOXg6BxGvbK3jaDiJitvboyg2qqJgCwch3MrqpgORlIUjBJGkW0bCb+jEUhYZ3UFvl2gwOxm+vWx184ONcQ42xQl5NerCaZKm3darpeA3NEJejQONcTbWtnDR1OIOx+hqUvP00tApE6xlpXVJksYi+RtrmHSeV2FZFjvr+ji5GbdY6ec2/Y6vrXuOwlTH/Q4UVvHwlZ/nlVmXjbgl7bbj1pZKZSxaUxaXTCvm1oumsL8xxpGWJA2RNKbt4Dc0GmMZWpOtNETSJzNfN6f44EiEVRvrqMz3ck5VPusPthBNWR2G1rYei1Lg05k3qYAT0Qx//9IWwgmTjO1Q25wkYws0BTy6StoSbnXzdrOanbZVWbbj0BTPuIGIrqEimFYaYk9DjOa2qul/3NGQCyIylsO2o2FsBzTNDa6EA45w2kqXnOyda1/kVE07xAybNbsasIU7zGdaDn6PTsaygZOBRzYT9OzyEGt3n+BENIXjuEvjPLqbO0pVFLfnxnRPrCneMTjuz6RmWWldkqSxSAZAw6TzvIoNh8N9qu2lCIcbtq/lG2/+jOpOxUpPBCbw75ffynPnLRnWYqU9EUDKdNhxPMq5VW5Cv/2NMZ56+yBHWhI4CIpDHppiJinTJmPZmN1MGqqLpAknm0h0s0E4ZfHm3iYAdhw/9XlLgG06ucKe7ccdBdDULuFkQzRD0KNxwhE8tmYvertemYmFfuZNmoDf8PHO/iYiKcstsWF3OmgPHNySHLbqtiWZAV1zh+Pe3NPIhTWFVE7w56qb1zYn+NOeE0TT7YdHBRnbJpa28eoKBT6DpGmjKAqFgZMBVH8nNctK65IkjUUj8y45DnSeVxFO9Z7d+YoDG/n2mqc4p1Ox0nhbsdL/GkHFStvrnB9HAJMm+Lj9shqmlYR4dM0+muMZJk7wc6QlQTzt4AhB0IBOHRen6C746avewpNs2x0B0bTdtnRepSzfQ2M0TThlkrYcJk7wUxz00pzIYNmnX6bEcdy8RB795PJ4VYGd9TFSpo3P0GlNZNjZVjC3O2lL0BzPoGsK+X53snjW6UxqlpXWJUkaa2QANEw6z6so6GJuRda59Xv5djfFSp/90LX8+NJbaAwWDnaTe5VNVNg5JBHtnsveshfUFDGjLI/a5kTuZiyEuwIrkbHx6iqR5MAVfO1NV6FEtr3tV64J3CChOZ7BcgQFPoN4xmZzbSuXTismnDBJn0Gz/R6VkNdAUxVsRxBv+/momuBn2YWTmVTg41OP/Rm7D4XLLAE6CpOLAqTaJc883UnNM8rymHZVSFZalyRpTJAB0DDpPK9iweQC1h9q6TAMNqmtWOknuihW+tvZV/D9K5dzqLBqCFvds4DHnSMT7aKehBs4uP8wNIWyoNsj0f5mrLbVljoeSeHTVWzRt+GjwZIbGuvUhSWEIGXabdmmNfweaE2Y1EVSJM0zC9q0tmX+QFsuI4VYysS0vVQU+Hh7fyOxtIWmuAFOb7y6SnHI22GC8plMapaV1iVJGitkADRMuppXMbsyj23HohQmwnxt3fMs3/QyhtPxhrpu8lwevOr2EVms1HEcRKfeABU38MkOIylAwKNTWuDmtel8M55WEuRQU5xExh6y+dtKu6/O4UtXbXCEQMUtZGoLgaEqJB2B7biFWNsft78hnKZquX/bQqCpCqZtkzZt6sMpdtRFcYSbFbsvB3eEoLLA12GCspzULEmSJAOgYTWjLI8Vl07hV+uPsu9EjEkewY2bX+CWN35BXqdipTtLpvDgVbezZtqCEbOyq/MNPmUJvJpAa0s2qLYFPqKtK0Vv+z7oUSnyedhZHyFgaEwrCbKtLkLIq1NdFKCmJMjh5gRaX7o4BoDPUFEVxZ3kHMuQjS001R3uEm3L5LNU3F4aQ1VJW46bJVp1Ey0GvToJs5eJS93IluYA9z3LtB07krI5EUvz/Pu1HGtJuM/3ceqT36Ox+OyOE5TlpGZJkiQZAA2rvQ1RVm9roKk1xuVrXuSTLz3OhHBTh22O5ZXwL1cu56VzrsJp1zswEmSHa0xL4PW4GZOriwI4jqAuksKyRVthUoeMJdxMzkBLwuQLP19PSchDSchLZb6PtGWz4WAzhqES9GhoikJqCKYABQyFCUEPeV6dc6ry+OP2BiJtk3gcB7rqZknbDj6vTmnIS104STRtURz0UFMUpD6cojGWyQ2f9SeE8xsqaQdsy8GyHYSAhOXgM1TmVBUwsTBAWdDDtrqeJ0BnGSosnVPJZdNPzfwtJzVLkjTeyQBomOxtiPLUnw5Q/eZqvv2rn1By9GCH5yP+ED+5+NM8s+D6EVus1BZt/1HcOl224w6fqIrCvIkTONAUpzmeIW25S829uoqiKGQsh+ORFM2xNEe9SdbbAstxC76alluXK3t79xsqpuX0ab5Lfym4FdljSXdOzQdHIm63VS8sB9KWQyJjIVAIeQ2mFAc53JKgOOSlqsBLXTiNwC3jYfXSWxPyauT7DEzbwbQFadOtD2Y6Aq+hcuXMEiYXBwGYEPJyfnUB6w+19hhcaQrMmzSB5QundNuTIyc1S5I0nskAaBg4juAvz/2OO378z0zfu6XDc5bh4f+74ia+O/fGEV+sVOBOxNVUd2m4okOeV+d4NM2R1gRXzChm85EwTfEMhT6dloRJc9JEIAh5dDKWIJw0sR23ZIUANDr2mmRMh3y/TtqysSxBpt2TCuTqePVnMbwCGFpbxmdVoSBg4NFVhBCk+7Cs3nEEibSFcATnTSpgxaU1zKrIzwURSdPi4Vd3sm5fEynT6bInSMENfJbOreCfbpjLwZY4v1p/lL0NURKmhYJCYyzDnIn5TC4Kdtj30hmlZGzB9mMRzC56goIelYXTS7h36exee3LkpGZJksYrRYhhXmozAkQiEQoKCgiHw+Tn5w/ui+3YQeLu/0vg1Zc7POwoCn9YsIR/v+KzbPMUDW4bzpDCyaXhAnfeStBjEPTpVOR72X8iDgrUFAWIZ2z8Hh3hOOxuiLVlN9bI8+mEUxYZy8FxRG71m0eFLhaR5YKdviSLLPBqFAY9xNMmKcuhJOjlrIoQKVPgN9zWHwsnaYplCHhU5lUX4jU0mqIpXt/pZmLu6nWy/SLFQYN8v8HEQj//eMMcakpPDVQdR3CwKcbq7Q1EUiYFPoNjLQnWH24hadoUBT1cMaOUj51XmQtS2pfUqA+neP79WqaXhtC66JGxHId9x6NMLQuRNm0iyQzFQS/FIR8fPbuMGlmZXZKkceBM7t+yB2ioHDsG990HTz5JwOl4h3+9rVjprhFQrLQvBCdXSymAI9xswUUBj5sbJ2CgALUtSTy6Ssin05J0gx1DU/B7dGzHveELIToEG10FP1l9CX7A7ZWKpi18ho7HAI+hUTUhSL7fXfIdSZocbE5QkuclkbHwGholIS9HWxO5WmBZnfMXCcBraOiaiq6qpOyuG6yqCtNK8/jyojz2NkR58k8HONqaZEZZHvk+A02FHfVRjkfTubIT7Xtjgh4dv9HzUvWA1+DWi6bIHhxJkqTTIAOgQea0tBL7x38m9OhPUFMdC51urpzJg1fdzjuTzxum1p05XXUnQhcFPfgMta22lJ8pRX7eOdBMImPTFEtj2e6S7oBHx9AUTNvN9txX/emmFMIhkXZfL9+nk7YcMu0ClYztTjL26AqaquaKjeqKSluB+I6UbKDnfmtoKhnLnafTWwFQxxE8+85h1h9qQQWOtiTRNZXCgIfppUGa4pkOZSeyOi9VB4imLDK2g6Eq1EfSnDdJLlWXJEk6XTIAGizpNCe+/2+Evv8g+ZHWDk8dnFDJw4tW8LsRWKy0vwKGjqaBobrZkf0enemlIQxN4byqAmIZk6MtKUoLPSQzNpYQ7pCXLbBsMSinnzDdadR2PINp2qAoRBJpmmNpNyeRAByb460ZAl6DQ00xDFUhnEifcqzcMv52WuNpkpaD39CJZzKkUh7+sPs49eE0FQVelsyuwOPRcBzBS5uP8vtt9QCU5HnJ01Uyps3+E1EONMaYXBhgV12Yo61JykNeXttZnzvOh88u4Vg4yfsHGqltSbqV5XFXd1UWBvn0BZPkMJckSdJpknOAGJw5QI1//w+U/NN9HR6L5hfy0MW38Ny8a0ZssdL+MjSFPK9OWZ6XopCX6aUhCgMGb+9rpDGaIWVaRNqqnmd7URwhMG3R7ySBZ0JrG8vKJjDMvv7pJCvszKOCqqoIBJqiUJrn44YPVeHVVV7YeISjrUmCHp2AR8MR0BRLk2n3+l5D5aKaIg41JTgRTbkJENuOE/JpueSHWaoCEwt8XDmrrEPVdkmSpPFGzgEaYRxH8MJF1/OZvB+SF23F9PlZ/+kv8telV9BqjP75GhogFDeYCXk0akqCTC8NUprnJWU6vL2vkT3HYxiaQnm+j+KQl8ZompZEJlfZXcVdPdbbEvGBYgv3h90WosOKsYEIwjIOKI5DUdDAEXAsnOSxtfuYUhzAdgR+Q0Nt6yFLtr0B2Y4bR0DKdHhzTyOaAvl+A6+ukLYEtc2JXFvVdq/nCDgaTvHOvkYmTvCfMnwmSZIk9U4GQIPgaGuSnQmFNz/7VSYe3c+7n7uLRGEJrX/cM9xNO2MqgHJyWGjx2eVMLQmx70SMQ00JDE2lMZrB0BSmlQRRVffWHfRooEBj1M2SnOfVEIqCZTtnXNG9r3SNtsnXA39sAaRNm3y/gWkpxE2H+tYkZfk+gl6deMoiZTq5Xp/2AVDuGAK8mjsvyac7tLQ7fjYrNbgBtiXcSeY7j7V2qNo+1sTiGf719V0caU4xqcjHPR+ZRSjYfV6sjYfrWPbIRizcX26/vHM+8ydXDll7JUkaPWQANAiyBT533Pg5drf7y9xQYYju9YPG0NzhnrTpruhaOreCD88qzy3f3tsQZf3BZsrzfbngB9whp4wl0DQ3eirNd8svWLZgz/HooCQ67My0OwYcAy1pOfgsd1WbR1VImm5yx3y/QSRpntLz1Hnw2QHSliCgQTzd8QfFEW3DeLgrzFRbkLEFexrjp1RtHyu++uxGXtlS12H133+vO8zSuZX85Nb5p2xfc2/H1BIWcNMjGwF4796r+D8/30B9JENFvofHP7eAsgnBU44hSdL4ofa+idRf7Qt8tnfzBSOncvvpEihYtkBVoCLfx8x2y7dnV+SjqSqWI/B7OpbtsIVwi4i29R4pilsUVdcUfB7tlCKig9L2QQ6yRNv8JpEtVoq7YsyyBUHvyb81coVhFXcOVXt2W5Evu1NjOzc9G1dbluh1Jdpo9NVnN/LbD+pOSX1gC/jtB3V89dmNHR7vHPx0dtGDa9h8JEp9JM3mI1EuenANVz70x4FutiRJo4gMgAZBdglzXThF+znminpqPpfRxrTd3D15foPF55QxqbDj0Etx0IOhqSQzHQt5aYqCqii5G7/eFiFoioJX1/AbJ38U29/zBjIYGuxpMkpbSRBFcYfaFAUmFwXxezSEELkEkqoCuqrg0VV8RsdAUVNOvi8djt3ptbI9WVNKAgO6FN6yHN470MQrW+t470ATVrtJWj09N5Bi8QyvbKnLfa8q7vvW/j14ZUsdsbg7nLrxcB2n43BLSgZBkjSOjb0/HUeArqpt+wyV7cfClOV5aIieXrXwoaa3ZV5uH5AI3KEaFVCEwv7GWIdVSPOrC6kpDrK7IUrQo+WGwTy6SsBQiKaEmxyxrYfIo6v4DZVEWrjV4hXwaQoJU6C1VY/vawLE3ngN1R2KG6gDduLXVTy6QsqElCPI92rMm1RAOGWy53iUpljGLR2CWxfN79HRVTe/j8B9T726e5sPelUi7Vbli2yCRgUc253I7dEUVl49c8AmQP9xx3GefvsgB5vimLaDoanUFAe57bIagG6fW3x2+YC8fta/vr4rd83b519qzxbudvddP5dlj2w8dYM+OtySoqE1LofDJGkckj1AgyRbbXtOVQGtCZMddREaomm8+sh8yzXcpIbZzMcezc3urCgd//LWFbd+VtoW/OVIK0+9fZC9DdGTz+sqt11WQ57P4HBLkmjKxHIcYmmLtC3wtfV6HGlJEU6aJE3LHRrTVPL8Bn5DA0VFVchlZdaUkxOHu7rV9/UdVRUFXVUG5YfeXc6ukTQd0rbAUBUmFgVItM0BOrsyn5rSoPv6als7FEHSdHI9U4oCaVtgOw4pS5zSTttxcyfZuO/Fpy+oZk5V4YC0/487jvPAKzvZ3RAlz6czsdBPnk9nd0OU//fSVv7fS1u7fO6BV3byxx3H+/16PfUmHWk+mTC0p1B1R13MPVa/X72j//PzDWd4BEmSRqNh7QF68803+f73v8+GDRuoq6vjxRdf5MYbb8w9L4Tgu9/9Lo8//jgtLS1cfPHF/PSnP+Xcc8/NbZNOp/nGN77BL37xC5LJJIsXL+aRRx5h0qRJw3BGHbWvtr31WJjn36+lOZ6mtiXV+85DQAVU1Q0yCnw6FYV+YkmLCUGD0qCHjbVht+gnAoFCwFDdVVTCIWPZ1IeTBDwqj7yxlxllQZriJvk+gwXVE7jxQ5X85oM6jrUmQYCmKRT6DUJFAZrjGVriGZoTaRDg0DY8JBw0RSFhObkeAAdyd8Hu5vBkb53d5fTRlWx5jJPDcnrbeQ9EZ5CuukNWsbSFpihMnODn+nlV+HSNfSdiHI+k8OoaN50/iaRp8+vNxzgRTZEwbTTFnT81Z2IBW46E3cczbh6gycUBphQH2Hw4TCxtusOHuCvoVlw6lXuumXXmjccNRp5++yDRlMnkQn+u1y7PpxIwVD44GkEA8ybmo2la7rmgR+NwS5Jn1h1k0cxS9D4G9z31NC0+u5xJRb4+Hce0bBxHoHNmQVB9ZHT0yEqSNLCGNQCKx+PMmzeP22+/nU996lOnPP/www/zgx/8gKeffpqzzjqLf/qnf+Lqq69m165d5OW5wy4rV67kN7/5Dc899xzFxcXcc889XHfddWzYsCH3y3o4ta/vtDpwnNqm+DC3qB3lZB6elO0wp6qAa86t4O29jby+o4Fw0mzX4yIIpzrO6zkaTlMXTndZiV1ti0baP9ccN9E1xZ3vIwTpTku/3KGp049IutuzqxVm/Zm+4iYe9PKJ8ydyx8Iannn3EBsOtRBLWhQFdHRdpTVhE/ToXH5WCcsvnpLLBJ1dHRf06Eyc4EdVFb521YwOGZ+zmaMzGbvLx1Mpi+c3HuZoS4qJhT5unj8Zn6/rj253r9mTjbUtHGyKUxz0dFi5BxDPOAjhJm2MZxzy/Sc/U6qqUhz0cKAxzsbaFi6aWtzre5ntaYqmTIqDHvwejWTGzvUmAdzzkVn897rDvQan+R6VN/ec4CefO5ev/Hxbr6/dnYr87pfVS5I0dg1rALR06VKWLl3a5XNCCP7t3/6N73znO9x0000APPPMM5SXl/Pss8/y5S9/mXA4zBNPPMHPfvYzPvrRjwLw85//nOrqav7whz9wzTXXDNm59KYy34emQmNs5Py1aagKtiNQVYWykDc38TZl2gS8Gh5NxWeoRFJWt8vHu4sjnHZzONoXErVsQcyxB3U5+kBzBNRFMryzv5kbz5/E9fMm0hgz2+qe+Qh4dBIZi7pwiv2NcQ63Jk4pbtqex6Nx3XkT+/y4z6ez4tJpvbZzb0OU3289zr4TMVKWjU/XmF4a4po55T1mi26KZzBtJzdZO2M5uWzUpu3mLkKA2UUCJb9HozmeoSne+891Tz1NnXuTLptRwpt7Grs9VmFAZ3djkif+tJ/SUN96jLrz+OcWnNH+kiSNTiNzQgpw4MAB6uvrWbJkSe4xr9fLokWLWLduHQAbNmzANM0O21RVVTFnzpzcNiPB3oYoj63dxzv7m4imR07OFssRKKpCZYGfy2eW0JIwefrtgzTHM5xdkY+nbZzoTIKVzrtml4CPNrYj2HYswst/OcarW+ppjmeYWRYiz2egqQp5PoOZZSGa24qbOkN8knsbojz19kG2HgszIWAwrSTEhIDB1mPhU+ZpdZZdudeayHCsNUVtS5IjLUlqW5K0Jk33GipgqKf+ukhmbAzN7QnqTU89TZ17k/75k3OpKug6sMn36eT7PPgMjanF7nkuOquk19fvyuRCn5wALUnj1IhdBVZf7xaQLC/vuMKkvLycQ4cO5bbxeDwUFhaesk12/66k02nS6ZNLbCKRyEA1+xTZG9Pu41HCSROPBqkREgPpqsK00iAX1BRRFPSStgTbjoW5ZFoRFfk+CvwG9ZGRMV9puCm4VeRf39lARYGfiYX+XGbm3DaKQmWBj70NsSHNzuw4gt9vPZ4LyrLtyvMZhLw6expiXVacz5pfXUhZnpftdRG8moLXo6MpKrYQJNMmjuNOfA961E6v69AUzzCrPI/51b1Pxm7f09SV9r1JF0wp4rMXT2HjwRM0xi3CKZMCn4GuCFrTDghBeb6PCQEDRVE4b2IBda0pItEY9cmTx1x2TohX9iWIpk/tvZpc6OPNby3utd2SJI1NIzYAyup8kxFCnPJYZ71t88ADD/Dd7353QNrXk+yNqSmWIW3a2A74DJ2UNfwRkApcMq2IedWFufdKU90hD01VUVWV8ycXtlUyH4VdNgNMAIqASMoiL2AR6Cb5oN+jcTySGtLszEdbk+w74aZbOJ2gTFUVphQF2FkfxXRAdwSq6vZ62ULBoyt4NI3alhTFoZPzdpriGfJ9BisurenTBOj2OaLyfD33JrVPJeHxZFhQ4MO0Hdbta8K2HfL8BtNLg7nzVVWVORPz2XTYYVF1gOmloVw7L5+Zwq+rbK9rpTVpy0zQkiQBIzgAqqioANxensrKk7V8Ghoacr1CFRUVZDIZWlpaOvQCNTQ0cOmll3Z77G9/+9vcfffdue8jkQjV1dUDfQq5G1O+T2d32sajq9iDUYjqNHgMt9p4+xum7QgM7WQbp5YEmVkaYmvd4PWQjRZK23/yfTpBw53zk+c7NbFlMmPj1bUhzc6cLb0S8HSdELG3oOxoaxIUhYXTi9hVH3PTEzgCTVUoDnk4qzxEPG1j2Q71kTTN8QyGpjKrPI8Vl/Y9D1B3OaKg696kbCqJ7LymE7EUqYxNdXGAmWUhioLeDsevnOCnMZZhanGI1oSZW303d2IBS87teR6UJEnjz4gNgKZOnUpFRQWrV6/m/PPPByCTybB27VoeeughABYsWIBhGKxevZply5YBUFdXx9atW3n44Ye7PbbX68Xr9Xb7/EDJ3pjyfQaKIvBoKs0jZPyrJOChst0cCyEE0ZRFTXHQTczX1ou26Kxidh+PkGlXxX1khHBDSwBeTeUjs8vQVJVtdRFCXr1DACmEoC6cYu7EggHNztyb9qVXTicoy/6