Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80dea10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e96defc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Data  \n",
    "data=pd.read_csv(\"C:\\\\Users\\\\busra\\\\OneDrive\\\\Masaüstü\\\\car.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24c41092",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print first 5 rows in the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24686f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows and columns\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e62bd6e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#Get some information about data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4feb3874",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the number of missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9709469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Petrol    239\n",
       "Diesel     60\n",
       "CNG         2\n",
       "Name: Fuel_Type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check distribution of categorical data\n",
    "data[\"Fuel_Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b306c3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dealer        195\n",
       "Individual    106\n",
       "Name: Seller_Type, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Seller_Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d8a07bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       261\n",
       "Automatic     40\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ff64b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding Fuel Type Column\n",
    "data.replace({\"Fuel_Type\":{\"Petrol\":0,\"Diesel\":1,\"CNG\":2}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d612379",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\"Seller_Type\":{\"Dealer\":0,\"Individual\":1}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85eaec4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\"Transmission\":{\"Manual\":0,\"Automatic\":1}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74f9743a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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          0   \n",
       "1      sx4  2013           4.75           9.54       43000          1   \n",
       "2     ciaz  2017           7.25           9.85        6900          0   \n",
       "3  wagon r  2011           2.85           4.15        5200          0   \n",
       "4    swift  2014           4.60           6.87       42450          1   \n",
       "\n",
       "   Seller_Type  Transmission  Owner  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "3            0             0      0  \n",
       "4            0             0      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "270a22ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data Target and features\n",
    "X=data.drop(columns=[\"Car_Name\",\"Selling_Price\"],axis=1)\n",
    "Y=data[\"Selling_Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9ef8f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
      "0    2014           5.59       27000          0            0             0   \n",
      "1    2013           9.54       43000          1            0             0   \n",
      "2    2017           9.85        6900          0            0             0   \n",
      "3    2011           4.15        5200          0            0             0   \n",
      "4    2014           6.87       42450          1            0             0   \n",
      "..    ...            ...         ...        ...          ...           ...   \n",
      "296  2016          11.60       33988          1            0             0   \n",
      "297  2015           5.90       60000          0            0             0   \n",
      "298  2009          11.00       87934          0            0             0   \n",
