car project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"car data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check mising or null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
    "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>current_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_year  \n",
       "0       Manual      0          2020  \n",
       "1       Manual      0          2020  \n",
       "2       Manual      0          2020  \n",
       "3       Manual      0          2020  \n",
       "4       Manual      0          2020  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset['current_year']=2020\n",
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_years']=final_dataset['current_year']-final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_year  no_years  \n",
       "0       Manual      0          2020         6  \n",
       "1       Manual      0          2020         7  \n",
       "2       Manual      0          2020         3  \n",
       "3       Manual      0          2020         9  \n",
       "4       Manual      0          2020         6  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop([\"Year\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Seller_Type</th>\n",
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       "      <td>9</td>\n",
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       "      <td>42450</td>\n",
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      ],
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       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_year  no_years  \n",
       "0       Manual      0          2020         6  \n",
       "1       Manual      0          2020         7  \n",
       "2       Manual      0          2020         3  \n",
       "3       Manual      0          2020         9  \n",
       "4       Manual      0          2020         6  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop([\"current_year\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  no_years  \n",
       "0       Manual      0         6  \n",
       "1       Manual      0         7  \n",
       "2       Manual      0         3  \n",
       "3       Manual      0         9  \n",
       "4       Manual      0         6  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_years  \\\n",
       "0           3.35           5.59       27000      0         6   \n",
       "1           4.75           9.54       43000      0         7   \n",
       "2           7.25           9.85        6900      0         3   \n",
       "3           2.85           4.15        5200      0         9   \n",
       "4           4.60           6.87       42450      0         6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "no_years                    -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                        no_years  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price          -0.236141          0.552339         -0.540571   \n",
       "Present_Price           0.047584          0.473306         -0.465244   \n",
       "Kms_Driven              0.524342          0.172515         -0.172874   \n",
       "Owner                   0.182104         -0.053469          0.055687   \n",
       "no_years                1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel       -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol        0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual  0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual    -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "no_years                              0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2ce2ad4fba8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xd+b9ep55NFM/uj5d/a5Pd1epqprjdU20TZekoNe5Wctm45MWJrtAfV37vc7N4UGvhSbgvR5Br+e4qXdtB2Ez0zrOdtzAOmxXevX2Gk/B0pZdIFx+a41c9OprTo/8ujJ4nCYQ+JjNy4o1s9namzqntx031PPtfA0265F0Q9ryG7b3EqXRMJ97W7e5MO/JUbyvp2U5SdUaxboPU0eY5dQa739djxPyfS9q/Xzu5efF7S2NGQiT3zTWTclLWw7YeymsNGaAvZfCijIHqh5o8TszpfPZh5kGUsrHpJRFKWVxenrad2GmoWN2Ktc2bHYqh3XLgdG4R0rr+KuV+mVu/ObZbFzn353TX63Ues4b9NjrltM1zJXB0zcfr9fyWsd1Thc0n2nUD/h0rrPmeMeVbdMlKeh1btay2fikhckuUF/Xfq9zc3jQa+E27sfSfL2DctW5HYTNTOs4Q9cC6zAaO703G0/B0pZdIFx+M41c9Opreo/8aiJ4nCsR+JhO4820mc3W3tQ5vaGHexvtfA0265F0Q9ryG7b3EqXRMJ97W7e5MO/JUbyvp2U5SdUaxboPU0eY5WQa739djxPyfS9q/Xzu5efF7S2NGQiT3zTWTclLWw7YeymsNGaAvZfCijIHqh5oWQKwt+XvWQDfC5pGCGEAeAWAK4MsrJA38di75ryVPjtVv5b/7M4cduQ07MhpODF/Y/y5xYvYu7N+D4vWeR45eADnFi9i5qYsTtx/d9u4E/NzOLd4sevvk0+9gJ35TH15UzmcfOoF7/fWWk4+9QJmp3LYmc/gY29rX+7J+TnMTo21DTt1uIisIdoeu3X6Zi3nFi825u8e31pLZ13nFi/i0Y7neOpwEYW8CQCYmci2rbPZqfq9E84ufKdtuiQV8iZOHS4G1rzZ+LQq5EzM7sx1vc47chpmd+YwMab5vhaGDpxd+A4+9rbX4tziRTxysH3+j7/9tdiZz3jbwQvL17zcdk7bzOi5xYtdyzo5P4eZiSx2jZtd407Mz2HXeH39Tuf9x0+nfP2ngarZnZnIYjyrYe/OXFc/qed5DIDs6qeP3n83pHTx589+3zfbgAtDR1dvO37oAPbuzOHU0y9662hmIotTh4s4u/Cdrhqa2Q2j8zXw660qvCajoGp+iVQVZpsr5Pzfkwu58NNE8RhJLSexWkOse7/P0Cda3o/CfF4Kuxy/7wBh3/dGJczrQFubqhlQtW6Klqo5ULVuio6qGVC1bopWlDkQUqp3OlTjwMnzAN4M4LsAvgbgX0gpv9kyzS8B+PtSyqNCiHcA+Dkp5dt7PW6xWJQLCwu+41xXYuV6FVXbgaYJZDSBiWz7ka2rlfplbgxNIGdqqNn1yyk5roSuCWgCkBIQGmDqGjZq7dNv1FzYTn3acVPDulUfn8vU/3eZ5bhwXYmsocGR0pvW0ASqtouMriFjCNRs1zsbwdAEshkNUgKWIyGlhGno3hepqxULNdtFzZVwXYmMrmFX3sTVDRuW7cA0dEzlMlit1Hz/zjUuG1Wz3frvjsSG7UIXQD6rw3Lq45rLbL1xca3mYHmt6q2DrKFBQnRNlyTXlSiVLe+5dtay2XgfiTyRXtkF6jd2+mG15r3OpqFhspHftarERFa05TdraHClRM2R0DRAuoAQ8HKlawK6JiAATGQFyo2TsW7KGChVLIhG1l0poQmBjK5ho1bfdvKmhkpNwnZcGLqGmYmsd1PVatXG5XXLq2PXuIls9saNpyzLxkr5xvjpvOnd2JV6S2t2gd75tW0XFdtCxarnybJdrwcLDYAU0DW09dMxQ4PluHAkkG/ppc1sNzMMADVHwnJc6I1xGV2gXG1fR811JyBRtd16b+3Ibhidr0Fnbx1l70u7tOZ3s97btO+hJ/t+7Jcevm+Qkkh9qei9Yba5jQ0bpcqN9+RCzuy6WeRm00TxGEktJ6law6z7zs/QMxNZZDI3zkYJ83kpzHJs260vx+czW4BU9N4wrwNtbQNkIBW9l9kloO8cpCK7APNL7L2ktqh6r5LJkVLaQohfBvCnAHQAj0spvymE+AiABSnlFwD8HoDPCiH+DvUzWd4xzDI1TWD3K8Z6TpPvPbpvU/loH8/Pzrz//0qbzrRfkmV6Mtvz70FkMjr2TI0P/ThR0jTR87ltNj6txsaMwAbRzO0w+W2dd0+IN6SpgOHZrIE92eD5TdPAHh5YGYiq2TUMDZPGGCaHyGe/vXRHR1uKat35PY6Kr8koqJpfIlWF2ebGxoxN3/M3myaKx0hqOUnVGmbdb/YZOsznpTDLMQwNN+/I9ZwmjcK8DrS1qZoBVeumaKmaA1XrpuiomgFV66ZoRZUDZZMkpfwigC92DPu3Lb9vAHhb0nURERERqYJn2xARERERERENT9V7tBAREREREREREREREY0cD7QQERERERERERERERENiAdaiIiIiIiIiIiIiIiIBsQDLURERERERERERERERAMSUspR15AaQogVAC9vMtkuAJcTKEd1XE91l6WU98a9kJDZBfi6BOF66ZZIdoFt23v5fOLF3js41hqPsLWmrfcmZSu+lmmQdK3svemz3deBqr13u79uANcBEG4dpC27AF87gOuAvVddXAdD9l4eaOmTEGJBSlkcdR1px/WUTnxd/HG9pN9We434fLYXldYPa42HSrWOgkrrh7WqY7s/f4DrQNXnr2rdUeI6UHcdqFp3lLb7OlD1+atad5S4DoZfB7x0GBERERERERERERER0YB4oIWIiIiIiIiIiIiIiGhAPNDSv8dGXYAiuJ7Sia+LP66X9NtqrxGfz/ai0vphrfFQqdZRUGn9sFZ1bPfnD3AdqPr8Va07SlwH6q4DVeuO0nZfB6o+f1XrjhLXwZDrgPdoISIiIiIiIiIiIiIiGhDPaCEiIiIiIiIiIiIiIhoQD7QQERERERERERERERENiAdaiIiIiIiIiIiIiIiIBsQDLURERERERERERERERAPigRYiIiIiIiIiIiIiIqIB8UALERERERERERERERHRgHighYiIiIiIiIiIiIiIaEA80EJERERERERERERERDQgHmghIiIiIiIiIiIiIiIaEA+0EBERERERERERERERDYgHWoiIiIiIiIiIiIiIiAbEAy1EREREREREREREREQD4oEWIiIiIiIiIiIiIiKiAfFACxERERERERERERER0YB4oIWIiIiIiIiIiIiIiGhAPNDS4t5775UA+MOfKH8SwezyJ4afxDC//InhJxHMLn9i+EkM88ufGH4SwezyJ4afxDC//In4JzHMLn9i+EkM88ufiH8C8UBLi8uXL4+6BKKBMLukMuaXVMXsksqYX1IVs0sqY35JVcwuqYz5paQYoy4giBDicQBvBbAspXxNY9gfAtjfmGQHgKtSyrt85n0JwHUADgBbSllMpGgiIiIiIiIiIiIiItpWUnugBcCnAXwCwOnmACnlzzd/F0J8DMAPe8z/j6WUPGRJRERERERERERERESxSe2BFinl00KIfX7jhBACwNsBvCnJmoiIiIiIiIiIiIiIiFqpeo+WHwdwSUr5rYDxEsCXhRCLQoj3JVgXERERERERERERERFtI6oeaHkngM/1GP8GKeXdAH4KwC8JId4YNKEQ4n0IPTm9AAAgAElEQVRCiAUhxMLKykrUdRLFhtkllTG/pCpml1TG/JKqmF1SGfNLqmJ2SWXML42CkFKOuoZAjUuH/bGU8jUtwwwA3wUwJ6VcCvEYvwFgTUr5m5tNWywW5cLCQuB415UolS1YtgPT0FHIm9A0sek42tYSCcFm2W21WVaZZWpI7EXvJ7/9iiLP3CaUlIrea9sulteqqDkuMrqGmYksDEPV/+NCCdkSvbdf+x56su95Xnr4vhgqoSGx95KqtmXvpXTqs4elJrv8zkQDZCA1+SWKqvem9h4tPfwTAM8FHWQRQuQBaFLK643f3wLgI8Mu1HUlLly6jiOnF7C0WsHsVA6nDhexf/ckAASO4xsLpUmvHGua2HQ8kUqiyDO3CRqUbbt47tJ1HD2z6GXn5Pwc7tg9yR1+REQxYe8lIpWp2sP4nYmYAVJZlL03tZ1aCPE5AH8DYL8QYkkI8d7GqHeg47JhQoibhRBfbPy5G8BfCiG+AeCrAJ6UUn5p2HpKZctrGACwtFrBkdMLKJWtnuOI0mSzrDLLtJVEkWduEzSo5bWq90ENqGfn6JlFLK9VR1wZEdHWxd5LRCpTtYfxOxMxA6SyKHtvas9okVK+M2D4e3yGfQ/ATzd+fxHAa6Oux7Idb4U3La1WYNmO93vQOKK02CzHm40nUkkUeeY2QYOqOa5vdmzHHVFFRERbH3svEalM1R7G70zEDJDKouy9qT2jJW1MQ8fsVK5t2OxUDqah9xxHlCabZZVZpq0kijxzm6BBZXTNNzuGzo9eRERxYe8lIpWp2sP4nYmYAVJZlL033d06RQp5E6cOF70V37zeYCFv9hxHlCabZZVZpq0kijxzm6BBzUxkcXJ+ri07J+fnMDORHXFlRERbF3svEalM1R7G70zEDJDKouy9QkoZdX3KKhaLcmFhIXC860qUyhYs24Fp6CjkTe+mTr3G0baWSAg2y26rzbLKLFNDYi96P/ntVxR55jahpFT0Xtt2sbxWhe24MHQNMxPZVN/IlFJhS/Tefu176Mm+53np4ftiqISGxN5LqtqWvZfSqc8elprs8jsTDZCB1OSXKKrem9p7tKSRpglMT/ofzeo1jihNNssqs0xbSRR55jZBgzIMDTfvyG0+IRERRYa9l4hUpmoP43cmYgZIZVH1Xv7XHiIiIiIiIiIiIiIiogHxjJYh8fRI2i6YddruuA1Qv5gZIqLksfcSkcpU7WGq1k3E7FKUeKBlCK4rceHSdRw5vYCl1Yp3s6f9uye5UdKWwqzTdsdtgPrFzBARJY+9l4hUpmoPU7VuImaXosZLhw2hVLa8jREAllYrOHJ6AaWyNeLKiKLFrNN2x22A+sXMEBElj72XiFSmag9TtW4iZpeixgMtQ7Bsx9sYm5ZWK7BsZ0QVEcWDWaftjtsA9YuZISJKHnsvEalM1R6mat1EzC5FjQdahmAaOmancm3DZqdyMA19RBURxYNZp+2O2wD1i5khIkoeey8RqUzVHqZq3UTMLkWNB1qGUMibOHW46G2UzWv5FfLmiCsjihazTtsdtwHqFzNDRJQ89l4iUpmqPUzVuomYXYqaMeoCVKZpAvt3T+LzD7wBlu3ANHQU8iZvmERbDrNO2x23AeoXM0NElDz2XiJSmao9TNW6iZhdihoPtAxJ0wSmJ7OjLoModsw6bXfcBqhfzAwRUfLYe4lIZar2MFXrJmJ2KUq8dBgREREREREREREREdGAUn1GixDicQBvBbAspXxNY9hvADgCYKUx2f8qpfyiz7z3AvgtADqA35VSPjxsPa4rUSpbPU8na50mn9WxbrmoOS4yuoaZiSwMg8e2KHlhshtmvqlcBteqNVQsB46UGMvo2JXP8rRKSiW/3APoyvRqpZbIacKDboekLsuysVK2YLsShiYwnTdhmqn+6EVEpLxazcHyWtXrvTMTWWQyvKntdqLqZy5V66ZoqdrDbNvF8lqV+7+2MWaAVBbVd/e0f9v/NIBPADjdMfzjUsrfDJpJCKED+D8B/ASAJQBfE0J8QUr57KCFuK7EhUvXceT0ApZWK94NkvbvnvQ+/LRO8/pbC5h/3S144IlnvOlPzs/hjt2TbDSUqDDZDTPfW+6cwQfvvQMr16t48Oz5vh6LKGlBuc8aGg4//tW2vvzbX3keX352OdY8D7odkrosy8aFlTKOnVn0XvMT83PYP53nwRYiopjUag6eW17r6r13zEwosaOShqfqZy5V66ZoqdrDbNvFc5eu42hL3dz/tb0wA6SyKL+7pzrtUsqnAVwZYNYfA/B3UsoXpZQWgD8A8LPD1FIqW96HHgBYWq3gyOkFlMqW7zRH3nird5ClOf3RM4tYXqsOUwZR38JkN8x8B+f24uKVineQpZ/HIkpaUO5fLq139eWDc3vbpokjz4Nuh6SulbLlfVAD6q/5sTOLWOFrTkQUm+W1qm/v5Xew7UPVz1yq1k3RUrWHLa9VvR3sAPd/bUfMAKksyu/uqT7Q0sMvCyHOCyEeF0JM+YzfA+Biy99LjWFdhBDvE0IsCCEWVlZW/CYBAFi2461w70FXK7Bsx3caXRO+09uO2/OJEYUVZXbDzLcjl8G4qQ/0WESdwuZ3UEG5Hzf1rmE7cpm2v+PI86DbIaVP2OzarvT/HODKuEskChR37yWKC3svhZXGz1xh8pvGuil5aethYXtvzXG5/2ubS2MG+LmXwoqy96p4oOUEgNsA3AXg+wA+5jON37m1vmtHSvmYlLIopSxOT08HLtQ0dMxO5dqGzU7lYBq67zSOK32nN3QVVzmlUZTZDTPf1UoN65Yz0GMRdQqb30EF5X7dcrqGXa3U2v6OI8+DboeUPmGza2jC/3MAL/9BIxR37yWKC3svhZXGz1xh8pvGuil5aethYXtvRte4/2ubS2MG+LmXwoqy9yrX9aSUl6SUjpTSBXAK9cuEdVoCsLfl71kA3xtmuYW8iVOHi96Kb14ztXlz5c5pTj39Ih69/+626U/Oz2FmIjtMGUR9C5PdMPOdW7yIvTtzOH7oQN+PRZS0oNzfUhjv6svnFi+2TRNHngfdDkld03kTJ+bn2l7zE/NzmOZrTkQUm5mJrG/v5Xew7UPVz1yq1k3RUrWHzUxkcbKjbu7/2l6YAVJZlN/dhZTpPo1aCLEPwB9LKV/T+PuVUsrvN37/dQD/QEr5jo55DADPA3gzgO8C+BqAfyGl/GavZRWLRbmwsBA43nUlSmULlu3ANHQU8mbXjelap8lndaxbLmzHhaFrmJnI8iZQ208i//UkiuyGmW8ql8G1ag0Vy4EjgbGMhl35LG/QuDUl9qJult9B+eUeQFemVyu1vreNqOrhthObVPRey7KxUrZguxKGJjCdN/u+mR5tO8r33kHse+jJvud56eH7YqiEhpSK3lurOVheq3q9d2Yim+qbSFP0BvjMlYrey8+KBPTdw1KRXaB+M/TltSr3f21jA2QgNfkl6vO7e2B2U/1tXwjxOQD3ANglhFgC8GEA9wgh7kL9UmAvAXh/Y9qbAfyulPKnpZS2EOKXAfwpAB3A45sdZAlD0wSmJ3sfje2cZsf4sEslGl6Y7Iadb6eRBfJRVUYUn6Dcdw4bZNuIsh7aukzTwB4eWCEiSlQmo2PPFL+EbWeqfuZStW6Klqo9zDA03Lwjt/mEtGUxA6SyqL67p/rbv5TynT6Dfy9g2u8B+OmWv78I4IsxlUZERERERERERERERJTuAy1p4ncJpdbLzUzlMrhSsbBRc6ALgZypY0eOp/pSemyWYb/LKkV5ujpPhaekhMl6M3uD5JJZprB4+RpivyBKHi/bSEQqU7WHNS8bVXNcZHjpsG1J1Qzw8zoB0X13T3+3TgHXlbhw6TqOnF7A0mrFu6nTb3/leXz52WW85c4ZfODNt+PomUVv/PFDB7D7pjHsK+S5gdLIbZbh2akcTv/ij6Fqu23TnDpcxP7dk0Nn2G/5UT02UaswWW9mD0DfuWSWKaxazcFzy2s41vLZ4MT8HO6YmeDBlm2C/YIoeZZl48JKuav37p/OK7Gjkoi2N1V7mG27eO7S9bZ9Yifn53DH7kkldrTT8FTNAD+vExDtd/f0pj1FSmXL2+gAYGm1gqNnFnFwbi8A4ODcXq+ZNMc/ePY8Xi6to1S2RlY3UdNmGV5areDl0nrXNEdOL0SSYb/lR/XYRK3CZL2ZvUFyySxTWMtrVe+DGlDPyrEzi1heq464MkoK+wVR8lbKlm/vXeF2R0QKULWHLa9Vu/aJHeXn3m1F1Qzw8zoB0X53T+8h8RSxbMdb2U1LqxXsyGUAADtyGd/x46YOy3YSq5MoyGYZBoBxU/edJooMBy2f2wdFLUzWW7PXby6ZZQrLdqVvVmxXjqgiShr7BVHy2HuJSGWq9rCa4/rX7bgjqoiSpmoG+HmdgGh7L89oCcE0dMxO5dqGzU7lcLVSAwBcrdR8x69b9ev7EY3aZhkGgHXL8Z0migwHLZ/bB0UtTNab2Rskl8wyhWVowjcrBk9B3zbYL4iSx95LRCpTtYdldM2/bp27HLcLVTPAz+sERNt70534lCjkTZw6XPRWevNag+cWLwIAzi1exMn5ubbxxw8dwC2Fce8G40SjtFmGZ6dyuKUw3jXNqcPFSDLst/yoHpuoVZisN7M3SC6ZZQprZiKLEx2fDU7Mz2FmIjviyigp7BdEyZvOm769d5rbHREpQNUeNjOR7dondpKfe7cVVTPAz+sERPvdXUiZ7lMQk1QsFuXCwoLvONeVKJUtWHb9LJWpXAarlVrb31cqFjZqLnQB5EwdO3Imb55EiQSgV3abNstw842kdZpCProMdy4/ysemWCT24oTJbz/CZL2ZvUFyySwrIRW9t1ZzsLxWhe1KGJrAzES275vpkdoG6BfK9t5h7Hvoyb7neenh+2KohIaUit5rWTZWypbXe6fzZqpvIk2psC17L6VTnz0sNdm1bbf+uddxYegaZiayqb4JOkVvgAykIr/8fk9A39/dAwPCT5whaZrA9GT7kazOv2cmx5IsiagvYTIcNCyu5RPFIWzWg6Yd5PGJ/GQyOvZMjY+6DBoh9gui5JmmgT08sEJEilK1hxmGhpt35DafkLYsVTPAz+sERPfdnYeXiYiIiIiIiIiIiIiIBqTeYfIRap4GV3NcZHgqJCnEdSUul6vYqDnQheCl7WhLc12JqxULFcuBIyXGMjp25bPMOyVOpdPQVaqViKgXfmcjIpWxh5GqmF1SWVT55YGWkGzbxXOXruPomUUsrVa8GzvdsXuSjYNSzXUlLly6jiOnF7zsHj90ALtvGsO+Qp470mhLcV2Jl0plXLq2gQfPnvcyf+pwEft3TzLvlBi/3pvWHKpUKxFRL/zORkQqYw8jVTG7pLIo88u0h7S8VvVWOAAsrVZw9MwilteqI66MqLdS2fJ2ngH17D549jxeLq2jVLZGXB1RtEplCy+X1r2DLEA980dOLzDvlCi/3pvWHKpUKxFRL/zORkQqYw8jVTG7pLIo88sDLSHVHNdb4U1LqxXYjjuiiojCsWzHN7vjpg7LdkZUFVE8LNvBuKn7Zp55pyQF9d405lClWomIeuF3NiJSGXsYqYrZJZVFmd/UHmgRQjwuhFgWQvy3lmHHhRDPCSHOCyE+L4TYETDvS0KI/08I8XUhxEIU9WR0DbNTubZhs1M5GHpqVyERAMA0dN/srlv16/ATbSWmoWPdcnwzz7xTkoJ6bxpzqFKtRES98DsbEamMPYxUxeySyqLMb5oT/2kA93YM+zMAr5FSHgDwPIB/3WP+fyylvEtKWYyimJmJLE7Oz3krvnm9tpmJbBQPTxSbQt7EqcPFtuweP3QAtxTGUcibI66OKFqFvIlbCuM4fuhAW+ZPHS4y75Qov96b1hyqVCsRUS/8zkZEKmMPI1Uxu6SyKPNrRF1cVKSUTwsh9nUM+3LLn/8PgENJ1WMYGu7YPYk/ev/rYDsuDF3DzESWN3Wi1NM0gf27J/GfHng9NmoudAHkTB07ciZvckxbjqYJ7CvksWM8gz983z+EI4GxjIZd+SzzTolq9t7PP/AGWHb9DMJCPp19V6VaiYh64Xc2IlIZexipitkllUWZ39QeaAnhFwH8YcA4CeDLQggJ4JNSyseiWKBhaLh5R27zCYlSRtMEZibHRl0GUSI0TWBnPgvkR10JbXeaJjA9qcb/4lKpViKiXvidjYhUxh5GqmJ2SWVR5VfJQ4tCiH8DwAbwRMAkb5BS3g3gpwD8khDijT0e631CiAUhxMLKykoM1RLFg9kllTG/pCpml1TG/JKqmF1SGfNLqmJ2SWXML42Ccme0CCHeDeCtAN4spZR+00gpv9f4d1kI8XkAPwbg6YBpHwPwGAAUi0Xfx2uqVm1cXrdguxKGJrBr3EQ2a8B1JUplK/ByG5uNJxpEP9m1bRcr5Sos24WuCWQ0gZ3jJjKZ9hsdx5FV5p/89JPfoAyFGa4JAU0AVcfFWEZvu4QYs6mONL1W/WR3Y8NGqXLjc0MhZ2JsLJ0fvdK0jik+/eSXKE22au+l7YG9l/qRph7W7z6H5bUqao6LDC8btS0F7TMdlbD5rdUcLK9VvbpnJrJd+8po67MsGyvlG/mdzpswzf7zq9QnTiHEvQA+BOAfSSnXA6bJA9CklNcbv78FwEeGXXa1auP5y2UcO7OIpdUKZqdyODE/h1cX8vj26jqOnF7whp86XMT+3ZPeDr8Ll64HjieKm227uHDpOt7fkt3jhw7g2qSNfVPj3htIHFll/mlYQRl69fQEvrWyFmr48UMH8NEvXcDKWtXLHwBmUxGq9pGNDRvfKvl/bkjbDj9V1zERUSeVei8RUSdVe5htu3ju0nUcban75Pwc7tg9yYMt20TQPtPbd+VHerBlM7Wag+eW17rqvmNmggdbthHLsnFhpTu/+6fzfR9sSW3HE0J8DsDfANgvhFgSQrwXwCcATAL4MyHE14UQJxvT3iyE+GJj1t0A/lII8Q0AXwXwpJTyS8PWc3nd8lY4ACytVnDszCJKFcvbMdEcfuT0AkplCwBQKvceTxS35bWqd5AFqGfwwbPnsXSlguW1qjddHFll/mlYQRlaXquGHv7g2fM4es9tbfljNtWh6mtVqgR/bkgbVdcxEVEnlXovEVEnVXvY8lrVO8gC1Os+emaxbX8DbW1B+0wvr6c/u351M7vby0rZP78rA3wfTu1hRSnlO30G/17AtN8D8NON318E8Nqo67Fd6a3wpqXVSuBwy3YAAJbt9BxPFLea4/pmcNzUYbs3zp6MI6vMPw0rKEN2QK6D8r4jl/F+b+aP2VSDqn2k1+eGtFF1HRMRdVKp9xIRdVK1hwV9B7Mdd0QVUdJUza6qdVO0osxBas9oSRtDE5idyrUNm53KBQ43jfopZqah9xxPFLeMrvlmcN1yYLRcEiaOrDL/NKygDBkBuQ7K+9VKzfvdNHRmUyGqvla9PjekjarrmIiok0q9l4iok6o9LOg7mKFzl+N2oWp2Va2bohVlDtj1Qto1buLE/Jy34pvXayvkTJw6XGwbfupwEYW8CQAo5HuPJ4rbzEQWn+zI7vFDBzC7M4eZiaw3XRxZZf5pWEEZmpnIhh5+/NABnHzqhbb8MZvqUPW1KuSCPzekjarrmIiok0q9l4iok6o9bGYii5MddZ+cn2vb30BbW9A+013j6c+uX93M7vYynffP7/QA34eFlPGeDtW4IX1FSukKIW4HcAeAP5FS1mJd8ACKxaJcWFgIHF+t2ri8bsF2JQxNYNe4iWzWgOtKlMoWLNuBaego5M22m8duNp62tERe6M2ya9suVspV1GwXmiaQ0QR2jptdN/eKI6vMv7ISe5E2y29QhsIM14SAJoCqIzGW0bArn/Xyx2yqY4DXKhW9d2PDRqly43NDIWem9kam3B5SIzW9N0n7Hnqy73leevi+GCqhIbH3kqq2Ze+ldOqzh6Umu7btYnmtCttxYegaZiayMAz+3+7tJGifaQ+pyG+t5tSz26h7ZiLbta+Mtj7LsrFSvpHf6bwJ0+y/9ybxifNpAD8uhJgC8BUACwB+HsD9CSw7UtmsgT0+TULTBKYng492bjaeKG6GoeGVr8htOl0cWWX+aVhBGep3+KDT0eip+lqNjRnYo8jOPVXXMRFRJ5V6LxFRJ1V7mGFouHnH5vscaOsK2meadpmMjj1T46Mug0bMNA3sCT6wEloSh5eFlHIdwM8B+B0p5T8HcGcCyyUiIiIiIiIiIiIiIopVEocahRDidaifwfLeBJcbG7/LawDwhuVMHbYrIV0J25VwXAlNE8iZGqZyWV6Kg0am38vcTeUyuFatoWI5cKTEWEb3Lr202XbAS89Q0npl0nVdrx/rmkDW0KBpArYrUbNd5pViwcvXEBElj72XiFSmag8b4LJRtMWoeiliVeumaEXVw5Loer8K4F8D+LyU8ptCiFsB/EUCy42F60pcuHQdR04vYGm14t0wNmtoOPz4VzE9kcUH792PT/3Vt/Hu1/8oPnTuvDfd8UMHsPsmG/sKeW60lLig7O7fPekdOGkd/5Y7Z/DBe+/AyvUqHjx7vm2eV09P4Fsra22PdfoXfwxV2w18fKI49erND//Jf+/qx4+/p4jrGzZ+9Q++zrxSLDY2bHyrVMaxM4texk7Mz+HVhbwSX5aJiFTE3ktEKlO1h1WrNp6/3F337bvyPNiyTWy2vymtVK2bohVlD4v10mFCCB3AP5VS/oyU8hEAkFK+KKX8QJzLjVOpbHkbIAAsrVZw5PQCXi6tY2m1gqP33IYHz57Hwbm93k695nQPnj2Pl0vrKJWtUT4F2qaCstvMY+f4g3N7cfFKxTvI0jrP8lq167FeLq33fHyiOPXqzX79+LurG95BltbpmVeKSqlieR/UgHrGjp1ZRKnCjBERxYW9l4hUpmoPu7zuX/fl9XTXTdHZbH9TWqlaN0Uryh4W66FlKaUjhJiLcxlJs2zHW/FNS6sVjJs6AGBHLoOl1Yr3r990lu0kVi9RU1B2m3nsHL8jl/Gm6ZzHdtyu4eOm3vPxieLUqzePozubzCvFzXalf/905YgqIiLa+th7iUhlqvYwVeum6Gy2vymtVK2bohVlD4v1jJaGvxVCfEEI8S4hxM81fxJYbixMQ8fsVK5t2OxUDutWfSO8Wqlhdirn/es3nWnoidVL1BSU3WYeO8dfrdSwbjm+8xi61jU8aFrmnZLQqzf79WPmleJmaMK/f/IUdCKi2LD3EpHKVO1hqtZN0dlsf1NaqVo3RSvKHpbEgZadAEoA3gTgnzZ+3prAcmNRyJs4dbjovQDN6/fdUhjH7FQOJ596AccPHcC5xYt45OCBtumOHzqAWwrj3g2aiZIUlN1mHjvHn1u8iL0767ntnGdmItv1WLcUxns+PlGcevVmv368Z2oMv/WOu5hXik0hZ+LE/Fxbxk7Mz6GQY8aIiOLC3ktEKlO1h+0a969713i666bobLa/Ka1UrZuiFWUPE1LyVL6mYrEoFxYWNp3OdSVKZQuWXT87pbkBNoflTB22KyFdCduVcFwJTRPImRqmclneUGl7SeTFHia7rXnsHD+Vy+BatYaK5cCRwFhGw658PcObbQd+j09KSeyFC5vfzfTKpOu6Xj/WNYGsoUHTBGxXoma7zOvWk4reu7Fho1SxYLsShiZQyJmpvpEppYJyvTcK+x56su95Xnr4vhgqoSGx95KqtmXvpXTqs4elJrvVqo3L6zfq3jVu9n0TaVLbZvubfKQivwPUTVtQnz0sMCCxdz0hxBiA9wL4HwCMNYdLKX8x7mXHRdMEpiezXcP9hhGlSVB2e43faWSBfPjH4nZAo8JMUtqMjRnYw517RESJYu8lIpWp2sOyWQN7eGBlW9tsf1NaqVo3RSuqHpbEpcM+C+BHAPwkgP8CYBbA9QSWS0REREREREREREREFKskDjf/PSnl24QQPyul/IwQ4vcB/GmYGYUQj6N+P5dlKeVrGsN2AvhDAPsAvATg7VLKVZ953w3gf2v8+X9IKT8z7BOp1Rwsr1W904jGTQ2T2fopZVcrVuPyShIZTUM2I1Cx6peqyegaZiayMAzNOyXNdV04EpBS9nVq2rDz03BUPaXQtl0sr1VRc1wYjcsm3ZQ18MOq43uZMNuVyBkaHAm4fWSsuX4EJKr2jfxP501c3bCVW29biarZDeK6EpfLVWzUHGR1DUII1BwXjith6BoACU0IaELAagw3dQ3TE/X/qdLcHlr78yiew1Z6TaibSpevYR6JaKtQqfdSPCzLxkr5Rgam8yZMM/0ZULVuipaqPUzVuik6ql4+rnV/2Sj3D9BoRdXDkkh8rfHvVSHEawD8APWDJGF8GsAnAJxuGfYQgK9IKR8WQjzU+PtDrTM1DsZ8GEARgASwKIT4gt8BmdBPoubgueU1HDuziKXVyo0b40y4AICXLq/jwbPnsbRawVvunMGvvPn2tmlPzs9h/8wE/u5yGR//swt49+t/FB86d94bf+pwEft3T/bcqeG6EhcuXR94fhpOc/0fOb2g1Hq3bRfPXbqOoy15fPT+u1Eez+Avnv0BfuOPn8Nb7pzBB++9AyvXq3jw7HlMT2TxwXv3e5kO81yb6+c/P3MR9712Dx544pm2beV3vvI8vvzssjLrbStRNbtBWp/P9EQWv/Ezd2Ldctry+h9+/i5Mjum4Uq61Df/ku+aQNTS851Nfa+vPd+yeTPTD1FZ7TajbxoaNb5XKXZ8bXl3Ip+5LJ/NIRFuFSr2X4mFZNi6sdGdg/3Q+1QctVK2boqVqD1O1bopOtWrj+cvdGbh9Vz7VB1v89peNYv8AjVaUPSyJ1DwmhJgC8L8D+AKAZwF8NMyMUsqnAVzpGPyzAJpnp3wGwD/zmfUnAfyZlPJK4+DKnwG4d4DaPctrVW+FA8DSagXHzizCdgDbgbcjDwAOzu3tmvbomUUsr1Vx5PQCDs7t9Q6SNG5pbCkAACAASURBVMcfOb2AUtnqWUOpbA01Pw2nuf5VW+/La1XvTQOo1/3AE8+gZku86c5XAqhn9uKVipfjo/fc1pbpMM+1uX4OFV/lHWRpznvszCIOzu0N/VgULVWzG6T1+Ry957a2gylA/fn92h9+Hbqmdw1//2cXcfFKxbc/j+o5NOtQ+TWhbqWK5fu5oVRJ32vMPBLRVqFS76V4rJT9M7CS8vc0VeumaKnaw1Stm6Jzed0/A5fX050Bv/1lo9g/QKMVZQ+L/bCilPJ3G7/+FwC3RvCQu6WU32889veFEDM+0+wBcLHl76XGsC5CiPcBeB8AvOpVrwpcqO1Kb4V7D7pagSslZOP3ph25jO+0zccIGm/ZTuDyAcCynaHmp+E013+rUa73sNmtOa5v3ZqoX3oOqGe2Obz5d7/Ptbl+dE34zttcRpjHomilLbtA+Pz6aX0+ndltambcb/i4qXcNsx23rxqGlcbXhMIZ9nOD7cpY6xsE87h9DNN7iUZpK/ZeikcaMxAmv2msm5KXthyw91JYacxAmPwG7S9Lev8AjVaU+Y39jBYhxG4hxO8JIf6k8fedQoj3xr1Yn2G+a0dK+ZiUsiilLE5PTwc+oKEJzE7l2obNTuWgCQFdtI+7Wqn5Ttt8jKDxptG+86+TaehDzU/Daa7/VqNc72Gzm9E137pdCQhR31SuVmpYtxxvukEy1lw/jit9571aqbX9zbwmJ23ZBcLn10/r8+nMblMz437D1y2na1j9vi7JSeNrQuEM+7nBSOGluJjH7WOY3ks0Slux91I80piBMPlNY92UvLTlgL2XwkpjBsLkN2h/WdL7B2i0osxvEsn5NIA/BXBz4+/nAfzaEI93SQjxSgBo/LvsM80SgL0tf88C+N4Qy8TMRBYn5ue8Fd+8XpuhA4YOHD90wBt3bvFi17Qn5+cwM5HFqcNFnFu8iEcOHmgbf+pwEYW82bOGQt4can4aTnP9q7beZyayONmRx0fvvxsZQ+DPn/0+gHpm9+7MeTk++dQLbZkO81yb6+fswnfw6P13d20r5xYvhn4sipaq2Q3S+nxOPvUCduYzXXn9Dz9/FxzX6Rr+yXfNYe/OnG9/HtVzaNah8mtC3Qo50/dzQyGXvteYeSSirUKl3kvxmM77Z2A65e9pqtZN0VK1h6laN0Vn17h/BnaNpzsDfvvLRrF/gEYryh4mmpcOiosQ4mtSyv9JCPG3Usr/sTHs61LKu0LOvw/AH0spX9P4+ziAkpTyYSHEQwB2Sik/2DHPTgCLAO5uDHoGwJyUsvN+L22KxaJcWFgIHF+rOVheq8J2JQxNYNzUMJk1oWkCVysWKpYDR0pkNA3ZjEDFcuG4EoauYWYiC8PQ4LoSpbIF13XhyPqlm0xDRyFvhrrh7LDz03Ca69+ynbDrPZEXZbPs2raL5bUqao4LQxPIGhpuyhr4YdXxnstULoNr1Vo9x67EmKHBkYDbR8aa60dAomrfyP903sTVDbuf9UYRS2t2gc3z68d1JS6Xq9ioucjqAkII1JwbmROQEEJAEwKW48J1JTK6hunGB6bltSpsx23rz0kb4DWh8FLRezc2bJQqlve5oZAzU3tDUOYxNVLde+Oy76En+57npYfvi6ESGhJ7L6WCZdlYKd/IwHTe3OyG8qnovQPUTVtQnz0sFdkF2HsJqFZtXF6/kYFd4yay2fT33ub+slHvH6DRiqr3JtH1ykKIAhqX7hJC/EMAPwwzoxDicwDuAbBLCLEE4MMAHgbwR43Lj30HwNsa0xYBHJVS/ksp5RUhxL8H8LXGQ31ks4MsYWQyOvZMjfuO25nPAvmOgZ1/A9A0genJwY+MDjs/DUfV9W8YGm7ekesaPt3xwX2n4ZPjPvRaP9MZXoJmlFTNbhBNE5iZHBt4fr/tIWlb7TWhbmNjBvYo8gWTeSSirUKl3kvxME0DexQ8QKFq3RQtVXuYqnVTdLJZA3t6H1hJpaD9ZbS9RNXDktgC/hWALwC4TQjxVwCmARwKM6OU8p0Bo97sM+0CgH/Z8vfjAB7vu1oiIiIiIiIiIiIiIqKQYj/QIqVcFEL8IwD7UT+15oKUsrbJbKnkd/klTQMcV3jXMb9etbC24XinGmUNDRMZA9csGxs1B7omIARgaBp2TWR5WQ5KhOtKrKxVvQxmNIFcVkBA4FqlPa9ly4GhifrfGQ2WI1GzXWQMDYYmULEcCCGgC0DTNF5ehhLReenGXeMmrlRq9X6sa9AFsGHXe3NGF3Bl/SZklivhuhL5rA5HAjXb9fIrGjmvWLxUEsVDpUso8NJhRLRVqNR7KR6qvqe17m/I8PI125aqPUzVuik6qmZA1fcMilZU+Y098UKIBdTPLPmclHI17uXFxbZdPHfpOo6eWcTSasW7ofgrxjP4i2d/gH9w2zQKExlcumbhWMs0j7+niB/Y1bb5Hjl4AJ/562/j1//Jfuz/kUluwBQr15W48INrOPLZGxk8fugAbpvJ49K1alteH73/bpz5m5fx1y+W8Mn5u+FK4NgTz7TN99EvXcDKWvVGjn9iP/bvZo4pPrWag+eW17ysvuXOGXzgzbe39dXWbJ6Yn0MuI7By3cKDZ89jeiKLD967Hw+ePd/Vh3/hDT/qzXfqcJFZpshsbNj4Vqnc1mNPzM/h1YV86r5wuK7EhUvXceT0glcrtwciUpFKvZfioep7mt/+hpPzc7hj9yQPtmwjqvYwVeum6KiaAVXfMyhaUeY3iXfsdwDYA+BrQog/EEL8pBBCubQur904WAIAS6sVPPDEM6jZEm+685U4cnoBli29F6U5zXdXN7rm+9C58zg4txdHPruAUtka2XOi7aFUtryDLEA9gw+ePe+b1weeeAZH3ngrllYrWL5ueQdZWuc7es9t7Tk+zRxTvJbXqm1ZPTi3t6uvtmbz2JlFAJp3YOXoPbd5vzenb+a3dT5mmaJUqlhdPfbYmUWUKunLWKlseV8uAHB7oKHse+jJvn+IoqJS76V4qPqe5re/4eiZRSyvVUdcGSVJ1R6mat0UHVUzoOp7BkUryvzGfqBFSvl3Usp/A+B2AL+P+tkt3xFC/DshxM64lx+VmuN6K7xpabUCTQBSSiytVuC4smuacVP3nW9HLoOl1Qos24m9dtreLNvxzaBfXpdWK9AbR+17Zbf1d+aY4mZ3ZLWZu1ad2dQEvGl6Td85H7NMUenMLVDPmO3KEVUULOh9gtsDEalGpd5L8VD1PS1of4PtuCOqiEZB1R6mat0UHVUzoOp7BkUryvwmcg6qEOIAgI8BOA7gHIBDAK4B+PMklh+FjK5hdirXNmx2KgdXAkIIzE7loGuia5p1y/Gd72qlhtmpHExDj7122t5MQ/fNoF9eZ6dycBqNpFd2W39njiluRkdWm7lr1ZlNV8Kbptf0nfMxyxSVztwC9YwZKTwFPeh9gtsDEalGpd5L8VD1PS1of4Oh87Jh24mqPUzVuik6qmZA1fcMilaU+Y39XVsIsQjg4wC+BuCAlPIDUsr/V0r5MQAvxr38qMxMZHFyfs5b8c37WWQMgT9/9vs4dbgI0xA40THNnqmxrvkeOXgA5xYv4tS7iijkzZE9J9oeCnkTp97VnsHjhw745vXR++/GqadfxOxUDjOTJk7cf3fXfCefeqE9x4eZY4rXzES2LavnFi929dXWbJ6YnwPg4vihA/XrWz/1gvd7c/pmflvnY5YpSoWc2dVjT8zPoZBLX8YKeROnDhfbauX2QEQqUqn3UjxUfU/z299wcn4OMxPZEVdGSVK1h6laN0VH1Qyo+p5B0Yoyv0LKeE/jEkLcKqUMPKAihHi3lPIzsRYRUrFYlAsLC4HjbdvF8loVNceFoQlkDQ2aBjiu8DbC61ULaxsObFd600xkDFyzbGzYDnQhoAlA1zTsmsjy5kpbXyIv8GbZdV2JlbUqqjUHmiaQ0QRyWQEBgWuV9ryuWw50TdT/zmiwHIma7SJjaDA0gYrlQAgBXQCapqGQN5njrSmxF3Wz/AJAreZgea3qZXXXuIkrlVq9H+sadAFs2PXenDEEXLf+PwksV8J1JfJZHY4Earbr5Vc0cl6xHJiGzixvLanovRsbNkoVy8ttIWem9maQritRKluwbG4PI5aq3juIpO658tLD9yWyHOoLey+lwgDvaanovc39DXbj8+3MRBaGwTNatps+e1gqsguw99JAGUhFfvk9iIDoem/sXa/XQZaGXwWQigMtmzEMDTfvyPWc5hW5LF7hMwnfYGiUNE1g901jvuMm/QcHyw9fD1G/Mhkde6bG24a9MhtRX2WmKSZjYwb2KPL+r2kC05P8X7NEpD6Vei/FQ9X3tDD7G2jrU7WHqVo3RUfVDKj6nkHRiiq/afjvETxMSERERERERERERERESkrDocZ4r10WIb9Lh+k6sF51kc/q3iWWcqaOmiOxUXNgNC5NU3MlTF2DrgE1R8JxpXc6kqEJOFICEImfHuy6EpfLVWzU6pc1y5k6duR4mtxW45ddQweEEKjWJCzHhetK6JqAEAAgsHMs03ba3K5xE9ms0fO0ymFPuWytM8PT5alFs1dVaw40IZDRBWyn3kfHTR2W7aLWcgm8bAa4VnG9/GZ0AaDeay27nq/pvImrG7aX16lcBquVGk8ZpsiodAkFnjJPRFuFSr2X4qFqBlStm6Klag5UrZuio2oGuB+KgOjym4bEK/Et3rZdPHfpOo6eWcTSasW7cfgrxjN4eeU6duTH8MATz2B6IosP3rsfD5497013/NABfPRLF7CyVsWJ+++GJoD3n3mmbXzO1PHoX/wdPvDm23HH7slENmrXlbhw6TqOnF5oq2X3TWPYV8hzB8sWEZTdH3lFFj+s1HD5erUtr48cPIDP/PW38Stvvh2/85Xn8eVnl70bQb26kMe3V9fbMnPqcBH7d08CQFeemuPCZMmvzpPzc4ltD5Renb3qLXfO4Ffe9Goce+IZvP7WAuZfdwseeOJGT/3Ue4qo2rItS36998T8HP7460v45H99ycvbb7dkvp/8EnXa2LDxrVIZx1pz2OijafvC4fd5gPknIhWp1HspHqpmQNW6KVqq5kDVuik6qmaA+6EIiDa/aUjNX426gDCW16rehgcAS6sVPPDEM6jZErfN3OTt5Dt6z23eTuvmdA+ePY+j99yGpdUKjj3xDJavW13jV8s1HJzbi6NnFrG8Vk3kOZXKlrdTpbWWl0vrKJWtRGqg+AVl17Illq5UuvL6oXPncXBuL46dWcTBub3e8GNnFlGqdGfmyOkFlMqWb56a4watM8ntgdKrM1sH5/biWKPnHnnjrV7/Beq5WVrd6MqSX+89dmYRh4qv8v4+2pH5fvJL1KlUsbwPakB7H02bYfs3EVFaqNR7KR6qZkDVuilaquZA1bopOqpmgPuhCIg2v7EfaBFCvEII8XEhxELj52NCiFc0x0spfznuGqJQc1xvhTctrVagCcB2pTduRy7jO92OXMb7fdzUu8aPm7o3r+24MT6TGyzb8a21fhkeJ5EaKH5B2XUal1wKymtrbpvDW7PeOtyyncA8hc1SUJ1JbQ+UXp3Zau2zuia6chOUa7/eq7f8b32/zLMX0qCC+qXtpu+KqcP2byKitFCp91I8VM2AqnVTtFTNgap1U3RUzQD3QxEQbX6TOKPlcQDXALy98XMNwKeGeUAhxH4hxNdbfq4JIX6tY5p7hBA/bJnm3w6zzIyuYXYq1zZsdioHVwKGJrxxVys13+muVmre7+uW0zV+3XK8eQ09mRONTEP3rXXdql+fnbaGoOzqmsC65QTmtTW3zeGtWW8dbhp6YJ7CZimozqS2B0qvzmy19lnHlV25Ccq1X+91Wt44/TLPXkiDCuqXRgovxTVs/yYiSguVei/FQ9UMqFo3RUvVHKhaN0VH1QxwPxQB0eY3ieTcJqX8sJTyxcbPvwNw6zAPKKW8IKW8S0p5F4A5AOsAPu8z6X9tTiel/Mgwy5yZyOLk/Jy34pv3ucgYAi8sX8Oj999dv5bfUy/g+KEDbdMdP3QAJ596wbtPwMyk2TV+Kp/BucWLODk/h5mJ7DClhlbImzh1uNhVyy2FcRTyZiI1UPyCsmsaArM7c115feTgAZxbvIgT83M4t3jRG35ifg6FXHdmTh0uopA3ffPUHDdonUluD5Rendk6t3gRJxo999TTL3r9F6jnZnZqrCtLfr33xPwczi58x/v7ZEfm+8kvUadCzsSJzhw2+mjaDNu/iVSx76En+/4htajUeykeqmZA1bopWqrmQNW6KTqqZoD7oQiINr9CynhP4xJC/A2AB6WUf9n4+w0AflNK+bqIHv8tAD4spXxDx/B7APwvUsq3hn2sYrEoFxYWAsfbtovltSpqjgtDE8gaGnQdWK+6yGd1WI5EzXaRM3XUHIlqzYGuCRiaQM2VMHUNugbUHAnHrf80x7tSQkJgZiKb6A2XXFficrmKjZoLXQA5U8eOnMmb30YnkRU5SHYNHRBCoFqTsBwXrpTQhYAQ9bJ3jmVQqliwXQlDE9g1biKbNeC6EqWyBcuun/lUyN/IS69xYTTrtB0Xhq4lvj1Qm8SawGb5BW70qmrNhSaAjC5gOxJ24xJ4lu2i1shq1tCQzQDXKq6X34wuAAg4st6nDV3DdN7E1Q3by+tULoPVSm3g/FKqpKL3bmzYbX20kDNTezPIYfs3RSZVvXcQSR2ceOnh+/qeZ5DaBlnONsbeS6kwQAZS0XuZXQL6zkEqsgswv6Ru7+V+KAKi671JdL1jAD7Tcl+WVQDvjvDx3wHgcwHjXieE+AaA76F+0OWbwyzIMDTcvCPXNXxqfJhHHS1NE5iZHBt1GRSzoOwCAAIGA8Aen6aiaQLTk/5H93uNC6NnnbStDdKrwkw+nWm/NNIw+SXqNDZm+PbRNBq2fxMRpYVKvZfioWoGVK2boqVqDlStm6Kjaga4H4qA6PKbxCG6/w7go6jfq+U/AfjPAP5ZFA8shDAB/AyA/+gz+hkAt0gpXwvgdxrL9XuM9wkhFoQQCysrK1GURZQIZpdUxvySqphdUhnzS6pidkllzC+pitkllTG/NApJHGr8vwBcRf3Ax3cjfuyfAvCMlPJS5wgp5bWW378ohHhUCLFLSnm5Y7rHADwG1E8l67WwatXG5fUbpxFNjGlwXKBmS4yZGiaz9Wu31S/F5WA8o6Nqu3BaL8kkgWxGQ61xyRvXlY2bLElICYxl6o+50XLZMU3TEr2EBy8fooZhsyslMKYZKFUsaAJwJbzx46aGnG4EnjYXlJFazamfctmYZ2Yii0wm/M2Umb3to5/8AvVsXNuwUK46bZcDk43cNi/F2BwG1IfbLZdo1OrXxUPNcQfKF/NJQH/ZVekSCsz39tBv7yVKi63aeykend99mpdAHpWw+U1b3TQaaeph7L3Uj7RlIGx+h92PRVtDVPlNIvGzUsp7Y3rsdyLgsmFCiB8BcElKKYUQP4b62TulQRdUrdp4/nIZx84sYmm14t0Y5+YdWazXbFyrAuuWg4rl4vDjX8Xrby1g/nW34IEnnvGmf+TgATx94RIOFveitGbhwbPnvXEfe9tr8Xt/+SJ+4Q0/inFTx2984VmsrFXxyMED+Mxffxu//hP7sX/3ZOw7PVxX4sKl6zhyesGr7dThYiLLpngEZXd2KosXrpTxW//383j3638UHzp3I4+ffNccTKOGX/jU19rmeXUhD9PUfTNyW2EcF1a6l3PHzESoNylmj4K4rsR3r67j6noNx1p66on774YmgPefaR+WMzWslmv49T/6hjf8+KEDGDfrOWz2137yxXxSvzY2bHyr1N0TX13Ip+5LJ/NNRFuFSr2X4hH03ef2XflUH7RQtW6Klqo9TNW6KTqqZqBWc/Dc8trA+7Foa4gyv0lcOuyvhRB/P+oHFUKMA/gJ1C9H1hx2VAhxtPHnIQD/rXGPlt8G8A4p5cD/c+/yuuWtcABYWq3g2JlFbFguDE3H0pUKbAd4ubSOpdUKjrzxVu8gS3P6D507j0PFV+G7qxveQZbmuH/1H7+Bg3N78eDZ87hSruHoPbd58xyc24sjpxdQKluDlh9aqWx5O1qatSW1bIpHUHbXqy7e/9lFHJzb6x1kaY5//2cXsXSl0jVPqWIFZmSl7L+c5bVqqDqZPQpSKluo2tI7yAI08vXEM1i+bnUNAzTvIEtzeLO3tvbXfvLFfFK/ShX/nliqpC8zzDcRbRUq9V6KR9B3n8vr6c6AqnVTtFTtYarWTdFRNQPLa9Wh9mPR1hBlfpM4rPg/A3iPEOLbAKoABAAppTwwzINKKdcBFDqGnWz5/RMAPjHMMlrZrvRWeNPSagW2Wz92M27qcKX0/se0rgnf6XVNYNzUfcftyGWwtFrBuKljHHrXcMt2ono6gSzb8a0tiWVTPHpltzVfneObWe6cBwEZ2Wwb2QyzR0Es24EmEDqnm03b2l/D5ov5pH4N2xOTxHwT0VahUu+leKiaAVXrpmipmgNV66boqJoBVeumaEWZgyQOtPxUAsuInaEJzE7l2lb87FQOhibgyvplwzQhsG7Vd0o4rvSd3nEl1i3Hd9zVSg2zUzmsWw4sx+0abhrxn7ZmGrpvbUksm+LRK7ut+eoc38xy5zxBGem1nDCYPQpiGjoqNTd0Tl2JntO29tew+WI+qV/D9sQkMd8UZN9DT466BKK+qNR7KR6qZkDVuilaquZA1bopOqpmQNW6KVpR5iD2S4dJKV/2+4l7uVHbNW427muRAwDvem1jpgbbdTC7MwdDB24pjNevbf70i3j0/rvbpn/k4AGcXfgO9kyN4fihA23jPva21+Lc4kUcP3QAO/MZnHzqBW+ec4sXcepwEYW8GfvzLORNnDpcbKstqWVTPIKyO57V8Ml3zeHc4kU8crA9j5981xxmd+a65inkzMCMTOf9lzMzkQ1VJ7NHQQp5E1lD4ERHTz1x/92YmTS7hgEuPv7217YNb/bW1v7aT76YT+pXIeffEwu59GWG+SairUKl3kvxCPrus2s83RlQtW6Klqo9TNW6KTqqZmBmIjvUfizaGqLMrxjitiVbTrFYlAsLC4Hjq1Ubl9ct2K6EoQlMjGlwXKBmS4yZGiaz9RfgcrmKjZqL8YyGqu3CkRK6EBACgASyGQ01R8J2JVxXwtA1CEi4EhjL1B9zo+ZA1wQMTUDTNBTyZmI3pHVdiVLZgmU7MA090WVvQYmsuEGyKyUwphkoVSxoGuC69TOx6pe305DT6+Oa8xRypncTqKCM1GoOlteq3jwzE9m+biDG7KVKYit+s/wC9Wxc27BQrjpeTjO6gJT10zw7hwE3hmvNXirqT6nmuAPli/lUSip678aGHdhH04b5To1U9d40n9Hy0sP39T3PIM9nkOVsY+y9lAqd3312jZub3VA+Fb13gLppC+qzh6UiuwB7Lw2UgVTkd9j9WLQ1RNV72fX6kM0a2BPig87M5FgC1cRH0wSmJ3n0divpld09Pd74gsYFZSST0bFnanywIns8LpGmCewYz2LH4PGKpAbmk/oxNmb07LFpwnwT0VahUu+leIT93p42qtZN0VK1h6laN0VH1QwMux+Ltoao8hv7pcOIiIiIiIiIiIiIiIi2KvUONY6Q60qsXK9iw65f1iujCeSyApWqRMbQcFM2g2vVGiqWA0dKjBk6shmBtQ0HtiuRaVy+ZsN2YegCuhCwHBemrsF2pTfNeFaHZcv6coRAPqvDciRq9uaXu0n60h+dy5vKZbBaqfHSIylj2y6Wr1dRc10YmkDW0GAawLWN+t8ZXcCVqF/qrpHD/JiGclWi5tSnGc9qWK+6yGd1rFsuao6LjK5h3NRQribzetu2i+W1qrfsmYksDIPHi7eD5msvIJHNaKhYrndK52ROw/XKjb81AUgJCAFICJi6gOVIuI3LOGqinnVdE9A0QNc07Mpn2asocipdQoH9lVST5sua0Wip1HspHqpmQNW6KVqq5kDVuik6qmbAsmyslG/UPZ03YZrpr5uiFVV+mZyQXFfiuR9cw/s+u4il1Yp3c+Vdk1nsyBm4eGUd+ayBctXGL/3+32JptYL3//g+vPWuWRw70z7PR790AStrVRz//9m7+yi57vrO85/vvfXQ1S3ZklstT5BkDF5jx5MjG3UHSJhkHZKwBjMhu1IYiIUHkpEjO5AMSRxIdjaTZedsMD4ZlidLsTIBhJywjDXJsMEhZHkIMyFk6TZYMxiEjWMjQWK1WpItdVdX1b33t3/Ug6q6q7qrS7e76td6v87p477P37r3U98u1c91a89O/emj39P/vGub7n34WGOdA3fs0gc//4Q++/gpvfqmrXrbq67XPQ892lh+6M4J3XD1xkVvCiaJ0/Fnz2vf4cll103rnCw83sG94/rA576tzz5+atWPj+5EUaJvPXte+5ty+MAdu3TlcFYzzxcVhqFG8hlNny+15PDg3nH9/fTzevsnjjVy+c3vP6cbX3BlSx4fuGOXjvztM/ryUzOrer3bPY6De8d149UbeTNwnatf+w987tv6rdfeqNOzlUZfffVNW/X2n3xJS5+9b/dOfezLf69f/Gcv1ue/+Y963c3bdHdTZpv7cH3dd/z0DfQqpGp+PtITM7Mt2Tywd1zXj44M3D846K8A1gufei9Wh68Z8LVupMvXHPhaN9LjawbK5UjHpxfXfcPYCIMtl5E088u/nrs0M1tuDLJI0smzxeqb0meKKkdOZ2YrOnGmqDOzlcY6eyauaVyk5m3233pd4/d9P/7ixpvb9XXufuhR7R7fIUnaPb6j8aZ2ffm+w5OamS23rbE+6LHcummdk4XH239kqlH7ah8f3Tl1odR480yqXpd7HnpUlcjp6iuHG9ldmMP9R6b00heONqbvfuhR/ej1Y4vyeM9Dj2rfj7941a93u8ex/8iUTl0orcrxMDjq1373+A5lgrClr+4e37Goz77z6DHtHt+hX/+Pj1X78ILMOib61gAAIABJREFUNvfh+rr0KqRtplhelM27j0xppjh4OaO/AlgvfOq9WB2+ZsDXupEuX3Pga91Ij68ZmJ5tX/c07w1cVtLML8NzXSpHceOE1508W9RwLlSUOA3nwkXbhIG13WZTIdv4fbl1NhWybZeXo7jrGtutm4ZOx6vXvtrHR3cqcdL2OgWmluy2WydO3KLpduuFtU8BrOb17vQ4ojhZleNhcNSv/aZCVrFrzWCnHlmf300frq9Lr0Kaog79Mmrqq4OC/gpgvfCp92J1+JoBX+tGunzNga91Iz2+ZsDXupGuNHPAJ1q6lMuE2r650DJv++aC5sqxMoFprhw3furixLXd5lyx0vh9uXXOFSttl+cyiwd2OtXYbt00dDpevfbVPj66kw2DttcpcWrJbrt1wqbbKNWn261XH5BZzevd6XFkQtrYele/9ueKFYXWmsFOPbI+v5s+XF+XXoU0ZTr0y8wA3p6O/gpgvfCp92J1+JoBX+tGunzNga91Iz2+ZsDXupGuNHPAv6C7NDqS04NvHm+c+Pp9/rdfVVAuY7pqJKsdVxV01Ui2sc7Dk9/Vgb2Ltzn4xe80fj/0paeq+2la58Adu3R06oQk6ejUCT1wx66W5YfunNDoSK5tjYfunOhq3bTOycLjHdw73qh9tY+P7mzdkNfBBTl84I5dymZMzz4318juwhwe3Duurz0z05g+cMcuffmJ6UV5fOCOXTr0padW/Xq3exwH945r64b8qhwPg6N+7Y9OnVCUxC199ejUiUV99r7dO3V06oR+/+durvbhBZlt7sP1delVSNtoIbcomwf2jmu0MHg5o78CWC986r1YHb5mwNe6kS5fc+Br3UiPrxkYG2lf9xjvDVxW0syvOcfHoeomJibc5ORkx+VJ4jR9vqRSFCsITNnAVMibiiWnbCbQFfmsni9VVCzHip00lAmUz5ouzMeKEqdsYMoEpvkoUSY0hWYqx4lyYaAocYoTp0xgGs6HKkdO81Gi0KSRfKhy7FSJEuUyoUZHch2/sDlJnGZmyypH8bLrpmHh8TYXsjpbrKzZ8T2wJg9+uexGUaJT50uqJIkygSmfCZTLSM/PV6ezoSlxUilKFNeyOjIUaLbkVImTWi4DzZUSjeRDzZUTRXGiTBhoOBdotrQ21zuKEp26UGoce+uGPF/UvHrW7Im7XH6li9fe5JTPBiqWE0W1nrmxEOh8sZrdMDAFgeQSyUxyMuVCUzl2cs4pMFNgUilOFJopCExhYNoykr/ce9V6MxC9d34+0kyx3MjqaCE3sF8GSX8dGAPVe69916fXqJrB9fR7bu93CT6h92Ig9JCBgei9ZBfSinMwENmVyC/87b3lcqTp2Yt1j43klMuR3ctNWr2X5KxAEJiuvnJo0fwrmmZdlclLI63Lr2z99NGqCgLT2Ma1+z9Q2x1vLY+P7mQygV6weXEQr1gmmwuzu3m4+t9Nw63zF06vlkwm0As2reETCgNj0bVf0GevWNyagb4bGspomyf/wKS/AlgvfOq9WB2+ZsDXupEuX3Pga91Ij68ZyOUy2sbAymUvrfzyvyoCAAAAAAAAAAD0yNshOzN7WtJ5SbGkyDk3sWC5SXq/pNdKmpP0Fufco5dyzPqtw+ajWGHt1mHDeVOpIuWypg256r3b6rfSMjOFJhXygc4X48bHjwq5QM5JpUqiSu1WN7narcRKsVOUJBqq3U6skjgVstUvaC7Htds6hYG2DGf1XCledIuuJHE6PVvSfCVWYNUazax6y5wgGKhbeTXfpiSs3VZt0GpcLyqVWNMXyqokSS1vgaq3GjQVy07DOem54sVbMWVDU5xIQ1mr3iasNv+KQvV2YqUoaXkOFDK5VG4xU89EJU6UbXPrmrW+NR4GQ5I4nb5QUrESaygTKHZq9I1saIpid7GXBqZE1dvg5cJAI3nT+fmLGc5nAjkZ2cGa8OkWCvRXAOuFT70Xq8PXDPhaN9Llaw58rRvp8TUDvtaNdKWVA9+T8xPOudMdlr1G0vW1n5dLOlD7b0+SxOlb//i87vr4lE6eLTa+UHnLxrzGNmR1di5SJXY69XxZ+w5PNtZ56F+9TKculHX3kYvbHdw7rqFsoLd85KuL9nX/Z76l6fNl/eZtN+jeh49pbENe/+5n/6men49078PHWvbxgc99W599/FTjS8ivH9ugJ6YvtBz//j07NZwL5SQ98IUn9Y6fvkE3XL2x72+eRFGibz17Xvubzst9u3fqY1/++4Gpcb2oVGIdP3Wh5Vzfv2entm0u6MJ8pKGM9Ox5tWT0gTt26dOPfU+vu2W7PljL2atv2qpf/amX6JfaPAe2bJA26tIGW9pl4uDecd149UZlMoGSxOn4s+db8n3ozgmyss4lidPxfzyvfR+f1NiGvH77tTfqHZ98TCfPFvXqm7bq7a+6Xnc/9Oiinve7n3pcL7t2k153y/a22f7ZXTvIDlbV/HykJ2ZmW/J3YO+4rh8dGbgX7vRXAOuFT70Xq8PXDPhaN9Llaw58rRvp8TUDvtaNdKWZg/V867DXSzrsqr4iaZOZ/UCvO5uZLTcGWSTp5NlideDjTFGzpUSVyGm+nDTepKivE1jQuFD1efuPTOnEmWLbfe0e36H9t17XGFTZf+t1OnW+3Jhu3sfu8R2N6X2HJ3XqQmnR8e99+JjOzFZ0drai3eM7tO/wpGZmy72ehtSculBqvKEuVWt959FjA1XjetHuXN/78DGVI6e7Pj6lfDa7KKP3PPSo9kxco7ubcrZ7fEdjkKV5PyfPFDVXqn4SJe069x+Zaux3Zra8KN9kZf2bmS1r38cnG/2wPsgiVTNZH2SRWnve/luva2S4XbbJDlbbTLG8KH93H5nSTHHwckd/BbBe+NR7sTp8zYCvdSNdvubA17qRHl8z4GvdSFeaOfB5oMVJ+qyZTZnZXW2Wb5N0omn6ZG1eCzO7y8wmzWxyenq648HKUdw44Y0dni1qOBcqSpwCk6LELVondovn1bdrN29TIatNhWxjm02FrIZzYdt9bCpkW6ajOOl4rPq+T54tqhzFHR/nWql0qHWQahx03Wa3XS6rg4C13HRYHgbWkrPmXDavV38ORHFySY+nUybq++30HCQrfuql9y7M4FKZ3FTINjK8cHl9PtlBLy6190aJW+0SV4z+evnoNr/AoFmPvRerYxAz0E1+B7FurL1BywG9F90axAzQe9GtNHPg80DLK51zu1S9Rdgvm9mPL1je7l4Xi86Qc+5B59yEc25ibGys48FymVDbNxda5m3fXNBcOVYmMCVOygS2aJ3QFs+rb9du3rliReeKlcY254oVzZXjtvs4V6y0TGfCoOOx6vvevrmgXKZ1kKcfsh1qHaQaB1232W2Xy+2bC0pcLTcdlseJa8lZcy6b16s/BzLhpbWTTpmo77fTc5Cs+KmX3rswg0tl8lyx0sjwwuX1+WQHvbjU3psZwFtx0V8vH93mFxg067H3YnUMYga6ye8g1o21N2g5oPeiW4OYAXovupVmDrwdaHHOfb/231OS/lTSyxasclLSjqbp7ZK+3+vxRkdyevDN440TX/8ugO1XFTSSD5TNmIZygQ7dOdGyTuISHdjbut3BvePacVWh7b6OTp3QwS9+pzq9uaCDX/yOtm7MNaab93F06kRj+tCdE9q6Ib/o+Pfv2amrRrLaPJLV0akTOnTnhEZHcr2ehtRs3ZDXwQXn5b7dOweqxvWi3bm+f89O5TKmB988rlKlsiijD9yxSw9PflcHmnJ2dOqE/qDDc2A4X/3i+rTrPLh3vLHf0ZHconyTlfVvdCSnQ2+eaPTD973h5kYGjk6d0IE7drXteQe/+J1Ghttlm+xgtY0Wcovyd2DvuEYLg5c7+iuA9cKn3ovV4WsGfK0b6fI1B77WjfT4mgFf60a60syBOeffx6HMbERS4Jw7X/v9ryS92zn3maZ1bpf0NkmvlfRySR9wzi0cjGkxMTHhJicnOy5PEqfp8yWVolhBYMoGpuG8qVSRclnThlz1AszMllWOYpmZQpMK+UDni7GixCkTmAq5QM5JpUqiSuIUBqZcaArNVIqdoiTRUBgoSpwqiVMhW/0/SstxojhxyoaBtgxn9VwpVjmKlcuEGh3JKQhMSeJ0erak+UqswKo1mpmccwqCoLHeIIii6vd6RHGiMDBlAhu4GlOwJg9kuexWKrGmL5RVSZJa3gJV+4WpWHYazknPFZNGRrOhKU6koaxprnxx/hWFQLMlp3KUtDwHCpmcMplLH7dtzkQmrA7eNO83SVzj+dWce6yKNTux3fTe0xeqfS2fCRQ7NfpGNjRFsavewjEw5QJTIqkcJcqGgUbypvPzFzOczwRyMrKz/g1E752fjzRTLDfyN1rIDeyXKtJfB8bA9F5JuvZdn16jagbX0++5vd8l+ITei4HQQwYGoveSXUgrzsFAZFciv6D3wm9p9V5fk3O1pD81M6n6GP7YOfcZM9svSc65g5IeUXWQ5UlJc5LeeqkHDQL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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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egx4VAAAAAACUwMdwjt+TWNsAAAAAAMBrUKgAAAAAAABeg0IFAAAAAADwGvSoAAAAAACgBIZz/B7F2gYAAAAAAF6DQgUAAAAAAPAaFCoAAAAAAIDXoFABAAAAAAC8Bs00AQAAAAAogY/hHL8nsbYBAAAAAIDXoFABAAAAAAC8Bpd+SLLW6oWPftOCFQmqGeCnF+/ppnbNw9xisrLz9MDYhdqVdEi+Pka9u0TroRs6ucXMXLRLD7y6UN+90l8dzqjnyUU4rf28cINefnGi8p1Wl1/ZVf+6/SK36Tk5eRr5+Bdav26PQkJra+y4mxQdfWz97tt7QEMHvai77u6vm2/tI0n6/NN5mvj9EslILVrW13MvXKeAgBoeXa6qYO/qNK34YqtsvlXznvXVdlBjt+nbFu7Tym+2qVZdf0lSy4ui1bxXAx3YeVDLPtmi3KN5Mj5G7QY1VuOujspYhCpj+aKdev+1n5Wfn6/+Q9rq6pvPdpu+ZsVejR+3UNu3punxF/qpe98zCqd99NYi/frzDlkrdTqvoe58qLuMMZ5ehGrhoxtG6rIOFyj54AF1eG5YZadTJVlr9cLYqZr/yybVrOmvl0ZdoXZtoovErd2QoBHPfK+j2bnqeUErjXzkssL/959/s0hfTFgiP18f9bywlR69f0DhfHv3pevSq97QPcP76l83dvfYcp3uflm4UWNfilW+M19DrzhPt97ex216Tk6enhrxtTYU7Mtffu0GNYh2HWtt3rRXzz/7gw4fOiofH6Mvvr2fffbf9PPC9XppzEQ58/N1xZXddNvtF7tNz8nJ1YjHvtD69bsVGlpHr467+YRjq/0aPGiM/n33AN1ya19Pp3/asNbqhTHfaf6CdapZs4ZeGnOj2rVrVCRu7bpdGjHiM9f2qEc7jXziKhljlJ5+WP958CMlJKQpOrqe3nj9NoWE1D7p6yYkpOne+z6QM98qL9ep66/vqX9e20OSNH36cr03fqbynVY9e7bTo49c7unVAZQrRlRIWrBir3buy9SsdwZr9J3n6dkPfi027pYhbTTj7UGa+OoArdiYogUrEgqnHcrK1RfTN+rMFhQoysLpzNeY57/Xe+Pv0OQfH9eM6Sv0x9ZEt5iJPyxRcHBtTZv1pG64qZfeeO1Ht+mvvDxJF3ZvU/g4KSldX36xQF9/96AmTXlc+U6rmdNXeGR5qpL8fKvfPtuiXg931MCXztXOJcnKSDhcJK7ReREa8Pw5GvD8OWreq4EkydffV93uaK1LXzxXvR7uqBVfblXO4VxPL0KV4XTm651XFui5Ny/T+AnXaV7cFu3ctt8txhEVqIee6avel7R0e379qn1av2qf3v36Wr33zbXavD5Za1bs9WT61coni6ep/9v/qew0qrQFv2zWjt1pipv8kJ57cqhGvRhbbNyoF2M1+sl/KG7yQ9qxO