czq4o4OyKfOrC7hL9gKFRWeDDAQ42xvnrD08nkXZ7foqDHuZXF/Z56TuczBH1wCs7OdyS7LAKrLvepPapJPadiPGL9w5TVeAn39/1eZaEvNxxeQ2KovRrJZwkSePPsAZAsViMvXv35r4/cOAAmzdvpqioiMmTJ7Ny5Uq+973vMXPmTGbOnMn3vvc9AoEAt956KwAFBQV84Qtf4J577qG4uJiioiK+8Y1vMHfu3NyqsOGUvTE5jkBXVWzHxBnsglR94DNUyif4iWds/B73xlEXdoc3Pn3BJF7f2ZDLYO33aJxdlc8HR9xcMLrqlnmwe30VV/tVYEq774f/XegfTVU4tyqfj89z67nVRVId3qPse1gU9LDk3PIhveFmS69sPRY+raCscwA1sVN5k3jKxKtrFPg8zKk6s3lN2d6ibB6gvvQmZVfTTZzgZ9vRCFuPhcnzdX+ekwoDMuCRJKlXwxoArV+/ng9/+MO577PDUitWrODpp5/mm9/8JslkkjvvvDOXCPG1117L5QAC+OEPf4iu6yxbtiyXCPHpp58eETmAsjemLUfDBAyNWtNGVQautEN/qQpMLQ5w22VTiSStDkn62g8TTCkO5IYdjkdSTC/NI+DR2Ho0QiJjd0g8qHdT4T5bvym7GEpVshNpNfJ8Bg6CutbUsL0X/RH0qCycXsy9S8/ODaO0H5oZ7qGWrkqv9CcoO9MAqr8Wn13OopmlbKxt6Vdv0pmepyRJUnuKECOgS2KYRSIRCgoKCIfD5OfnD+ixs6vAdtRF2FkXIWU6AzqEpAIVIRXDMAh4NGaV51FTFqIpliFgqDgCGuMmhQEPi88u4+KaYnRd7TVhXlfPZzJ2LiGfV1fRFMH2+jjNsTQJ08Lv0Ti3soAlZ5ej6gpN8QzNsTRFfi9ChVDbvI+pJUEmFvg50pLgzb0NrFp/hLpwipBXY1pJgJBP53gkw4lwnLqYhe0IQrpCfkDDQaM1bmHbNkkLPDpMmuBj6dwK9p1IsrMuTDRtYagqQoViv0GB30silWJLfQIElOR5uH5+BRkTiv06+xsTHAuncISDsAUBr1uOY1Z5HsV5fq4+p4ya4lNXUJ1O0sHB1D4PUNpyh71mlIX6FJRlf06zuY06Bxa3X1YzYubQnMl5SpI0tpzJ/VsGQAxuAATuL+zn36vl+fW1RAZwDpACXDE1n3tvmMPs8glDfvMdaQFAbXOCH67ezYSA0eVcmGjKpDVh8rdXnzVkS9SH2plck9EUWIy0nz1JkobHmdy/R+wk6LEmz6e1FcAcuESAAnjzQIQ3/30dNcV+/mvFhUN6o+ou0/FwOdPVUGPBmVyT9hOOR3pgMdJ+9iRJGn1GbCbosWJvQ5Qn/3SQt/c14dFVDG1wbiYHm5Is/693esz4O9a1n8zbleFYot4fjiOobU6wsz5CbXNiyLNJw8nAYnZFPtVFcjKxJElj18i8E4wRjiN49t3DvH+wmWTaojGa7rIw50Cpi2T4zcZavr7k7HF54xrqybwD6XTreEmSJEmnR/YADaJ1+xr5/bZ6TkRSNMUHN/jJemHzMTex3TiUXSVUFPSwpyFGNGViOQ7RlMmehtiIXSV0JnW8JEmSpNMje4AGieMIfrX+iFv9XQjSQ7TeO5K0xvQcl950zh48HEvU+zNB90zreEmSJEmnRwZAg6S2JcHG2hZsx0EZwqkcfu/IneMyVIZzMm9/h7LOtI6XJEmSdHrG951yEO0/EaM5lkZTFOwhzDSw9OyyETnHZaid7iqhM11G3j6XTsDjJ5Gx2HoszLFwsstcOnLlmiRJ0vCQAdAgaYplsIWb/Xiohr/yvCqfuXSqHCrpB8tychmJkxmboy0JDjQm+j0R+XSHss60jpckSZJ0euRv1UFSnOdBCEFyKGY+A3lejX+75XzOKh/4RI6jUXc9OamUlctmHU9bHGiMUduSIpmxSZo2PkPj/MkFzK4o6LX3pr3THcoaTSvXZPJBSZLGEhkADZLJRQHMAQx+AgqUTPBQ25LpUFy02K/y8fMn8dmLajirQi6Xhu7n4RwLJ/ntX+qIpc1cDTIFKA4aeHSVtAUp02bjoVYCHp2pJaE+T0Q+3aGs0VLfSi7TlyRprJEB0CDZeKi1zxXTe6IA00sDgEJZvo/r5k3gYGOcZRdWU1HgG9d/iXfVI7G/MdblPJwXNh1h/4m4W9Fe6ViVvjFu4tdV8gMGKhBJWWyubWVKUQBVVXuciJxtQ304he0I4mmLfH//hrIGc+XaQPTanM7cJkmSpJFOBkCDwJ0PUj8gxxKAEAp5fp3meIYT0TQ+w/3rezyvCuqqR2JaSZDmROaUeTh+XeNQoxv8KIAt6NCLJoCk5VCAQFVV/B6N1oRJXTjFxMJAt7037duQNC1qm5McOBHnoqmFFId8ue36MpQ1GCvXBqLXRi7TlyRprJIB0CB4e18j2+rCA3Y8n6FiaCqxlEldOMml00tGxJyQ4dJdj8T7h5o53JTg/MkTOsylee9QU7dJKNs/HE/b5PtVDFUh6QgSptuH11XvTec2VHn8+A2N9w+2sHZ3IxfWFFI5wd+voayBrG81UL02cpm+JEljlcwEPcD2NkT5xXuHyVjOgBxPAVQF4mmLRMahOOQdEXNChkvnHok8n4GmKuT5DCZO8JMwbY6FU4i21ANCCI62dMyM3d0PfcZ2EAJMR6CpCgFDy/XezCgL5YLO7tpQXRRk0Vml+D0au+qjHDgRpzVhMndiwZAOE/X0Hs0sC9Ecz/DatuN9qjV2cm5T138r+T0aacuWy/QlSRp1ZA/QAMreeOJpi3yfTnPc5EynQauKOyfFtAUzykLcddWMcT3foqceCa+uEfLqnIimiabcuTjRlIXd+UafHffqxFBVEhmLtOVQEvIS8mpdltDoqQ3FIS+XTS/mWGuKZRdWM700NORztAay10Yu05ckaaySPUADKHvjmVYSIt/vQRuAd9dvaBQFPVw4tYj/d93Z436lV089Enk+nZKQh3jaIm25w1cZ2yHkNciGAdkeNa1TPKIApXkeLEegqyrl+V4iKbvL3pveekUCXh1dU6go8A1LRfWB7LXJLtOva9erltVV75gkSdJoIf9sG0DZG0