      "299  2017          12.50        9000          1            0             0   \n",
      "300  2016           5.90        5464          0            0             0   \n",
      "\n",
      "     Owner  \n",
      "0        0  \n",
      "1        0  \n",
      "2        0  \n",
      "3        0  \n",
      "4        0  \n",
      "..     ...  \n",
      "296      0  \n",
      "297      0  \n",
      "298      0  \n",
      "299      0  \n",
      "300      0  \n",
      "\n",
      "[301 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff3889c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.35\n",
      "1       4.75\n",
      "2       7.25\n",
      "3       2.85\n",
      "4       4.60\n",
      "       ...  \n",
      "296     9.50\n",
      "297     4.00\n",
      "298     3.35\n",
      "299    11.50\n",
      "300     5.30\n",
      "Name: Selling_Price, Length: 301, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2f9ebe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into train and test\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.1,random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e310cc",
   "metadata": {},
   "source": [
    "Model Training "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e629fe",
   "metadata": {},
   "source": [
    "# - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4177a328",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading linear regression model\n",
    "lin_reg_model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8bef2922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac75b722",
   "metadata": {},
   "source": [
    "Model Evulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "942eea2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction on Train Data\n",
    "train_data_predict=lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "27ea121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squarred error on train data :  0.8799451660493708\n"
     ]
    }
   ],
   "source": [
    "#R squarred error on train data\n",
    "train_score=metrics.r2_score(Y_train,train_data_predict)\n",
    "print(\"R squarred error on train data : \",train_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c38e9b",
   "metadata": {},
   "source": [
    "Visualize Actual Prices and Predicted Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e314beba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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69eurPf/uu++KHj16CC8vL+Hi4iLatWsnFixYIEpKSmo9rmb49ZYtW2rdT/MZ3rp1q8bnt27dKvr06SPc3NyEm5ubaNeunYiNjRVpaWk6+y1fvlyEhIQIuVwuunfvLo4cOVLtM27IZ/HOO++I++67T9jZ2VW7lkxZoz62fh2StMmEqNImSURERCQR7CNDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSZfMT4qnVamRkZMDDw8PkKx0TERGReQghkJeXh8DAQNjZ6W93sfkgk5GRgRYtWli6DCIiIqqHGzdu4P7779f7vM0HGc301Ddu3ICnp6eFqyEiIiJD5ObmokWLFnUuM2HzQUZzO8nT05NBhoiISGLq6hbCzr5EREQkWQwyREREJFkMMkRERCRZDDJEREQkWQwyREREJFkMMkRERCRZDDJEREQkWQwyREREJFkMMkRERCRZNj+zLxEREZleuVrgVPpd3Mwrgp+HM3qEeMPervEXZ2aQISIiIqPsSclE3I5UZKqKtNsCFM6YNywcMREBjVoLby0RERGRwfakZGL6+iSdEAMAWaoiTF+fhD0pmY1aD4MMERERGaRcLRC3IxWihuc02+J2pKJcXdMe5sEgQ0RERAY5lX63WktMZQJApqoIp9LvNlpNDDJERERkkJt5+kNMffYzBQYZIiIiMoifh7NJ9zMFBhkiIiIySI8QbwQonKFvkLUMFaOXeoR4N1pNDDJERERkEHs7GeYNCweAamFG83jesPBGnU+GQYaIiIgMFhMRgIQJXaFU6N4+UiqckTCha6PPI8MJ8YiIiMgoMREBGBSu5My+REREJE32djJEhja3dBm8tURERETSxSBDREREkmU1QWbhwoWQyWR48cUXtduKiooQGxuL5s2bw93dHaNGjUJ2drbliiQiIiKrYhVB5vTp0/j000/RqVMnne0vvfQSduzYgS1btuDw4cPIyMjAyJEjLVQlERERWRuLB5n8/Hw88cQTWLVqFZo1a6bdrlKpsHr1anzwwQcYMGAAunXrhrVr1+Knn37CiRMnLFgxERERWQuLB5nY2FgMHToU0dHROtvPnj2L0tJSne3t2rVDy5YtkZiYqPd4xcXFyM3N1fkhIiIi22TR4ddffvklkpKScPr06WrPZWVlwcnJCV5eXjrb/f39kZWVpfeY8fHxiIuLM3WpREREZIUs1iJz48YNzJo1Cxs2bICzs+kWl5o7dy5UKpX258aNGyY7NhEREVkXiwWZs2fP4ubNm+jatSscHBzg4OCAw4cP48MPP4SDgwP8/f1RUlKCnJwcnddlZ2dDqVTqPa5cLoenp6fODxEREdkmi91aGjhwIC5evKizbfLkyWjXrh