00LFm2WJC1Z9ofi52/Qj9/cp2nfPaB/3eBejHhx3DR1P79lcS+Jk3A68/XSC5P0f+/fph+mPKKZ038vsi+f/MNSBQXX0pSZIzTsxh56c9w0SVJenlNPPv61Rj59hX6Y8og+/OQu+fn5VsZiVBlOZ76ef+47vffBnZry4xOaPu03/bF1n1vMxO+XKDiktmbMelo33NhL416d4jb95ZcmqXv3tp5M+7S0YME67diZrLiZo/Tcs8M0avQ3xcaNevZrjX72OsXNHKUdO5O1YOF6SdIHH85St26tFDfrWXXr1koffDirxNeNiAjRN18/rNhJT2jCt4/oww/jlJScrgMHDumVVyfp04/v17SpTykt7aAWL97omZVQjRjjUyX/vJX3ZuZB8b/u0ZBezWSM0VmtwpV5OEfJ+7PcYmoF+KlrhyhJkn8NX7VtFqbEtGMxb321Sv8a2lb+/uxcy2Ltmp1q1ChcDWPCVcPfT/0HdNJPc9e4xcybu0aDh54jSbq435laumSLrLWSpLlzVqthw3pqfkaU2zxOZ76yj+YqL8+po0dzFOEI8cwCVSH7/8hUoKOWAh215Ovno0ZdHdqzIrVU8wbXr62gqNqSpNp1A1QzuIayD1Ko+Ks2r0tWg5gQ1W8Yoho1fNXz4hZaMn+7W0xkg2A1bRFeZKSEMUY5OU7l5eYrN9cpZ16+QsNqeTL9amXh1pXafzizstOo0uLnr9fQSzu59tkdGinz0FElp7iv8+SUTB06dFSdOjaSMUZDL+2k+HmuHwZff79Uw2/uKX9/16DSemGBhfPN+Wm9GkaHqUVzRoCVxdo1uxQTU08NY+qphr+fLhl4lub9tM4tZt7cdRo0pIsk6aJ+HfVrwb588aLNatGyvlq1dhW6Q0PryNeXw9O/Y83qnWrUKEIxBcdWAwZ21twTjq3mzl2jIUPOlST1u+QsLV2yufDYKn7OajWMKXpshaLi567W0CHnubZHZzVVZuYRJSdnuMUkJ2e4tkedXL81hg45T/Hxq46bv6skaeiQrprj9nzR1/X395O/v2u0UU5OnvILPrPde1LVpLFDYWFBkqRu3VprVtzvHlkHQEUp1Z7AGFPHFJRbjDEtjTGDjTFVZkxe0v4jqh9eu/BxVL3aStp/5KTxmYdz9NPyBHXrEClJWr9tv/alHVHvLg0rPNeqJikpQ5FRdQsfR0aFFtnAHx/j5+erwKCaSk8/rCNHsvW/j+J117/7u8VHRobqplt6q1/fZ9W359MKDKyl8y9oXfELU8UcOZCt2vUCCh/XDgtQ1oHsInG7l6Vq+shl+vnttTqcdrTI9LQ/MpWfZxXo4MfxX5WackgRkcd+TIVHBiotpejoluK06RiljmdHa9iAjzWs/yfq3LWRGjUNO/WMgJdKSs5UVOSx4nOUI1hJJxQqklJOiIkMVlKyK2bHrjQt/32HrrrxXV1/+wdavW6PJOlIVo4+/HS+7hnufskCTi05KUOR9UMLH0dGhiolqeiPtagoV4xrX15L6elHtGtHioyR/n37B/rnla/rk49+8mjuVVFycnrhupZcn0fyiZ9HUoai6h//eRx3bPXfOfr3vwcIp5aUlK6o445jo6LqKik53T0mOV1Rkcc+j6jIukpKcsWkpR2Uo+BkmsMRov37D57ydfft269BQ55Xrz4jdfu/+inSEarGjRzatj1JexLSlJfnVHz8KiUmHqiYhQY8pLQl6wWSahpjoiXFS7pF0icVlZTH2aJPnezy7Txnvh4a97NuGNhKMVFBys+3evHj3/TYzZ0rNseqqrh1L3PqGGP07v/N1A039lLtOgFu0zIzjuinuWs1Y/bTmjNvtLKysjV1yvJyTLo6c/9sos8K1+BxXTXwhXMU2a6ulnzgPswwKz1bi8dv0Hm3t5LxoSfCX1bMd+DEr8nJ7N2drt07DujzaTfpi+k3adXyPVz6gdOaPck+obQxTqdTmZlZmvDpXXr0/gF64PGvZa3V2+/P0U3XXaA6tQOKzoyyK9Vn4hoB+fuK7XrhlWH63+d3a278Wi1dssVDSVZNpfuOFA0yMnrn/2bohpuKHluheMWux9Ks61Psw0t63fr1w/Rj7JOKm/WsJsUuUWpqpkJCamvUM9fqPw9+pGHXj1N0dD35+jLKG6e30jbTNNbaI8aYf0l621r7ijGmxPFExpjhkoZL0vvPDNLwq7r8zVTL15czNum72X9IkjqcEaZ9qcdGUCSmHZGjbu1i53v6vaVqXD9YNw1ynaE/nJWrLbsydONTcyRJqelZ+veL8/XuiJ401CyFyKgQJR1X8U1KTFeEI7jYmKioUOXlOXXo4FGFhNTWmtU7NSdupV5/bYoOHsySMT4KCKihevWC1DA6TGEFw3n7XtxRK1du12WDvev/oLerXTdAR9KOjaA4sj+7sGnmnwKCjg2sat6rgVZ9u63wcW5Wnua/tkYdr2yq8DO49ObvCHcEKiXpUOHj1KRDqhdep1TzLpq3Ta3bR6pWbddn16VbY21cm6gOnRtUSK5ARfhywmJNmOQqOHdoG63E484OJyZnyhEe5BYf5Qh2j0nKlCPCFRPpCNHFfdrJGKOO7WPkY4wOpB/WqrW7NSt+rV59a6YyD7qaOgYE+On6a7p5YAlPb47IECXtO3YWOSmpmH15ZIgSE9MVWbgvz1JISG05IkN0dpfmqlvXtU27sHtrbVy/R+d1beHRZahKIiNDlZh4is8jKlSJ+1xn7QuPrUJra83qHZo9a6XGvVpwbOVjFBBQQ9cN6+HpxfBaX345XxO+/0WS1KF9Y7eRC4mJB+SIcD/miYqsq8SkY59HYtIBORyuERb16gUpOTlDDkeIkpMzCi/diIqqe8rXjXSEqsUZ9bX8t63qf0ln9endUX16d5QkfTvhZ/lwgqjcmeJOHFUFXvpfpbQjKowxppukYZKmFTxXYpHDWvuBtbaLtbaLtxUpJGnYgFaaPG6gJo8bqL7nxih23jZZa7VyU6qCavvLUcw13G98tVIHj+TqiVuPddsPquOvJZ9eqbnjh2ru+KE6s2U4RYoyaNe+kXbuTNWePWnKzcnTzBm/q1fv9m4xvXq315TJyyRJs+NW6dzzWsgYo0+/uE8z5zyjmXOe0bAbeuq24Rfpn8O6K6p+qFav2qmsrBxZa7V0yRY1axZZGYt3WgtrFqSDSVk6lJIlZ16+di1JVsNO4W4xWenHChkJK1IV3MBV4HPm5Wvhm2vV5IJINTqXa73/rpZtHdq7K0OJCZnKzXVq/uwt6tqjSanmjYgM0poVe+XMy1denlNrViQopkndU88IeJFhV3dT7Nf3Kvbre3VRr7aaPO131z57zS4FBdaUI8L9R5gjIlh16gRo5ZpdstZq8rTf1benqzHgRb3aasky14mK7TtTlZvnVN3QOvrqozs0d+qjmjv1Ud103fm645ZeFClKqV37GO3alaqEgn35rOkr1at3O7eYnr3b6cdYV7FpTtxqnXPeGTLG6PwLWmnL5n3KyspRXp5Tvy3fpmbN2Wf/He07NNKunSmFx1Yzpq9Q794d3GJ6926v2FhX8/i4WSt1XlfXsdVnXzyguPhRiosfpetv7Knbh19MkeIEw4b1VOykJxQ76Qld1LejJscudW2PVm5XUFCtwks5/uRwhLi2Ryu3u7ZHsUvVt4+roNCnT0dNjl0iSZocu+TY8707FPu6iYkHdPRojiQpI+OIVqzYpqZNXd+XtLSDhc9/9fUCXXXlBR5ZH6gejDH9jTGbjDFbjTGPFzM9wBjzbcH0pcaYJn/3PUs7ouJ+SSMkTbLWrjPGNJNUZS4i7Hl2Ay1YkaB+/56imgG+GnPPsQOToQ9O1+RxA5WYekTvf79OzaKDdfnDMyTp/9m77/gqy/Px4587CRsSZsIWQURAVJwoMsRVRwX3rFtb/TlaW62K/YrWvVprbR11tmrdgAO3gnuCAxXBgQzZQpgBcu7fHycNK8CJ5CTHnM/79corec5znyfXw0NyTq7nuq+bY/fbksP33mJ9h1UK8vJyuXjooZxx2m2UJhIMOXgXtujahltveZYePTuyx6CtOfjQPlz8x/9wwL5XUNC0IdfdcPwGj7nNtp3Ya59tOfKwG8jNzaF79/YcdsRu1XRGtUdObg47Ht+V1677hBgjnfu3oaB9Iz55/Fuab96E9tu3ZMIL05g2dg45OYG6jevQ57RkpdH3785i1oQFlCxawbdvJDu/9zltK5pt1mRD31LrkZuXwxkX9OOSc0ZSWhrZ56DubNalBfff9i5bdi+kz4DNmTB+Jn++YBSLikt4941v+c/t73H7I8ew+55d+PiDqZxx9H8hwI67dqRP/81r+pRqrQdPvpyBW25Py8ZNmXLVSC59+k7ufuupjT9RKRuwezdGvzmBvQffSIP6dbhq2KHl+wYffQsjHjobgGEXDeaiYY+xbNlK+vfdkv59kyt5HDp4By6+7AkOPOKv1MnL45phh7lc7ybKy8vlj0MP5szT7ySRiAw+eCe6bNGaf9zyHD16dmDgoJ4MOXRnLrnwIQ76xdXkFzTkmhuOAyC/oCHHndCf4468mRBg937d6TfA1SY2RV5eLhdfchi/PvUflCYSHHxIH7bo2oa//+0Zem7dkT0G9eKQw3bloj/+m/32vZyCgoZcf+OJNR32z9KAAVszesx49t73UhrUr8tVV/2qfN/gg69ixJMXAzDs0qPLlyft368n/fsnE3mnn7oPvz3vLh577C3atG3OzX85dYPH/frrGVxz3eOEEIgxcvLJe9Fty+TyzFde9ShfTkj23Pl/Z+xfnsCQNlUIIRe4FdgbmAq8H0IYGWP8fLVhpwA/xhi3CCEcBVwLHLlJ37eiOVAVBHZNjPH8n/pN4vjLa2uhzM/O8q12qukQVObqD4bXdAgqc3z3nhsfpGrR5Y8P1nQIWk28/ie/9KuKLalfd+ODVC3q5HgtMkWdlO+5qlrk7Fm7s77x1dr5N23YY4PXrWxmxbAY475l2xcBxBivXm3M82Vj3g4h5AEzgFZxY8mGDdjoT3eMsTSEsMPGxkmSJEmSVCvFRE1HkB4bTy+1A6astj0V2GV9Y2KMK0MIC4AWwJyfGlaqacixIYSRwKNA+Zp4McYnfuo3liRJkiRJNWf1RTDK3BFjvGP1IRU8be1KiVTGVEqqiYrmwFxg9cXFI2CiQpIkSZKkn6GypMQdGxgyFeiw2nZ7YO217v83ZmrZ1I8CYN6mxJVSoiLGeNKmfBNJkiRJkvSz8z7QNYSwOTANOAo4Zq0xI4ETgLeBw4BXNqU/BaSYqAgh1CfZybMnUP9/j8cYT96Uby5JkiRJkjJTWc+Js4DngVzg7rKVQC8HPogxjgTuAv4dQphEspLiqE39vqlO/fg38CWwL3A5cCzwxaZ+c0mSJEmSMl5tbaaZghjjs8Czaz32f6t9vQw4vCq/Z06K47aIMf4JWBxjvA84AOhVlYFIkiRJkiSlmqhYUfZ5fghha5LNMTqlJSJJkiRJkpS1Up36cUcIoRnwJ5KNMhoD/7fhp0iSJEmSJFVOqqt+/Kvsy9FA5/SFI0mSJElShsniHhU1IaWpHyGEohDCXSGEUWXbPUIIp6Q3NEmSJEmSlG1S7VFxL8nlSNqWbX8F/DYdAUmSJEmSpOyVaqKiZYzxESABybVUgdK0RSVJkiRJkrJSqs00F4cQWgARIITQB1iQtqgkSZIkScoU9qioVqkmKn5PcrWPLiGEN4FWwGFpi0qSJEmSJGWlVFf9+DCEMADoBgRgQoxxRVojkyRJkiRJWSfVVT8+AE4HpscYPzNJIUmSJEmS0iHVqR9HAScB75clLe4BXogxxrRFJkmSJElSJkjYo6I6pVRREWOcFGMcCmwJPAjcDXwfQrgshNA8nQFKkiRJkqTskerypIQQtgFuBK4HHifZTLMYeCU9oUmSJEmSpGyT0tSPEMKHwHzgLuDCGGNJ2a53Qwh90xWcJEmSJEnKLqn2qDg8xvhNRTtijIeEEE6IMd5XhXFJkiRJkqQslOrypBUmKVZzLmCiQpIkSZJU+0SbaVanlHtUbESoouNIkiRJkqQsVlWJCpcplSRJkiRJm8yKCkmSJEmSlDFSbaa5MW9W0XEkSZIkScos9qioVilVVIQQCkIIfwkhfFD2cWMIoeB/+2OMZ6UvREmSJEmSlC1SnfpxN1AMHFH2UQzck66gJEmSJElSdkp16keXGOOhq21fFkIYl46AJEmSJElS9ko1UbE0hLB7jPENgBBCX2Bp+sKSJEmSJClD2KOiWqWaqDgDuG+1vhQ/AiekJyRJkiRJkpStUk1UfAFcB3QBmgILgCHAJ2mKS5IkSZIkZaFUExUjgPnAR8C09IUjSZIkSZKyWaqJivYxxl+kNRJJkiRJkjJRwh4V1SnV5UnfCiH0SmskkiRJkiQp66VaUbE7cGII4VugBAhAjDFuk7bIJEmSJElS1kk1UbFfWqOQJEmSJEkixURFjHFyugORJEmSJElKtaJCkiRJkqTsFG2mWZ1SbaYpSZIkSZKUdiYqJEmSJElSxjBRIUmSJEmSMoY9KiRJkiRJ2hB7VFQrKyokSZIkSVLGMFEhSZIkSZIyhokKSZIkSZKUMexRIUmSJEnShtijolpZUSFJkiRJkjKGiQpJkiRJkpQxTFRIkiRJkqSMYY8KSZIkSZI2IMbSmg4hLUJNB7AeVlRIkiRJkqSMYaJCkiRJkiRlDBMVkiRJkiQpY1RLj4o/zp9aHd9GKWj/2Q81HYLKNK+fw+DOvWo6DJXZLLdNTYcgIN74R0i4TnmmCOdfX9MhqMwDt/1Y0yFoNUeNGlTTIQhIAGHnXWs6DJUJzWs6AtUmNtOUaohJisxhkiKDmKSQlOFMUmQOkxSqVr5HqVZO/ZAkSZIkSRnDRIUkSZIkScoYJiokSZIkSVLGsEeFJEmSJEkbEu1RUZ2sqJAkSZIkSRnDRIUkSZIkScoYJiokSZIkSVLGsEeFJEmSJEkbYo+KamVFhSRJkiRJyhgmKiRJkiRJUsYwUSFJkiRJkjKGPSokSZIkSdoQe1RUKysqJEmSJElSxjBRIUmSJEmSMoaJCkmSJEmSlDFMVEiSJEmSpIxhM01JkiRJkjbEZprVyooKSZIkSZKUMUxUSJIkSZKkjGGiQpIkSZIkZQx7VEiSJEmStCEJe1RUJysqJEmSJElSxjBRIUmSJEmSMoaJCkmSJEmSlDHsUSFJkiRJ0oZEe1RUJysqJEmSJElSxjBRIUmSJEmSMoaJCkmSJEmSlDHsUSFJkiRJ0obYo6JaWVEhSZIkSZIyhokKSZIkSZKUMUxUSJIkSZKkjGGiQpIkSZIkZQybaUqSJEmStCE206xWVlRIkiRJkqSMYaJCkiRJkiRlDBMVkiRJkiQpY9ijQpIkSZKkDUnYo6I6WVEhSZIkSZIyhokKSZIkSZKUMUxUSJIkSZKkjGGPCkmSJEmSNiTao6I6WVEhSZIkSZIyhokKSZIkSZKUMUxUSJIkSZKkjGGPCkmSJEmSNsQeFdXKijtAtY8AACAASURBVApJkiRJkpQxTFRIkiRJkqSMYaJCkiRJkiRlDBMVkiRJkiQpY9hMU5IkSZKkDUnYTLM6WVEhSZIkSZIyhokKSZIkSZKUMUxUSJIkSZKkjGGPCkmSJEmSNiQRazqCrGKiApj56Tw+fXASxEjHfm3Y8oCOa+z//o0ZjH/kG+o3qwtA5z3bsVn/NgCMOGU0+e0bAdCwRX12OWfr6g2+lpk8bi5v3DORRCLSY8827DCkU4XjJr0zi+dv+ozDr96Rwi75THh9BmNHfl++f+73izji2p1o1alJNUVeO73/1nf884bRJBKRXwzpyVEn7rTG/k8+msZtN47mm0lzuPjK/ei/V1cAxn0whdtuGlM+bsp3P3LxVfvRd2CXao3/5y7GyJXXPcXoNydQv34drrnscHp2b7fOuM8+n8pFlz7KspKVDOjbjaEX/JIQArfc9iKPPPE+zZslf0edd9a+DOi3FZ98NoU//fmJ8u9x9m/2Yu9B/u7akBgjV17/dNm1qMs1ww6t+Fp8MY2LLn2MZSUrktfi/AMJIQDw7/++xX8eeYe83BwG7N6NC87dr/x503+YzwGH/5WzTt+TU47vV23nVdvd9auhHNirL7MW/kivPx9b0+FkhR1uHkrb/Qewcsky3jnxQn4c+/k6YzoesR89h55ByM1h+jOjGffH6wFo2KENu953LXWaNiHk5vLxhTcwfdSYdZ6vjYsxctUTExnz+Vzq18nhqmN70LPDuu+JTvvnOGYXL2dlIrJj5wL+dHg3cnMCfx/1DY++PZ3mjZPvfX97QGcG9GxZ3afxsxVj5Mq/vMCYt76mfv06XP2nA+nZrc064z778gcu+vNTlJSspP9uXRj6u30IIfDlxJlcet0olixZTrs2Bdxw2RAaN6rH8hWlXHrts3z2xQ/k5AQu/t0+7LL9ZjVwhlL6ZX2iIiYin/xnIrv9fhsaNK/H6Ms/ovV2Lchv12iNce12bsU2x3Vd5/m5dXPY47IdqyvcWi2RiIy5awIHXdKbxi3q8ehFH7D5jq1o3n7Na7F86Uo+GTWFoq755Y9169eabv1aA8kkxbPXfWKSYhOVlib4+7Wvcc2tB9OyqDFnH/9fdu3fmc06tygfU9i6CX8YtjeP/fujNZ673Y4duO3B5B8FxQuWcdLB97JDnzUTgNq4MW9M4Lvv5/DCiD/w8adTGHbVcB799/9bZ9ywq4Zz+SWHsN02HTntrHsY8+ZXDNi9GwAnHrc7pxzff43xXbsU8fgDZ5GXl8us2cUMPvJm9ujfnby83Go5r5+jMW9+xXdT5vLC8N/z8WdTGHb1CB69/8x1xg27egSXX3Iw2/XqwGnn3MeYt75iQN9uvPP+17w8+gue+u851K2bx9x5i9Z43tU3PUO/3basrtPJGve+/Qx/f+0x7j/x/2o6lKzQdr/+NOnaiae67kOLXbZlp38O44U+R6wxpm7zpvS+/gKe2+EQSub8SJ97r6FoUB9mvvIOW19yBpMfGcWk2x4iv3sXBj57ByM337OGzubnbcznc5k8ewnPXdKHjycXc/mjE3j4vHXfr/7lpK1pXD+PGCPn3v0Zz42bxQHbFwFwwsCOnDzI1+6fYszbXzN5yjyef/QMPh4/ncuue45H7jppnXGXXTeKyy/cn+22bsfp5/2X19/5mv67bsElVz/DBWftyc7bb8bjT43jrv+8zbm/HsijI8YC8NQDpzN33mJOO++/PHb3yeTkhOo+RSntsr5HxY/fFNOosAGNChuQk5dDu10KmTFubk2HlZVmTSqmoHVDCooakJuXQ9fdCvn2/dnrjHv34W/Y/qDNyK1T8X/fr96YSde+RekOt9abMH4mbTsU0KZ9AXXq5DJgny15a/Q3a4xp3Tafzl1bETbwAvn6yxPZcbdO1K9fJ90h1zovj/6cIQduTwiB7bbpSPHCpcyaXbzGmFmzi1m0uITe225GCIEhB27Py6+N3+BxGzSoW56UKFm+svyOv9bv5dGfM+SA3slr0asjxYuWVXwtFi2j9zYdk9figN68/FrybvJDj73L6ScOoG7d5P2BFs0blz/vpVc/p3275nTtUlh9J5QlXp80jnmLizc+UFWi3eA9+fb+4QDMffdj6jbNp37rVmuMady5Awu/+o6SOT8CMOOlt+lw6L5A8i50nfzkz0bdgiYsnT6rGqOvXV75bA6Dd2qd/J3VqYDipSuZtaBknXGN6yd/J61MRFaUJvDVoGq8POYrBu+3TfLff+t2ydeMOQvXGDNrzkIWLV5O717tCSEweL9teGn0VwB8O3kuO/VOJol227kzL7w2AYCvv53Drjt2AqBF80bkN67PZ19Mr74Tk6rRRisqQgjnbGh/jPFvVRdO9Vs2fzkNmtcr327QrB4/frPum5rpH85h7lcLaFTUgF5Hd6FB8/oAJFYkeO2yD8nJDXTdvyNttrcs7qdaNK+Exi1WXYvGLeoxc+Ka12L2twtZNKeETju0ZOxT3699CAAmvT2T/c/fJq2xZoM5sxbRqmhVVUqrwsZ8+dmMSh/ntRe+4tBje1dlaFlj5qxiWrduWr7duqiAmbOKKWyVv+aYwoJ1xvzPA/99i+FPf8TWPdpx4XkHUJDfEICPP/2ei4c9xvQf5nPdFUdYTbERM2cV07potX/nwnxmzl7rWsxea0xRfvm1+O77uXww9jv+cusL1KuXxwW/3Z9terZnydLl3HnfaO7+x8nc/e/Xq++EpDRo2K6IJVNWvU4smTqDhu2KWDZj1U2HhZMmk79VZxpt1o4lU2fQfsie5NZNJrI/HfZ3Br1wF93OPo68Rg14ea9170ArNTPnl9C6af3y7dYF9Zi1oITCgnrrjD31n+P4dHIx/bq3YN/tViVMH3h9KiPe+4GtO+ZzwZAtKGjoDYdUzZy9kDZFq14fWrfKZ+bshRS2bLLGmNaFq7ZbFzZh5uxkMqNr51a88vpX7Nm/G8+98gU/lL2WdOtayMtjvmL/vXoyY1Yx4yf8wA+zFrJNz2o6sWyXSNR0BFkllYqKVhv5qFAI4fQQwgchhA8+HvFFVcSaFrGinihrpZNbb9eCva/bhT0u35FWPZrx0b8mlO/b+/o+DLx0B3Y4vTufPjSJxbOWpjfg2qzCa7HqYsRE5I37JtL3+C3We4gZExeQVzeXFh0br3eMfrrK3nmfO2cx302ay467On/yp4gV/IJa+xLECn5w/jfm6MP78OJTFzDiv+dQ2DKfa256pnzMtr068szj5/HYf87i9rtfo6RkRZXGXttU9Fqx9s/DhsaUlpZSXLyUR+47gwvO3Y/fXvgQMUZuue0lTjimL40arvvHg/SzU8FrxNq/x1bML+b9M4bR9+G/sPfrD7D4u2kkVpYC0OnoA/jm3icZ3mEAr+1/Orv9+7oKj6mN28hbqjX864ztGPPnvixfmeCdr5KVLkf1bc8Lf9qVJy/YmVb5dblu+KT0BVsrVfTavM4LeAVjkp+vGnogDzz+IYeceBeLl5RQp+xmwqEHbkfrwnwOO/kurvrrC/Tu1Z68XH9GVDtttKIixvinn3LgGOMdwB0AF7x5esa2SG3QrC5L560qhVv6Ywn1m675hrFu41UZ5E4D2vD5Y9+s9vzk2EaFDWi5VVMWfL+IRoUN0hx17dS4RT0WzV11LRbNLaFRWQNTgOXLSpk3ZTHDL0vOz1syfznPXPcJB1ywDYVdklnrSW/OctpHFWlZ2JjZM1eVKc6etYjmrRpt4BnrGvPiV+y2Rxfv1lfCAw+/zSNPvAdAr57tmTFjfvm+GTMXrHEHH6B1YQEzZi2ocEzLFqvu1Bx+yE785pz71vl+XToX0qBBXb6aNJNePdtX6bn83D3wyNs88uQHAPTq0Y4ZM1f7d55VvMadMUhWWawxZmYxha2SY4oKC9h7UE9CCGyzdQdyQuDH+Yv5+LMpPP/yZ9zwt+coXriMnJxAvXp5HHfkrtVwhtKm63rmMWxxWrIPxdz3P6Vhh9bl+xq2b13h9I1pT7/KtKdfBaDLaUcQS5N3KTufchiv/eJUAOa8M47c+vWo17IZJbPnpfs0aoUHXp/KY28npwFs3bEJM+YvK983Y0EJrfLXnxCtVyeXQb1a8spns+m7VXNa5q96/3X4rm35zR2fpC/wWuKBxz7g0ZHJ96i9urflh5mrqhtnzC6msOWaN9GKCpswY9aq91kzZq2quOjcqSV333wMAN9+P5fRbyYTRXl5OVz0273Ln3PUafeyWYfmaTkfqaal3KMihLBFCOH5EMLHZdvbhBAuSl9o1aPp5vksnrmUxbOXkliZYNq7s2i9XYs1xiybv+qP5x/GzqVJm2Tp9PLFKyhdkXxxLVm4gnkTi2lctk+VV9ilCQt+WELxrKWUrkww8a1ZdNpx1VSaeg3zOOWufhx/624cf+tuFHXNXyNJERORSe+YqKgq3XoUMW3KfH6YtoAVK0oZ/cJX7Nq/c6WO8erzX7HHvjYIrIxjj9yVEQ+fy4iHz2WvPXoy/OmPiDEy7pPvadK4/jqJisJW+TRqWI9xn3xPjJHhT3/EngN6AKzRQ+GlV8bTtUvyZ2PKtHmsLLuDOW36j3z73WzatW1WTWf483HsEbsy4qGzGfHQ2ew1sAfDnxmbvBafbuBaNKrHuE/LrsUzY8uvxV4De/DO+18D8O3kOaxYWUqzpo148K5f88rTF/DK0xdwwjG78euTBpqk0M/KxH88yKjeQxjVewhTh7/E5scPAaDFLtuyYsHCNaZ9/E+9Vsk/rOo0zWfLM4/h6389CsCS73+gaM/k///8rTqTU7+eSYpKOLZfe568YGeevGBn9uzVihHvz0j+zvpuAU3q564z7WNxyaq+FStLE4z+fC6dC5M3JFbvZ/HiJ7Pp2qZyNyqy0bGH7cjw+09j+P2nsWf/LRkx6pPkv/9n02jSqN46ye3Clk1o1Kgu4z6bRoyREaM+Yc/+yfdMc+ctBpKN5m+7502OOnh7AJYuW8GSpcsBePO9b8jLy2GLzddb4C79rFVm1Y9/ARcDt5Ztfwo8BFxd1UFVp5zcwDbHbcHbN31KTEQ67t6a/HaN+OLJb2naqQlterfkm5emMWPcXEJOoG7jPHqfshUAi35Ywrj7JhJCsuS36/4d1lktRKnLyc2h38lbMvLKccREpPsebWnRoTHvPvwNhV2asPmOG/5FPP2L+TRuUY+CIitaqkJuXg5nnT+Qi88eTqI0su9BPejUpQX33fY2W3YvYtcBnZkwfgaXnf8MC4uX8c7r3/LvO97hzkd+BcCM6cXMnrmQbbb3Lv1PNWD3box+40v2Puh6GtSvw1XDDi/fN/jImxnx8LkADLt4SNnypCvo37cb/ctW/Lj+5lF8OWE6hEC7Ns24/JKDAfhw7Hfcec9r5OXlkpMTGHbxkPIlTFWxAbt3Y/SbE9h78I1l1+LQ8n2Dj76FEQ+dDcCwiwZz0bDHWLZsJf37bkn/vsk3nYcO3oGLL3uCA4/4K3Xy8rhm2GE2Ma0GD558OQO33J6WjZsy5aqRXPr0ndz91lM1HVatNf3Z0bTdfwC/nPQipUuW8s5JF5fv22/scEb1TiYxdrh5KM22Tb6X+uzyW1k48TsAPvr9Nexy5xVs9bsTIUbeOfHC6j6FWmNAjxaM+Xwu+/75berXzeWqY7qX7zv4uvd48oKdWVqS4P/d+QnLVyYojdCnazOO7NsWgBtGTuLLaYsIQLsWDRh2RLcaOpOfpwG7bcGYt75mn8P/Qf16dbjqkgPL9w05/k6G338aAJee/wsuvuJplpWsoF+fLvTfNbmM+zMvjueBxz8EYJ+B3TjkwG0BmPvjYk797UPkhEBRqyZc+3+Dq/nMspw9KqpVqGgOdIUDQ3g/xrhTCGFsjLF32WPjYozbbey5mTz1I9u0b2wJfqYY3LlXTYegMpvlrru2uWqIbwIySjj/+poOQWUeuO3Hmg5BZY4aNaimQ1CZsLMVaJkkND++Vmfg44Qra+XftKHb0Iy8bpVZnnRuCGFzylq/hBCGAJVfAkCSJEmSJGk9KjP14yzgLmCrEMJk4Afg6LREJUmSJEmSslLKiYoY4yRgUAihgOSUkfkbe44kSZIkST97iVo58yNjVWbVj7NCCPkxxgXANSGE90IIe6YxNkmSJEmSlGUq06Pi9BhjcQhhH6A9cAZwXXrCkiRJkiRJ2agyiYr/1brsB9wTY/ywks+XJEmSJEnaoMokGj4OITwL/BIYFUJozKrkhSRJkiRJ0iarzKofJwE7AJNijEtCCC2BU9ITliRJkiRJGSKRqOkIskrKFRUxxlKgM8neFAANKvN8SZIkSZKkjanMqh9/B/YAjit7aDFwWzqCkiRJkiRJ2akyUz92izFuH0IYCxBjnBdCqJumuCRJkiRJUhaqTKJiRQghh7IGmiGEFoATdSRJkiRJtZs9KqpVZXpM3Ao8DrQKIVwGvAFcm5aoJEmSJElSVkq5oiLGeH8I4UNgLyAAh8cYP0tbZJIkSZIkKetsNFERQmgUY1wcQsgHpgD3rLYvP8ZYnM4AJUmSJElS9kilouIxYD9gPMn+FGGtzx3TFp0kSZIkSTUtEWs6gqyy0URFjHG/ss8d0h+OJEmSJEnKZin1qAgh5AL7AFuVPfQ58FKMsTRdgUmSJEmSpOyTSo+KNsArwFxgLMkpH4cCfwkhDIoxzkhviJIkSZIkKVukUlFxFfCvGOONqz8YQvgdcDVwUjoCkyRJkiQpIyQSNR1BVkklUbFrjHGdZESM8S8hhC/TEJMkSZIkScpSOSmMWfoT90mSJEmSJFVKKhUVBSGEgyp4PAD5VRyPJEmSJEnKYqkkKt4EDl/PvreqMBZJkiRJkpTlNpqoiDH+KpUDhRCOizH+Z9NDkiRJkiQpgyRiTUeQVVLpUZGq86rwWJIkSZIkKQtVZaIiVOGxJEmSJElSFqrKRIW1MJIkSZIkaZOk0kwzVVZUSJIkSZJqn0SipiPIKlVZUfFOFR5LkiRJkiRloZQTFSGEViGE20MIT5dt9wghnPi//THGM9IQnyRJkiRJyiKVqai4FxgNdCjbngj8vqoDkiRJkiRJ2asyPSoKY4wPhhDOB4gxrgghlKYpLkmSJEmSMoM9KqpVZSoqFocQmlO2ukcIYSdgYVqikiRJkiRJWakyFRV/AJ4COocQRgPtgMPSEpUkSZIkScpKKScqYowfhBD2ALqTXIr08xjj8rRFJkmSJEmSsk7KiYoQQj3g18DuJKd/vB5CuDPGWJKu4CRJkiRJqmkxxpoOIS1CTQewHpWZ+nEfUALcWbZ9dNljR1V1UJIkSZIkKTtVJlHRI8a4zWrbL4YQPq7qgCRJkiRJUvaqzKof48pW+gAghLAD8HbVhyRJkiRJkrJVZSoqtgfeDSF8U7a9OTA+hDAWiDHG7as8OkmSJEmSlFUqk6gYnLYoJEmSJEnKVIlETUeQVSqTqDgNuCfGOCFdwUiSJEmSpOxWmR4V3wH3hxDeDCGcGkJokqaYJEmSJElSlko5URFjvC3GuAvJyoqtgE9DCPeHEPqlLTpJkiRJkpRVKjP1gxBCDskmmp2AH4EJwMUhhLkxxuOqPjxJkiRJkmqYPSqqVcqJihDCdcAhwGjgphjjW6vts2+FJEmSJEnaZBtNVIQQOsYYvwcmAdvFGBdVMGzXKo9MkiRJkiRlnVR6VAwHiDHesZ4kBTHGeVUalSRJkiRJykqpTP0IaY9CkiRJkqRMlYg1HUFWSSVR0S6E8Lf17YwxnlOF8UiSJEmSpCyWSqJiKfBhugORJEmSJElKJVExN8Z4X9ojkSRJkiRJWS+VRMXyVA4UQugZYxy/ifFIkiRJkpRZEomajiCrbHTVjxhjnxSP9e9NjEWSJEmSJGW5VCoqUrXe1UEue3ZqFX4bbYrcNo1rOgSVqdM8pWIlVYNlbevWdAgqk4jercgkD9z2Y02HoDLH/qZZTYegMo27NajpEFSmfyOvRSZpWtMBqFbZaEVFJbheiyRJkiRJ2iRVmaiQJEmSJEnaJFU59cM6dkmSJElS7WMzzWq10URFCGH7De2PMX5U9jnVppuSJEmSJEkVSqWi4sYN7IvAoCqKRZIkSZIkZbmNJipijHtURyCSJEmSJEkp96gIITQEzgM6xhhPDyF0BbrFGJ9OW3SSJEmSJNW0hItcVqfKrPpxD8mGmbuVbU8FrqjyiCRJkiRJUtaqTKKiS4zxOmAFQIxxKRDSEpUkSZIkScpKlUlULA8hNCDZQJMQQhegJC1RSZIkSZKkrJRyjwrgUuA5oEMI4QGgL3BiOoKSJEmSJCljJBI1HUFWSTlREWN8MYTwEdCH5JSPc2OMc9IWmSRJkiRJyjqVWfWjf9mXC8s+9wghEGMcU/VhSZIkSZKkbFSZqR/nr/Z1fWBn4ENgUJVGJEmSJEmSslZlpn78cvXtEEIH4Loqj0iSJEmSpExij4pqVZlVP9Y2Fdi6qgKRJEmSJEmqTI+KWyhbmpRkgmM74ON0BCVJkiRJkrJTZXpUfLDa1yuBh2KMb1ZxPJIkSZIkKYttNFERQugYY/w+xnhfdQQkSZIkSZKyVyoVFcOB7QFCCI/HGA9Nb0iSJEmSJGWQRNz4GFWZVJpphtW+7pyuQCRJkiRJklJJVMT1fC1JkiRJklSlUpn6sW0IoZhkZUWDsq8p244xxvy0RSdJkiRJkrLKRhMVMcbc6ghEkiRJkqSMlEjUdARZJZWpH5IkSZIkSdXCRIUkSZIkScoYJiokSZIkSVLGSKWZpiRJkiRJ2cseFdXKigpJkiRJkpQxTFRIkiRJkqSMYaJCkiRJkiRlDHtUSJIkSZK0IYlY0xFkFSsqJEmSJElSxjBRIUmSJEmSMoaJCkmSJEmSlDFMVEiSJEmSpIxhM01JkiRJkjYkkajpCLKKFRWSJEmSJCljmKiQJEmSJEkZw0SFJEmSJEnKGPaokCRJkiRpA2JprOkQMk4IoTnwMNAJ+A44Isb443rG5gNfAE/GGM/a2LGtqJAkSZIkSZV1IfByjLEr8HLZ9vr8GRid6oFNVEiSJEmSpMoaDNxX9vV9wJCKBoUQdgCKgBdSPbCJCkmSJEmSVFlFMcYfAMo+F649IISQA9wInF+ZA9ujQpIkSZKkDUnUzh4VIYTTgdNXe+iOGOMdq+1/CWhdwVOHpvgtzgSejTFOCSGkHJeJCkmSJEmSslBZUuKODezfa337QggzQwhtYow/hBDaALMqGLYr0C+EcCbQGKgbQlgUY9xQPwsTFZIkSZIkqdJGAicA15R9HrH2gBjjsf/7OoRwIrDjxpIUYI8KSZIkSZJUedcAe4cQJgJ7l20TQtgxhPCvTTmwFRWSJEmSJG1Iae3sUbEpYoxzgT0rePwD4NQKHr8XuDeVY1tRIUmSJEmSMoaJCkmSJEmSlDFMVEiSJEmSpIxhokKSJEmSJGUMm2lKkiRJkrQBMWEzzepkRYUkSZIkScoYVlSUqXPAr8npthOsKGH54zcRp3+9zpi6p1xDaNIcVpYAUHLPJbB4Qfn+nJ59qXfMUJb941zitInVFnttE2PkmjHTeX1yMfXzcrhirw70KGy43vFnP/0tUxcs58ljuwHwh1Hf8d385DVaWFJKk3q5PHZ0t2qJvbaJMXLlrW8y5r3J1K+Xx9UXDKJn11brjPvL3e8y4sUJFC8s4aOnTyt//Op/vMm7H08DYOmylcybv5T3R5xSbfH/3L35+hdce/VwEqUJDj6sD6ectubqT8uXr2TohQ/yxfgpFDRtxHU3HU+7ds2ZNm0eBx94DZ06FQLQa9vN+NOwwwFYsXwlV1/5BO+/9zU5OYGzz92PvfbZttrP7efszde/5PprRpAoTTDk0F04+bRBa+xfvnwlf7roIb4YP5WCpg259sZf0bZdcwC+mjCdKy57nMWLlpGTE/jPw+dSr16dmjiNWmOHm4fSdv8BrFyyjHdOvJAfx36+zpiOR+xHz6FnEHJzmP7MaMb98XoAGnZow673XUudpk0Iubl8fOENTB81prpPISvc9auhHNirL7MW/kivPx9b0+HUel9+MIuR//ycRCKy8y86MOjILdbY//Yzk3nrqcmEnEC9+rkcdm4vijZrAsD0b4p5/G+fUrJkJSEncM7f+lKnbm5NnMbP1ttvfMVN1z5LIpHgoEN24IRTBqyxf/nylVw29DG+/Hw6BQUNueL6I2nbrhkrVqzk6stH8OX46YScwHl/3J8dduoMwD//9iLPPjWWhcXLeO3d/6uJ05KqlYkKIGfLHQkt21Fy06mEDt2oe9BZlNz2uwrHLn/0+oqTEHUbkLfrYBLff5nmaGu/1ycvZPL8Ep751VZ8MnMJV7w2jQeP6Frh2JcmzadBnTULg27Yr1P519e/Pp3G9Swc+qnGvPc9k6fN5/n7juHjL2Zy2c1jeOTvh64zbo8+m3Hs4K35xQkPrvH4RWf2Lf/6309+yheT5qQ95tqitDTBVVc8we3/+g1FRQUcc+RfGLhHT7ps0bp8zJOPv0t+fgOefn4oo54dy19vfJrrbzoegPYdWvLIk39Y57h33v4SzZs35qlRF5FIJFiwYEm1nVNtUFqa4Jorn+Sfd55OUVEBxx55MwP26LHGdRn++Ls0yW/AyOcu4rlnx3LzTc9w7Y2/YuXKUi658CH+fPXRdNuqLfPnLyYvzzf/m6Ltfv1p0rUTT3Xdhxa7bMtO/xzGC32OWGNM3eZN6X39BTy3wyGUzPmRPvdeQ9GgPsx85R22vuQMJj8yikm3PUR+9y4MfPYORm6+znLwqgL3vv0Mf3/tMe4/0T+w0i1RGnny1vGcftUuFLSsz9/OeYOefYrKExEAvQe2ZdcDNgNg/NszGXnHF5x25c6UliZ46LpxHH3BdrTtnM/i4uXk5vo+qjJKSxNcf9VT3HLHSRQW5XPi0bfRb2B3OncpLB8z8okPaZLfgMefOY8XRn3CrX99niuvP4rhj38AwINPnM28FgV+QwAAIABJREFUuYv47Zn3c+9DvyEnJ4fdB2zF4Uf34bAD/1JTpyZVK3/zALnd+1A69mUA4pQJUL8RNGlWqWPU2etXrHz9MeLK5ekIMau8+s0CDurejBAC27ZuxMKSUmYvXrHOuCXLS7l/3Gx+vVNRhceJMfL8pPnsv2XlrqVWefmt7xi8dzdCCGzXozXFi0qYNXfxOuO269GawhaNNnisZ16dyAGDttjgGK3y2aff06FjS9p3aEGdunn8Yr/evPbKZ2uMefWVzzhoyE4A7L3PNrz3zkRi3PD8yeFPvsfJZZUZOTk5NGvWOD0nUEt99un3dOjQovy67Lv/drz26vg1xrz2ynh+OXhHAPZa7bq8/dZXdN2yDd22agtA06aN/ANgE7UbvCff3j8cgLnvfkzdpvnUb71m1Vfjzh1Y+NV3lMz5EYAZL71Nh0P3BZKvE3Xykz8DdQuasHT6rGqMPru8Pmkc8xYX13QYWeH7CfNp2aYhLdo0JK9ODtsNaMv4t2euMaZ+o1WVXMuXrSSE5NdffTiHNps3oW3nfAAa5dclJzdUW+y1weefTaV9xxa0a9+cOnXy2PsXvRjz6hdrjBnz2hcccFBvAAbt3ZP33/2GGCPffj2bnXbpAkDzFo1p0qQ+X4yfDkCvbTvQslUTVINKY+38yFC+QwJCfkvigtnl27F4DiG/ZYVj6x7yO+qddQt5exy96vltOhMKWpGY8F7aY80GsxavoHXjVS+gRY3rMGvRuomKW96dwQm9C6mfV/F/4w+nL6ZFwzw2a1ovbbHWdjPnLKZNq1V/yLZu1ZiZc9ZNVGzMtJkLmTZjIX22a1eV4dVqs2YuoHXrpuXbha2bMnPWgvWOycvLpXGT+syfn7w+06bN44hDbuTk4//ORx98A0Bx8VIAbr3lOY489Eb+8Nv7mDtnYXWcTq0xa+YCitqsui5FRU2ZPXOt6zJr7evSgPnzl/D9d7MJAc487Q6OPuwv3HvXq9Uae23UsF0RS6bMKN9eMnUGDdutmbxeOGky+Vt1ptFm7Qi5ubQfsieNOiQrYD4d9nc2P+6XDJkymoHP3sEHZ19RrfFL6VA8dxlNWzUo3y5oWZ8Fc5etM+7Nkd9x9Umv8sxdXzL4jJ4AzJm2mBACd178Ln/9f6/z6qPrToXWhs2aWUxRUUH5dmFRPrNnrZmkmz2zmMKyMXl5uTRuXI8F85fQtVtrxrz6BStXljJ96jy+/GI6M2es+RojZYuNTv0IIZyzof0xxr9VXTg1pKJEcQV3JZc/ej0Uz4W6Dah7zFBytxtE6cevUmf/01nx+E3pjzNLVHhDeK1r9OXspUyZv5w/9itgWnHFVSyjvprP/l2bVrhPKargYoRQ+Tsrz746iX36dfbucSVUVBkR1vpBqHBMCLRqlc/zL/+Jpk0b8fn4Kfz27Ht4YuQFlJaWMnPGfHr37sT5fxzM/fe+xo3Xj+Sqa50vvknC2tel4iGlpQnGfvQt/3n4t9SvX4dfn3I73Xu2Z5c+FU9tUwoq+H209s/FivnFvH/GMPo+/BdIJJj91lgad+4AQKejD+Cbe5/ky5vuoWWf7djt39fxzNYHrueFSPp5WN/voLX1PagTfQ/qxNhXp/HyQxM56g/bUVqa4Nvx8zj3b7tTp14ut1/4Du23KKBr74pv4Ck1a793qvCtbgj8csj2fPfNbE48+p+0btOUXtt2JHc9N+Sk2i6VHhXrds5LQQjhdOB0gFv268kpvTv+lMOkTe4uB5K3U7L0MzF1IqFg1WmG/JbEhXPXfVJx2WPLl1L68WvkdOhG6RfvkFO0GXVPvTb53MbNqHfc/1Hyn8ttqFkJD30yh8fHJ/99ty5syIzVKihmLlpBYaM1m819PGMxn89ewr73fs7KBMxbupKTnpjEPYckpxasTERe+noBDx/lHwCV9cCIz3j02WQzul5bFvLD7EXl+2bMXkRhi/U3Nl2fZ1+dxJ/O6VdlMWaDotZNmTFjfvn2rBnzKSzMr3BMUeumrFxZyqKFyygoaEgIgbp1k7/ee/TsQIcOLZj83Wx69GxP/QZ1GbRXLwD22Xdbnnz83eo7qVqgsKiAmT+sui4zZ86n1drXpahgreuylIKChhQWFbDDjl1o1iw5TWr3flvx5edTTVRUUtczj2GL05J9KOa+/ykNO6zqD9KwfesKp29Me/pVpj2drGDpctoRxNIEAJ1POYzXfnEqAHPeGUdu/XrUa9mMktnz0n0aUtoUtKzP/NlLy7cXzFlGfvP66x2/7YC2PHFLcmph05YN6NyrBY0K6gKw1U6FTJu0wERFJRQW5TNztUq7WTOL15myUViUn6zQa12QfJ1YVEJ+QQNCCPzugv3Lx536q9vp0LFFtcUuZZKNpuhijH/a0McGnndHjHHHGOOOmZakACh992lK/n42JX8/m9Iv3ia3d3LOdujQDUoWw8If13xCTg40LHszmpNL7lY7k5g5GUqWsOyqoym54SRKbjiJxJQvTVL8BEdv05LHju7GY0d3Y1DnAkZ+8SMxRj6esZjGdXNotVai4sheLXnl5J48f2IP7j9sCzo1rVeepAB4Z8pCNm9Wj9aN61b3qfzsHTt4a4bffgTDbz+CPftuzogXJxBjZNznM2jSqN5Ge1Gs7ZspP7JgUQm9e1TcS0QV67l1B76fPJupU+eyYvlKnhs1lgF7bL3GmIF79GTk8PcBePGFT9h5ly0IITBv3iJKy/4QmzplLpMnz6Z9++aEEBgwsAfvv5cs5X33nYl06dIapa7n1h34/vs5TCu7Ls8/O46Be/RcY8yAPXry1IhkQ7SXXviEncquy259uzHxqx9YunQ5K1eW8uEH39C5iz8XlTXxHw8yqvcQRvUewtThL7H58UMAaLHLtqxYsJBlM2av85x6rZKrrtRpms+WZx7D1/96FIAl3/9A0Z67ApC/VWdy6tczSaGfvQ7dCpgzfTHzZixh5YoE40ZPp0efNX/XzJ62ahrnl+/NomW75Gv7lju0Ysa3xSxfVkppaYJvPp1LUUd7GVVG957tmDJ5LtOnzmPFipW8+Nyn9B+41Rpj+g3cimdGjgXglRfHs+POnQkhsGzpcpYuSVYKv/v2JHJzc9ZowqkaVpqonR8ZKuVVP0IIbYGbgd3LHhoD/C7GOD0dgVWnxIT3iVvuRL3z7kouT/rEqm669c66hZK/nw25dah34p8hNw9CDomvx1H6/nM1GHXt1a9TE8ZMLmb/+7+kfp0crtizQ/m+wx6akNJSo6O+ms/+WzrtY1MN2KUjY96bzD7HP0j9enlcdf4e5fuG/PoRht+evKt5/R1v8/QrE1laspIBR93PYft15+wTkk0en3llEgcM3OInTRnJZnl5uVw09BDOOO0OEokEQw7emS26tubWW0bRs2cHBg7amoMP3YWhf3yQA/e9kvymDbnuhuSKHx998DW33vIceXk55OTkcMmlh1PQNPkm9LfnHcjQCx/k+muG06xZYy6/8qiaPM2fnby8XP449GDOPP1OEonI4IN3ossWrfnHLc/Ro2cHBg7qyZBDd+aSCx/ioF9cTX5BQ6654TgA8gsactwJ/TnuyJsJAXbv151+A3rU8Bn9vE1/djRt9x/ALye9SOmSpbxz0sXl+/YbO5xRvZNJjB1uHkqzbZN/KHx2+a0snPgdAB/9/hp2ufMKtvrdiRAj75x4YXWfQtZ48OTLGbjl9rRs3JQpV43k0qfv5O63nqrpsGql3Nwchpy5NXcOfS+5POk+7WndqQnP3z+B9l2b0nPXIt4a+R0Tx84hJy+Hho3zOPL3yWWqGzapQ79DNudv57wBIVlR0X0XE6qVkZeXyx8uPpBzzriPRGmCXw7Zgc5bFHH7rS/RvUc7+u/RnYMO3oFhFz/GoQfcRH5BA6647kgA5s1bzLm/uY+cnECrwiYMu+qw8uPectNzPP/sJyxbtoID97qOwYfswGlnukqRaq+wsQ7x5QNDeB54DLi/7KFfAYfHGPfd2HOXDt3fyZ4ZIreNWfFMUWfwbjUdgsqUtLX0PlMkYuZm9rPR8DrrLnGrmnHsb1zBKlOMuMDX70zRv+2uNR2CVtO03uG1+q7UyruPqZV/0+ad/GBGXrfKdGcpijHeGWMsKfv4F2CKVZIkSZIkVZmUp34A80IIRwEPl20fATiRU5IkSZJUq8VErSyoyFiVqag4GTgemAPMJjn145R0BCVJkiRJkrJTShUVIYRc4KAY4/4bHSxJkiRJkvQTpVRREWMsBQ5NcyySJEmSJCnLVaZHxeshhJuB/wLliy/HGD+p8qgkSZIkSVJWqkyiYkDZ5+1XeywC/asuHEmSJEmSMkypzTSrU2USFcfFGCev/kAIYbMqjkeSJEmSJGWxyqz68WSKj0mSJEmSJP0kG62oCCFsCXQHCkIIB622Kx+on67AJEmSJElS9kll6kdP4BCgKXD4ao8vBH6djqAkSZIkScoYCXtUVKeNJipijE8CT4YQdo8xvlENMUmSJEmSpCxVmR4VM0IIz4cQPgYIIWwTQrgoTXFJkiRJkqQsVJlExb+Ay4BE2fanwHFVHpEkSZIkScpalVmetFGM8a0QAgAxxhhCWJGesCRJkiRJygyx1B4V1akyFRVzQwibAxEghDAEmJGWqCRJkiRJUlaqTEXFWcBdwFYhhMnAD8BRaYlKkiRJkiRlpZQTFTHGScCgEEIBEGKM89MXliRJkiRJykYbTVSEEHYCbgO6kGygeWqMcUK6A5MkSZIkKSMkEhsfoyqTSo+KfwCXAO3Kvr45rRFJkiRJkqSslUqiIjfGOCrGuDjG+BBQmO6gJEmSJElSdkqlR0XTEMJB69uOMY6s+rAkSZIkSVI2SiVR8SZw+Hq2I2CiQpIkSZIkVYmNJipijL9K5UAhhONijP/Z9JAkSZIkScogpbGmI8gqqfSoSNV5VXgsSZIkSZKUhaoyURGq8FiSJEmSJCkLVWWiwloYSZIkSZK0SVJpppkqKyokSZIkSbVOTHhfvjpVZUXFO1V4LEmSJEmSlIVSTlSEEFqFEG4PITxdtt0jhHDi//bHGM9IQ3ySJEmSJCmLVKai4l5gNNChbHsi8PuqDkiSJEmSJGWvyvSoKIwxPhhCOB8gxrgihFCaprgkSZIkScoMpfaoqE6VqahYHEJoTtnqHiGEnYCFaYlKkiRJkiRlpcpUVPwBeAroHEIYDbQDDktLVJIkSZIkKSulnKiIMX4QQtgD6E5yKdLPY4zL0xaZJEmSJEnKOiknKkII9YBfA7uTnP7xegjhzhhjSbqCkyRJkiSpxtmjolpVZurHfUAJcGfZ9tFljx1V1UFJkiRJkqTsVJlERY8Y4zarbb8YQvi4qgOSJEmSJEnZqzKrfowrW+kDgBDCDsDbVR+SJEmSJEnKVpWpqNgeeDeE8E3Z9ubA+BDCWCDGGLev8ugkSZIkSVJWqUyiYnDaopCk/8/efYdJWZ3/H3+f3aWXRcruAoJKEQQxVsQGosYWFXuJmmLERI010cSSWKJYYokl8atEk/iLmtgoNkRBxYIaCyIgYkWpS5EiZWF3zu+PGRaQsrO6szvuvF/XtdfOPM+Z4TPzuDPOPefcjyRJkpSlYsJmmrWpOoWKwcA/YowfZiqMJEmSJEnKbdXpUfE5cH8I4dUQwukhhBYZyiRJkiRJknJU2oWKGOP/xRh3Jzmzoifwfgjh/hDCPhlLJ0mSJEmSckp1ln4QQsgj2URza+Ar4EPg0hDCghjjKTUfT5IkSZKkOlaRqOsEOSXtQkUI4UbgaOAl4JYY42vr7LNvhSRJkiRJ+s6qLFSEEDrHGL8APgZ2jDF+vZFhe9R4MkmSJEmSlHPS6VExHCDGeM8mihTEGBfWaCpJkiRJkpST0ln6ETKeQpIkSZKkLBUTsa4j5JR0ChUdQwi3b2pnjPHcGswjSZIkSZJyWDqFihXA25kOIkmSJEmSlE6hYkGM8V8ZTyJJkiRJknJeOoWKVencUQihd4xx8nfMI0mSJElSdqmwR0VtqvKsHzHGfmne1//7jlkkSZIkSVKOS+f0pOny7CCSJEmSJOk7qclChXNhJEmSJEnSd5JOj4rvLLqeJ2skFpfVdQSt0ax1XSdQypJVC+o6glK2aFRc1xG0jhOf2a+uIyileY8mdR1BKYNufK2uIyhl2R0D6zqCpAypyUJFWk03JUmSJEn6Xkn45XttqrJQEULYeXP7Y4zvpH6n23RTkiRJkiRpo9KZUXHzZvZFwHmhkiRJkiSpRlRZqIgxuvhLkiRJkiTVirR7VIQQmgIXAp1jjGeEELoDPWKMT2YsnSRJkiRJdcwTRNSu6pye9B8kG2bumbo+A7imxhNJkiRJkqScVZ1CRdcY443AaoAY4wogZCSVJEmSJEnKSdUpVKwKITQh2UCTEEJXoCwjqSRJkiRJUk5Ku0cFcAUwCugUQngA2Av4WSZCSZIkSZKUNRL2qKhNaRcqYozPhRDeAfqRXPJxXoxxfsaSSZIkSZKknFOds370T11cmvrdK4RAjHFczceSJEmSJEm5qDpLPy5a53JjoC/wNrBfjSaSJEmSJEk5qzpLPw5f93oIoRNwY40nkiRJkiQpm1Qk6jpBTqnOWT++aQawfU0FkSRJkiRJqk6PijtInZqUZIFjR+C9TISSJEmSJEm5qTo9Kt5a53I58FCM8dUaziNJkiRJknJYlYWKEELnGOMXMcZ/1UYgSZIkSZKUu9KZUTEc2BkghPBYjPGYzEaSJEmSJCl7xESsepBqTDrNNMM6l7tkKogkSZIkSVI6hYq4icuSJEmSJEk1Kp2lHz8IISwhObOiSeoyqesxxtgyY+kkSZIkSVJOqbJQEWPMr40gkiRJkiRlpQoXF9SmdJZ+SJIkSZIk1QoLFZIkSZIkKWtYqJAkSZIkSVkjnWaakiRJkiTlrJiwR0VtckaFJEmSJEnKGhYqJEmSJElS1rBQIUmSJEmSsoY9KiRJkiRJ2oxYYY+K2uSMCkmSJEmSlDUsVEiSJEmSpKxhoUKSJEmSJGUNCxWSJEmSJClr2ExTkiRJkqTNiAmbadYmZ1RIkiRJkqSsYaFCkiRJkiRlDQsVkiRJkiQpa9ijQpIkSZKkzUhU2KOiNjmjQpIkSZIkZQ0LFZIkSZIkKWtYqJAkSZIkSVnDHhWSJEmSJG1GTNijojY5o0KSJEmSJGUNCxWSJEmSJClrWKiQJEmSJElZwx4VkiRJkiRtRkwk6jpCTnFGhSRJkiRJyhoWKiRJkiRJUtawUCFJkiRJkrKGhQpJkiRJkpQ1bKYpSZIkSdJmxIpY1xFyijMqJEmSJElS1rBQIUmSJEmSsoaFCkmSJEmSlDXsUZHS4PBfkd9jN1hdRtkjNxNnfbLBmEZn3EBo0RpWlwGw8t7LYNli8nc5gIaHnE5cMh+A1eOfoOJ/z9Zq/vqk4MDB5HXdBVaXsfrJ24hzPt1gTMNTroHmraE8eSxWPXglLF9M6NSLBgeeTijamtXDbiIx9bVaTl9/xRi59tbRjHvtExo3bsB1fziM3j3abzDu1v97gRHPvM+SpSt5Z+zFdZC0fnrj1U+47cbRJBKRw47akVNO23O9/RPe/oLb/zyaTz8q5Yrrj2LgD7cD4KOpc7h5yCiWfV1GXn4ePzl9L/Y/qFddPIR645WXp3D9kMepSCQ45tg9OH3wD9fbv2rVai753b+ZMuVLWrVqxk23/IyOHdtU7p89ayFHHD6Es84+hJ+ftn9tx69XYowMefwjxk1ZQOMGeQw5uRe9O7XYYNzguyYwb8kqyhORXbsU8ofjepCfF7jzmU95ZPwsWjdvCMD5P+rCgN5ta/th1AtT3ypl5F1TSCQifQ/uxH4ndFtv//inpvPaE9MJeYFGjfM59rw+FG+VPFazPl3CY7e/T9nyckJe4Nzb96JBw/y6eBg54d5TL+OwPntRuvQr+vzp5LqOUy+9+vJU/nz9CBIVCY48ZndOG7zfevtXrSrnD5c8xAeTZ1DYqik33HwqHTq2BmDah7O45qrHWPb1SvLyAv/+73k0atSAs88Yyrx5S6ioSLDTLttwyeVHk5/vd861KSbsUVGbLFQAeT12I69tB1be9AvyOvWk4ZG/puxvF2x07Kr/3Ehi5kcbbC+f+BKrR96V6aj1Xl7XXQit27Pqrl8ROmxLg4PPZNU/L9ro2NUjbiHO/nj9jUvms/qJ2yjY/ahaSJtbxo3/hOlfLuTZR87kvcmzuOrGUTx87883GDdw7205+dhdOfh4/x5qSkVFgluuG8Wt//dj2hW3ZPDJ97HXgO5s07Vd5ZjikpZcevXh/Of+N9a7baMmDbjsT0fQaavWzC9dyi9+fC999+hCi5aNa/th1AsVFQmu+dMjDL33bEqKW3HC8TcxcOD2dO22tmj3+KOv07KwKc88+0eefuptbrlpJDffuvZv5Ybrh7HPPhaLasK4KQuYPm85oy7vx3vTl3D1Ix/y3wt33WDcrT/fnuaNC4gxct59kxg1oZQf7VwMwE/37cxp+3Wu7ej1SqIiMuyvkzljyO4Utm3M7ee+Qu9+xZWFCICd9u3AHj/aCoDJ4+cy8p4PGHxtXyoqEjx04wROunhHOnRpybIlq/zwlWH/HP8Ud774KPf/7I91HaVeqqhIcP21w7hr6BkUFxdy8gm3MWBgL7p2K6kcM/yxN2jRsgkjR13CqKff5bZbnuKGm0+lvLyCy3//EH+67iR69OzAokXLKChIFu1uuOVUmjdvTIyR355/P889+x4HH7pTXT1MKeN8JwDye/Wj/J0xACS+nEpo0hxabFHHqXJT3rZ9qZj4AgBx1jRo3Ayap38s4uJSYul0iIlMRcxZY8ZNY9AhOxBCYMftO7Lk65WUzl+6wbgdt+9IUdsNv9HUt/fBpFl07NSaDltuQYMG+ex/UC9eeXHaemPad2xFt22LCSGst73zVm3otFXyW5q2RS3YonUzFn21vNay1zfvT5xO587t6NSpLQ0aFnDIoTszduz7640ZO/Z9Bg3qC8CBB+3IG69PI8bktzBjnp/Ilp3arPc/rPr2xk6az6DdSpKvS1sXsmRFOaWLyzYY17xx8nuZ8kRkdUWCsMEIfRdffLiItu2b0qZ9Uwoa5LHjgA5MHj93vTGNmzWovLxqZTlrXqqmvT2f9tu0oEOXlgA0a9mQvHyPUCa9/PEEFi5bUtcx6q1J739Bp05t2LJTGxo0LOCgQ3fkxRcmrzfmxbGTOXxQsqh6wIE78ObrHxFjZPxr0+i+bXt69OwAQKtWzSoLd82bJ79gKC9PUL66fIP3e6m+cUYFkNeyDRWL5ldej4vnk9eyLYmlX20wtuFxF0AiQfmkVykf+1Dl9oLt9yZ/mz4k5s9k9ZN3ExfP3+C2qlpo0aZyCQ1AXDI/ue3rDY9Fg8POgZigYup4Kl55uDZj5qS585bSvrhl5fWSdi2ZO2+pRYlaMK90KUUla5/ndsUt+eD9mdW+nynvz6R8dQUdO1mI/bZKSxdRUtKq8npxcSvenzh9/TFzF1PSPjmmoCCf5i0as2jRMho1asB9f3+eofeezT/+MaZWc9dXcxeVUdJq7eygksJGlC4uo6iw0QZjT79rAu9PX8I+27XhoB2LKrc/8PIMRrw5m+07t+TiI7tR2LTBBrfV5i1ZsJJW7ZpUXi9s25gvPly0wbhXR37OuGGfUbE6wS9v6AfA/JnLCCEw9NI3WLZ4FT/YtwMDj+taa9mlmlY6dzHF7dd/n5j0zfeJ0sWV7yXJ94kmLFq0nC8+n0cIcNbge/jqq2UcdMiO/OwXAytvd9bge5g06Uv22rsnBxy4Q+08IKmOVFmoCCEMAza5ICfGeHSNJqoLG6lIxo085FX/uZG4ZAE0bEKjUy4n7rw/Fe+MoeKDN1gx4SWoWE3B7ofS8PjfUDb0ktpIXv9srDocN3IsRtwCSxdCwyY0OOZ3xD4DSbz/Qi0EzGUbHger+bVkY6/A1Xzu589byjWXj+SyPx1BXp7H7dvayMvRBn8HcSODAoG/3vkMp/50X5o22/BDtL6d6vxp/P3MHSlbXcFF90/h9WlfsVfP1py415acedA2BOD2pz/lxuEfc+2Pt8tk5Hpp438XG27b64it2euIrXn3hZmMeegjTvztjlRUJPhs8kLOu31vGjTK5+7fv86W3QrpvpO9QlSPbPA+sfEhFRUJ3n3nM/793/Np3LgBv/zF3WzXe0t279cdgL8NPYOystVcevGD/O+Nj+m357a1kV4pscIeFbUpnRkVd36bOw4hnAGcAXDHQb05bcdO3+ZuMqag32EU9D0YgMSMaYRWbSFV7AyFbZMFiW+o3LZqBeXvvUDelttS8c4YWL52+nv5m6NocMhpGc9fn+Tvcij5OyWb0SVmfUxo2bbyfz5Dy7bErxdueKOlqW2rVpCYPI68Dt0tVGTAA4++xSMj3wWgz3YdmD137VTROfOWUNS2eV1FyyntiltQOmft68y8uUto2y79537Z12VcfM5/GXz2vvTeoWMmIuaM4uJWzJmz9pviuXMX0a6o5fpjSloxZ/YiSkq2oLy8gq+XrqSwVVPen/g5zz07gVtuGsnSpSuSTQUbNeDHJ/ev7YfxvfbAyzN4dPwsALbv3II5i1ZW7puzuIx2LTddCGrUIJ/9+rRl7KR57NWzNW1bNqzcd9weHfjVPRMzF7weK2zbmEXzVlReXzx/JS1bb7oPzg8GdODxOyYB0KptE7r0aUOzwuSx6LlbETM/XmyhQt9bRcWFzJ1dxftEcSFz5iyiuKRV6n1iBYWFTSkqLmSXXbuyxRbNANh7n55MnTKjslAB0KhRAwYM7MWLYydZqFC9VmWPihjjmM39bOZ298QYd40x7pptRQqA8tefZOXtv2bl7b+mfPJ4CnZOdl7P69STuHIZfHPZR14eNE29yOTlk99zd+LcVGVjnX4W+b36kSj9sjYeQr1R8fbTrPr7Baz6+wUkpr1O/g7JKW69gC4+AAAgAElEQVShw7ZQtgy+uewj5EGT1DT4vHzyuu1GnPdFLafODScfuyvD7x/M8PsHs3//bRnxzERijEyYNJMWzRq57KOW9OzdgRlfLGTWzEWsXl3BmGensPeA9P7nZPXqCi698FEOPmwHBh7oN8Xf1fZ9OvPF9HnMmLGA1avKeebpdxg4sM96YwYO3J4RI94EYPSzE9i9X3dCCNz/7/MZPeZKRo+5klN+MoDBZ/zQIsW3cPI+WzLs4r4Mu7gv+/dpx4j/zUm+Ln2+mBaN8zdY9rGsbG3fivKKBC9NWUCXouSHgHX7WTw3cR7d2zervQdSj3TqUcj8WctYOGc55asTTHhpFr36Fa83Zt7MZZWXp75ZStuOyed6213aMeezJaxaWUFFRYJP319AcWeL4Pr+6r19J774Yj4zU+8Tzz49gX0H9l5vzICBvXlixFsAPD96Irvt3o0QAnvu1YOPps1mxYpVlJdX8PZbn9KlazHLl5Uxb17yy6Ly8gpefXkqW29TtMG/LdUnafeoCCF0Ba4FegGVZfIY4/e+lJf48H8keu5G44vug9UrWfXIrZX7Gp97Jytv/zXkN6DxaddAfgHk5VHx8buUvzkKgAZ7DiK/Vz9IVBCXL2XVIzfX1UP53kt8/DZ5XXel4Vn/lzo96R2V+xqefiur/n4BFDSg4UlXQl7yWCQ+e4+Kd0cDENp3o+Gxl0Dj5uR1343Y/yRW3XNOHT2a+mXAnt0Y99onHHjc32jcqAFDLj+sct+RPxnK8PsHA/DnO8fw5OjJrFi5mgFH3M6xR+zIOaf7Yey7KCjI44LfH8RvznyIRCLBjwb9gG26tePvf3uJnr3as/e+2/LBpFlcduGjLF2yktfGfcR9d43j/z3+S8aOnsJ773zBkkXLeWbkewBcevXhdO9pM8dvo6Agn0svP5Zfnv43KhIJjjq6H926t+fO25+i9/adGbhfH44+dg8u+d3/45CDrqawsCl/vvlndR273hrQqw3jpizgoD+Np3HDfIass2zjqBvfZNjFfVlRluDsoRNZVZ6gIkK/7ltwwl7JRnU3jfyYqTO/JgAd2zThyuN71NEj+X7Lz8/jyLO2Z+hlbyZPT3rglpRs3YJn7/+QLbu3ovcexbw28nM+enc+eQV5NG1ewAm/+QEATVs0YJ+jt+H2c1+BkJxRsd3uxVX8i/ouHjztavbddmfaNm/Fl0NGcsWTQ7nvtSfqOla9UVCQz+8uO4qzzhhKIhEZdNRudO1Wwt/uGEWv3p3Yd7/eHHlMXy7//UMccfB1tCxsyvU3nQJAy8KmnPLT/pxywm2EAHvvsx37DOjFgvlLOf/s+1i9OlnQ2233bhx7wh51/EilzAobW0u70YEhvAxcA9wEHAn8HEjEGKs8t9Hy3x/igp4skdeiYdWDVCsanXlMXUdQyrwmVY9R7diikR9Qskn+6OF1HUEpT/bwhSpbDLrxtbqOoJRld1xW1xG0jqYFh9frJlilx+9RLz/TFj08PiuPW3VOT9o0xvgsQIzxkxjj5cDAKm4jSZIkSZKUtuqcnrQsJFubfxJC+BUwE3BxlCRJkiRJqjHVKVRcADQHziXZq6IQ8PQWkiRJkiSpxqRdqIgxvpG6uBQ4NTNxJEmSJElSLqvOWT+eAzZoIBJjPLBGE0mSJEmSlEUSiXrZSzNrVWfpx+XrXG4MHAOUbWKsJEmSJElStX2bpR9rvBRCeKmG80iSJEmSpBxWnaUfLde5mgfsArSv8USSJEmSJClnVWfpx2SSPSoCUA58BgzORChJkiRJkrJFrLBHRW2qztKPTpkMIkmSJEmSVGWhIoRwxOb2xxhH1lwcSZIkSZKUy9KZUXFc6ndbYE/gxdT1AcBLgIUKSZIkSZJUI6osVMQYTwUIIYwEesUYZ6audwRuz2w8SZIkSZLqVkzYo6I25VVjbJc1RYqUWUCPGs4jSZIkSZJyWHXO+jEuhPAU8BDJs3+cCIzLSCpJkiRJkpSTqlOoOJtkv4p9UtfvBx6t8USSJEmSJClnVef0pBF4OPUjSZIkSVJOsEdF7Urn9KQvxRgHhBC+Irnko3IXyfpF64ylkyRJkiRJOSWdGRUDU7/bZjKIJEmSJElSlWf9iDEmUhdvBHaOMVas+5PZeJIkSZIkKZdU5/SkU4BrQwjTQgjXhRB2zFQoSZIkSZKUm6rTTPNe4N4QQjvgWOAvIYSSGGPPjKWTJEmSJKmOxQqbadam6syoWKMTsDXQEfisRtNIkiRJkqSclvaMihDCtSRnUnwJ/BfYPca4MFPBJEmSJElS7km7UAHMBvrHGOdmKowkSZIkScptVRYqQgjdY4wfAeOA4hBC8br7Y4wTMxVOkiRJkqS6FhOJqgepxqQzo+L3wC+Av25kXwT612giSZIkSZKUs6osVMQYf5H6vU/m40iSJEmSpFyWztKPIza3P8Y4subiSJIkSZKkXJbO0o/jUr/bAnsCL6auDwBeAixUSJIkSZLqrVgR6zpCTkln6cepACGEkUCvGOPM1PWOwO2ZjSdJkiRJknJJXjXGdllTpEiZBfSo4TySJEmSJCmHpbP0Y41xIYSngIdInu3jRJKnLJUkSZIkSaoR1SlUnE2yX8Was3/cDzxa44kkSZIkScoiMWGPitqUdqEixhiBh1M/kiRJkiRJNS7tHhUhhEEhhA9CCItDCEtCCEtDCEsyGU6SJEmSJOWW6iz9uBk4Ksb4fqbCSJIkSZKk3Fads37MtUghSZIkSZIyqTozKv4XQngAGA6UrdkYYxxZ46kkSZIkScoSCZtp1qrqFCraAAngiHW2RcBChSRJkiRJqhHVOevHqZkMIkmSJEmSVGWhIoRwK8mZExsVY7ywRhNJkiRJkqSclc6MikkZTyFJkiRJUpaKFfaoqE1VFipijPemc0chhL/EGM//7pEkSZIkSVKuqs7pSavSvwbvS5IkSZIk5aCaLFRIkiRJkiR9J9U5PakkSZIkSTknJuxRUZtqckZFqMH7kiRJkiRJOajahYoQQqNN7LrzO2aRJEmSJEk5Lu1CRQihbwjhfeCj1PUfhBDuWLM/3bODSJIkSZIkbUp1elTcDhwGDAeIMb4XQhiYkVSSJEmSJGWJWGGPitpUnaUfeTHG6d/YVlGTYSRJkiRJUm6rzoyKL0MIfYEYQsgHzgGmZSaWJEmSJEnKRdWZUXEmcCHQGSgF+qW2SZIkSZIk1Yi0Z1TEGEuBEzOYRZIkSZIk5bi0CxUhhK2BW4E9UpteBX4TY/y8xlNJkiRJkpQlYsJmmrWpOj0qHgLuAU5IXf9xatsem7xFSuO9O1U/mTKjIL+uE2iNFYvqOoFSippsVdcRVKk6b0vKtNi3yrd41ZL+zZrUdQSlLLvDk95li2bnXFvXEbSOeNfhdR1B9Uh1z/rxjxjjqtTPP6t5e0mSJEmSpM2qzldXY0MIvwX+A0SSMyueCCG0BIgxLslAPkmSJEmSlEOqU6g4JfX7vG9s/yXJwkXnGkkkSZIkSVIWsUdF7arOWT9sNCFJkiRJkjIq7R4TIYTXQwhnhBBaZDKQJEmSJEnKXdVphvkzoCvwXgjh3yGE/TMTSZIkSZIk5arqLP2YCvwuhHApcARwfwhhFXAfcEeM0XMtSpIkSZLqnVhhj4raVK3Ti4YQegHXA9cBI0g22FwFjK35aJIkSZIkKdekPaMihPAGsILkDIo/xhhXpHa9GkLYKxPhJEmSJElSbqmyUBFCODrG+Dhwaoxx2sbGxBiPqPFkkiRJkiQp56Qzo+Jy4PFNFSkkSZIkSarPEgl7VNSmavWokCRJkiRJyqR0ZlT0DCFM3Mj2AMQY4w41nEmSJEmSJOWodAoVnwGHZzqIJEmSJElSOoWKVTHG6RlPIkmSJEmScl46hYpX07mjEMJPY4z/+o55JEmSJEnKKolEXSfILVU204wx/jrN+zrvO2aRJEmSJEk5ribP+hFq8L4kSZIkSVIOqslChSeWlSRJkiQpB4QQWocQngshfJT6vcUmxt0YQpgcQvgghHB7CKHKSQ7OqJAkSZIkaTMSifr58x39HhgTY+wOjEldX08IYU9gL2AHYHtgN2BAVXecVqEihJAXQji+imFpNd2UJEmSJEnfe4OANSfU+Bdw5EbGRKAx0BBoBDQA5lZ1x2kVKmKMCWCzTTWr0XRTkiRJkiR9vxXHGGcDpH4XfXNAjHE88AIwO/XzbIzxg6ruOJ3Tk67xXAjht8B/gWXr/MMLq3EfkiRJkiQpC4QQzgDOWGfTPTHGe9bZ/zxQspGbXpbm/XcDtgO2TG16LoTQP8Y4bnO3q06h4rTU77PX2RaBLtW4D0mSJEmSvldqoJ9DVkoVJe7ZzP4DNrUvhDA3hNA+xjg7hNAeKN3IsKOA12OMX6du8wzQD9hsoSLtZpoxxm028mORQpIkSZKk3DMS+Gnq8k+BERsZ8wUwIIRQEEJoQLKRZpVLP9IuVIQQmoYQLg8h3JO63j2EcFi6t5ckSZIkSfXG9cAPQwgfAT9MXSeEsGsI4e+pMY8CnwDvA+8B78UYn6jqjquz9OMfwNvAnqnrM4BHgCercR+SJEmSJOl7Lsa4ANh/I9vfAk5PXa4Aflnd+65OoaJrjPGEEMJJqX9wRQghVPcflCRJkiTp+yQR6zpBbkl76QewKoTQhGQDTUIIXYGyjKSSJEmSJEk5qTozKq4ARgGdQggPAHsBP8tEKEmSJEmSlJvSLlTEGJ8LIbxD8lQiATgvxjg/Y8kkSZIkSVLOqbJQEULY+RubZqd+dw4hdI4xvlPzsSRJkiRJUi5KZ0bFzZvZF4H9aiiLJEmSJElZJ5Go6wS5pcpCRYxxYG0EkSRJkiRJSmfpx9Gb2x9jfLzm4kiSJEmSpFyWztKPwzezLwIWKiRJkiRJUo1IZ+nHz2sjiCRJkiRJ2cgeFbUrL92BIYTiEMK9IYRnUtd7hRB+kblokiRJkiQp16RdqAD+CTwLdEhdnwacX9OBJEmSJElS7qpOoaJtjPFhIAEQYywHKjKSSpIkSZIk5aR0mmmusSyE0IZkA01CCP2AxRlJJUmSJElSlrBHRe2qTqHiQmAk0DWE8CrQDjg2I6kkSZIkSVJOqnLpRwhhtxBCSYzxHWAAcClQBowGZmQ4nyRJkiRJyiHp9Ki4G1iVurwncBnwV+Ar4J4M5ZIkSZIkSTkonaUf+THGhanLJwD3xBgfAx4LIUzIXDRJkiRJkuqePSpqVzozKvJDCGsKGvsDY9fZV50eF5IkSZIkSZuVTqHhIeClEMJ8YAXwMkAIoRue9UOSJEmSJNWgKgsVMcZrQwhjgPbA6BhjTO3KA87JZDhJkiRJkpRb0lq6EWN8fSPbptV8HEmSJEmSsos9KmpXOj0qJEmSJEmSaoWFCkmSJEmSlDUsVEiSJEmSpKxhoUKSJEmSJGWNtJppSpIkSZKUq2ymWbucUSFJkiRJkrKGMyqAGCNDhn/CuA8W0LhhPkNO7EHvLVtsMG7wPROZt2QV5YnIrl0K+cPR3cnPC1xw/xQ+n7ccgCUrymnZpIBhv9m1th9GvRFjZMjjHzFuygIaN8hjyMm96N1pI8fjrgnrH4/jepCfF7jzmU95ZPwsWjdvCMD5P+rCgN5ta/th1AsxRq6982XGvTGdxo0bcN3F+9N723YbjLv13tcZMfpDlixdyTtP/3K9fc+8+BF3/ut/BKBH17bcfPmBtZS+fokxcu2NI3nplak0btyA668+nt7bbbnBuElTZnDJHx9mZdlqBuzdk8suPoIQAnfcNZqHH3+T1ls0A+DCcw5mwD7b1fbDyHoxRq4d8ggvjZucfJ6H/ITevTtvMG7S5C+45JL7k89z/95cdulxhBBYtGgZF1x4LzNnLqBjxzb85dbTKSxsusn7nTlzAeecew8ViUj56gpOOWUAJ53YH4Cnn36Lu+4eRaIiMmBAby6+6OjafjqyUoyRa28dzbjXPkm+Lv3hMHr3aL/BuElTZ3PJn56grKyc/nt25bILDiSEwNSP5nLFjc+wfPkqOrYv5KarjqR5s0asWl3BFTc8zaQPZpOXF7j0ggPZfeet6uARfn+Mf2Uat9zwNIlEgiOO3oWf/mLAevtXrSrnqsseZeqUWRQWNuWaP59Ah45bsHp1OdddPYKpk2cR8gIX/u5QdtmtCwB33f4cTz/xLkuXrOTFN/5YFw/re+vVl6fy5+tHkKhIcOQxu3Pa4P3W279qVTl/uOQhPpg8g8JWTbnh5lPp0LE1ANM+nMU1Vz3Gsq9XkpcX+Pd/z6NRowacfcZQ5s1bQkVFgp122YZLLj+a/Hy/56wp9556GYf12YvSpV/R508n13UcKav4SgOMm7qQ6fOXM+qSvlx13LZc/dhHGx136096Mfy3u/LERbuy8OvVjHpvXuX2Yb/ZlWG/2ZUDd2jHAX38UPxdjJuygOnzljPq8n5cdWJPrn7kw42Ou/Xn2zP8d3154vd9k8djQmnlvp/u25lhF/dl2MV9LVJ8B+PemM70mYt59v+dwtUX7stVf3lxo+MG7rE1D//t2A22fz5jEfc8+A4P3n40T/7jx1x69t4ZTlx/jXtlKp9/MZ/RIy/mT384hiuvHbbRcVdeO4yr/3AMo0dezOdfzGfcq2v/fn52yj6MePgCRjx8gUWKTRg3bjKfTy9l9Kgr+dNVJ3Pl1f/Z6Lgrr3qIq6/6MaNHXcnn00sZ9/IUAO4Z+ix77NGD0c9exR579OCeoc9u9n7btSvkPw/9lhHDLuXh/17E0KGjmVu6iK+++pobbxrGv/5xHk89+QcWLFjK+PFTa+dJyHLjxn/C9C8X8uwjZ3L17w/lqhtHbXTcVTc+w9W/P5RnHzmT6V8u5OXXPwHg8uue4jdnDuSJB87ghwN6cO+/xwPwyIh3AXjigTO477Yfc8Ptz5NIxNp5UN9DFRUJ/jzkCf5y10/4z/BzGf3M+3z6Sel6Y0Y+/jYtWjbhsacu5MRT9+Svf0n+PQx/7C0AHnz8HO64+2fcdtMoEqk51XsP6Mk/Hjyzdh9MPVBRkeD6a4dx5/+dzmMjL2LU0+/yycdz1hsz/LE3aNGyCSNHXcLJP+nPbbc8BUB5eQWX//4hLvvjMTw28iKG/vNMCgryAbjhllN5eNhveHTEb/lq4TKee/a9Wn9s9dk/xz/FwXdcUNcxpKxkoQIYO2kBg3YpIYTAjlu1ZMmKckqXlG0wrnnj5ASU8kRkdUWC8I39MUZGTZjHj3YqqoXU9dfYSfMZtFvqeGxdmDwei6t/PPTdjXntMwb9sEfyWPQqYcnXqyhdsGyDcTv2KqGoTbMNtj/y1BR+PKgPhS0aA9Bmi6YZz1xfjXlxCkcetnPyWOywFUuWrqB03pL1xpTOW8LXy1ay0w+2IoTAkYftzJgXJtdR4u+nMWMncuSg3ZPP847bsGTJckpLF683prR0MV9/vZKdduqSfJ4H7c6YMe+tc/t+ABw5qB/Pr7d9w/tt2LCAhg0bAMlvOxMx+cH4yxnz2XqrIlq3Ts4m22OPnjw7+t1aeQ6y3Zhx0xh0yA7J53L7jiz5eiWl85euN6Z0/lK+XraKnfpsSQiBQYfswPMvTQPgs+kL2G2n5CyZPft2YfSLyWLeJ5/NZ49dtwagTetmtGzemEkfzKq9B/Y9M2XSDLbs3IaOW7amQYMCfnhwH8a98MF6Y8a9+AE/OmInAPb7YW/+98anxBj57JN57LZ7VwBat2lOixaN+WBy8rnu84NOtG234SxKbd6k97+gU6c2bNmpDQ0aFnDQoTvy4jde/18cO5nDByVn/B5w4A68+fpHxBgZ/9o0um/bnh49OwDQqlWzylkTzZsn37/LyxOUry4nBP9vqya9/PEEFi5bUvVAZYVEon7+ZKvNFipCCMNCCI9v6qe2Qmba3MVllLRqVHm9pLARpYtXbXTs6XdPZO8rxtOsUQEH/WD9KfBvfbqYNi0asHU7P4x9F3MXlVHSqnHl9eTx2LBQAXD6XRPY+7JXksdjx7UFogdensGg69/gsgc/YPHy1RnPXF/Nnb+M9kXNK6+XtGvG3PkbFio25fMZi/h8xiJOOucxTjj7UV5+c3omYuaEuaWLKSlpVXm9pLgVc7/xAXpu6WJKigs3OeaB/7zG4cfdwiVXPMziJcszH/p7aO7cRZSUbFF5vaRkC+aWLlp/TOkiSorXPRZbMHducsyCBUspKkoeg6KiQhYuXFrl/c6evZDDB13DvvtdxuBfHEhxUSu26lzEp5/NZcbMBZSXVzBmzHvMmfNVZh7098zceUtpX9yy8npJu5bMnbd0gzElRWs/7JYUtagc071LO8a+nCxajBr7AbNLkx8SenQvYsy4aZSXJ5gxaxGTP5zN7NL171drlc5dQvE6rzdFxS2ZV7r+B655c5dQlBpTUJBP8+aNWLxoOd17lDDuhQ8oL69g1oyFTP1gFnPnrP96puopnbuY4vZrX5eKi1sxb+6GRdY17yMFBfk0b9GERYuW88Xn8wgBzhp8Dycdeyv/vPeF9W531uB72L//lTRt1pgDDtwh8w9Gkqi6R8WdtZKijm1sYuem6sV//+UOlK1OcNEDH/D6R1+xV4/WlfueerfU2RQ1YKPHYxMH5O9n7kjZ6gouun8Kr0/7ir16tubEvbbkzIO2IQC3P/0pNw7/mGt/7DT3b2UjB6M6X6aUVySYPmMR9996JHPnLePk8x7niftOomXzRlXfWOuJGz0WIe0xJx2/B2edcQAhwG1/Hc31Nz/JdVcdn4mo32txI0/ihs/zxsZ8+/tt3741T4y4nLmlizj713dz0EE70bZtS6684kQuuPBe8kJgp5268OWX86vxSOqzqo/R5l67hlx2GNfcOpq/3vcK++3TnQapKe7HHLYjn36+gGNPu5cOJYXs1GdLCvL99rg6Nvhb2cSYw4/cmc8/ncfPTrqLkvat6PODzuQXOMm3xqX1HpFcNvLuO5/x7/+eT+PGDfjlL+5mu95bsnu/7gD8begZlJWt5tKLH+R/b3xMvz23rY30knLcZgsVMcYx3/aOQwhnAGcA3HX23pxxcHZ9UHzglZk8+sZsALbv1II5i9Z+Yz9ncRntChtu8raNGuSxX+82jJ28oLJQUV4Ref79+Tx6wS6ZDV5PPfDyDB4dn5z2uX3nFsxZtLJy35zFZbRruekPto0a5LNfn7aMnTSPvXq2pm3LtcfuuD068Kt7JmYueD30wPD3eeSp5HTRPj2KmV36deW+OfOWbXSJx6aUtGvOD7YrpkFBPlu2b8k2nVoxfcYi+vQsrvHc9dED/3mNhx9/A4A+vTsxZ87ab/bnzF1EUbuW640vKS5kzjrfoK07pm2btd8uH3d0X3517j8yGf175YEHXuLhR18FoM/2W603c2HOnK8oale43viS4i2YM3fdY/EVRUXJbynbtGlBaeliiooKKS1dXLl0o6Rkiyrvt7ioFd27teettz/m4IN2Zr+BO7DfwOS3l/99+BXy8nL3Q/MDj77FIyOTS1/6bNeB2XPXfnM/Z94Sito2X298cVEL5qwzG2JO6VKK2iaPRZet23LfbT8G4LMvFvDSqx8DUFCQxyXn/7DyNicO/idbdVr7ZYTWV1TckrnrvN6Uzl2ywZKNouKWyW/6SwopL6/g66/LaFnYhBACF1x8aOW400+9m06d29Ra9vqoqLiQubPXvi7NnbuIdkXrv0cUFxcyZ84iiktaJY/H0hUUFjalqLiQXXbtyhapZst779OTqVNmVBYqABo1asCAgb14cewkCxWSakVa5esQQtcQwn9CCBNDCNPW/GzuNjHGe2KMu8YYd822IgXAyXt3rGyAuf/2bRnx9hxijEyYvoQWjQso+sYH42VlFZV9K8orIi99sJAuRWuXeIz/6Cu2KWq63hISpe/kfbasbH65f592jPhf6nh8vpgWjfMpKvzm8Vjbt6K8IsFLUxbQpSj5BrvuMpHnJs6je/v0P1gLTj6yD8OHnsjwoSey/97bMOK5D5PHYsocWjRrWK1CxQF7bcMbE2YC8NXiFXw+YzFbti+s4lZa4+QT96xsfnnAwN4Mf/Kd5LGYOJ0WzZtsUKgoateSZk0bMWHidGKMDH/yHfbftxfAev0snh87ie7dSmr1sWSzk08ewIhhlzJi2KUcsP8ODB/xRvJ5nvAZLVo0qVzKsUZRUSHNmjViwoTPks/ziDfYf79kQWG//XZg+IjXARg+4vW12wf22ej9zpnzFStXJpcaLl68nHfe+ZRttkkW8hYsWFq5/cGHxnHcsXvVyvORjU4+dleG3z+Y4fcPZv/+2zLimYnJ53LSTFo0a1RZhFijqG0LmjVryIRJM4kxMuKZiezfP/nhasHC5PK1RCLyf/94lROP2hmAFStXs3xF8li8+uanFBTk0W2bDc9ypKTtenfky+kLmDVjIatXl/PcqPfpv2/P9cbss29PnkoVmMY+N5ld+yb7uqxcsYoVy5PP9RvjPyY/P48uXZ2R+l303r4TX3wxn5kzFrB6VTnPPj2BfQf2Xm/MgIG9eWJEspHp86Mnstvu3QghsOdePfho2mxWrFhFeXkFb7/1KV26FrN8WRnzUu8d5eUVvPryVLbexuOk3FXXvSRyrUdFuqcn/SdwDXATcAjwcyCLH1b1DNiuNeM+WMhB171J4wbJ05OucdTNbzHsN7uyYlUFZ983mVXlCSoSkX7dWnHCHh0qxz3tso8aM6BXG8ZNWcBBfxqfPF3sOss2jrrxTYZd3JcVZQnOHjoxeTwi9Ou+BSfslTweN438mKkzvyYAHds04crje2ziX1JVBuy+FePemM6Bp/ybxo0LGHLx/pX7jhz8H4YPPRGAP9/9Gk+OmcaKsnIGHP9Pjj20F+f8rC9779aZV976kh/9/EHy8gIX/XJPtihsvKl/TpsxYJ+evPTKVH54+A00adyQIVcdV7lv0PG3MuLhZNfwKy87qvL0pP336kn/vZMfHP78l6eZ+oEo10oAACAASURBVOEsCNCxwxZcffkxdfI4st2AAdvz0rjJ/PCgK5LP85BTK/cNOmoII4ZdCsCVV5xUeXrS/vv0pn//5AeCM04/kPMvvJdHH32N9h1ac9utp2/2fj/5ZA7X3/gYIQRijJx22gH02LYjANcOeYSpH84A4OwzD60sYOS6AXt2Y9xrn3DgcX+jcaMGDLn8sMp9R/5kKMPvHwzAFRcdzKXXPMnKstXs068r/fdINm986rnJPPDY2wAcuG8Pjj7sBwAs+GoZp5//EHkhUNyuBTf8cVAtP7Lvl4KCfH576WGce+a/SFQkOPzIXejSrZi7//o82/XqSP+B23HEUbtw5aWPcsyPbqFlYROuufEEABYuXMZ5v/oXeXmBdkUtuHLI2rNG3XHLKJ59eiIrV67msANuZNDRuzD4rP03FUMpBQX5/O6yozjrjKEkEpFBR+1G124l/O2OUfTq3Yl99+vNkcf05fLfP8QRB19Hy8KmXH/TKQC0LGzKKT/tzykn3EYIsPc+27HPgF4smL+U88++j9WrK6ioSLDb7t049oQ96viR1i8PnnY1+267M22bt+LLISO54smh3PfaE3UdS8oKYWPrZjcYFMLbMcZdQgjvxxj7pLa9HGPcJ51/JPHkGZ7fK1uk1uKq7oU+2TfTKFeF1lvVdQSt0ah51WNUa+KimXUdQSmLmzWp6whKaZhvwT1bNDvn2rqOoHXEu16v12sUX2jfs15+ph04e2pWHrd0Z1SUhWSHpE9CCL8CZgJOH5AkSZIkSTUq3ULFBUBz4FzgWqAQOC1ToSRJkiRJyhbprERQzUmrUBFjfCN1cSlw6ubGSpIkSZIkfVtpFSpCCM+xkdNhxxgPrPFEkiRJkiQpZ6W79OPydS43Bo4ByjYxVpIkSZIk6Vup7tKPNV4KIbyUgTySJEmSJCmHpbv0o+U6V/OAXYD2GUkkSZIkSVIWSSTqOkFuSXfpx2SSPSoCUA58BgzOVChJkiRJkpSb0l360SnTQSRJkiRJktKdUUEIoS+w9bq3iTE+mIFMkiRJkiQpR6Xbo+KfQC9gAlCR2hwBCxWSJEmSpHrNHhW1K90ZFf2AXjFGD48kSZIkScqYvDTHTQbaZjKIJEmSJElSujMqCoEPQgivA2VrNsYYj85IKkmSJEmSlJPSLVRcl9EUkiRJkiRlKXtU1K50T086JtNBJEmSJEmS0upREULYLYTweghhcQhhZQihLISwJNPhJEmSJElSbkl36cffgFOA/wB9gZ8BnTKUSZIkSZIk5ah0CxV5McYPQwgFMcbVwNAQwmvAHzOYTZIkSZKkOmePitqVbqFiWQihIfBeCGEIMBtonrlYkiRJkiQpF6XVo4LkUo884NdABdAdODZDmSRJkiRJUo5K96wfn6YurgT+kLk4kiRJkiQpl222UBFCeGdz+2OMO9dsHEmSJEmSlMuqmlHREFgNPAg8BZRlPJEkSZIkSVnEZpq1a7M9KmKM2wOnAq2AB0gu++gKfBZj/CTz8SRJkiRJUi6psplmjHFSjPGyGONOwDMkZ1f8JuPJJEmSJElSzqmymWYIoQQ4geRZPr4GLgIey3AuSZIkSZKUg6pqpjmG5LKPR0ieonTeOvtaxhiXZDSdJEmSJEl1zB4VtauqGRU9gAicDZy1zvaQ2t45Q7kkSZIkSVIO2myhIsa4ZTp3EkLoGWOcWjORJEmSJElSrqqymWaaHqyh+5EkSZIkSTmsymaaaQo1dD+SJEmSJGUVe1TUrpqaURFr6H4kSZIkSVIOq6lChSRJkiRJ0ndWU4WKihq6H0mSJEmSlMPS7lERQigheTrSytvEGF9L/d6t5qNJkiRJklT3EjY7qFVpFSpCCEOAU4CprJ09EYFDM5RLkiRJkiTloHRnVBwDbBtjXJnJMJIkSZIkKbel26Pis2qMlSRJkiRJ+lbSnVGxFHg3hPA8ULZmY4zxwoykkiRJkiRJOSndQsWo1I8kSZIkSTklkajrBLklrUJFjPHeEEIB0C216eMYY3nmYkmSJEmSpFyU7lk/9gH+HzATCEBJCOHUGOOrmQwnSZIkSZJyS7pLP24FDo0xTgEIIWxHsnCxa6aCSZIkSZKk3JNuoaLhmiIFQIzxgxBCwwxlkiRJkiQpa9ijonalW6h4J4RwN8lZFAAnA+9mJpIkSZIkScpV6RYqfgWcC1xMskfFOOCOTIWSJEmSJEm5Kd2zfqwEbkz9SJIkSZIkZcRmCxUhhIdijCeFEN4F4jf3xxh3zlgySZIkSZKygD0qaldVMyouSv0+NtNBJEmSJEmS8ja3M8Y4I3VxFvBpjPGT1PUewPRMBpMkSZIkSblns4WKdbwMNAkhtAdeAs4E7stYKkmSJEmSlJPSPetHXoxxeQjhNODOGOP1IYQJmQwmSZIkSVI2sEdF7Up3RkVeCGE34MfAk6lt+ZmJJEmSJEmSclW6hYoLgauAp2KMk0IIXUguB5EkSZIkSaoxaS39iDGOBcYChBACMDfGeFYmg0mSJEmSpNwTYoxVDwrhfuDXQDnwFtAWuD7GeEtm42WXEMIZMcZ76jqHPBbZxGORPTwW2cXjkT08FtnDY5E9PBbZw2MhbSjdpR99YoxLgCOB0cCWwM8yFSqLnVHXAVTJY5E9PBbZw2ORXTwe2cNjkT08FtnDY5E9PBbSN6RbqGgYQigABgHDY4yrAPueSpIkSZKkGpVuoeLvwBfAFsBLIYTOwNcZSyVJkiRJknJSWoWKGOOtMcYOMcYDY7KpxZfAfpmNlpVcO5Y9PBbZw2ORPTwW2cXjkT08FtnDY5E9PBbZw2MhfcNmm2mGEE6KMT4UQjh3Y/tjjLdnLJkkSZIkSco5VZ2edIvU73aZDiJJkiRJkpTW6UklSZIkSZJqQ1UzKgBINc/8NbD1ureJMR6dmViSJEmSJCkXpVWoAEYC9wPP4WlJJSkrhBDygXNjjLfWdZZcF0JoBqyIMSZCCNsCPYFnYoyr6ziaJCmLhBCGAZuc0u4XwVJSWks/Qghvxhj71kKerBNCKAaGAB1ijIeEEHoBe8QY763jaDkn9T//dwHFMcbtQwg7AEfEGK+p42g5KYTQDhjMhjOtTqurTLkohPBijHHfus6R60IIbwP7kOzt9DrwFrA8xnhynQbLQZtqAL6GjcBrj8cie/jhOHuEEPbf3P4Y45jayiJls3RnVNwRQrgceBYoW7MxxjgxI6myyz+BfwCXpa5PA/4LWKiofUOBi4C7IfnfXwjhQcBCRd0YAbwMPA9U1HGWXPZqCOFOkq9Ly9ZsjDG+U3eRclKIMS4PIfwCuCPGeGMI4d26DpWjbACePTwW2ePOug6gJAsRUnrSLVRsC5wOHMLapR8R6J+JUFmmbYzx4RDCJQAxxvIQgh/K6kbTGOObIYR1t5XXVRjRNMb4u7oOIfZM/b56nW0R2K8OsuSyEELYAzgZ+EVqW7rvsapBMcY/1HUGJXkssocfjrNPCKErcC3QC2i8ZnuMcds6CyVlkXT/J+p4YOsYY1mVI+ufZSGENqSmy4UQ+gGL6zZSzpqfelFfcyyOBWbXbaSc9mQI4dAY49N1HSSXxRgH1nUGAXAecAkwLMY4OYTQBXihjjPltBBCN+CvQEmM8Qep5YI/ijFeV8fRck4IoQNwG7B3atM44IIY4/9v786j9Srrs49/r4QqIAQVUByIlKBgZFYQgcISfbFYBwoqoICCFmdQ1FqF2loLVd46IU4oowOCBRVKZVA0IIgMCRBAVAYFB7QqkgiRyev9496HnIQz9S3Z9865r89aZ53n2fucta6VJ+d59r6H3++X9VK1KTfHg3IiZVXwv1Mmgw8gtQAjHjRjij93DbDmigwyYIdSionOkXQxpajoW+tGatabKds+NpH0C+BtwBvrRmraIZTBij9JWiRpsaRFtUO1RtLjJR0n6Zvd87nd9oPoSVfU9MW2X2L7QwC2b7Y94f78WOE+D7yfpRf+C4F968Vp2gnAeZSaRhtQirOfUDFPy06k/NuLcnN8GvCVmoEatrrtcwFs32T7cCCTDxGdqa6oWBu4QdIPWLZGxbQvvGN7vqSdgY0pb+o/ShX3OmzfDDy/q64/w/bi2plaZrvVwcuhOZHU0anK9gOSnlk7RzzEo2xfMrJd0LYl5fO7jsfb/tyo55+X9JZqadq2uu1zJf277ZuAwyVdVDtUo+5ReYO6SdIbgF8Aj6ucKWIwpjpQccQKTTFgkt4MfMn2dd3zx0jax/anKkdrjqQjgaNs/6F7/hjgHd0IdPSs+3B9FfCXtj8gaX3gCbYvqxytNamjMwwLJJ0JfJVli5qeUS9S834n6S9Zul1wd+D2upGa9XtJe1MGUaFsKf59xTwty83xcLwdWAM4mHKvtRaQzmkRnam2J10N+FM3GzGHsrrgPNvTvpChpKtsb7ncsQW2t6qVqVVj/btLmm9761qZWibp05Ql1bvYfno3cHSe7W0qR2uKpO8CewLn2966q6PzIds7103WFkljLWN32vXW09WoOBbYDvhvSk2jfWzfUjVYgyRtAHwKeDZl4OhS4K15Lfon6dnA9ZRWyiM3xx+yfXHVYBERy5nqioqLgJ0krQXMAxYAewP7r6hgAzJDktyN6HR7kR9ROVOrZkp65EhR124A7ZGVM7Xs2d2N8QIA23dIyt9G/5avo7Mu8LK6kdpj+4DaGWJZtm8EdumuXTSyGi/61V03vcT2C2tnCbD9g+7hYmC/mllaJ+l8uhVfo9netUKciMGZ6kDFjK4//IHAMbY/KOmqFRlsQM4FTpP0GcqbyRuAc+pGatYXgW93M5emLI87qW6kpt3XXYCODOKtS6pV9y51dIZB0qqUtqTPYNlK+llRUUlXA+Fk23dK+oykrYH3pE1jv7oaLnsCR9fOErk5HpjRW5dXpayObLHDYsSYpjxQIWkb4JXAQd2xmSsm0uC8G3g9pbuEKFWrP181UaNsHyVpIfA8ymvxgZFqyVHF0cDXgMdJOoIyi596IXVsS6mkvwqwtSRsn1w3UnO+ANwAvAD4F0r9lh9WTRQH2T5G0q7Akymf48cCKXzav4skfZzSXWJ0DZdr6kVqVm6OB2LU6pYR8yTNqxImYoCmWqNiF+CdwMW2j+j6w7/T9ptWdMCIGC5Jm7B04OjbtnNj1jNJXwDmAFcBI0U0ndaY/RqpoSPpGtubS/oL4Fzbu9TO1ipJV9veQtJHge/ZPj01puoYp6uEbe/Ue5h4CEnzUteof5JmjXo6gzKI+mnbT6sUKWJQprSiwvYFwAWjnt8MTOtBCkmn2X5FN4M/1hK5zSvEapKk79neUdJiln0tRLnQmTXOr8YK1M2OnWr7k7WzNO5ZwFxPZdQ5VqSR7TZ/kLQppbvEBvXiBHC1pP8CngYcJmkNxvg8j17sa/tnow9IekqtMC0b5+b4CZXitO46ynuSgPuBW4C/q5ooYkCmNFDRVc4+lKVLi4Fpv5/tkO77i6qmCGzv2H1fs3aWWMZ8Sv/1p1G2gJxq+4rKmVp0LbAepaNB1HNs1/nmHynFTdcA3lc3UvMOoNyE3djV2VqHUkck+vc1YPkOXWMdixUvN8cDYXv92hkihmyqWz+uAo4DrmTp0uKx9lZNK12hwHNtP792ltZJmgFcY3vT2lliWZIeS9njujcw2/ZTK0dqiqTvAFsClzFqn7Htl1QLFTEQkvYG5nTbVtcHHmf7ytq5WtENZD8d+Ajw9lGnZgHvtT23SrCIgZA0usYUALa/XC1QxIBMtZjmn21/YoUmGaCuUvXdktayfWftPC2z/WdJV0uabfvW2nliGRsBm1A+aK+vG6VJ/1w7QICkxwNHAk+0vZukucBzbB9XOVqzJB0D/AWwE3AEpYjjZ4BtauZqzDOAPYBHAy8fdXwxpVB59ETShIPXts/sK0sUkk4E5rJcjSkgAxURTH2g4huSDqIs0xs9Y7dohaQalj8BC7t2TqMrVadQXf+eAFwn6TKWfS0yc1yBpA9RLkBvAk6jdGH5Q91UTZoDXGT7J7WDNO5E4ATgsO75j4FTKasRo47tbW8taQGA7d9LekTtUC2x/TXga5J2tP292nkaNzJQtA6wPfDd7vnOwDzKlrXo13aUGlNp7R4xhqkOVLyu+/6Po44ZmP3wxhmks7uvqO/9tQPEMm6hzBj/tnaQxm0A7NsVprsSuIgycHFV1VTtWcf2aZLeA2D7fkkPTPZLsULd120bNICktYHcENRxu6RzgfW6TiybA39j+99qB2uF7f0AJJ1JuTn+Rff8SZR249G/6ygDR7+pHSRiiKba9aPJYi+StqLM3F+Xtot1SdqdssVgoe1za+dpmaRNbN9AqYkwW9IyA5a259dJ1ibb7wOQtBqlINq7gI8BM2vmatBd3Y3wyE3xdkC2DNb1SeB0YF1J7wdeQQa8a/k88F7KawKwEDgFyEBF/zYcGaTo/BLYuFaYxq0F/FDSpSy7Yn2PepEihmOqKyqQtAllH9WqI8emc7EXSe8D9qXMUB4l6d9sf65yrCZJ+hRln+slwAckbWv7A5VjtexQ4CDgw2OcM7BLv3HaJulwYAdKl4kFwDspqyqiX++gLJ2eI+liYF3gZXUjtc32yZKuBJ5P6XDwctvXVo7VqkfZvkQSUPqKS7pvkt+JFeNCSWdTBopMKYR9Yd1IzcpAXcQEptr143BgV0rBvHOBFwDfm84jfpKuA7bpWpqtDZxjOwW4KpB0LbBFV9x0dcqy9mfWztWybjn1c2xfXDtL6yTNp7SYO5uyz/hS23+qm6pNklahzEwK+JHt3IhVIOlRtu+SNGus843U1xoUSecAbwRO7+qG7A68wfZfV47WHJXRopcDf9UduhD4D0/lhiAiokdTHahYSGl/N7/bW/gE4LPTuYihpCtH3wwv/zz6I2m+7a3Hex51SPq+7efUzhEgaU1gx+7rFcCvbe9YN1VbJF0BHA+cYvuO2nlaJumbXeeV2ygzxhr93XYL9bUGRdJGwLGU4oH/DfwK2Nv2T2vmiqhJ0jbAJygtfB9JeY+6x/aYg6wRrZnq1o8l3Wz2/d0F8e3Ahisw1xDM6QoOQXnjGP08nSb6tYmka7rHI6/FNSy96Ny8XrSmnSdpT+CMzMTUI2lTyszYzsCzgNvI1o8a9gYOAC7vBi1OAM7L30b/bO/WfW+yvtYQ2b4R2EXSWpRJsnSI6pmkebZ3lnQHXS2dkVOUa6nHVorWsk9Rtpl/BdgWeA2Q962IzlRXVHwWeDfwKuBgYBHwQ9v7r9h49UjaeaLztuf1laV1XTeDcdn+WV9ZYilJi4FHUXp/L2HpxU5mAnok6VuUNnPzgAW2/1g3Udu6bVEvAj5N6TBxPPBx27+vGqwxkmaydMsqwPXAt2ynE0uPuhnjz1DaKC8EXmf7R3VTtUnSDNt/7v42HiJ/G/0bWa0taaHtzbpjl9jevna2iCGYdKCi28u2nu1fdc83Amalsn8h6XTbe9bOEdmKEG3p6iEcCRwI3EoZKHoyZSb/sNRH6F/XcvEA4IWUek5fomzH2c/2ljWztaTbnnoB8DtKgVlRtq8+FtjF9u0V4zVF0uXA+yh1EF4CvDp1KeqS9GHgK7Yvr52ldZIupBT7PZ7yOf4r4O+yUjiimOqKitRnGIekBba3qp0j8lr0qbtJ3o1lZyvPtX1/vVRtkfRRYE3g7bYXd8dmAf9O2a53SM18rem6S/wBOI5SMPCeUefOmM7Fp4dG0gnAtbY/vNzxtwOb2z6gTrL2pMbU8Eh6LbAXsAGlfe+ptq+qGqpRkjaktIddldI5ai3gGNs/rhosYiCmOlDxaeBzWUXxUPnQHY68Fv2Q9ETgO5SR/5HZyq2A9YDn2v5lxXjNkPQT4GnL10DolvXeYPupdZK1SdKGtm+e4PyrbZ/UZ6ZWSbrB9ib/03Px8JN0M/C2UYc+Nvq57TMf8kvRC0kjLZT3oqyczt9FRAzKhMU0Ja3SzZDuCPydpJuAu1i6Fz03hRHtORL4tO2PjT4o6WBKT/BXV0nVHo9VqLErfJwCjj2baJCicwiQgYp+LPn/PBcPv4sprTDHem4gAxX1rE9ZVfEk4Ma6UdrStRUfV+6vIorJun5cBmwN7N5DlpWVageIB+W16Md2tl+z/EHbR0tKkbT+XC9pf9snjz4oaV/ghkqZYnx5f+rPWpLG6swlIMV+e2R7v6n8nKR9bX9xRecJkHQEZSXFbcCpwLNT7Ld3jwDuA74MnA3cM/GPR7RpsoEKAdi+qYcsK6t31w7QCkkfsv3uCY5N6YIo/tcmmpG8u7cU8WbgDEkHAldSZie3AVYD/rZmsBhTVrn0Z/lZ/NEu6TNITNmhQAYq+vErYCfbv64dpFW2N+1ai+9DKbp8NWXQ4lu2/1w1XMSATFijQtLPgY+Md972uOemC0kLeegF5p3AFcC/2v5d/6naNFYNCknXpDpyv7o9x+8c6xRwlO05PUdqmqRdgGdQ/v2vs/3typFiDCn2OzyZxR+O/H2seJKeavsnXXeih7B9Td+ZopC0F/BJ4EO2/2/tPBFDMdmKipnAGrS9ZPWbwAOUkU6Avbvvi4ATgRdXyNQUSW8E3gRsKGn0B+malJmz6Nc8xv9/f2GfQQJsX0BpxRjDlveq4cks/nBkxdGK9w/Aayk3xMszsFO/cdomaT1KIdOXAX8E3kXpwhIRnclWVDTfRUHSxbZ3GOuYpIW2N6uVrRWS1gIeQynU+A+jTi3OvsrhSpeDaEn3PvXPwF91h+YB/2L7zmqhYkKZxR+OvBbREknfBh4NfLX7+u/R520vqpErYmimVKOicWtIerbtHwBI2payygTg/nqx2tFd6N8J7NO1Xnw85f/uGpLWsH1r1YAxnnQ5iJYcD1wLvKJ7vh9wArBHtUQxmcziD8eltQNMd+MUmH1QWsX2amPK+8+bKSuGR6g7PrtGqIihmWxFxWNbn7GWtA3lAnRkC8wi4HXAdcDf2D6tYrymSHoLZcby18BIsSGnRsUwZYYsWiLpKttbTnYshiPvUf2RtC7wr8CTbL9I0lxgW9sn1k3WDklf6B6uA2wPfLd7vjMwz/ZLa+SK8UnaxHa6eEWzJlxR0fogBYDty4HNumW9sv2HUaczSNGvtwEbp4DpSiOzldGSJZJ2tP09AEk7MHGHnKgvs/j9OZHS3WCkS9dPKK0xT6yUpzkjrWIlnQnMtf2L7vmTgKNrZotxfRloegt+tG2yrR/Nk/RIYE9gA2AVqeyGsf0vFWO16jbKFpBYOWTrWLTkjcBJ3aA2wB3Aqyvmad5ks/i231gzX2MeZ/vLkt4FYPs+SQ/UDtWoDUcGKTq/pGxFiOHJdVQ0LQMVk/sG5eb4SuCeylladzPwXUlnM+q1aKFN7koqXQ6iJT8EjgLmUIqk3QnsDqTlXz0nkln8obhL0mPpVtp122oX143UrAu766hTKK/H3qRj11BlZWo0LQMVk3uy7b+uHSIAuLX7ekT3FRVJOoRSLHAx8HlgK+AfbJ8HYPstFeNF9O0bwB+A+cAvJvnZ6Edm8YfjncBZlDbj84AnUdoyRv/eDLycpR2KTgb+o16ciIixZaBicpdI2sz2wtpBWmf7/QCSHmX7rtp5ggNtf1zSC4B1gQMoAxfn1Y0VUUUGtYcns/gDYfsKSc8Fnk5Zzn697Xsrx2qSSxX900idtZVBBlajaRmomNyOwGsk3ULZbiDSaaIKSc8BjqN0YJktaQvg9bbfNPFvxgoysnfyhcAJtq/WSBGXiPZkUHt4Mos/EF29r9dTrqkMXCTpc7azpbZnkl4KfBB4IuVzfOS6dlbVYI2StB6lHemD92S2L+m+b1MrV8QQTNieNEDSU8Y6bvtnfWdpnaQfUC4yzxxpKSfpWtub1k3WJkknUC78/xLYApgJfNf2M6sGi6hA0vXARkAGtQdE0iPILH51kr5C+bv4YndoH2B123vXS9UmSTcCf5tB1fokHQnsC9zA0tUTtv3CeqkihiMrKsYhaZbtRWSZ6KDYvm25Sfssi6vntcCWwM2275a0NmX7R0SLdqsdIJaVWfxBmbvcoN35kq6ulqZtv84gxWDsCTzN9p9qB4kYogxUjO/LwIso3T7Msi2CDGxYI1TjbpO0PeBuluxgSqX9qMD2nyXdD+wkafR7SbocRHOyym6QTqLM4n+ue75Pdyyz+P27StI2ti8HkPRM4PuVM7XqcklfAr7Osh3UzqwXqVm3ADNqh4gYqmz9iJWGpHWAjwPPpwwcnQccYvt3VYM1StLxwObAdcCfu8O2fWC9VBERhaRrlt96I+lq21vUytQqSdcCcyltxqFsGbyOsirStreula01kr4wxmHb3r/3MI2T9FXKddS3WHbQ6NBqoSIGJCsqxiFpwg9N2/P7yhKF7d8Cr6qdIx60ne25tUNERIwjs/jD8dLaAaKwvV/tDPGgc7qviBhDVlSMQ9J3Jjht27v0FiYAkHQU8K/AEsob+xbA22x/ccJfjBVC0nHAh21fXztLRMTyMos/HJI+SOkO9aPaWVol6aN0rXrHkln8Orqtsxt1T2+0fX/NPBFDkhUV47D93NoZ4iF2tf33kv4W+DnwcuA7LK0iHv06Cfi+pNtJl4OIGJ7M4g/HT4GTu7pGJwCn2k6x8n5dWztALEvSXwFfAH5BuYZaT9J+ti+umyxiGLKiYhyS9pjovO0z+soShaTrbD9D0ueA022fk/3G9XQtzg4FFrK0RkWKCkbEIGQWf3gkzQUOpLQavxD4nO2L6qaK0SR9zPbbaudogaQrgP1HVqZKejrwBdvPqpssYhiyomJ8L57gnIEMVPTvLEk3ULZ+vEnSukBaOtVza6qER8SA/ZTM4g+GpBmU7TcbAHcAPwLeK+l3tvetmS2WsVPtAA15xOjts7Z/2HW1iwiyoiJWMpIeAyyy/YCkRwFr2r69dq4WSfoU8GjgLJatVp1BvIgYjMzi19fVmNoDmAccZ/uSUed+ZHvjauFiiPG+wQAADIhJREFUGZLmp35LPySdSLl+GunE8ipgdduvrhYqYkCyomISkh4PHAk80fZu3QXPc2wfVzlacyStDrwZmA0cBDwR2Bj4z5q5GrYa5QN211HHstooIgYjs/h1SZpt+1bgRmBL238c48ee03OsiKF4A3Aw8PeUGhUXAp+omihiQLKiYhKSvklZMnqY7S266rwLbG9WOVpzJJ0KXEnZz7eppNWA79vesnK0pkh6su2fj3PuxbbP6jtTRMTyMotfX2bnVz6SFtjeqnaOiIgZtQOsBNaxfRpdscCubdADdSM1a47to4D7AGwvoYxAR7++LWmD5Q9KOgD4WO9pIiJGkTS7ezgyi//a0YMUnczi9yOf0QMl6ZHjnDqm1yANknRK932BpPnLf9XOFzEU2foxubskrU3Xe1rSdsCddSM1695uFcXIazGHUbURojdvB86X9ELbPwGQ9B7glcDOVZNFRMDXga1tHzveD9j+fY95WvYkSUePd9L2wX2GCZC0LXAcsBYwW9IWwOtsvxUgW5t78a7u+8uqpogYuAxUTO5Q4ExgjqSLgXXJG0st/wScA6wv6UvADsBrqiZqkO3/knQP8E1JuwOvA7YBdrJ9R910ERGZxR+QJZQtmzEcRwMvogzoYftqSc+tG6kto7bP/hL4k213k28bA+fVSxYxLKlRMQ5J2wC32b69q0vxemBP4HrgfZmN6ZckAU8G7ga2o1yIXmr7t1WDNUzSjpQLnUuAV9hOq9iIqE7Sb4CvjHc+s/j9SY2K4ZF0me1tR9eikHS17S1qZ2uNpCso7WDXAi4HFgB32N6/arCIgciKivF9Fnh+93h74DDgrcCWwLFkVUWvutHmr9t+JnB27Twtk7SYsv1GwCOB5wG/6QaTbHtWzXwR0bzM4g/HvVP5IUnPsH3dig4TANzWbf+wpJmUa9sfV87Uqhm275Z0IHCM7Q9Kuqp2qIihyEDF+GaOWjWxF3Cs7dOB0/MmUs2lkraxfXntIC2zvWbtDBERE/id7ZNqhwiwvd0Uf/QLQFZe9OONlO0fs4HfAOd3x6J/M7oV3K8EDuqOzayYJ2JQMlAxvpmSVum6fDyPpW8gkH+3Wp4LvEHST4G7KDP6tr151VQRETEkmcVf+aSuSE9s/wbYu3aOAEodvPcDZ9u+VtKGwEWVM0UMRmpUjEPSYcALgd9SRp237rYfbAScZHuHqgEbJOkpYx23/bO+s0RExMot9ROGI69Ff7r24h9laYvei4F32P5ppUjBg7XYVrd9V+0sEUMxo3aAobJ9BPAO4ERgRy8d0ZlB2c8XPZG0qqS3Udo5/TXwC9s/G/mqHC8iIlZOmcWPFp1C6WY3u/s6qzsWPZN0sqRZklYHrgNukXRo7VwRQ5GBignYvtT210aPbtr+se35NXM16CTgWcBCYDfgw3XjRETENJAlpcMxpe068bCYYfsE2/d2XyeS+4FaNrO9CNid0pb0ycBrqiaKGJDUWoiVwVzbmwFIOg64rHKeiIiImISkCbdzjEz8/A+Kbsb/3gWS3klp4WtKwfizJM0C6G6cox+PkLQK8FLg07bvlfTn2qEihiIDFbEyuG/kge37yza+iIiI/5XM4q94E62ANLBLX0HiQft23w9Z7vjrKa/J7H7jNO3zwK3AtcA8SbOBP9aNFDEcKaYZgyfpAUqXDyh7ilcD7mZp149ZtbJFRMSwTHUWPyJiSLqCmn9hO4OoEWSgIiIiIqYRSd+Z4LRtZxa/Z12xwEOB2bYPkvRUYGPb/1k5WnMkXQocD5xie3HtPC2StI/tUyQdPNZ520f3nSliiLL1IyIiIqYN28+tnSEe4gTgSmD77vnPga8CGajo32uAA4CrJV0CnGD723UjNecx3fd1q6aIGLisqIiIiIhpJ7P4wyHpCtvPkrTA9lbdsattb1E7W6skzQReAhxDqddyPPAJ23+oGiwiopMVFRERETEdZRZ/OO6VtBpdS1hJc4B76kZql6S5lFUVLwa+AXwJ2BG4AJiwxks8fLrimW8BNmDUPZntPWplihiSDFRERETEdDTH9l6S9gGwvURpG1XLPwHnAOtL+hKwA2ULQvRM0g+AJZQVFO+zvaQ7dbGkHeola9KZwMnA+UDakkYsJwMVERERMR1lFn8gbJ8vaT6wHaVj1yG2f1s5VlMk7WH7DGA/2z8e62dsv6TnWK271/ZHaoeIGKrUqIiIiIhpR9L/AQ4H5gLn0c3i2/5uzVwtkrTTWMdtX9h3llZJmm872zoGRNJ+wFOAcxk1iGr7mmqhIgYkAxURERExLUlam6Wz+JdmFr8OSWeNeroqsC1wZVrF9icDFcMj6QPA64CbWbr1w7bHHNiLaE0GKiIiImLaySz+cElaHzjK9j61s7RC0t3AjWOdotwcb95zpOZJ+hGwue1sSYsYQ2pURERExHT0rlGPH5zFBzKLX9/PgU1rh2jMLZQuHzEc1wBrkto5EWPKQEVERERMO7aXuSkbmcWvFKdpkj5BV9QUmAFsCVxdL1GT7rX9s9ohYhlrAzd0nVhG16hIe9IIMlARERERbcgsfj1XjHp8P3CK7YtrhWnUlP69Jb3a9kkrOkwAcETtABFDlhoVERERMe2MM4v/U9v71kvVFkmzbd9aO0dMXYpu9qdrn/wn2+7aJ28MnGf7/srRIgYhAxUREREx7Uh69ain91MGKTKL36PRN72STre9Z+1MMTFJC2xvVTtHCyRdAewErAVcDiwA7rC9f9VgEQORrR8RERExbYzM4mf5+iBo1OMNq6WI/4nMYPZnhu27JR0IHGP7g5Kuqh0qYihm1A4QERER8TD6+sgDSafXDBLL3PTmBnjloMl/JB4mMyRtA7wS+M/u2MyKeSIGJQMVERERMZ1kFn84tpC0SNJiYPPu8SJJiyUtqh2uNZJmSHrFJD+W7VH9ORR4P3C27WslbQhcVDlTxGCkRkVERERMG8vVRUhhwIhRJF1oe6faOSIiJpOBioiIiJg2JD0A3EVZWbEacPfIKcC2Z9XKFlGbpH8ElgCnUv5OALD9+2qhGiVpI8qqig0YVTfQ9q61MkUMSQYqIiIiIiIaIOmWMQ7bdrZJ9awrnHkccCXwwMhx2z+oFipiQDJQERERERER0aNsTYuYWIppRkREREQ0QNLqkg6XdGz3/KmSXlQ7V6O+IekgSetKmjXyVTtUxFBkRUVERERERAMknUrZarC/7U0lrQZ83/aWlaM1R9JtYxy27dm9h4kYoFUm/5GIiIiIiJgG5tjeS9I+ALaXSNJkvxQPP9vr184QMWQZqIiIiIiIaMO93SoKA0iaA9xTN1K7JG0CzAVWHTlm+8v1EkUMRwYqIiIiIiLa8E/AOcD6kr4E7AC8pmqiRkk6HNgV2AQ4F3gB8D0gAxURpEZFREREREQzJK0NbAcIuNT2bytHapKkhcCWwHzbW0h6AvBZ2y+pHC1iELKiIiIiIiJiGpO0fBvMX3XfZ0uabXt+35mCJbYfkHS/pDWB24ENa4eKGIoMVERERERETG8fnuCcgV36ChIPWiDp0cDxwBXAIiADRhGdbP2IiIiIiIjoSddpZT3bv+qebwTMysqWiKUyUBERERERMY1J2mOi87bP6CtLFJKutP3M2jkihipbPyIiIiIiprcXT3DOQAYq+neZpK2ziiJibFlRERERERER0QNJq9i+v+v68XTgJuAuShcW216+8GlEk7KiIiIiIiKiAZIeDxwJPNH2bpLmAs+xfVzlaC25DNga2L12kIghy4qKiIiIiIgGSPomcAJwmO0tJK0CLLC9WeVozZC0wPZWtXNEDF1WVEREREREtGEd26dJeg9AtwXhgdqhGrOupEPHO2n7I32GiRiqDFRERERERLThLklrUwpoImk74M66kZozE1iDUpMiIsaRrR8REREREQ2QtDXwCWBT4FpgXeBltq+pGqwhkuanYGbE5GbUDhARERERESuOpG0krde1wtwZeC9wD3Ae8POq4dqTlRQRU5CBioiIiIiI6e2zwL3d4+2Bw4BPAncAx9YK1ajn1Q4QsTJIjYqIiIiIiOltpu3fd4/3Ao61fTpwuqSrKuZqzqjXISImkBUVERERERHT28yuFSmUGf0LRp3LxGVEDE7emCIiIiIiprdTgHmSfgssAS4CkLQR6foREQOUrh8REREREdNc14r0CcB5tu/qjj0NWKMrshkRMRgZqIiIiIiIiIiIwUiNioiIiIiIiIgYjAxURERERERERMRgZKAiIiIiIiIiIgYjAxURERERERERMRgZqIiIiIiIiIiIwfh/KIasa+IE5d4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=final_dataset.corr()\n",
    "top_corr_features=corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "g=sns.heatmap(final_dataset[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_dataset.iloc[:,1:]#independent and dependent features\n",
    "\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_years</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_years  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0         6                 0   \n",
       "1           9.54       43000      0         7                 1   \n",
       "2           9.85        6900      0         3                 0   \n",
       "3           4.15        5200      0         9                 0   \n",
       "4           6.87       42450      0         6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arun kumar\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "                    max_features='auto', max_leaf_nodes=None,\n",
       "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                    min_samples_leaf=1, min_samples_split=2,\n",
       "                    min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                    oob_score=False, random_state=None, verbose=0,\n",
       "                    warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##feature importance\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model=ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.31602671 0.05018644 0.00047992 0.07038708 0.25769632 0.00075539\n",
      " 0.13822246 0.16624567]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances=pd.Series(model.feature_importances_,index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_random=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "#hypoparameters\n",
    "import numpy as np\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   0.6s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   0.5s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   0.7s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.0s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   0.9s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.2s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.2s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   0.4s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   0.7s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   0.9s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.2s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.2s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.3s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.2s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.4s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.6s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.8s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   32.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                   iid='warn', n_iter=10, n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.596447074482658"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ce30fe32b0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ce310539b0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.6137558196721267\n",
      "MSE: 1.2519699329217193\n",
      "RMSE: 1.1189146227133326\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}