+V18+00iC7j0exnDMbCvPqKhdNLebTF0wa1z0/WT31SCiKwsQJfhoiaY62JvEZGpriLvnK9+uEkxYOoAhQFPf/2Vu6z1CxHDi/egLXzatiXvWEbicij/RekYFs32hZpi9JktRfMgAaQO1vPNWFAWaWBdlyNHraw2CGCjfOn8j/XTILXZedddB74sCk6fCR2WUUBj3sPxEnZdp4dZXyfB/VhQp7jsdzc33A7QmaVZHHXR+ZQWnIy/zqwl7f65GevHCg2zcSCsxKkiQNNBkADaD2N56ZZSHmVRdyLJyiMWae1vE+NLmQz1w0WQY/7fSlR+IzF09mWsnJJeWN0TS/21JHS8JkfnURB5tiNCdMHCE4b2IBX7xyWr9u4iO9V2Qw2jecBWYlSZIGgyI6D+yPQ5FIhIKCAsLhMPn5Z1ZKov3yY7+h8pcjYWqb42T6kRVRARafXca9S2fLv6670T7HTdpyh3RmlIW67ZHo7/aD0YahNtLbJ0mSdKbO5P4tAyAGNgAC98bz6tZ6freljqZ4Btt2yNgOPkMjbdrYjiCRcecGCcDQwKNASZ6HT55fzfXzJlJTKhPL9aa/WY4Ho5bVSK+PNdLbJ0mSdCbO5P4th8AGwYyyPK6fp7LlSJhJhX72NsTJ8+n4DC23TdqySZkOcycWYDuCv736LJlIrp/6mzhwIBMNDuYxB9JIb58kSdJwkZNLBknStNE0haKgF1UBT6d5PIamYjsOmqrIRHKSJEmSNMRkADRIsivCbMdB11RMu+NIo2k7aKqK7QiZSE6SJEmShpgMgAZJdkVYNGVRGDCIpcwO5RlibY9HU5ZMJCdJkiRJQ0x2OwyS9kuR42kbTVVoimXwGipp00bXVHRNpTgkE8lJkiRJ0lCTPUCDKJtA7pJpxVQXBdA1hWjKQtdUqosCLJxWPKRFMiVJkiRJcskeoEHWPoFcNG0SS1mEvHquerns+ZEkSZKkoScDoCEglyJLkiRJ0sgih8AkSZIkSRp3ZAAkSZIkSdK4IwMgSZIkSZLGnTETAD3yyCNMnToVn8/HggULeOutt4a7SZIkSZIkjVBjIgB6/vnnWblyJd/5znfYtGkTV1xxBUuXLuXw4cPD3TRJkiRJkkagMVEN/uKLL2b+/Pk8+uijucfOPvtsbrzxRh544IFe9x/oavCSJEmSJA2+M7l/j/oeoEwmw4YNG1iyZEmHx5csWcK6deu63CedThOJRDp8SZIkSZI0foz6AKixsRHbtikvL+/weHl5OfX19V3u88ADD1BQUJD7qq6uHoqmSpIkSZI0Qoz6AChLUTpmVBZCnPJY1re//W3C4XDuq7a2diiaKEmSJEnSCDHqM0GXlJSgadopvT0NDQ2n9Apleb1evF5v7vvsNCg5FCZJkiRJo0f2vn0605lHfQDk8XhYsGABq1ev5pOf/GTu8dWrV/OJT3yiT8eIRqMAcihMkiRJkkahaDRKQUFBv/YZ9QEQwN13383y5cu54IILWLhwIY8//jiHDx/mK1/5Sp/2r6qqora2lry8vG6HzXoTiUSorq6mtrZ2TK8kGy/nCePnXMfLecL4Odfxcp4gz3Us6s95CiGIRqNUVVX1+3XGRAB0880309TUxD/8wz9QV1fHnDlz+N3vfseUKVP6tL+qqkyaNGlA2pKfnz+mfzCzxst5wvg51/FynjB+znW8nCfIcx2L+nqe/e35yRoTARDAnXfeyZ133jnczZAkSZIkaRQYM6vAJEmSJEmS+koGQAPE6/Vy3333dVhdNhaNl/OE8XOu4+U8Yfyc63g5T5DnOhYN1XmOiVIYkiRJkiRJ/SF7gCRJkiRJGndkACRJkiRJ0rgjAyBJkiRJksYdGQBJkiRJkjTuyACojx555BGmTp2Kz+djwYIFvPXWWz1uv3btWhYsWIDP52PatGk89thjQ9TS0/fAAw9w4YUXkpeXR1lZGTfeeCO7du3qcZ81a9agKMopXzt37hyiVp+e+++//5Q2V1RU9LjPaLymNTU1XV6fu+66q8vtR9P1fPPNN7n++uupqqpCURReeumlDs8LIbj//vupqqrC7/dz1VVXsW3btl6Pu2rVKs455xy8Xi/nnHMOL7744iCdQd/0dJ6mafKtb32LuXPnEgwGqaqq4vOf/zzHjh3r8ZhPP/10l9c5lUoN8tn0rLdretttt53S5ksuuaTX4460awq9n2tX10dRFL7//e93e8yReF37cl8Zrs+qDID64Pnnn2flypV85zvfYdOmTVxxxRUsXbqUw4cPd7n9gQMH+NjHPsYVV1zBpk2b+Lu/+zv+5m/+hlWrVg1xy/tn7dq13HXXXbzzzjusXr0ay7JYsmQJ8Xi813137dpFXV1d7mvmzJlD0OIzc+6553Zo85YtW7rddrRe0/fff7/DOa5evRqAT3/60z3uNxquZzweZ968efzkJz/p8vmHH36YH/zgB/zkJz/h/fffp6KigquvvjpX+68rf/7zn7n55ptZvnw5f/nLX1i+fDnLli3j3XffHazT6FVP55lIJNi4cSN///d/z8aNG3nhhRfYvXs3N9xwQ6/Hzc/P73CN6+rq8Pl8g3EKfdbbNQW49tprO7T5d7/7XY/HHInXFHo/187X5sknn0RRFD71qU/1eNyRdl37cl8Zts+qkHp10UUXia985SsdHps9e7a49957u9z+m9/8ppg9e3aHx7785S+LSy65ZNDaOBgaGhoEINauXdvtNm+88YYAREtLy9A1bADcd999Yt68eX3efqxc069//eti+vTpwnGcLp8frdcTEC+++GLue8dxREVFhXjwwQdzj6VSKVFQUCAee+yxbo+zbNkyce2113Z47JprrhG33HLLgLf5dHQ+z6689957AhCHDh3qdpunnnpKFBQUDGzjBlhX57pixQrxiU98ol/HGenXVIi+XddPfOIT4iMf+UiP24yG69r5vjKcn1XZA9SLTCbDhg0bWLJkSYfHlyxZwrp167rc589//vMp219zzTWsX78e0zQHra0DLRwOA1BUVNTrtueffz6VlZUsXryYN954Y7CbNiD27NlDVVUVU6dO5ZZbbmH//v3dbjsWrmkmk+HnP/85d9xxR69Ff0fj9WzvwIED1NfXd7hmXq+XRYsWdfu5he6vc0/7jDThcBhFUZgwYUKP28ViMaZMmcKkSZO47rrr2LRp09A08AytWbOGsrIyzjrrLL70pS/R0NDQ4/Zj4ZoeP36cl19+mS984Qu9bjvSr2vn+8pwflZlANSLxsZGbNumvLy8w+Pl5eXU19d3uU99fX2X21uWRWNj46C1dSAJIbj77ru5/PLLmTNnTrfbVVZW8vjjj7Nq1SpeeOEFZs2axeLFi3nzzTeHsLX9d/HFF/Pf//3f/P73v+c///M/qa+v59JLL6WpqanL7cfCNX3ppZdobW3ltttu63ab0Xo9O8t+Nvvzuc3u1999RpJUKsW9997Lrbfe2mMRydmzZ/P000/z61//ml/84hf4fD4uu+wy9uzZM4St7b+lS5fyP//zP7z++uv867/+K++//z4f+chHSKfT3e4z2q8pwDPPPENeXh433XRTj9uN9Ova1X1lOD+rY6YY6mDr/BezEKLHv6K72r6rx0eqr371q3zwwQf86U9/6nG7WbNmMWvWrNz3CxcupLa2ln/5l3/hyiuvHOxmnralS5fm/j137lwWLlzI9OnTeeaZZ7j77ru73Ge0X9MnnniCpUuXUlVV1e02o/V6dqe/n9vT3WckME2TW265BcdxeOSRR3rc9pJLLukwefiyyy5j/vz5/PjHP+ZHP/rRYDf1tN188825f8+ZM4cLLriAKVOm8PLLL/cYHIzWa5r15JNP8tnPfrbXuTwj/br2dF8Zjs+q7AHqRUlJCZqmnRJVNjQ0nBJ9ZlVUVHS5va7rFBcXD1pbB8rXvvY1fv3rX/PGG28wadKkfu9/ySWXjJi/OPoqGAwyd+7cbts92q/poUOH+MMf/sAXv/jFfu87Gq9ndkVffz632f36u89IYJomy5Yt48CBA6xevbrH3p+uqKrKhRdeOOquc2VlJVOmTOmx3aP1mma99dZb7Nq167Q+uyPpunZ3XxnOz6oMgHrh8XhYsGBBbvVM1urVq7n00ku73GfhwoWnbP/aa69xwQUXYBjGoLX1TAkh+OpXv8oLL7zA66+/ztSpU0/rOJs2baKysnKAWze40uk0O3bs6Lbdo/WaZj311FOUlZXx8Y9/vN/7jsbrOXXqVCoqKjpcs0wmw9q1a7v93EL317mnfYZbNvjZs2cPf/jDH04rIBdCsHnz5lF3nZuamqitre2x3aPxmrb3xBNPsGDBAubNm9fvfUfCde3tvjKsn9U+T5cex5577jlhGIZ44oknxPbt28XKlStFMBgUBw8eFEIIce+994rly5fntt+/f78IBALib//2b8X27dvFE088IQzDEL/61a+G6xT65K//+q9FQUGBWLNmjairq8t9JRKJ3Dadz/WHP/yhePHFF8Xu3bvF1q1bxb333isAsWrVquE4hT675557xJo1a8T+/fvFO++8I6677jqRl5c35q6pEELYti0mT54svvWtb53y3Gi+ntFoVGzatEls2rRJAOIHP/iB2LRpU27104MPPigKCgrECy+8ILZs2SI+85nPiMrKShGJRHLHWL58eYfVnG+//bbQNE08+OCDYseOHeLBBx8Uuq6Ld955Z8jPL6un8zRNU9xwww1i0qRJYvPmzR0+t+l0OneMzud5//33i1dffVXs27dPbNq0Sdx+++1C13Xx7rvvDscp5vR0rtFoVNxzzz1i3bp14sCBA+KNN94QCxcuFBMnThx111SI3n9+hRAiHA6LQCAgHn300S6PMRqua1/uK8P1WZUBUB/99Kc/FVOmTBEej0fMnz+/w9LwFStWiEWLFnXYfs2aNeL8888XHo9H1NTUdPsDPJIAXX499dRTuW06n+tDDz0kpk+fLnw+nygsLBSXX365ePnll4e+8f108803i8rKSmEYhqiqqhI33XST2LZtW+75sXJNhRDi97//vQDErl27TnluNF/P7JL9zl8rVqwQQrjLa++77z5RUVEhvF6vuPLKK8WWLVs6HGPRokW57bP+93//V8yaNUsYhiFmz5497MFfT+d54MCBbj+3b7zxRu4Ync9z5cqVYvLkycLj8YjS0lKxZMkSsW7duqE/uU56OtdEIiGWLFkiSktLhWEYYvLkyWLFihXi8OHDHY4xGq6pEL3//AohxH/8x38Iv98vWltbuzzGaLiufbmvDNdnVWlroCRJkiRJ0rgh5wBJkiRJkjTuyABIkiRJkqRxRwZAkiRJkiSNOzIAkiRJkiRp3JEBkCRJkiRJ444MgCRJkiRJGndkACRJkiRJ0rgjAyBJksakq666ipUrVw53MyRJGqFkACRJ0oh32223oSgKiqJgGAbTpk3jG9/4BvF4vNt9XnjhBf7xH/9xCFspSdJoog93AyRJkvri2muv5amnnsI0Td566y2++MUvEo/HefTRRztsZ5omhmFQVFQ0TC2VJGk0kD1AkiSNCl6vl4qKCqqrq7n11lv57Gc/y0svvcT999/Phz70IZ588kmmTZuG1+tFCHHKEFg6neab3/wm1dXVeL1eZs6cyRNPPJF7fvv27XzsYx8jFApRXl7O8uXLaWxsHIYzlSRpKMgASJKkUcnv92OaJgB79+7ll7/8JatWrWLz5s1dbv/5z3+e5557jh/96Efs2LGDxx57jFAoBEBdXR2LFi3iQx/6EOvXr+fVV1/l+PHjLFu2bKhOR5KkISaHwCRJGnXee+89nn32WRYvXgxAJpPhZz/7GaWlpV1uv3v3bn75y1+yevVqPvrRjwIwbdq03POPPvoo8+fP53vf+17usSeffJLq6mp2797NWWedNYhnI0nScJA9QJIkjQq//e1vCYVC+Hw+Fi5cyJVXXsmPf/xjAKZMmdJt8AOwefNmNE1j0aJFXT6/YcMG3njjDUKhUO5r9uzZAOzbt2/gT0aSpGEne4AkSRoVPvzhD/Poo49iGAZVVVUYhpF7LhgM9riv3+/v8XnHcbj++ut56KGHTnmusrLy9BosSdKIJgMgSZJGhWAwyIwZM05r37lz5+I4DmvXrs0NgbU3f/58Vq1aRU1NDboufy1K0nggh8AkSRrzampqWLFiBXfccQcvvfQSBw4cYM2aNfzyl78E4K677qK5uZnPfOYzvPfee+zfv5/XXnuNO+64A9u2h7n1kiQNBhkASZI0Ljz66KP81V/9FXfeeSezZ8/mS1/6Ui6RYlVVFW+//Ta2bXPNNdcwZ84cvv71r1NQUICqyl+TkjQWKUIIMdyNkCRJkiRJGkryTxtJkiRJksYdGQBJkiRJkjTuyABIkiRJkqRxRwZAkiRJkiSNOzIAkiRJkiRp3JEBkCRJkiRJ444MgCRJkiRJGndkACRJkiRJ0rgjAyBJkiRJksYdGQBJkiRJkjTuyABIkiRJkqRxRwZAkiRJkiSNO/8/UwBuCxD1Pa4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "x = df['price(crore)']\n",