3mzJmDFi1awNHREfv378eoUaMAAGlpabh+/ToiIyMtUTIRERFZGYsFGQ8PD0REROhsc3NzQ/PmzbXbp0yZgtmzZ8Pb2xuenp544YUXEBkZiV69elmiZCIiIrIyVr3W0pIlS2BnZ4dRo0ahuLgYgwcPxvLlyy1dFhEREVkJmRBCWLoIc8rNzYVCoYBKpWJ/GSIiIokw9Pvb4vPIEBEREdUXgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSZZFg0xCQgI6deoET09PeHp6IjIyErt379Y+X1RUhNjYWDRv3hzu7u4YNWoUsrOzLVgxERERWROLBpn7778fCxcuxNmzZ3HmzBkMGDAAw4cPx88//wwAeOmll7Bjxw5s2bIFhw8fRkZGBkaOHGnJkomIiMiKyIQQwtJFVObt7Y3Fixdj9OjR8PX1xcaNGzF69GgAwKVLl9C+fXskJiaiV69eBh0vNzcXCoUCKpUKnp6e5iydiIiITMTQ72+r6SNTXl6OL7/8EgUFBYiMjMTZs2dRWlqK6Oho7T7t2rVDy5YtkZiYqPc4xcXFyM3N1fkhIiIi22TxIHPx4kW4u7tDLpdj2rRp2LZtG8LDw5GVlQUnJyd4eXnp7O/v74+srCy9x4uPj4dCodD+tGjRwszvgIiIiCzF4kGmbdu2OH/+PE6ePInp06dj4sSJSE1Nrffx5s6dC5VKpf25ceOGCaslIiIia+Jg6QKcnJzQunVrAEC3bt1w+vRpLFu2DGPGjEFJSQlycnJ0WmWys7OhVCr1Hk8ul0Mul5u7bCIiIrICFm+RqUqtVqO4uBjdunWDo6Mj9u/fr30uLS0N169fR2RkpAUrJCIiImth0RaZuXPnYsiQIWjZsiXy8vKwceNGHDp0CD/88AMUCgWmTJmC2bNnw9vbG56ennjhhRcQGRlp8IglIiIism0WDTI3b97EU089hczMTCgUCnTq1Ak//PADBg0aBABYsmQJ7OzsMGrUKBQXF2Pw4MFYvny5JUsmIiIiK2J188iYGueRISIikh7JzSNDREREZCwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIskwSZnJwcUxyGiIiIyChGB5lFixZh8+bN2sePP/44mjdvjvvuuw8XLlwwaXFEREREtTE6yKxYsQItWrQAAOzduxd79+7F7t27MWTIELz66qsmL5CIiIhIHwdjX5CVlaUNMjt37sTjjz+ORx55BMHBwejZs6fJCyQiIiLSx+gWmWbNmuHGjRsAgD179iA6OhoAIIRAeXm5aasjIiIiqoXRLTIjR47E+PHjERYWhjt37mDIkCEAgHPnzqF169YmL5CIiIhIH6ODzJIlSxAcHIwbN27g/fffh7u7OwAgMzMTzz//vMkLJCIiItJHJoQQli7CnHJzc6FQKKBSqeDp6WnpcoiIiMgAhn5/12semS+++AJ9+vRBYGAgrl27BgBYunQptm/fXr9qiYiIiOrB6CCTkJCA2bNnY8iQIcjJydF28PXy8sLSpUtNXR8RERGRXkYHmY8++girVq3CG2+8AXt7e+327t274+LFiyYtjoiIiKg2RgeZ9PR0PPDAA9W2y+VyFBQUmKQoIiIiIkMYHWRCQkJw/vz5atv37NmD9u3bm6ImIiIiIoMYPfx69uzZiI2NRVFREYQQOHXqFDZt2oT4+Hh89tln5qiRiIiIqEZGB5lnnnkGLi4u+Pe//43CwkKMHz8egYGBWLZsGcaOHWuOGomIiIhq1KB5ZAoLC5Gfnw8/Pz9T1mRSnEeGiIhIegz9/ja6RSY9PR1lZWUICwuDq6srXF1dAQCXL1+Go6MjgoOD6100ERERkTGM7uw7adIk/PTTT9W2nzx5EpMmTTJFTUREREQGMTrInDt3Dr179662vVevXjWOZiIiIiIyF6ODjEwmQ15eXrXtKpVKO8svERERUWMwOshERUUhPj5eJ7SUl5cjPj4effr0MWlxRERERLUxurPvokWLEBUVhbZt26Jv374AgKNHjyI3NxcHDhwweYFERERE+hjdIhMeHo7k5GQ8/vjjuHnzJvLy8vDUU0/h0qVLiIiIMEeNRERERDVq0DwyUsB5ZIiIiKTHpPPIJCcnIyIiAnZ2dkhOTq51306dOhlXKREREVE9GRRkunTpgqysLPj5+aFLly6QyWSoqSFHJpNx5BIRERE1GoOCTHp6Onx9fbX/JiIiIrIGBgWZoKAgAEBpaSni4uLw5ptvIiQkxKyFERERUf2UqwVOpd/Fzbwi+Hk4o0eIN+ztZJYuyyyMGn7t6OiIrVu34s033zRXPURERNQAe1IyEbcjFZmqIu22AIUz5g0LR0xEgAUrMw+jh1+PGDEC3377rRlKISIioobYk5KJ6euTdEIMAGSpijB9fRL2pGRaqDLzMXpCvLCwMLz99ts4fvw4unXrBjc3N53nZ86cabLiiIiIyDDlaoG4HamoaU4VAUAGIG5HKgaFK23qNpPR88jU1jdGJpPh999/b3BRpsR5ZIiIqClIvHIH41adqHO/TVN7ITK0eSNU1DAmnUemMo5aIiIisj4384rq3smI/aTCqCBz4sQJ7NixAyUlJRg4cCBiYmLMVRcREREZwc/D2aT7SYXBnX2//vpr9O7dG8uWLcNnn32GoUOH4j//+Y85ayMiIiID9QjxRoDCGfp6v8hQMXqpR4h3Y5ZldgYHmfj4eEydOhUqlQp//fUX3n33Xbz33nvmrI2IiIgMZG8nw7xh4QBQLcxoHs8bFm5THX0BIzr7uru74/z582jdujUAoKSkBG5ubvjzzz/h5+dn1iIbgp19iYgqNKVJ0poyW5lHxuSdfQsLC3UO5OTkBGdnZ+Tn51t1kCEiItv5cqO6xUQEYFC4ssmEVqM6+3722Wdwd3fXPi4rK8O6devg4+Oj3cZ5ZIiIrItmkrSqze+aSdISJnRlmLEx9nYySQyxNgWDby0FBwdDJqs9zXEeGSJqCqR0i6ZcLdBn0YFqM71qyAAoFc44NmeA1b4HappMfmvp6tWrpqiLiEjSpHaL5lT6Xb0hBqiY8TVTVYRT6XebzF/wZFuMXmuJiEjqytUCiVfuYPv5P5F45Q7K1YZNcC7FdWya6iRp1HRYNMjEx8fjwQcfhIeHB/z8/DBixAikpaXp7FNUVITY2Fg0b94c7u7uGDVqFLKzsy1UMRFJ3Z6UTPRZdADjVp3ArC/PY9yqE+iz6ECdIaSudWyAinVsDA1FjaWpTpJGTYdFg8zhw4cRGxuLEydOYO/evSgtLcUjjzyCgoIC7T4vvfQSduzYgS1btuDw4cPIyMjAyJEjLVg1EUlVQ1pUjLlFY02a6iRp1HQYvdaSKe3Zs0fn8bp16+Dn54ezZ88iKioKKpUKq1evxsaNGzFgwAAAwNq1a9G+fXucOHECvXr1skTZRCRBDV0ZWKq3aDSTpE1fnwQZoPP+bXmSNGo6rKqPjEqlAgB4e1f8ZXD27FmUlpYiOjpau0+7du3QsmVLJCYmWqRGIpKmhraoSPkWTUxEABImdIVSoVubUuHModckeQa1yOTm5hp8wPoOcVar1XjxxRfRu3dvREREAACysrLg5OQELy8vnX39/f2RlZVV43GKi4tRXFysfWxM7URkuxraoqK5RZOlKqqxVUczjNlab9E0tUnSqOkwKMh4eXnVOYeMRnl5eb0KiY2NRUpKCo4dO1av12vEx8cjLi6uQccgItvT0BYVW7hF05QmSaOmw6BbSwcPHsSBAwdw4MABrFmzBn5+fnjttdewbds2bNu2Da+99hr8/f2xZs2aehUxY8YM7Ny5EwcPHsT999+v3a5UKlFSUoKcnByd/bOzs6FUKms81ty5c6FSqbQ/N27cqFdNRGRbTNHplbdoiKyPwTP7agwcOBDPPPMMxo0bp7N948aNWLlyJQ4dOmTwsYQQeOGFF7Bt2zYcOnQIYWFhOs+rVCr4+vpi06ZNGDVqFAAgLS0N7dq1Q2JiokGdfTmzLxFpaEYtATW3qBgaRqQ0sy+RVBn6/W10kHF1dcWFCxeqhY5ff/0VXbp0QWFhocHHev7557Fx40Zs374dbdu21W5XKBRwcXEBAEyfPh27du3CunXr4OnpiRdeeAEA8NNPPxn0OxhkiKgyqc3MS9RUmS3ItG3bFsOHD8f777+vs/21117D9u3bq01oVxt9/W7Wrl2LSZMmAaiYEO/ll1/Gpk2bUFxcjMGDB2P58uV6by1VxSBDRFWxRYXI+pktyOzatQujRo1C69at0bNnTwDAqVOncPnyZWzduhWPPvpowyo3MQYZIiIi6TFbkAGAGzduICEhAZcuXQIAtG/fHtOmTUOLFi3qX7GZMMgQUVNSUqbGF4lXce1uIYK8XfFkZDCcHKxqyjAig5g1yEgJgwwRNRXxu1Kx6mg6Ki/3ZCcDpvYNwdxHwy1XGFE9GPr9Xa+YfvToUUyYMAEPPfQQ/vzzTwDAF1980eA5YIiIqH7id6Xi0yO6IQYA1AL49Eg64nelWqYwIjMzOshs3boVgwcPhouLC5KSkrSz6KpUKrz33nsmL5CIiGpXUqbGqqPpte6z6mg6SsrUjVQRUeMxOsi8++67WLFiBVatWgVHR0ft9t69eyMpKcmkxRERUd2+SLxarSWmKrWo2I/I1hgdZNLS0hAVFVVtu0KhqDYDLxERmd+1u4bN32XofkRSYnSQUSqV+O2336ptP3bsGFq1amWSooiIyHBB3q4m3Y9ISowOMlOnTsWsWbNw8uRJyGQyZGRkYMOGDXjllVcwffp0c9RIRES1eDIyGHXN52cnq9iPyNYYtPp1Za+//jrUajUGDhyIwsJCREVFQS6X45VXXtEuH0BERI3HycEOU/uG4NMj+jv8Tu0bwvlkyCbVex6ZkpIS/Pbbb8jPz0d4eDjc3d1NXZtJcB4ZImoqOI8M2RKzTYj39NNPY9myZfDw8NDZXlBQgBdeeAFr1qypX8VmwiBDRE3JvZJyvLcrFVfvFCK4uSv+9Wg4XJzsLV0WkdHMFmTs7e2RmZkJPz8/ne23b9+GUqlEWVlZ/So2EwYZImoquLI32RJDv78N7iOTm5sLIQSEEMjLy4Ozs7P2ufLycuzatatauCEi6eIK0dKyJyUT09cnoepfplmqIkxfn4SECV0ZZsgmGRxkvLy8IJPJIJPJ0KZNm2rPy2QyxMXFmbQ4IrKM2v6yHxSuZMCxMuVqgbgdqdVCDAAIADIAcTtSMShcyc+KbI7BQebgwYMQQmDAgAHYunUrvL29tc85OTkhKCgIgYGBZimSiBpPbX/ZT1ufBC9XR+QUlmq389aFfo3VqnUq/a5O6KxKAMhUFeFU+l1EhjY3+e8nsiSDg0y/fv0AAOnp6WjZsiVkMqZ6IltT11/2AHRCDMBbF/o0Zn+Vm3n6Q0x99iOSEqMnFThw4AC+/vrratu3bNmCzz//3CRFEZFl1PWXfU00ASduRyrK61rwp4nQtGpVPZea0LcnJdOkv8/Pw7nunYzYj0hKjA4y8fHx8PHxqbbdz8+Pq18TSVx9/2KvfOuiqTOkVcvUoa9HiDcCFM7Q104uQ0VrUI8Qbz17EEmX0UHm+vXrCAkJqbY9KCgI169fN0lRRGQZDf2LvbYgVK4WSLxyB9vP/4nEK3dstvXGmP4qpmJvJ8O8YRUT3lUNM5rH84aFs6Mv2SSjlyjw8/NDcnIygoODdbZfuHABzZuzExmRlGn+ss9SFdXYolAXfUGoKc1vYqn+KjERAUiY0LXaeVba6Hkm0jA6yIwbNw4zZ86Eh4cHoqKiAACHDx/GrFmzMHbsWJMXSESNR/OX/fT1SZABBocZGSq+MGu6ddHU5jexZH+VmIgADo+nJsfoIPPOO+/g6tWrGDhwIBwcKl6uVqvx1FNPsY8MkQ3Q95e9Zth11YBT262Lpji/SV2tWrWFPlOwt5NxiDU1KfVeNPLXX3/FhQsX4OLigo4dOyIoKMjUtZkElyggqp+a5kDZm5pl1C2ixCt3MG7ViTp/16apvWzqy1fTCgXUHPpsrRWKyBxMvkRBVW3atKlxhl8isg01/WVv7K2Lpjq/CfurEDUeg4LM7Nmz8c4778DNzQ2zZ8+udd8PPvjAJIURkXUy5tZFU57fhP1ViBqHQUHm3LlzKC0t1f5bH872S0SVWbq/iKWxvwqR+dW7j4xUsI8MkWWxvwgR1Yeh399GT4hHRGQMTX8RpUL39pFS4cwQQ0QNZtCtpZEjRxp8wG+++abexRCRbWrM/iKNteI0EVkHg4KMQqHQ/lsIgW3btkGhUKB79+4AgLNnzyInJ8eowENETUtj9BdpSjMIE1EFo/vIzJkzB3fv3sWKFStgb28PACgvL8fzzz8PT09PLF682CyF1hf7yBA1DfpmEDZVXxy29BA1LkO/v40OMr6+vjh27Bjatm2rsz0tLQ0PPfQQ7ty5U7+KzYRBhsj2lasF+iw6oHexRs3oqGNzBtQrfLClh6jxma2zb1lZGS5dulRt+6VLl6BWq409HBFRg5lzxWlNS0/V42vWitqTkmn0MYnIdIye2Xfy5MmYMmUKrly5gh49egAATp48iYULF2Ly5MkmL5CIqC7mmkG4Ka4VRSQ1RgeZ//znP1Aqlfjvf/+LzMyKv0QCAgLw6quv4uWXXzZ5gUREdTHXDMLGtPRw4jsiyzA6yNjZ2eG1117Da6+9htzcXABg3xMisihzzSDcVNeKIpKSek2IV1ZWhn379mHTpk3aZQkyMjKQn59v0uKIiAxhbyfDvGHhAP4epaSheTxvWLjRt3+a8lpRRFJhdJC5du0aOnbsiOHDhyM2Nha3bt0CACxatAivvPKKyQskIjKEOWYQ1rT06Is/MlSMXrLVtaKIpMDoW0uzZs1C9+7dceHCBTRv/vc94cceewxTp041aXFERMYw9QzCmpae6euTIEPNa0XVp6WHiEzH6CBz9OhR/PTTT3ByctLZHhwcjD///NNkhRER1YepZxDWtPRUnUdGyXlkiKyC0UFGrVajvLy82vY//vgDHh4eJimKiMiaNOZaUURkHKP7yDzyyCNYunSp9rFMJkN+fj7mzZuHRx991JS1ERFZDU1Lz/Au9yEytDlDDJGVMHqJghs3biAmJgZCCFy+fBndu3fH5cuX4ePjgyNHjsDPz89ctdYLlyggIiKSHrOttQRUDL/evHkzLly4gPz8fHTt2hVPPPEEXFxcGlS0OTDIEBERSY9ZgkxpaSnatWuHnTt3on379iYp1NwYZIiIiKTHLItGOjo6oqiIM1gSERGRdTC6s29sbCwWLVqEsrIyc9RDREREZDCjh1+fPn0a+/fvx48//oiOHTvCzc1N5/lvvvnGZMURERER1cboIOPl5YVRo0aZoxYiIiIioxgdZNauXWuOOoiIiIiMZnAfGbVajUWLFqF379548MEH8frrr+PevXvmrI2IiIioVgYHmQULFuBf//oX3N3dcd9992HZsmWIjY01Z21E1MjK1QKJV+5g+/k/kXjlDsrVRk8zRUTUqAyeRyYsLAyvvPIKnnvuOQDAvn37MHToUNy7dw92dkYPfmo0nEeGyDB7UjKrLYwYwIURichCTD6PzPXr13XWUoqOjoZMJkNGRkbDKiUii9uTkonp65N0QgwAZKmKMH19EvakZFqoMiKi2hkcZMrKyuDs7KyzzdHREaWlpSYviogaT7laIG5HKmpqmtVsi9uRyttMRGSVDB61JITApEmTIJfLtduKioowbdo0nblkOI8MkbScSr9brSWmMgEgU1WEU+l3ERnavPEKIyIygMFBZuLEidW2TZgwwaTFkPSUqwVOpd/Fzbwi+Hk4o0eIN+ztZJYui4xwM8+wZUcM3c+UeH0RUV0MDjKcP4aqYudQ2+Dn4Vz3TkbsZyq8vojIENY73IisGjuH2o4eId4IUDhDXzuHDIDSUw61EI02LJvXFxEZikGGjMbOobbF3k6GecPCAaBamJGh4jMtKlPjic9OYtaX5zFu1Qn0WXTAbGGC1xcRGYNBhoxmTOdQqWmqE8LFRAQgYUJXKBW6t48Uro4AgJxC3dGJ5mwZseXri4hMz+i1loisuXNoQzT1PhkxEQEYFK7Udq71cZPj5S0XAFSfYkGgorUmbkcqBoUrTdoB11avLyIyDwaZJq4+o0KstXNoQ2j6ZFRtf9G0PCRM6GrSMFP5vPu4yQEZcDu/2OIjc+ztZNoh1olX7iArt/GHZdvi9UVE5mPRIHPkyBEsXrwYZ8+eRWZmJrZt24YRI0ZonxdCYN68eVi1ahVycnLQu3dvJCQkICwszHJF25D6tkBoOodmqYpq7McgA6BUVHwhS0FdfTJM3fJQ03mvzNvNEY91uQ/R4UqjQo2phypbqmXE1q4vIjIvi/aRKSgoQOfOnfHJJ5/U+Pz777+PDz/8ECtWrMDJkyfh5uaGwYMHo6iITcoN1ZBRIXV1DgWAecPCJTPfR2P2ydB33iu7W1CK1cevGtWpdk9KJvosOoBxq06YrEOupVpGbO36IiLzsmiQGTJkCN5991089thj1Z4TQmDp0qX497//jeHDh6NTp0743//+h4yMDHz77beNX6wNMcWoEH2dQ5UKZ5PfhjG3xmp5qO2862NIsDTXUGVDhmUHmKllxJauLyIyL6vtI5Oeno6srCxER0drtykUCvTs2ROJiYkYO3Zsja8rLi5GcXGx9nFubq7Za5UaU01JX7VzqKX7d9RXY7U81HXea1LXrS1z3hbTtIxMX5+kHYat0RgtI7ZyfRGReVnt8OusrCwAgL+/v852f39/7XM1iY+Ph0Kh0P60aNHCrHVKkSlbIDSdQ4d3uQ+Roc0l+SXTWC0P9W3RqenWlmaY+JK9v5r1tpilW0Zs4foiIvOy2haZ+po7dy5mz56tfZybm8swUwVHhehqrJaHhp5PTRCqq7Nwba+tD7aMEJE1s9ogo1QqAQDZ2dkICPj7r77s7Gx06dJF7+vkcrnOCt1UnRRGhZSrBU78fgeJV+4AEIhs5YNeDfyLvLZRPTERAXg2KgSrjqZDVDopMhkwtW+ISVoe6jrvdfHzcNY7TNyQ1zZE5WHZRETWxGqDTEhICJRKJfbv368NLrm5uTh58iSmT59u2eIkztJ9H+qyJyUTr39zUWc22Y8PXoGXqyPeGxGBZm5yo1sG6hpqviclEyuPpFcLCGoBfHokHa5ODpgxIKxB56S2814bTbDsFtQM/RYfNCrEWEMoJSIyJ5kQwmJzsOfn5+O3334DADzwwAP44IMP8PDDD8Pb2xstW7bEokWLsHDhQnz++ecICQnBm2++ieTkZKSmpsLZ2bC/MHNzc6FQKKBSqeDp6WnOtyM51jiT7Z6UTExbn2Tw/obUq68VQxNJPhnfFe98X/etGqWnM+b/s+HnxphbQ5oaEyZ0hcLFCeNWnTD491R+LUf5EJHUGPr9bdEgc+jQITz88MPVtk+cOBHr1q3TToi3cuVK5OTkoE+fPli+fDnatGlj8O9gkKmdqSdRa2gtvRceqHU22arq+rIuVwv0WXRAb2iQAfB2c8KdghKDf58pgkFNM/vu/yUb357PwN1KtVQOatvP/4lZX543+HdYOpQSETWEJIJMY2CQkY7EK3eManGozNXJHi9Ft8HEh4Lh5PD3YLyGHLMmmls1x+YMMEvgqy1YGvpeZjwcit6tfdkhl4gkzdDvb6vtI0O2wZgWn4aMrCksKceCXb8gfvcvmNo3BHMfDW/wMWtirvWFNGrrVGtoJ+2XBrVlgCGiJoNBhszG2D44phjuremcCwBzHw03+Jjebo74q6DU4I60N/OKGv22nLV30iYisgSrnRCPpK0+0+b3CPGG0tM0c9esOpqOkjI1ugU1g7ebk979NJPdvT2sg1Gjga7eLmjQ2kaaCe22n/8TiVfu1LocRGWWnqCOiMjasI8MmZwhHWz19TMxdtRSbUZ3vQ/Hr9yptQ4AeDYqBN9dyDR4FJGXqyP+qjQ0vKrl4x/Ao50C9T5vitFi1tRJm4jIHAz9/maLDJlcQ1eT9nJ1NEkdXyf9WWsdCldHDO0UgJVH0g2eJVcAqCv6z9h0DruSa26ZMdUCj5y6n4ioAoMMGcSYWyH1XctJ8yWfU0trhynlFJZiZ3Km0bPk5tyrvT61AJ7fWD2UmGLVcSIi0sXOvlQnc3XarbxfbV/yUlV11WlTrTpORER/Y4uMjapvZ9Kq6ttpt7bVpIHqq0nX9SUvRVVvn5ly1XEiIqrAFhkbVFcLiiEdRcvVAieu3MHrWy/qvRUiQ/VWB8CwNYXyisqw4PuK1/YI8bbZL+/K74urjhMRmR6DjI3Rt65QlqoI09YnYVgnJY5evqPTz6PqbSJD1wKq7VaIZphw1cUfNfKLy7Dm+FWsOX4VAQpnjH2wZb3er7WrHEqksOo4EZHUMMjYEEM6k+5Izqr2nOY2UcKErgBQYxCqjabVoWpLT1mZGioDOu5mqoqwdN+v8HJ1hKrQ8EnprFlNoYQT2hERmR6DjA2pbz+TyreJhBBGBwk/D2ejVnTWR/Plru92lCE8nR2QW1RW7xoqc5fbo6C4XG/ricLFATn3qv+u2kKJpqWq6rlScoFHIqJ6YZCxIQ3pZ6K5TWSsAIUz/iooQexG41pxavr9fxWW4qXoMHx5+ka9anF2tMO8YR3w8pYLDajkb33DfLE7JUtv68nCUZ0AwOhQEhMRgEHhSk5oR0RkAgwyNsQSnUT/0UmJd7433bDpYB83HJszAEv2/oqPD/5m1GsfbuuHQC8XE1UC7E7J0k7OV7mfT9WgUp9QUtvikEREZDgGGRtSV2dSc1h3/BpKTTiBm4+bHPZ2MvRu7WN0kJnQK8jk5yCnsBQyAC9Ft0Gwj2uNQYWhhIjIcjiPjA3RdCYFUOscLvoEKJyh9JQb9VpThhgAeHnLBexJyUSPEG94uRi+VIGXS0Um35mcgbEPttD2tanJqAfuw+SHggw+tgDw5enr+EenQC4HQERkZbhopA2qb8fbFZVGLQH173DbEJqIkDChK9Ky8rFk368Gvc7L1VHn9k9Nt4SqDjNftu+ywccHgE1Te7HlhYiokRj6/c1bSzaoamfSq7cLsfT/f2HXFE68XB2xcGRH7Rd8TaNqGkvlEVSHX30Ya39KN2jtpar7aIZ913ZLKNjH1ajabHXSPiIiKWOQsVFV+220VbpXCydero6Y/FAIZgxorfMFrwlCr205j63nMhq1buDvEVRnr/2FhSM7Ytr/byEy9hgyVNwSOjZnQI23g4ztHM0Zd4mIrA+DTBNR05DfbkHNcPbaX9iZnKFtsQAq5qPZl5plkRBT2c28Igzvch9WTOiK+d+lIivXuBaRuhZhNKZjcNW1oYiIyDowyEiMIesk6VO5lWZPSib6LT5YrYUGqH6bxlI0LSAxEQHwkDviidUn63UcfbeEKs+0WxsZOOMuEZG1YpCRkLoWgzTmODUtQ2AtAQaoGIVUuQXkdkFxvY9V2y0hfTPtatTn/BIRUeNhkJGI2haD1KyTZMiXbW3rMVmTnHtl+CElC492qnhP9emfYugijJVvu2XlFuFufjG83ZygVLhwxl0iIivHICMBdS0GqRnlMyhcWeeXbn3XY7KEGZuS8DEewKOdAo2e6M7YRRg5qR0RkTRxQjwJqCt8VO7UWq4WSLxyB9vP/4nEK3dQXmXCOikNIVYL4PmN57AnJdPoyf6