07-Car-Price-Prediction/Car_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "414b4319",
   "metadata": {},
   "source": [
    "# Car Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b98d5b",
   "metadata": {},
   "source": [
    "### Importing all dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0d05d579",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7096bc01",
   "metadata": {},
   "source": [
    "### Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b39794c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "car_dataset = pd.read_csv(\"data/car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e6f72af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "    }\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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a0dba757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "212e9596",
   "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": [
    "# information extraction\n",
    "car_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3ee3acba",
   "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": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing value \n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aa4d3f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel type\n",
      "Petrol    239\n",
      "Diesel     60\n",
      "CNG         2\n",
      "Name: Fuel_Type, dtype: int64\n",
      "Seller type\n",
      "Dealer        195\n",
      "Individual    106\n",
      "Name: Seller_Type, dtype: int64\n",
      "Transission type\n",
      "Manual       261\n",
      "Automatic     40\n",
      "Name: Transmission, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking the distribution of categorical data\n",
    "print(\"Fuel type\")\n",
    "print(car_dataset.Fuel_Type.value_counts())\n",
    "print(\"Seller type\")\n",
    "print(car_dataset.Seller_Type.value_counts())\n",
    "print(\"Transission type\")\n",
    "print(car_dataset.Transmission.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "aa7518f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding categorical data\n",
    "\n",
    "# fuel type\n",
    "car_dataset.replace({\"Fuel_Type\":{\"Petrol\": 0, \"Diesel\": 1, \"CNG\": 2}}, inplace = True)\n",
    "\n",
    "# seller type\n",
    "car_dataset.replace({\"Seller_Type\":{\"Dealer\": 0, \"Individual\": 1}}, inplace = True)\n",
    "\n",
    "# transmission type\n",
    "car_dataset.replace({\"Transmission\":{\"Manual\": 0, \"Automatic\": 1}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e7b4e1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "64c8d847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features and labels\n",
    "X = car_dataset.drop([\"Car_Name\", \"Selling_Price\"], axis = 1)\n",
    "Y = car_dataset[\"Selling_Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "65807fbb",
   "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",
      "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(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8102e4",
   "metadata": {},
   "source": [
    "### Split Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "75cb4aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e2ccbe3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
      "204  2015          4.430       28282          0            0             0   \n",
      "249  2016          7.600       17000          0            0             0   \n",
      "277  2015         13.600       21780          0            0             0   \n",
      "194  2008          0.787       50000          0            1             0   \n",
      "244  2013          9.400       49000          1            0             0   \n",
      "..    ...            ...         ...        ...          ...           ...   \n",
      "75   2015          6.800       36000          0            0             0   \n",
      "22   2011          8.010       50000          0            0             1   \n",