    "y = df['area(sq. yard)']\n",
    "\n",
    "# Calculate the Pearson correlation coefficient\n",
    "corr_coeff = df['price(crore)'].corr(df['area(sq. yard)'])\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.scatter(x, y, alpha=0.5)\n",
    "\n",
    "# Add the Pearson correlation line\n",
    "sns.regplot(x=x, y=y, scatter=False, color='red')\n",
    "\n",
    "# Add the correlation coefficient as a text annotation\n",
    "plt.text(x.min(), y.max(), f'Correlation: {corr_coeff:.2f}', ha='left', va='top')\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Area')\n",
    "plt.title('Scatter Plot with Pearson Correlation Line')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642eb0b6",
   "metadata": {},
   "source": [
    "<h3>applying regression tree and calculating value of MSE RMSE R2 of the model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "5b8aaf51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 2.061051982828634\n",
      "Root Mean Squared Error (RMSE): 1.4356364382491251\n",
      "R-squared: 0.8934209077002013\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['bedrooms', 'bathrooms', 'area(sq. yard)', 'House', 'Flat','Bahria Town','Clifton','DHA']], df['price(crore)'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize and train the decision tree regression model\n",
    "modelDTR = DecisionTreeRegressor()\n",
    "modelDTR.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "predictions = modelDTR.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "r_squared = modelDTR.score(X_test, y_test)\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"R-squared:\", r_squared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "8d6aad05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Price: [11.25]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Iqbal Siddiqui\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Prepare the new input data\n",
    "new_data = [[5, 5, 600, 1, 0, 0, 0, 1]]  # Example input data with feature values\n",
    "\n",
    "# Make predictions on the new input data\n",
    "predicted_price = modelDTR.predict(new_data)\n",
    "\n",
    "# Print the predicted price\n",
    "print(\"Predicted Price:\", predicted_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "973e7612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the actual prices\n",
    "plt.scatter(y_test, y_test, color='blue', label='Actual')\n",
    "\n",
    "# Plotting the predicted prices\n",
    "plt.scatter(y_test, predictions, color='red', label='Predicted')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Actual Prices (crore)')\n",
    "plt.ylabel('Predicted Prices (crore)')\n",