UCmeDW6mIiEi62CIjAYaGj72pWZj91fla+9BIcQixprVJX3+WAIUz3hzaHs3c5FyEkYioiWGQkQBDw8ea41erbavah0Zzi0Yqt5cA3SHUXDmaiIgq460lCdCEj9q+qvV9j2tuLMXtSEW5WsDeToZ/dpbe7ZbKrVKa/izDu9zHtY+IiJo4BhkJqK1/iOZxbWs3Vu1D892FTHOUaVZSvCVGRETmxyAjEZr+IUqF7he6UuGMKb2DDTrG7pRMrDueLqnbSjJwVl0iItKPfWQkRF//kBO/38HqGvrHVPW/xGvmL9KEjB1CTURETQ+DjMRUne9kT0om5n/3swUrMh07me4tMiVn1SUiojowyEiYvtl+pcTbzRFv/qMDlJ5/L2LJ0UhERGQoBhmJkspSA3W5W1AKpaeztpWJs+sSEZEx2NlXoqS01EBdpDTbMBERWRcGGYmypS9/Dq0mIqL64q0lEyhXC5z4/Q4Sr9wBIBDZyge9DJyorVwt6jVL7e+38k1QuWUZujo1ERGRPgwyDbQnJROvf3MROYWl2m0fH7wCL1dHLBzZsdYRN3tSMmtcN6iukTrlaiHJodSiymOAQ6uJiKhheGupAfakZGLa+iSdEKORU1iKaeuTsCel5ll0NSOOqvZz0ayNpO91QEX/mL9q+J3Wxs3JHismdMUKPRP5cXVqIiJqKLbI1FO5WmD+d6l17jf/u58xKFyp0+pQ24gjgYrWCs2KzzW1Vkilf8zKJ7ujd5gPAHChRyIiMgsGmXo6lX4XWbl1B4qs3GLtys2VX1vbiKPKayPVNBw5/VZBvWpuLJq+L70q1V51Ij8iIiJTYJCpJ2NaRarua+hrNftV7hC89+cs7LyYZXihJlK1j0tt+wHs+0JERI2DQaaejBky7OMmr9dr/Tyca+wQ3Nim9A7GrpSsap2S/9k5AN9dyNTZzmUFiIioMTHI1FO3oGYGt1KgSsNEjxBvBCickaUq0vt6fw8nnPz9Dpbuv9ywQk0gOlyJfw0Nr7GPy2sx7dn3hYiILIZBpp7OXvvL4OUBbucX6zy2t5PhzaHt8fzGc3pfk51XYhUhRukp14aTmvq4sO8LERFZEoNMPRnTR6byraRytcDHBy7j0yO/m6Msk5v/zw5sYSEiIqvFIFNPhvZzcbADVh+7gj/+KkRGThHWHP8dqntlZq7ONF6KDmNfFyIismoMMvWk6edSVyfcMjWw75db2PfLrUaqTL83h7aHj4ccPm5yvLzlArJz9ffRCVA4Y8aAsEatj4iIyFic2bee7O1kmDcsvGo/Xqvm4yHH8C73oXeYD+b/MxxAtX7IkP3/Hw6fJiIiKWCQaYCYiAA8GxVi6TIMVvl2WExEABK4dAAREUkcby01QLla4LsL+tdEsibN3ZyqrTIdExHApQOIiEjSGGQaoK6lBqzJO8MjagwoHD5NRERSxltLDSCVxRsHtvPFo514q4iIiGwPg0wD+LjL697JCjzTN9TSJRAREZkFby01wKn0O5YuoVaaVair9o0hIiKyFWyRqaddyRlYtv83i9ZQW59crkJNRERNAVtk6mFXciZmbNK/TlJjeHNoezwZGYzTV+9i/YlrOPLrLRSUlGuf5yrURETUFDDIGGlPSiae35hk6TLg4yHHgUvZiNuRqjNyysvFEZN7B2PGgDC2xBARkc3jrSUjlKsF4nakWroMAMDV2wWYvj6p2vBv1b1SLN13GXtTsyxUGRERUeNhkDGCNcwbI0PFOkibTl2vcZ0kzba4HakoV+tbSYmIiMg2MMgYwVrmjRn7YEtk5RbrfV4AyFQV4VT63cYrioiIyALYR8YIldcqMhcvV0c4O9gjK7d6aNL0f2np7WLQsawleBEREZkLg4wReoR4w9vNCXcLSsz2OxaO7Khd/yhLdQ/Hf7uNvb/chOpeKXLulWLJvsvwdnMy6FiNEbyIiIgsSRK3lj755BMEBwfD2dkZPXv2xKlTpyxSh72dDMO7BJrt+C9FhyEmIkC7/pGLkz22Jv0J1b1Snf3+qiNIafrRcCI8IiKydVYfZDZv3ozZs2dj3rx5SEpKQufOnTF48GDcvHnTIvUEKszTyhGgcMaMAWHax5oRUrV16K0JJ8IjIqKmxOqDzAcffICpU6di8uTJCA8Px4oVK+Dq6oo1a9ZYpJ6cwtK6d6qHqsHD0BFS3m6OOo+VCmckTOjKifCIiKhJsOo+MiUlJTh79izmzp2r3WZnZ4fo6GgkJibW+Jri4mIUF/89oic3N9ekNZljQLPmllJlhnbUffMfHaD0dMbNvCL4eVTcTmJLDBERNRVWHWRu376N8vJy+Pv762z39/fHpUuXanxNfHw84uLizFZTM1fDOtoao6W3a7VthnbUVXo6IzK0ualLIiIikgSrv7VkrLlz50KlUml/bty4YdLj+3jITXo8ADWOguoR4o0AhTP0ta2wQy8REZGVBxkfHx/Y29sjOztbZ3t2djaUSmWNr5HL5fD09NT5MSU/MwQZb/fqx7S3k2HesHAAqBZm2KGXiIioglUHGScnJ3Tr1g379+/XblOr1di/fz8iIyMtU5QZOskoPWu+jRQTEYCECV2hrDJSih16iYiIKlh1HxkAmD17NiZOnIju3bujR48eWLp0KQoKCjB58mSL1HO7QP/SAPVR1+2hmIgA7QR57NBLRESky+qDzJgxY3Dr1i289dZbyMrKQpcuXbBnz55qHYAby9XbhSY7lgyG3R7STJBHREREumRCCJteIjk3NxcKhQIqlarB/WXK1QK9Fx6ocR2k+ngpug1mRYfVvSMREVETY+j3t1X3kbE2p9LvmizEAECwT/Vh10RERGQ4BhkjmHo1aS7qSERE1DBW30fGmvi4mWbotQwVI484BwwREVHDsEXGGCYYKMQ5YIiIiEyHLTJGuJ1v3NBrL9eKBR0rLzSpVDhj3rBwzgFDRERkAgwyRjCmT8vMh1tj1qA2AMA5YIiIiMyEQcYIPUK84eHsgLyisjr3XZd4FbMGteEcMERERGbEPjJGsLeToWvLZgbtm1tUhlPpd81cERERUdPGIGMk3xoWeNTH1MO1iYiISBeDjBHK1QJHL98yeH/OE0NERGReDDJGOJV+F9l5ho1cUnrKOU8MERGRmTHIGMGYW0XjerTk6CQiIiIzY5AxgjErXwf7uJmxEiIiIgIYZAxWrhbYdOq6wfuzfwwREZH5McgYyJiVr5u5OrJ/DBERUSNgkDGQMf1jhBnrICIior8xyBjImFtFOYWlnAyPiIioETDIGKhHiDeUnoZPhrcvNcuM1RARERHAIGMwezsZxvVoafD+q49fxZ6UTDNWRERERAwyRjB2SHXcjlSUq9ljhoiIyFwYZIxg7JDqTFUR+8oQERGZEYOMEXqEeCNA4Qxj5uvlwpFERETmwyBjBHs7GeYNCzfqNZwYj4iIyHwYZIwUExGAhAldofSsPaDIAAQonDkxHhERkRkxyNRDTEQAjr8+AC9Fh9X4vObW07xh4Vw4koiIyIwYZOrJ3k6GWdFtsGJCVwQodFtnlApnJEzoipiIAAtVR0RE1DQ4WLoAqYuJCMCgcCVOpd/Fzbwi+HlU3E5iSwwREZH5MciYgL2dDJGhzS1dBhERUZPDW0tEREQkWQwyREREJFkMMkRERCRZDDJEREQkWQwyREREJFkMMkRERCRZDDJEREQkWQwyREREJFkMMkRERCRZNj+zrxACAJCbm2vhSoiIiMhQmu9tzfe4PjYfZPLy8gAALVq0sHAlREREZKy8vDwoFAq9z8tEXVFH4tRqNTIyMuDh4QGZzDQLOebm5qJFixa4ceMGPD09TXJMqWnq56Cpv3+A5wDgOWjq7x/gOTDn+xdCIC8vD4GBgbCz098TxuZbZOzs7HD//feb5dienp5N8sKtrKmfg6b+/gGeA4DnoKm/f4DnwFzvv7aWGA129iUiIiLJYpAhIiIiyWKQqQe5XI558+ZBLpdbuhSLaernoKm/f4DnAOA5aOrvH+A5sIb3b/OdfYmIiMh2sUWGiIiIJItBhoiIiCSLQYaIiIgki0GGiIiIJItBph4++eQTBAcHw9nZGT179sSpU6csXVKjmD9/PmQymc5Pu3btLF2WWR05cgTDhg1DYGAgZDIZvv32W53nhRB46623EBAQABcXF0RHR+Py5cuWKdZM6joHkyZNqnZdxMTEWKZYM4iPj8eDDz4IDw8P+Pn5YcSIEUhLS9PZp6ioCLGxsWjevDnc3d0xatQoZGdnW6hi0zLk/ffv37/aNTBt2jQLVWx6CQkJ6NSpk3bSt8jISOzevVv7vC1//hp1nQNLXgMMMkbavHkzZs+ejXnz5iEpKQmdO3fG4MGDcfPmTUuX1ig6dOiAzMxM7c+xY8csXZJZFRQUoHPnzvjkk09qfP7999/Hhx9+iBUrVuDkyZNwc3PD4MGDUVRU1MiVmk9d5wAAYmJidK6LTZs2NWKF5nX48GHExsbixIkT2Lt3L0pLS/HII4+goKBAu89LL72EHTt2YMuWLTh8+DAyMjIwcuRIC1ZtOoa8fwCYOnWqzjXw/vvvW6hi07v//vuxcOFCnD17FmfOnMGAAQMwfPhw/PzzzwBs+/PXqOscABa8BgQZpUePHiI2Nlb7uLy8XAQGBor4+HgLVtU45s2bJzp37mzpMiwGgNi2bZv2sVqtFkqlUixevFi7LScnR8jlcrFp0yYLVGh+Vc+BEEJMnDhRDB8+3CL1WMLNmzcFAHH48GEhRMVn7ujoKLZs2aLd55dffhEARGJioqXKNJuq718IIfr16ydmzZpluaIsoFmzZuKzzz5rcp9/ZZpzIIRlrwG2yBihpKQEZ8+eRXR0tHabnZ0doqOjkZiYaMHKGs/ly5cRGBiIVq1a4YknnsD169ctXZLFpKenIysrS+d6UCgU6NmzZ5O5HjQOHToEPz8/tG3bFtOnT8edO3csXZLZqFQqAIC3tzcA4OzZsygtLdW5Dtq1a4eWLVva5HVQ9f1rbNiwAT4+PoiIiMDcuXNRWFhoifLMrry8HF9++SUKCgoQGRnZ5D5/oPo50LDUNWDzi0aa0u3bt1FeXg5/f3+d7f7+/rh06ZKFqmo8PXv2xLp169C2bVtkZmYiLi4Offv2RUpKCjw8PCxdXqPLysoCgBqvB81zTUFMTAxGjhyJkJAQXLlyBf/6178wZMgQJCYmwt7e3tLlmZRarcaLL76I3r17IyIiAkDFdeDk5AQvLy+dfW3xOqjp/QPA+PHjERQUhMDAQCQnJ2POnDlIS0vDN998Y8FqTevixYuIjIxEUVER3N3dsW3bNoSHh+P8+fNN5vPXdw4Ay14DDDJksCFDhmj/3alTJ/Ts2RNBQUH46quvMGXKFAtWRpY0duxY7b87duyITp06ITQ0FIcOHcLAgQMtWJnpxcbGIiUlxeb7humj7/0/++yz2n937NgRAQEBGDhwIK5cuYLQ0NDGLtMs2rZti/Pnz0OlUuHrr7/GxIkTcfjwYUuX1aj0nYPw8HCLXgO8tWQEHx8f2NvbV+uNnp2dDaVSaaGqLMfLywtt2rTBb7/9ZulSLELzmfN60NWqVSv4+PjY3HUxY8YM7Ny5EwcPHsT999+v3a5UKlFSUoKcnByd/W3tOtD3/mvSs2dPALCpa8DJyQmtW7dGt27dEB8fj86dO2PZsmVN5vMH9J+DmjTmNcAgYwQnJyd069YN+/fv125Tq9XYv3+/zn3CpiI/Px9XrlxBQECApUuxiJCQECiVSp3rITc3FydPnmyS14PGH3/8gTt37tjMdSGEwIwZM7Bt2zYcOHAAISEhOs9369YNjo6OOtdBWloarl+/bhPXQV3vvybnz58HAJu5BmqiVqtRXFxs859/bTTnoCaNeg1YpIuxhH355ZdCLpeLdevWidTUVPHss88KLy8vkZWVZenSzO7ll18Whw4dEunp6eL48eMiOjpa+Pj4iJs3b1q6NLPJy8sT586dE+fOnRMAxAcffCDOnTsnrl27JoQQYuHChcLLy0ts375dJCcni+HDh4uQkBBx7949C1duOrWdg7y8PPHKK6+IxMREkZ6eLvbt2ye6du0qwsLCRFFRkaVLN4np06cLhUIhDh06JDIzM7U/hYWF2n2mTZsmWrZsKQ4cOCDOnDkjIiMjRWRkpAWrNp263v9vv/0m3n77bXHmzBmRnp4utm/fLlq1aiWioqIsXLnpvP766+Lw4cMiPT1dJCcni9dff13IZDLx448/CiFs+/PXqO0cWPoaYJCph48++ki0bNlSODk5iR49eogTJ05YuqRGMWbMGBEQECCcnJzEfffdJ8aMGSN+++03S5dlVgcPHhQAqv1MnDhRCFExBPvNN98U/v7+Qi6Xi4EDB4q0tDTLFm1itZ2DwsJC8cgjjwhfX1/h6OgogoKCxNSpU20q2Nf03gGItWvXave5d++eeP7550WzZs2Eq6ureOyxx0RmZqblijahut7/9evXRVRUlPD29hZyuVy0bt1avPrqq0KlUlm2cBN6+umnRVBQkHBychK+vr5i4MCB2hAjhG1//hq1nQNLXwMyIYQwf7sPERERkemxjwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMEUmKTCbDt99+a/LjBgcHY+nSpSY/LhGZF4MMEdUoMTER9vb2GDp0qNGvtWQomDRpEmQyGWQymXaRu7fffhtlZWW1vu706dM6K/gSkTQwyBBRjVavXo0XXngBR44cQUZGhqXLMUpMTAwyMzNx+fJlvPzyy5g/fz4WL15c474lJSUAAF9fX7i6ujZmmURkAgwyRFRNfn4+Nm/ejOnTp2Po0KFYt25dtX127NiBBx98EM7OzvDx8cFjjz0GAOjfvz+uXbuGl156SdsyAgDz589Hly5ddI6xdOlSBAcHax+fPn0agwYNgo+PDxQKBfr164ekpCSj65fL5VAqlQgKCsL06dMRHR2N7777DkBFi82IESOwYMECBAYGom3btgCqtyLl5OTgueeeg7+/P5ydnREREYGdO3dqnz927Bj69u0LFxcXtGjRAjNnzkRBQYH2+eXLlyMsLAzOzs7w9/fH6NGjjX4fRFQ3Bhkiquarr75Cu3bt0LZtW0yYMAFr1qxB5WXZvv/+ezz22GN49NFHce7cOezfvx89evQAAHzzzTe4//778fbbbyMzMxOZmZkG/968vDxMnDgRx44dw4kTJxAWFoZHH30UeXl5DXo/Li4u2pYXANi/fz/S0tKwd+9enXCioVarMWTIEBw/fhzr169HamoqFi5cCHt7ewDAlStXEBMTg1GjRiE5ORmbN2/GsWPHMGPGDADAmTNnMHPmTLz99ttIS0vDnj17EBUV1aD3QEQ1c7B0AURkfVavXo0JEyYAqLhNo1KpcPjwYfTv3x8AsGDBAowdOxZxcXHa13Tu3BkA4O3tDXt7e3h4eECpVBr1ewcMGKDzeOXKlfDy8sLhw4fxj3/8w+j3IYTA/v378cMPP+CFF17Qbndzc8Nnn30GJyenGl+3b98+nDp1Cr/88gvatGkDAGjVqpX2+fj4eDzxxBN48cUXAQBhYWH48MMP0a9fPyQkJOD69etwc3PDP/7xD3h4eCAoKAgPPPCA0fUTUd3YIkNEOtLS0nDq1CmMGzcOAODg4IAxY8Zg9erV2n3Onz+PgQMHmvx3Z2dnY+rUqQgLC4NCoYCnpyfy8/Nx/fp1o46zc+dOuLu7w9nZGUOGDMGYMWMwf/587fMdO3bUG2KAivd3//33a0NMVRcuXMC6devg7u6u/Rk8eDDUajXS09MxaNAgBAUFoVWrVnjyySexYcMGFBYWGvUeiMgwbJEhIh2rV69GWVkZAgMDtduEEJDL5fj444+hUCjg4uJi9HHt7Ox0bk8BQGlpqc7jiRMn4s6dO1i2bBmCgoIgl8sRGRmpc1vIEA8//DASEhLg5OSEwMBAODjo/q/Ozc2t1tfX9f7y8/Px3HPPYebMmdWea9myJZycnJCUlIRDhw7hxx9/xFtvvYX58+fj9OnT8PLyMuq9EFHt2CJDRFplZWX43//+h//+9784f/689ufChQsIDAzEpk2bAACdOnXC/v379R7HyckJ5eXlOtt8fX2RlZWlE2bOnz+vs8/x48cxc+ZMPProo+jQoQPkcjlu375t9Ptwc3ND69at0bJly2ohxhCdOnXCH3/8gV9//bXG57t27YrU1FS0bt262o+mpcfBwQHR0dF4//33kZycjKtXr+LAgQNG10JEtWOLDBFp7dy5E3/99RemTJkChUKh89yoUaOwevVqTJs2DfPmzcPAgQMRGhqKsWPHoqysDLt27cKcOXMAVIwAOnLkCMaOHQu5XA4fHx/0798ft27dwvvvv4/Ro0djz5492L17Nzw9PbW/IywsDF988QW6d++O3NxcvPrqq/Vq/Wmofv36ISoqCqNGjcIHH3yA1q1b49KlS5DJZIiJicGcOXPQq1cvzJgxA8888wzc3NyQmpqKvXv34uOPP8bOnTvx+++/IyoqCs2aNcOuXbugVqu1I6SIyHTYIkNEWqtXr0Z0dHS1EANUBJkzZ84gOTkZ/fv3x5YtW/Ddd9+hS5cuGDBgAE6dOqXd9+2338bVq1cRGhoKX19fAED79u2xfPlyfPLJJ+jcuTNOnTqFV155pdrv/+uvv9C1a1c8+eSTmDlzJvz8/Mz7pvXYunUrHnzwQYwbNw7h4eF47bXXtK1MnTp1wuHDh/Hrr7+ib9++eOCBB/DWW29pb8d5eXnhm2++wYABA9C+fXusWLECmzZtQocOHSzyXohsmUxUvWlNREREJBFskSEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIsn6f1tq4WKZKziwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train,train_data_predict)\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51bf27b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction on Test Data\n",
    "test_data_predict=lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78922f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squarred error in test data :  0.8365766715026374\n"
     ]
    }
   ],
   "source": [
    "#R2 squarred error in test data\n",
    "test_score=metrics.r2_score(Y_test,test_data_predict)\n",
    "print(\"R squarred error in test data : \",test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af5b5e7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize Actual Prices and Predicted Prices\n",
    "plt.scatter(Y_test, test_data_predict)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e0f725",
   "metadata": {},
   "source": [
    "# - Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31fab156",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading linear regression model\n",
    "lass_reg_model=Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d0bead4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lass_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6a70e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction on Train Data\n",
    "train_data_predict=lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f6282a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squarred error on train data :  0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "#R squarred error on train data\n",
    "train_score=metrics.r2_score(Y_train,train_data_predict)\n",
    "print(\"R squarred error on train data : \",train_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fc48bc",
   "metadata": {},
   "source": [
    "Visualize Actual Prices and Predicted Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e9126530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train,train_data_predict)\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Predicted Prices\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5519bfae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction on Test Data\n",
    "test_data_predict=lass_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "41b58033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squarred error in test data :  0.8709167941173195\n"
     ]
    }
   ],
   "source": [
    "#R2 squarred error in test data\n",
    "test_score=metrics.r2_score(Y_test,test_data_predict)\n",
    "print(\"R squarred error in test data : \",test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eeb8f5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize Actual Prices and Predicted Prices\n",
    "plt.scatter(Y_test, test_data_predict)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}