      "72   2013         18.610       56001          0            0             0   \n",
      "15   2016         10.790       43000          1            0             0   \n",
      "168  2013          0.730       12000          0            1             0   \n",
      "\n",
      "     Owner  \n",
      "204      0  \n",
      "249      0  \n",
      "277      0  \n",
      "194      0  \n",
      "244      0  \n",
      "..     ...  \n",
      "75       0  \n",
      "22       0  \n",
      "72       0  \n",
      "15       0  \n",
      "168      0  \n",
      "\n",
      "[270 rows x 7 columns]\n",
      "99      9.65\n",
      "161     0.45\n",
      "89      4.75\n",
      "30      3.10\n",
      "232    11.45\n",
      "290     4.50\n",
      "35      2.95\n",
      "7       6.50\n",
      "183     0.27\n",
      "13      6.10\n",
      "269     6.70\n",
      "65      4.75\n",
      "178     0.35\n",
      "258     8.40\n",
      "227     2.55\n",
      "133     0.72\n",
      "130     0.75\n",
      "156     0.48\n",
      "237    11.25\n",
      "262     4.00\n",
      "112     1.15\n",
      "282     8.25\n",
      "164     0.45\n",
      "275    10.90\n",
      "154     0.50\n",
      "29      7.45\n",
      "141     0.60\n",
      "192     0.20\n",
      "216     2.90\n",
      "3       2.85\n",
      "159     0.45\n",
      "Name: Selling_Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300c262f",
   "metadata": {},
   "source": [
    "### Model Training on Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6e0d3a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "740073e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e611ff3",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f17f9d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction on training data\n",
    "train_prediction = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9f441d69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8799451660493695\n"
     ]
    }
   ],
   "source": [
    "# R squraed error on training data\n",
    "error_score = metrics.r2_score(Y_train, train_prediction)\n",
    "print(error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0001b1d",
   "metadata": {},
   "source": [
    "### Visualise the actual prices and predicted prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1299a53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dhg0B5H+gmBtIa8ng2ho1auD3339HXl6eVYGmevXqaNasGd5//32zj+s/tKtVq4bY2Firaiyobt26Vp1Pc/TjbP755x8EBgaa3Uf/c/j000+LvJunuA/WatWqISkpyWT7jRs3jI5vb+bOx549e3Djxg3ExMQYWiAA4P79+2VSkyVKe91Y+rthi3Px/fffIyMjA9u3b0dQUJBhe0WckqKssWWGTOh0OmzcuBH16tXD3r17Tb6mTJmCpKQk7N69GwDQt29f7N2719DlYU5xf13WqVMHf//9t9EdDHfv3jXcSaOnUChQuXJloyb4R48e4fPPPy/V+33ppZdw9OhR/P7779i5cyfCw8ONXqPw++jSpQsiIyMB5Dcp20vDhg0RHByM06dPo02bNma/vL29AeTfwfLbb78ZWhGA/J/j1q1bS3ydvn37IjMzs8S7Qor6a/upp57C2bNnUa9ePbM16sNMt27dkJ6ejh07dhg935JByvbSu3dvuLi4FDvnT6dOnVClShXEx8cX+XMo3MpWUI8ePRAfH4+4uDij7Zs2bYJCoUC3bt1s8l6sacHRBxz9c/VWr15tk5psoUuXLkhPTzf8f6Onb00tiaW/G1LORVHn2twxhBBYs2aNRbWS9dgyQyZ2796NGzduIDIy0uwt06GhoVi2bBnWrl2Lp556CvPnz8fu3bvRuXNnzJgxA02bNsX9+/cRFRWFyZMno1GjRqhXrx7c3d3x5ZdfonHjxvDy8kJAQAACAgLwwgsvYPXq1Rg5ciTGjh2Lu3fvYtGiRSZzUvTv3x+LFy/GiBEj8Morr+Du3bv46KOPTP7zkWr48OGYPHkyhg8fjqysLMMdQnqzZ8/GP//8gx49eqB27dq4f/8+PvnkE6P+dHtZvXo1+vbtiyeffBKjRo1CrVq1kJKSgj///BNxcXH4+uuvAQDvvvsuduzYge7du2P27Nnw8PDA8uXLix3LoTd8+HCsX78e48ePx/nz59GtWzfk5eXh6NGjaNy4sWHMVNOmTRETE4OdO3fC398f3t7eaNiwIebPn4/o6Gh07NgRb7zxBho2bIjMzExcuXIFu3btwqpVq1C7dm28+OKL+Pjjj/Hiiy/i/fffR3BwMHbt2oWff/7ZruewOHXq1MGMGTPw3nvv4dGjRxg+fDjUajXi4+Nx584dzJs3D15eXvj0008RHh6OlJQUDBkyBDVr1sTt27dx+vRp3L59u9gw9NZbb2HTpk3o378/5s+fj6CgIPz0009YsWIFJkyYgAYNGtjkvehn6P7ss8/g7e0NlUoFrVZrthtIr2PHjqhatSrGjx+POXPmwNXVFV9++SVOnz5tk5psITw8HB9//DFGjhyJBQsWoH79+ti9e7fhuimpNdHS3w0p56Jp06YA8rs8+/btCxcXFzRr1gy9evWCm5sbhg8fjqlTpyIzMxMrV67EvXv3bHQ2qEgOHX5MTunpp58Wbm5u4tatW0XuM2zYMFG5cmXDHQLXrl0To0ePFhqNRri6uoqAgADx3HPPiZs3bxqe89VXX4lGjRoJV1dXAUDMmTPH8NjGjRtF48aNhUqlEiEhIWLr1q1m72Zat26daNiwoVAqlaJu3boiIiJCrF27tsQ7dkoyYsQIAUB06tTJ5LEff/xR9O3bV9SqVUu4ubmJmjVrin79+okDBw6UeNygoCDRv3//YvfR38Xy4Ycfmn389OnT4rnnnhM1a9YUrq6uQqPRiO7duxvuMtM7ePCg6NChg1AqlUKj0Yi3335bfPbZZxadm0ePHonZs2eL4OBg4ebmJqpVqya6d+8uDh06ZNjn1KlTolOnTsLDw0MAMDrG7du3xRtvvCG0Wq1wdXUVvr6+onXr1mLmzJniwYMHhv3++ecf8cwzzwgvLy/h7e0tnnnmGXHo0CFJdzN9/fXXxe6nv+vm9u3bRT5W2KZNm0Tbtm2FSqUSXl5eomXLlib17Nu3T/Tv31/4+voKV1dXUatWLdG/f/8S6xFCiMTERDFixAhRrVo14erqKho2bCg+/PBDw91oQhR/HRT+fSnKkiVLhFarFS4uLkbntEuXLqJJkyZmn3Po0CERFhYmPDw8RI0aNcSYMWNEXFycyc+kqLuZzF3fha+xou5m8vT0NHmuude5evWqGDx4sNF1s2vXLrN3x5lj6e+GpeciKytLjBkzRtSoUUMoFAqj4+zcuVM0b95cqFQqUatWLfH222+L3bt3F3mnGdmGQggrZhsjIiJyoIULF+Ldd9/F1atXy+QGBHJu7GYiIiKntmzZMgBAo0aNkJOTgz179mDp0qUYOXIkgwwBYJghIiIn5+HhgY8//hhXrlxBVlYWHnvsMbzzzjt49913HV0aOQl2MxEREZGs8dZsIiIikjWGGSIiIpI1hhkiIiKStXI/ADgvLw83btyAt7e3RdObExERkeMJIZCenm7R2nHlPszcuHGjyDVXiIiIyLldu3atxFvwy32Y0a9dc+3aNZPp8YmIiMg5paWlITAw0PA5XpxyH2b0XUs+Pj4MM0RERDJjyRARDgAmIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZK/czABMREZHt6fIEYhNScCs9EzW9VWin9YVLJccs6MwwQ0RERJJEnU3CvJ3xSErNNGzzV6swZ0AI+oT6l3k97GYiIiIii0WdTcKEL+KMggwAJKdmYsIXcYg6m1TmNTHMEBERkUV0eQLzdsZDmHlMv23eznjo8sztYT8MM0RERGSR2IQUkxaZggSApNRMxCaklF1RYJghIiIiC91KLzrIWLOfrTDMEBERkUVqeqtsup+tMMwQERGRRdppfeGvVqGoG7AVyL+rqZ3WtyzLYpghIiIiy7hUUmDOgBAAMAk0+u/nDAgp8/lmGGaIiIjIYn1C/bFyZCto1MZdSRq1CitHtnLIPDOcNI+IiIgk6RPqj14hGs4ATERERPLlUkmBsHrVHF0GAHYzERERkcwxzBAREZGsOU2YiYiIgEKhwKRJkwzbhBCYO3cuAgIC4O7ujq5du+LcuXOOK5KIiIicjlOEmWPHjuGzzz5Ds2bNjLYvWrQIixcvxrJly3Ds2DFoNBr06tUL6enpDqqUiIiInI3Dw8yDBw/w/PPPY82aNahataphuxACS5YswcyZMzF48GCEhoZi48aNePjwITZv3uzAiomIiMiZODzMTJw4Ef3790fPnj2NtickJCA5ORm9e/c2bFMqlejSpQsOHTpU5PGysrKQlpZm9EVERETll0Nvzd6yZQtOnDiB48ePmzyWnJwMAPDz8zPa7ufnh8TExCKPGRERgXnz5tm2UCIiInJaDmuZuXbtGt588018+eWXUKmKXpBKoTCegEcIYbKtoOnTpyM1NdXwde3aNZvVTERERM7HYS0zJ06cwK1bt9C6dWvDNp1Oh/3792PZsmU4f/48gPwWGn//f6dGvnXrlklrTUFKpRJKpdJ+hRMREZFTcVjLTI8ePXDmzBmcOnXK8NWmTRs8//zzOHXqFOrWrQuNRoPo6GjDc7Kzs7Fv3z507NjRUWUTERGRk3FYy4y3tzdCQ0ONtnl6eqJatWqG7ZMmTcLChQsRHByM4OBgLFy4EB4eHhgxYoQjSiYiIiIn5NRrM02dOhWPHj3Cq6++inv37qF9+/b45Zdf4O3t7ejSiIiIyEkohBDC0UXYU1paGtRqNVJTU+Hj4+PocoiIiMgCUj6/HT7PDBEREVFpMMwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkawxzBAREZGsMcwQERGRrDHMEBERkaw5NMysXLkSzZo1g4+PD3x8fBAWFobdu3cbHhdCYO7cuQgICIC7uzu6du2Kc+fOObBiIiIicjYODTO1a9fGBx98gOPHj+P48ePo3r07Bg0aZAgsixYtwuLFi7Fs2TIcO3YMGo0GvXr1Qnp6uiPLJiIiIieiEEIIRxdRkK+vLz788EOMHj0aAQEBmDRpEt555x0AQFZWFvz8/BAZGYlx48ZZdLy0tDSo1WqkpqbCx8fHnqUTERGRjUj5/HaaMTM6nQ5btmxBRkYGwsLCkJCQgOTkZPTu3duwj1KpRJcuXXDo0KEij5OVlYW0tDSjLyIiIiq/HB5mzpw5Ay8vLyiVSowfPx7fffcdQkJCkJycDADw8/Mz2t/Pz8/wmDkRERFQq9WGr8DAQLvWT0RERI7l8DDTsGFDnDp1CkeOHMGECRMQHh6O+Ph4w+MKhcJofyGEybaCpk+fjtTUVMPXtWvX7FY7EREROV5lRxfg5uaG+vXrAwDatGmDY8eO4ZNPPjGMk0lOToa/v79h/1u3bpm01hSkVCqhVCrtWzQRERE5DYe3zBQmhEBWVha0Wi00Gg2io6MNj2VnZ2Pfvn3o2LGjAyskIiIiZ+LQlpkZM2agb9++CAwMRHp6OrZs2YKYmBhERUVBoVBg0qRJWLhwIYKDgxEcHIyFCxfCw8MDI0aMcGTZRERE5EQcGmZu3ryJF154AUlJSVCr1WjWrBmioqLQq1cvAMDUqVPx6NEjvPrqq7h37x7at2+PX375Bd7e3o4sm4iIiJyI080zY2ucZ4aIiEh+ZDnPDBEREZE1GGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1qwOM9nZ2Th//jxyc3NtWQ8RERGRJJLDzMOHD/Hyyy/Dw8MDTZo0wdWrVwEAb7zxBj744AObF0hERERUHMlhZvr06Th9+jRiYmKgUqkM23v27ImtW7fatDgiIiKiklSW+oTvv/8eW7duRYcOHaBQKAzbQ0JCcOnSJZsWR0RERFQSyS0zt2/fRs2aNU22Z2RkGIUbIiIiorIgOcy0bdsWP/30k+F7fYBZs2YNwsLCbFcZERERkQUkdzNFRESgT58+iI+PR25uLj755BOcO3cOhw8fxr59++xRIxEREVGRJLfMdOzYEQcPHsTDhw9Rr149/PLLL/Dz88Phw4fRunVre9RIREREVCSFEEI4ugh7SktLg1qtRmpqKnx8fBxdDhEREVlAyue35JaZXbt24eeffzbZ/vPPP2P37t1SD0dERERUKpLDzLRp06DT6Uy2CyEwbdo0mxRFREREZCnJYebChQsICQkx2d6oUSNcvHjRJkURERERWUpymFGr1bh8+bLJ9osXL8LT09MmRRERERFZSnKYGThwICZNmmQ02+/FixcxZcoUDBw40KbFEREREZVEcpj58MMP4enpiUaNGkGr1UKr1aJx48aoVq0aPvroI3vUSERERFQkyZPmqdVqHDp0CNHR0Th9+jTc3d3RrFkzdO7c2R71ERERERWL88wQERGR05Hy+W1Ry8zSpUvxyiuvQKVSYenSpcXu+8Ybb1heKREREVEpWdQyo9Vqcfz4cVSrVg1arbbogykUZu90ciS2zBAREcmPzVtmEhISzP6biIiIyNEk3c2Uk5ODunXrIj4+3l71EBEREUkiKcy4uroiKysLCoXCXvUQERERSSJ5npnXX38dkZGRyM3NtUc9RERERJJInmfm6NGj+O233/DLL7+gadOmJksYbN++3WbFEREREZVEcpipUqUKnnnmGXvUQkRERDagyxOITUjBrfRM1PRWoZ3WFy6Vyu8QEclhZv369faog4iIiGwg6mwS5u2MR1JqpmGbv1qFOQNC0CfU34GV2Y/FY2by8vLw4YcfolOnTmjXrh1mzJiBzMzMkp9IREREZSLqbBImfBFnFGQAIDk1ExO+iEPU2SQHVWZfFoeZyMhITJs2DZ6envD398fixYs52y8REZGT0OUJzNsZD3Mz4eq3zdsZD11e+VvFyOIws2HDBnz66af45Zdf8MMPP+D777/Hpk2bUM6XdiIiIpKF2IQUkxaZggSApNRMxCaklF1RZcTiMJOYmIinnnrK8P2TTz4JIQRu3Lhhl8KIiIjIcrfSLRv6Yel+cmJxmMnOzoa7u7vhe4VCATc3N2RlZdmlMCIiIrJcTW+VTfeTE0l3M82aNQseHh6G77Ozs/H+++9DrVYbti1evNh21REREZFF2ml94a9WITk10+y4GQUAjTr/Nu3yxuIw07lzZ5w/f95oW8eOHY1WyeYyB0RERI7hUkmBOQNCMOGLOCgAo0Cj/3SeMyCkXM43oxDlfASvlCXEiYjKs4o2kVpFVV7mmZHy+S150jwiIpKf8vIBRyXrE+qPXiGaChVc2TJDRFTO6SdSK/yfvf6jbeXIVgw05HSkfH5LXjWbiIjyu2wOX7qLH05dx+FLd512IrKKPJEaVRzsZiIikkhOXTZSJlILq1et7AojsiG2zBBRhWRty4rc1r6pyBOpUcVhUcvMH3/8YfEBmzVrZvG+ERER2L59O/766y+4u7ujY8eOiIyMRMOGDQ37CCEwb948fPbZZ7h37x7at2+P5cuXo0mTJha/DhFRQda2rJTUZaNAfpdNrxCN0wy2rMgTqVHFYVGYadGiBRQKBYQQJc4lo9PpLH7xffv2YeLEiWjbti1yc3Mxc+ZM9O7dG/Hx8fD09AQALFq0CIsXL8aGDRvQoEEDLFiwAL169cL58+fh7e1t8WsREQFFD4bVt6wUNxhWjl02FXkiNao4LOpmSkhIwOXLl5GQkIBvv/0WWq0WK1aswMmTJ3Hy5EmsWLEC9erVw7fffivpxaOiojBq1Cg0adIEzZs3x/r163H16lWcOHECQH6rzJIlSzBz5kwMHjwYoaGh2LhxIx4+fIjNmzdLf7dEVKGVdjCsHLts9BOpAf/evaRX3idSo4rDopaZoKAgw7+fffZZLF26FP369TNsa9asGQIDAzFr1iw8/fTTVheTmpoKAPD1zf8LISEhAcnJyejdu7dhH6VSiS5duuDQoUMYN26c1a9FRBVPaVtW5Npl0yfUHytHtjLpWtM46aBlIqkk38105swZaLVak+1arRbx8fFWFyKEwOTJk/H4448jNDQUAJCcnAwA8PPzM9rXz88PiYmJZo+TlZVltPhlWlqa1TURUflS2pYVOXfZVMSJ1KjikHw3U+PGjbFgwQJkZv77y56VlYUFCxagcePGVhfy2muv4Y8//sBXX31l8ljhcTrFjd2JiIiAWq02fAUGBlpdExGVL6VtWZF7l41LJQXC6lXDoBa1EFavmtPWSSSV5DCzatUq/PrrrwgMDETPnj3Rs2dP1K5dG9HR0Vi1apVVRbz++uvYsWMH9u7di9q1axu2azQaAP+20OjdunXLpLVGb/r06UhNTTV8Xbt2zaqaiKj80besFPURrkD+XU3Ftazou2w0auPAo1GrOJMukYNI7mZq164dEhIS8MUXX+Cvv/6CEAJDhw7FiBEjDHcgWUoIgddffx3fffcdYmJiTLqvtFotNBoNoqOj0bJlSwBAdnY29u3bh8jISLPHVCqVUCqVUt8WEVUAtlpVmF02RM7FoWszvfrqq9i8eTN++OEHo7ll1Go13N3dAQCRkZGIiIjA+vXrERwcjIULFyImJsbiW7O5NhMRFSanGXyJKiopn99WhZnPP/8cq1evxuXLl3H48GEEBQXh448/Rt26dTFo0CCLj1PUuJf169dj1KhRAP6dNG/16tVGk+bpBwmXhGGGiMzR5Qm2rBA5MbuGmZUrV2L27NmYNGkSFixYgHPnzqFu3brYsGEDNm7ciL1795aqeFtjmCEiIpIfu66a/emnn2LNmjWYOXMmKlf+d8hNmzZtcObMGenVEhGRzWTn5mHtgcuY/cNZrD1wGdm5eY4uicjuJA8ATkhIMAzGLUipVCIjI8MmRRERkXQRu+Kx5kACCk5g/P6uPzH2CS2m9wtxXGFEdia5ZUar1eLUqVMm23fv3o2QEP6yEBE5QsSueKzebxxkACBPAKv3JyBil/WTmhI5O8ktM2+//TYmTpyIzMxMCCEQGxuLr776ChEREfjvf/9rjxqJiKgY2bl5WHMgodh91hxIwJTejeBWWfLfsEROT3KYeemll5Cbm4upU6fi4cOHGDFiBGrVqoVPPvkEw4YNs0eNRERUjM8PXzFpkSksT+Tv9/ITdcumKKIyJDnMAMDYsWMxduxY3LlzB3l5eahZs6at6yIiIgslpjy06X5EciO5vbF79+64f/8+AKB69eqGIJOWlobu3bvbtDgiIipZkK+HTfcjkhvJYSYmJgbZ2dkm2zMzM3HgwAGbFEVERJZ7IawOSprvr5Iifz+i8sjibqY//vjD8O/4+HijxR91Oh2ioqJQq1Yt21ZHREQlcqtcCWOf0GL1/qIHAY99QsvBv1RuWRxmWrRoAYVCAYVCYbY7yd3dHZ9++qlNiyMiIsvo55EpPM9MJQU4zwyVexYvZ5CYmAghBOrWrYvY2FjUqFHD8Jibmxtq1qwJFxcXuxVqLS5nQEQVyaNsHRbuiseVuw9Rp5oHZvQLgbub8/3fTFQSKZ/fFrfMBAUFAQDy8jg1NhGRMyq8GviBC8Cvf97iauBU7knuQI2IiMC6detMtq9btw6RkZE2KYqInIMuT+Dwpbv44dR1HL50F7qSJjMhh4k6m4QJX8QZgoxecmomJnwRh6izSQ6qjMj+JM8zs3r1amzevNlke5MmTTBs2DC88847NimMiByr8F/5AOCvVmFW/8ao6qnErfRM1PRWoZ3WFy4l3UpDdqXLE5i3Mx7moqYAoAAwb2c8eoVo+LOicklymElOToa/v2lzZY0aNZCUxORPVB7o/8ov/OGYlJqJVzefNNrmr1axG6MIujyB2IQUuwe/2IQUkxaZggTyf3axCSkIq1fN5q9P5GiSw0xgYCAOHjwIrVZrtP3gwYMICAiwWWFE5BjF/ZVvjr4bY+XIVgw0BRTVsmWP4HcrveggY81+RHIjeczMmDFjMGnSJKxfvx6JiYlITEzEunXr8NZbb2Hs2LH2qJGIylBJf+UXpg8983bGc0zN/5T1+JWa3iqb7kckN5JbZqZOnYqUlBS8+uqrhpmAVSoV3nnnHUyfPt3mBRJR2bLmr3d2Y/zLEeNX2ml94a9WITk10+zrKgBo1PndXETlkeSWGYVCgcjISNy+fRtHjhzB6dOnkZKSgtmzZ9ujPiIqY6X56724IFRR7oySMn7FVlwqKTBnQP6keIXjkf77OQNCOPiXyi2rVs0GAC8vL7Rt29aWtRCREyjpr/ziFBWEynL8iKM5avxKn1B/rBzZyuQ8a8rpeSYqyKIwM3jwYGzYsAE+Pj4YPHhwsftu377dJoURkWPo/8qf8EUcFIBFgaa4boyi7owqrwOHHTl+pU+oP3qFaMrkDioiZ2JRmFGr1VAoFIZ/E1H5VtRf+eYU141REec/cfT4FZdKigo/bokqHovXZpIrrs1EZL3C86Tcy8jGez9Z3l10+NJdDF9zpMTX+Wpsh3L1AaxvjQKMW7b0ca28tUYR2YNd1mYioorH3F/5T4Za3o1RUec/4fgVorJlUZhp2bKloZupJHFxcaUqiIicm5RujIo8/wnHrxCVHYvCzNNPP234d2ZmJlasWIGQkBCEhYUBAI4cOYJz587h1VdftUuRRCRPjh4/4mgcv0JUNiwKM3PmzDH8e8yYMXjjjTfw3nvvmexz7do121ZHRLJW3J1RnP+EiGxF8gBgtVqN48ePIzg42Gj7hQsX0KZNG6Smptq0wNLiAGAix6tI88wQkW3YdQCwu7s7fv/9d5Mw8/vvv0OlKn/93kRUemU5fqSsVqomIuchOcxMmjQJEyZMwIkTJ9ChQwcA+WNm1q1bxyUNiKhIZTF+hC1ARBWTVfPMbNu2DZ988gn+/PNPAEDjxo3x5ptv4rnnnrN5gaXFbiaiiqGomYZtNbcLW3yIypaUz29OmkdEsqfLE3g8ck+RsxXr75r6/Z3uVgUQtvgQlT0pn9+SV80GgPv37+O///0vZsyYgZSU/JVf4+LicP36dWsOR0RUKvZcqVrf4lP4+Pq1paLOJkk+JhHZluQxM3/88Qd69uwJtVqNK1euYMyYMfD19cV3332HxMREbNq0yR51EhEVyV4zDVfEtaWI5Ehyy8zkyZMxatQoXLhwwejupb59+2L//v02LY6IyBL2mmnYni0+RGQ7ksPMsWPHMG7cOJPttWrVQnJysk2KIiKSQj/TcFFtIwrkj3GROtNwRV1bikhuJIcZlUqFtLQ0k+3nz59HjRo1bFIUEZEU+pmGAZgEmtLMNFyR15YikhPJYWbQoEGYP38+cnJyAAAKhQJXr17FtGnT8Mwzz9i8QCIiS+hXqtaojYOFRq2y+rZse7X4EJFtSb41Oy0tDf369cO5c+eQnp6OgIAAJCcnIywsDLt27YKnp6e9arUKb80mqlhsPR+M/m4mwPzaUqWdv4aIzCuTeWb27NmDuLg45OXloVWrVujZs6dVxdobwwwRlRbnmSEqe3YLM7m5uVCpVDh16hRCQ0NLXWhZYJghIlvgDMBEZctuC01WrlwZQUFB0Ol0pSqQiEhuymJtKSKyjuQBwO+++y6mT59umPmXiIiIyJEkzwC8dOlSXLx4EQEBAQgKCjIZ8BsXF2ez4oiIiIhKIjnMDBo0CAoF+4mJiIjIOXDVbCIiInI6dlk1++HDh5g4cSJq1aqFmjVrYsSIEbhz506piyUiIiIqDYvDzJw5c7Bhwwb0798fw4YNQ3R0NCZMmGDP2oiIiIhKZPGYme3bt2Pt2rUYNmwYAGDkyJHo1KkTdDodXFxc7FYgERERUXEsbpm5du0annjiCcP37dq1Q+XKlXHjxg27FEZERERkCYvDjE6ng5ubm9G2ypUrIzc31+ZFEREREVnK4m4mIQRGjRoFpVJp2JaZmYnx48cbzTWzfft221ZIRGWGU/YTkRxZHGbCw8NNto0cOdKmxRCR43AxRSKSK84zQ0SIOpuECV/EofB/Bvo2mZUjWzHQEFGZsss8M0RUPunyBObtjDcJMgAM2+btjIcur1z/3UNEMsYwQ1TBxSakGHUtFSYAJKVmIjaBi8sSkXOSvDYTUUEcMCp/t9KLDjLW7GdLvL6IyBIMM2Q1DhgtH2p6q2y6n63w+iIiS7GbiayiHzBauHsiOTUTE76IQ9TZJAdVRlK10/rCX61CUe0dCuSHiNZBVXH40l38cOo6Dl+6a9cxNLy+iEgKtsyQZCUNGFUgf8BorxANuwRkwKWSAnMGhGDCF3FQAEY/V/1Pb2Bzf3T5cG+ZtJLw+iIiqdgyQ5KV5wGjujxRZq0PzqRPqD9WjmwFjdq4K0mjVuGVzlp8tj+hzFpJyvP1RUT2wZYZksyZB4yWRkUfo9En1B+9QjRGA25bB1VFlw/3lmkrSXm9vojIfhhmKjhr7hZx1gGjpVHUpHH61gdbTxpX8LxX91QCCuDOgyyH37HjUkmBsHrVDN8fvnTX4laSgs8rjfJ4fRGRfTk0zOzfvx8ffvghTpw4gaSkJHz33Xd4+umnDY8LITBv3jx89tlnuHfvHtq3b4/ly5ejSZMmjiu6HLG2JUI/YDQ5NdPsX+wK5HdPtNP62r5oOyjrMRrmzntBvp6u+H8taqFniEZSsLHHbcyOaCUpb9cXEdmfQ8fMZGRkoHnz5li2bJnZxxctWoTFixdj2bJlOHbsGDQaDXr16oX09PQyrrT8Kc3dIvoBowBM7oDRfz9nQIhsBmeW5RiNos57QSkZOVh78AqGrzmCxyP3WDQmJepsEh6P3IPha47gzS2nJD23OI5oJSlv1xcR2Z9Dw0zfvn2xYMECDB482OQxIQSWLFmCmTNnYvDgwQgNDcXGjRvx8OFDbN682QHVlh+2mL6+uAGjclvHp6xaH4o770WxJFza8zZmS2/btnUrSXm6vojI/px2zExCQgKSk5PRu3dvwzalUokuXbrg0KFDGDdunNnnZWVlISsry/B9Wlqa3WuVGyktEcWNgzA3YFSOM7SWVetDSefdnJK6uezdRWbJbdv2aiUpL9cXEdmf096anZycDADw8/Mz2u7n52d4zJyIiAio1WrDV2BgoF3rlCNbtkToB4wOalELYfWqyfKDpqxaH6xt2THXzaW/hfzj6L/t3kXmyFaS8nB9EZH9OW3LjJ5CYfyflxDCZFtB06dPx+TJkw3fp6WlMdAUwrtFjJVV60Npz6c+DJU0gLi451qLrSRE5MycNsxoNBoA+S00/v7//uV369Ytk9aagpRKJZRKpd3rkzM53C2iyxM4cvkuDl+6C0AgrG51dCjlX+bF3e3TJ9Qfr3TWYs2BBIgCJ0WhAMY+obVJ60NJ570kNb1VRd5CbslzS6vwbdtERM7CacOMVquFRqNBdHQ0WrZsCQDIzs7Gvn37EBkZ6eDq5M2R4yAsEXU2CdO2n8H9hzmGbcv2XkIVD1d8MLipVS0EJd2GHnU2CZ/tTzAJCXkCWL0/AR5ulfFa9+BSnZPizntx9OGyuAnsSnoub2MmovJMIYRw2HztDx48wMWLFwEALVu2xOLFi9GtWzf4+vriscceQ2RkJCIiIrB+/XoEBwdj4cKFiImJwfnz5+Ht7W3Ra6SlpUGtViM1NRU+Pj72fDuy44wz3kadTcL4L+KK3aeKh6tR0Cmp5qJaM/SxZPmIVnjvp5K7bTQ+KswdWPpzI6WbSF/jypGtoHZ3w/A1Ryx+nYLP5d0/RCQ3Uj6/HRpmYmJi0K1bN5Pt4eHh2LBhg2HSvNWrVxtNmhcaGmrxazDMFM8eE62VppZOH+xBcpq08R3FfWjr8gQej9xTZHBQAPD1dMPdjGyLX8sW4cDcDMC//XkT35+6gZQCtRQMaj+cuo43t5yy+DUcHUyJiEpDNmGmLDDMyMfhS3cltTwU5qOqjKMzesLdzcVmxyxM323z+zvd7RL6iguXlr6X17rVQ6f6NThAl4hkTcrnt9OOmaHyQUrLT2nvuEnLzEXInCi88oQW0/uF2OSYhdljLaKCihtka+nA7bd6NWSIIaIKhWGG7EbqmBxb3HEj/jdgFwCm9wux+Ji+nq64l5Fj8eDaW+mZZd5F5+wDt4mIHMVpJ80jebNmiv12Wl9ofGwzt82aAwnIzs1D66Cq8PV0K3I//YR48wc0kXSX0JU7GaVaC0k/6d0Pp67j8KW7xS4dURCn+SciMsUxM2Rzlgy6LWrciSV3M1lqSKtaOHjpbrF1AMArnbXYcTrJ4ruLqni44l6Bu6kKWzGiJfo1CyjycVvcReZMA7eJiOxByuc3W2bI5kq7CnUVD1eb1PFN3PVi61B7uKJ/M398tj/B4tl0BYAcXfH5/7WvTmLXH+ZbaGy1KCSn+Sci+hfDDFlESreItWs/6T/o7xfT6mFL9x/m4Mc/kiTPpvsgK7fYx/ME8Opm02Bii9XKiYjIFAcAU4nsNZC34H7FfdDLVeHVqm21WjkRERljy0w5Ze0A08KsHchb3CrUgOkq1CV90MtR4a40W65WTkRE/2LLTDlUUkuKJYNHdXkCRy7dxbRvzxTZLaKAaesDYNkaROmZuXj/p/znttP6ltsP8ILvi6uVExHZB8NMOVPUOkTJqZkY/0UcBjTT4MCFu7j/qOi1jSxdO6i4bhH9LcSFF4zUe5CVi3UHr2DdwSvwV6swrO1jVr1fZ1cwmMhhtXIiIjlimClHLBlguvOPZJPH9F1GK0e2AgCzYag4+taHwi0+ubl5SLVgMG9SaiaW/Po3qni4IvWh5RPXOTNzwYST3hER2QfDTDli7biTgl1GQgjJYaKmt0rSStBF0X/AF9U1ZQkfVWWkZRZ/t5GlJvUIxtbj14psSSlKccFE32JV+FxpuCgkEZHVGGbKkdKMO9F3GUnlr1bhXkY2Jm6W1ppj7vXvPczBWz2DseXYNatD0fMdHsPKmMulqORfuXkCs/o3xsTNJ4tsSTE34V5JwaRPqD96hWg46R0RkY0wzJQjjhg42q+pBu/9ZLtbqutU98Tv73THx9F/Y9nei5Kf76KwXSBYtvci/NWqEgPL1D6NJQeT4haUJCIiaRhmypGSBpjaw9rfr9j0eNU9lXCppECn+tWtCjNhdavj27jrNjsHSamZ+Gx/ApaPaIWqnm5mAwuDCRGRY3GemXJEP8AUQLFzvBTFX62Cxkdp1XNtZcrXpxF1NgnttL6o4i5tWQNfT1fcepCFYW0DDWNvzHmmZS281DHI4uMKAO/9FI92Wl8uH0BE5IS40GQ5ZO1g3FUF7mYCrB+EWxr6iLByZCucT36Aj3/926rj6Nd3KnhbeOFb0D/59YKk4381tgNbYIiIyoiUz292M5VDhQeYXrnzEEv+96FtLqBU8XDFB4ObGj7kzd1tU1YK3lm17+1uWH8owaq1mvS3hL/VswHqVPcwO5alTnUPSccsrxP7ERHJHcNMOVV4HEdDjZdJQKni4YqXOmrxWvf6Rh/y+jA09etT+PbkjTKtG/j3zqoTiffwweCmGP+/liKpx1AA2HLsKn5/p7vZbiGpA6Y5My8RkXNimKkgzN0O3DqoKk4k3sOPf9wwtFwA+fPV/Bqf7JAgU9Ct9EwMalELq0a2wtwd8UhOk9YyUtLCjVIGTBdeS4qIiJwHw4wMWbK2kjkFW2uiziahy4d7TVpqAFjVrWMP+paQPqH+8Fa64vm1R606TlHdQwVn5C2OApyZl4jImTHMyExJi0haegxzSxY4S4gBgEoK4F5GtuH7OxlZVh+ruO6hombk1ZN6bomIqOwxzMhIcYtI6tdWKulDt7j1m5xJngAmbo7Dykr578ma8SqWLtxYsAsuOS0TKQ+y4OvpBo3anTPzEhHJAMOMTJS0iKT+DqBeIZpiP3ytXb/JEQSA6dvPoFeIRvKEgFIXbuTEd0RE8sVJ82SipBCiH+x65NJdHL50Fz+cuo7Dl+5Cl2f80S+324vvPczBsj0XJE8IqFGrLGqpIiIi+WPLjExYGkImbo7D/UdFTxQnx9uL1x+8gte6Bxc5vsVfrcKs/o1R1VPJhRuJiCoghhmZsDSEFAwygOl4Gn13jVy6moD896S/vZorThMRUWHsZpIJfQiR+pGt72SatzMeujwBl0oKDGwuv66Xgi1T+vEtXCeJiIgAhhnZKM0ikgUnj9PlCew4nWTz+uxNjt1jRERUNhhmZEQ/ZkSjNv5gt3R16YMX7+Dj6POy6mJSgLPvEhFR8ThmRmbMjRnJ1eXhhXWxJT532d6LZVChZVSulZCZk1fsPlJvryYiooqJYUaGCi9LMHfHOQdXJF3W/4JMFQ/XImce1nD2XSIisgDDjIwVNSOwHOgn+lNVroQvx7THnQdZqO6lBET+0gW8S4mIiCzFMCNTclmWoDgCQHJaFiopFBjUopajyyEiIpniAGCZktOyBCWR26zERETkXBhmZKo8BQDedk1ERKXBbiYb0OUJHLmcvyYSIBBWtzo6WDiZmy5PWDWb7eXbD2xQuWNZuqo1ERFRcRhmSinqbBKmbT9jdEfOsr2XUMXDFR8MblrsnThRZ5PMrjNU0h08ujyBTYcTbfMGyogCMBrfw9uuiYjIVtjNVApRZ5Mw/os4s7cW33+Yg/FfxCHqrPnZdvV3IhUe96JfS6mo5wH542XuFXE7szPxdHPBqpGtsMrMRH9c1ZqIiGyFLTNW0uUJzN0RX+J+c3ecQ68QjVHrQ3F3IulvWZ63M97keXpyGS/z2Qtt0Cm4OgBwcUgiIrIbhhkrxSakIDmt5FCRnJZlWPG54HOLuxOp4FpKBZ+nV91TaVXNZUU/FqZDgdoLTvRHRERkSwwzVpLSOlJ4X0ufq9+v4CDhK3cysPb3BMsLtZHCY16K2w/gWBgiIio7DDNWknI7ceGWFEufW9NbZXaQcFl7uVMd7DqbbDJQeWBzf+w4nWS0nUsQEBFRWWOYsVLroKoWt1agUANFO60v/NUqJKdmFvn8ml6uOHr5Lpb8dqF0hdpAzxANZvQPMTvmZWqfxhwLQ0REDsUwY6UTifcsXkrgzoMso+9dKikwq39jvLr5ZJHPufUgxymCjMZHaQgo5sa8cCwMERE5GsOMlaSMmSnYraTLE1i25wJW779sj7Jsbu7AJmxpISIip8YwYyVLx724uiiwbM/f2Pd3Fagqu2D9oQSkPsq1c3W28VbPYI59ISIip8cwYyX9uJeSBubm6AQOXkrBwUspZVRZ0Wb1b4zq3kpU91RiytencTOt6DE7/moVXuseXKb1ERERWYMzAFvJpZICcwaEFB7b69SqeysxqEUtdAqujrkDQwCYjE2G4n9fvLWaiIjkgmGmFPqE+uOVzlpHl2Gxgl1jfUL9sZLLDBARUTnAbqZS0OUJ7Dhd9BpKzqSap5vJ6tR9Qv25zAAREckew0wplLQsgTN5b1Co2ZDCW6uJiEju2M1UCnJZ8LFHoxro14zdRkREVD4xzJRCdS/nXvBRb8wT9RxdAhERkd2wm6kUYhPuOrqEYulXry48VoaIiKg8YcuMlaLOJuGT3y46tIbixuly9WoiIqoo2DJjBV2ewLRv/3BoDbP6N8YLYXVw7EoKvjiSiP1/30ZGts7wOFevJiKiioJhxgrL9lzEfQcvSVDdW4k9f93EvJ3xRndUVXF3xUud6uC17sFskSEiogqB3UwS6fIE1h9McHQZuHInAxO+iDO5NTz1UQ6W/HoB0fHJDqqMiIiobDHMSBSbkIL7j3Ic9voK5K+b9FXsVbPrKum3zdsZD11eUSsvERERlR8MMxI5w9wyw9o+huS0rCIfFwCSUjMRm+D4xS2JiIjsjWNmJCq4vpE9VPFwhaqyC5LTTEOTfjzMY77uFh3LGYIXERGRvTHMSNRO6wtfTzekZGTb5fgfDG5qWC8pOfURDl68g+g/byH1UQ7uP8rBx79egK+nm0XHsnfwIiIicgay6GZasWIFtFotVCoVWrdujQMHDjisFpdKCgy009IAb/UMRp9Qf8N6Se5uLvg27jpSC43RuVdCkNKPq+FkeUREVBE4fZjZunUrJk2ahJkzZ+LkyZN44okn0LdvX1y9etVhNeXk5dn8mP5qFV7rHmz4XpcnMG9nfLGDfM3hZHlERFTROH2YWbx4MV5++WWMGTMGjRs3xpIlSxAYGIiVK1c6rKabxQy+tVbh8GHpity+nq5G32vUKqwc2YqT5RERUYXh1GNmsrOzceLECUybNs1oe+/evXHo0CGzz8nKykJW1r9hIy0tzeZ1ebm52PR4+u6lgiwdvDvrqSbQ+KhwKz0TNb3zu5bYIkNERBWJU7fM3LlzBzqdDn5+fkbb/fz8kJxsflK4iIgIqNVqw1dgYKDN6woJUNv0eI/5ephss3TwrsZHhbB61TCoRS2E1avGIENERBWOU4cZPYXC+ANaCGGyTW/69OlITU01fF27ds3m9dy4/8imxzN3Z1Q7rS/81SoUFU04yJeIiCifU4eZ6tWrw8XFxaQV5tatWyatNXpKpRI+Pj5GX7akyxP49uQ/Nj2mr5fSZJtLJQXmDAgBAJNAw0G+RERE/3LqMOPm5obWrVsjOjraaHt0dDQ6duzokJpiE1KQnqkreUcJND7mu5T6hPpj5chW0KiNH+cgXyIion859QBgAJg8eTJeeOEFtGnTBmFhYfjss89w9epVjB8/3iH12HpW3ZK6ivqE+hsm0eMgXyIiIlNOH2aGDh2Ku3fvYv78+UhKSkJoaCh27dqFoKAgh9Rz5c5Dmx1LAcu6ivST6BEREZEphRCiXC+tnJaWBrVajdTU1FKPn9HlCXT6YI/ZdZOs8VbPBnizZ3DJOxIREVUwUj6/nXrMjLOJTUixWZABgDrVTW/JJiIiImkYZiSw9XgZLgRJRERUek4/ZsaZVPc0vYXaGgrk35HEOWKIiIhKjy0zUtjgBiLOEUNERGRbbJmR4M4DaQtMVvHIXwTy/sMcwzaNWoU5A0I4RwwREZGNMMxIIGWMyxvd6uPNXg0AgHPEEBER2RHDjATttL7wVlVGemZuiftuOHwFb/ZqwDliiIiI7IxjZiRwqaRAq8eqWrRvWmYuYhNS7FwRERERMcxIVMPMopBFsfWt3ERERGSKYUYCXZ7AgQu3Ld6f88gQERHZH8OMBLEJKbiZbtkdTZUUQOsgy7qkiIiIyHoMMxJI6TbKE8CJxHt2rIaIiIgAhhlJpHYbccwMERGR/THMSNA6qCqkTBHDMTNERET2xzAjwYnEe8gTlu3rz7WXiIiIygTDjARSuo1m9efaS0RERGWBYUYCKd1GVT3d7FgJERER6THMSNA6qKrFC2f/Gp9s11qIiIgoH8OMBCcS78HCITNYe/AKos4m2bUeIiIiYpiRROqt1vN2xkNn6YhhIiIisgrDjARSb7VOSs3kYpNERER2xjAjQTutL/zVKovHzQCcOI+IiMjeGGYkcKmkwJwBIZKew4nziIiI7IthRqI+of5YObIVND7FhxQFOHEeERFRWWCYsUKfUH8cnNYdb/UMNvu4vhtqzgBOnEdERGRvDDNWcqmkwJs9G2DVyFbwVxu30mjUKqwc2Qp9Qv0dVB0REVHFUdnRBchdn1B/9ArRIDYhBbfSM1HTO79riS0yREREZYNhxgZcKikQVq+ao8sgIiKqkNjNRERERLLGMENERESyxjBDREREssYwQ0RERLLGMENERESyxjBDREREssYwQ0RERLLGMENERESyxjBDREREslbuZwAWQgAA0tLSHFwJERERWUr/ua3/HC9OuQ8z6enpAIDAwEAHV0JERERSpaenQ61WF7uPQlgSeWQsLy8PN27cgLe3NxQK2yz+mJaWhsDAQFy7dg0+Pj42OabcVPRzUNHfP8BzAPAcVPT3D/Ac2PP9CyGQnp6OgIAAVKpU/KiYct8yU6lSJdSuXdsux/bx8amQF29BFf0cVPT3D/AcADwHFf39AzwH9nr/JbXI6HEAMBEREckawwwRERHJGsOMFZRKJebMmQOlUunoUhymop+Div7+AZ4DgOegor9/gOfAWd5/uR8ATEREROUbW2aIiIhI1hhmiIiISNYYZoiIiEjWGGaIiIhI1hhmrLBixQpotVqoVCq0bt0aBw4ccHRJZWbu3LlQKBRGXxqNxtFl2c3+/fsxYMAABAQEQKFQ4Pvvvzd6XAiBuXPnIiAgAO7u7ujatSvOnTvnmGLtpKRzMGrUKJNrokOHDo4p1g4iIiLQtm1beHt7o2bNmnj66adx/vx5o33K83Vgyfsv79fAypUr0axZM8PEcGFhYdi9e7fh8fL889cr6Rw4+hpgmJFo69atmDRpEmbOnImTJ0/iiSeeQN++fXH16lVHl1ZmmjRpgqSkJMPXmTNnHF2S3WRkZKB58+ZYtmyZ2ccXLVqExYsXY9myZTh27Bg0Gg169eplWBOsPCjpHABAnz59jK6JXbt2lWGF9rVv3z5MnDgRR44cQXR0NHJzc9G7d29kZGQY9inP14El7x8o39dA7dq18cEHH+D48eM4fvw4unfvjkGDBhkCS3n++euVdA4AB18DgiRp166dGD9+vNG2Ro0aiWnTpjmoorI1Z84c0bx5c0eX4RAAxHfffWf4Pi8vT2g0GvHBBx8YtmVmZgq1Wi1WrVrlgArtr/A5EEKI8PBwMWjQIIfU4wi3bt0SAMS+ffuEEBXvOij8/oWoeNeAEEJUrVpV/Pe//61wP/+C9OdACMdfA2yZkSA7OxsnTpxA7969jbb37t0bhw4dclBVZe/ChQsICAiAVqvFsGHDcPnyZUeX5BAJCQlITk42uh6USiW6dOlSoa4HAIiJiUHNmjXRoEEDjB07Frdu3XJ0SXaTmpoKAPD19QVQ8a6Dwu9fr6JcAzqdDlu2bEFGRgbCwsIq3M8fMD0Heo68Bsr9QpO2dOfOHeh0Ovj5+Rlt9/PzQ3JysoOqKlvt27fHpk2b0KBBA9y8eRMLFixAx44dce7cOVSrVs3R5ZUp/c/c3PWQmJjoiJIcom/fvnj22WcRFBSEhIQEzJo1C927d8eJEyccPiuorQkhMHnyZDz++OMIDQ0FULGuA3PvH6gY18CZM2cQFhaGzMxMeHl54bvvvkNISIghsFSEn39R5wBw/DXAMGMFhUJh9L0QwmRbedW3b1/Dv5s2bYqwsDDUq1cPGzduxOTJkx1YmeNU5OsBAIYOHWr4d2hoKNq0aYOgoCD89NNPGDx4sAMrs73XXnsNf/zxB37//XeTxyrCdVDU+68I10DDhg1x6tQp3L9/H99++y3Cw8Oxb98+w+MV4edf1DkICQlx+DXAbiYJqlevDhcXF5NWmFu3bpmk8orC09MTTZs2xYULFxxdSpnT38XF68GYv78/goKCyt018frrr2PHjh3Yu3cvateubdheUa6Dot6/OeXxGnBzc0P9+vXRpk0bREREoHnz5vjkk08qzM8fKPocmFPW1wDDjARubm5o3bo1oqOjjbZHR0ejY8eODqrKsbKysvDnn3/C39/f0aWUOa1WC41GY3Q9ZGdnY9++fRX2egCAu3fv4tq1a+XmmhBC4LXXXsP27duxZ88eaLVao8fL+3VQ0vs3p7xdA+YIIZCVlVXuf/7F0Z8Dc8r8GnDUyGO52rJli3B1dRVr164V8fHxYtKkScLT01NcuXLF0aWViSlTpoiYmBhx+fJlceTIEfHUU08Jb2/vcvv+09PTxcmTJ8XJkycFALF48WJx8uRJkZiYKIQQ4oMPPhBqtVps375dnDlzRgwfPlz4+/uLtLQ0B1duO8Wdg/T0dDFlyhRx6NAhkZCQIPbu3SvCwsJErVq1ys05mDBhglCr1SImJkYkJSUZvh4+fGjYpzxfByW9/4pwDUyfPl3s379fJCQkiD/++EPMmDFDVKpUSfzyyy9CiPL989cr7hw4wzXAMGOF5cuXi6CgIOHm5iZatWpldItieTd06FDh7+8vXF1dRUBAgBg8eLA4d+6co8uym7179woAJl/h4eFCiPzbcufMmSM0Go1QKpWic+fO4syZM44t2saKOwcPHz4UvXv3FjVq1BCurq7iscceE+Hh4eLq1auOLttmzL13AGL9+vWGfcrzdVDS+68I18Do0aMN/+fXqFFD9OjRwxBkhCjfP3+94s6BM1wDCiGEKJs2ICIiIiLb45gZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSIiIpI1hhkiIiKSNYYZIiIikjWGGSKSNYVCge+//97mx+3atSsmTZpk8+MSke0xzBCRRQ4dOgQXFxf06dNH8nPr1KmDJUuW2L4oC4waNQoKhQIKhQKurq6oW7cu/vOf/yAjI6PY523fvh3vvfdeGVVJRKXBMENEFlm3bh1ef/11/P7777h69aqjy5GkT58+SEpKwuXLl7FgwQKsWLEC//nPf8zum5OTAwDw9fWFt7d3WZZJRFZimCGiEmVkZGDbtm2YMGECnnrqKWzYsMFknx07dqBNmzZQqVSoXr06Bg8eDCC/uyYxMRFvvfWWoYUEAObOnYsWLVoYHWPJkiWoU6eO4ftjx46hV69eqF69OtRqNbp06YK4uDjJ9SuVSmg0GgQGBmLEiBF4/vnnDV1T+jrWrVuHunXrQqlUQghh0s2UlZWFqVOnIjAwEEqlEsHBwVi7dq3h8fj4ePTr1w9eXl7w8/PDCy+8gDt37kiulYikY5ghohJt3boVDRs2RMOGDTFy5EisX78eBZd1++mnnzB48GD0798fJ0+exG+//YY2bdoAyO+uqV27NubPn4+kpCQkJSVZ/Lrp6ekIDw/HgQMHcOTIEQQHB6Nfv35IT08v1ftxd3c3tMAAwMWLF7Ft2zZ8++23OHXqlNnnvPjii9iyZQuWLl2KP//8E6tWrYKXlxcAICkpCV26dEGLFi1w/PhxREVF4ebNm3juuedKVScRWaayowsgIue3du1ajBw5EkB+l82DBw/w22+/oWfPngCA999/H8OGDcO8efMMz2nevDmA/O4aFxcXeHt7Q6PRSHrd7t27G32/evVqVK1aFfv27cNTTz1l1XuJjY3F5s2b0aNHD8O27OxsfP7556hRo4bZ5/z999/Ytm0boqOjDe+5bt26hsdXrlyJVq1aYeHChYZt69atQ2BgIP7++280aNDAqlqJyDJsmSGiYp0/fx6xsbEYNmwYAKBy5coYOnQo1q1bZ9jn1KlTRuHAVm7duoXx48ejQYMGUKvVUKvVePDggeQxOz/++CO8vLygUqkQFhaGzp0749NPPzU8HhQUVGSQAfLfn4uLC7p06WL28RMnTmDv3r3w8vIyfDVq1AgAcOnSJUm1EpF0bJkhomKtXbsWubm5qFWrlmGbEAKurq64d+8eqlatCnd3d8nHrVSpklFXFQCjrh8g/06k27dvY8mSJQgKCoJSqURYWBiys7MlvVa3bt2wcuVKuLq6IiAgAK6urkaPe3p6Fvv8kt5fXl4eBgwYgMjISJPH/P39JdVKRNKxZYaIipSbm4tNmzbh//7v/3Dq1CnD1+nTpxEUFIQvv/wSANCsWTP89ttvRR7Hzc0NOp3OaFuNGjWQnJxsFGgKj1c5cOAA3njjDfTr1w9NmjSBUqm0alCtp6cn6tevj6CgIJMgY4mmTZsiLy8P+/btM/t4q1atcO7cOdSpUwf169c3+iopKBFR6THMEFGRfvzxR9y7dw8vv/wyQkNDjb6GDBliuJtnzpw5+OqrrzBnzhz8+eefOHPmDBYtWmQ4Tp06dbB//35cv37dEEa6du2K27dvY9GiRbh06RKWL1+O3bt3G71+/fr18fnnn+PPP//E0aNH8fzzz1vVClRaderUQXh4OEaPHo3vv/8eCQkJiImJwbZt2wAAEydOREpKCoYPH47Y2FhcvnwZv/zyC0aPHm0S4ojI9hhmiKhIa9euRc+ePaFWq00ee+aZZ3Dq1CnExcWha9eu+Prrr7Fjxw60aNEC3bt3x9GjRw37zp8/H1euXEG9evUMY1MaN26MFStWYPny5WjevDliY2NN5n5Zt24d7t27h5YtW+KFF17AG2+8gZo1a9r3TRdh5cqVGDJkCF599VU0atQIY8eONUy8FxAQgIMHD0Kn0+HJJ59EaGgo3nzzTajValSqxP9miexNIQp3WhMRERHJCP9kICIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWfv/3ieDkLbWYSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data plot\n",
    "plt.scatter(Y_train, train_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual vs Predicted Price on training data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f1b8f56d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on test data\n",
    "test_prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5a2b6097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8365766715026903\n"
     ]
    }
   ],
   "source": [
    "# r2 error\n",
    "test_error_score = metrics.r2_score(Y_test, test_prediction)\n",
    "print(test_error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "78a9b578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting actual vs prediction on test data\n",
    "plt.scatter(Y_test, test_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual vs Predicted Price on test data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06553d13",
   "metadata": {},
   "source": [
    "### Model training on Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "891c4a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e6ddaa5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1e665cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on training data\n",
    "training_prediction = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8def4867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "# r2 error score on training data\n",
    "training_error_score = metrics.r2_score(Y_train, training_prediction)\n",
    "print(training_error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8c2919b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data plot\n",
    "plt.scatter(Y_train, training_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual vs Predicted Price on training data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3e7bf17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on test data\n",
    "training_prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "295747f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8365766715026903\n"
     ]
    }
   ],
   "source": [
    "# r2 error score on test data\n",
    "test_error_score = metrics.r2_score(Y_test, test_prediction)\n",
    "print(test_error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "a75b56a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test data plot\n",
    "plt.scatter(Y_test, test_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual vs Predicted Price on training data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232693d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}