    "plt.title('Decision Tree Regression')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Display the chart\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b196d1a",
   "metadata": {},
   "source": [
    "<h2>applying random forest regressor and calculating values of MSE RMSE and R-Squared.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "37a95632",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['bedrooms', 'bathrooms', 'area(sq. yard)', 'Flat', 'House','Bahria Town','Clifton','DHA']], df['price(crore)'], test_size=0.2, random_state=42)\n",
    "modelRFG = RandomForestRegressor()\n",
    "modelRFG.fit(X_train, y_train)\n",
    "predictions = modelRFG.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "78b75821",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 1.9798397544910473\n",
      "Root Mean Squared Error (RMSE): 1.4070677860327296\n",
      "R-squared: 0.897620474548576\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "r_squared = modelRFG.score(X_test, y_test)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"R-squared:\", r_squared)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e4e3a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the new input data\n",
    "new_data = [[3, 4, 500, 1, 0, 1, 0, 0]]  # Example input data with feature values\n",
    "\n",
    "# Make predictions on the new input data\n",
    "predicted_price = modelRFG.predict(new_data)\n",
    "\n",
    "# Print the predicted price\n",
    "print(\"Predicted Price:\", predicted_price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11515fcb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Plotting the actual prices\n",
    "plt.scatter(y_test, y_test, color='blue', label='Actual')\n",
    "\n",
    "# Plotting the predicted prices\n",
    "plt.scatter(y_test, predictions, color='red', label='Predicted')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Actual Prices (crore)')\n",
    "plt.ylabel('Predicted Prices (crore)')\n",
    "plt.title('Forest Tree Regression')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Display the chart\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bd3d01",
   "metadata": {},
   "source": [
    "<h2>we notice that the random forest regressor yields the best result with minimum Mean Squared Error value and maximum R Squared value, so we will choose this model for our prediction</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8558fcd",
   "metadata": {},
   "source": [
    "<h3>For Frontend purposes</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "349f2047",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Assuming your trained model is stored in the variable `model`\n",
    "with open('housingProject.pickle', 'wb') as f:\n",
    "    pickle.dump(modelRFG, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "262fa48f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {\n",
    "    'data_columns': [col.lower() for col in df.columns]\n",
    "}\n",
    "with open('columns.json', 'w') as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  }
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