car_price_prediction_LinReg_LassReg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9580cb32",
   "metadata": {},
   "source": [
    "Importing the dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b9d5e97",
   "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": "6cf5ee71",
   "metadata": {},
   "source": [
    "Data collection and processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b80cd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data from csv filr to pandas dataframe\n",
    "car_dataset = pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca4f9ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting first 5 rows of the dataframe\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0782009c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and column \n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b957f5f1",
   "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": [
    "# getting some information about the dataset\n",
    "car_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0628a047",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of missing values\n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65171e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel_Type\n",
      "Petrol    239\n",
      "Diesel     60\n",
      "CNG         2\n",
      "Name: count, dtype: int64\n",
      "Seller_Type\n",
      "Dealer        195\n",
      "Individual    106\n",
      "Name: count, dtype: int64\n",
      "Transmission\n",
      "Manual       261\n",
      "Automatic     40\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking the distribution of categorical data\n",
    "print(car_dataset.Fuel_Type.value_counts())\n",
    "print(car_dataset.Seller_Type.value_counts())\n",
    "print(car_dataset.Transmission.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b2ec73",
   "metadata": {},
   "source": [
    " Encoding the categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7562889b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding \"Fuel_Type\" column\n",
    "car_dataset.replace({\"Fuel_Type\":{'Petrol':0, 'Diesel':1, 'CNG':2}}, inplace=True)\n",
    "\n",
    "# encoding \"Seller_Type\" column\n",
    "car_dataset.replace({\"Seller_Type\":{'Dealer':0, 'Individual':1}}, inplace=True)\n",
    "\n",
    "# encoding \"Transmission\" column\n",
    "car_dataset.replace({\"Transmission\":{'Manual':0, 'Automatic':1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd29bc74",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d223458",
   "metadata": {},
   "source": [
    "Splitting the data into Feature and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "413e168b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = car_dataset.drop([\"Car_Name\",\"Selling_Price\"], axis=1)\n",
    "Y = car_dataset[\"Selling_Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0359498c",
   "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": 18,
   "id": "4f2a7715",
   "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": "markdown",
   "id": "168142f9",
   "metadata": {},
   "source": [
    "splitting the data into trainig and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "994b675d",
   "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": "markdown",
   "id": "1c41be15",
   "metadata": {},
   "source": [
    "Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f241c3",
   "metadata": {},
   "source": [
    "1. Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e50d4bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading Linear regression Model\n",
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bbd3c11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the data to the model i.e is X_train and y_train\n",
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc9df875",
   "metadata": {},
   "source": [
    "Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d2bd6263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on training data\n",
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7e20a5cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R square Error: 0.8799451660493705\n"
     ]
    }
   ],
   "source": [
    "# R square error \n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R square Error:\", error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d27b18",
   "metadata": {},
   "source": [
    "Visualize the actual price and predicted prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "33c630c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Acatual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Vs Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6e19d9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on testing data\n",
    "test_data_prediction = lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ea5888e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R square Error: 0.836576671502687\n"
     ]
    }
   ],
   "source": [
    "# R square error \n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R square Error:\", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f0d4cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Acatual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Vs Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edd5deb",
   "metadata": {},
   "source": [
    "2. Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72382dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading Lasso regression Model\n",
    "lass_reg_model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dc1cbac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lass_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e57ed32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction = lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c509065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d0240775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ef98733c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Testing data\n",
    "test_data_prediction = lass_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e1328a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8709167941173195\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1617e7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cv6zCw8Md68c0b9682HtQGBq6deumPXv2aOfOnU7XWLx4canvUzhAePbs2Vc8r6Q/m/3799eBAwcUHR1dbI2Ff3a7desmSXrnnXfcrtFbOnToIJvNpjlz5pS4XtT111+vRo0aaefOncV+vrZt2yoyMrLI62rWrKnhw4frnnvu0d69e8s0cw/mQssOAs7x48f10Ucf6dZbb9Xtt99e7Dn//Oc/9fbbbys5OVn33HOP5s+fr5EjR2rv3r3q1q2b7Ha7vv76azVp0sTROtCsWTOtXbtWy5cvV1xcnCIjI3X99derb9++ioqKcsyWqVSpkhYsWKAjR444vWeHDh1UrVo1jRw5UklJSQoJCdE777xT5EvQ11y5Hx07dtSf/vQn3X///dq2bZtuvvlmRUREKDMzUxs3blSzZs00atQoffLJJ5o1a5YGDhyoBg0ayDAMLV26VKdOnVLPnj1LraVXr1669tprNXr0aGVlZTl1YUnSnDlztGbNGvXr109169ZVbm6uI8T26NHDK/en0CuvvKJOnTqpc+fOGjVqlOrXr6/Tp09r//79Wr58uWN23fjx4zVv3jz169dP06ZNc8zGcqU1oXPnzrr33ns1bdo0/fzzz+rfv79CQ0O1Y8cOhYeH69FHH5X0W+vZkiVL1KBBA1mtVjVr1kzjx4/XBx98oJtvvlmPPfaYmjdvLrvdrsOHD+vzzz/Xn//8Z914443q1auXbr75Zj3xxBM6c+aM2rZtq6+++srxi4EvXHPNNXrxxRf14IMPqkePHnrooYdUs2ZN7d+/Xzt37tRrr70m6eIvC3369NGtt96q4cOHq3bt2jpx4oS+/fZbpaam6j//+Y8k6cYbb1T//v3VvHlzVatWTd9++60WLlyo9u3bl2nWHkzGl6OjgbJ4+eWXS50BNGfOHEOS8cEHHxiGYRjnzp0znnnmGaNRo0ZG5cqVjejoaKN79+7Gpk2bHK9JS0szOnbsaISHhxuSnGa/pKSkGB06dDAiIiKM2rVrG0lJSca///3vIrOxNm3aZLRv394IDw83atSoYTz44INGamqqIcmYP3++4zx3Z2P98ssvVzyvcOZRSc9dPpPHlfthGIYxb94848YbbzQiIiKMsLAwo2HDhsZ9991nbNu2zTAMw/juu++Me+65x2jYsKERFhZm2Gw2o127dsaCBQtK/WyFnnzySUOSUadOHcfMmkKbN282fv/73xv16tUzQkNDjejoaKNLly7Gxx9/XOp1r3RPLiXJGDNmTLHPZWRkGCNGjDBq165thISEGDVq1DA6dOhgTJs2zem89PR0o2fPnobVajWioqKMBx54wPjoo49KnY1lGIZRUFBg/POf/zQSExONypUrGzabzWjfvr2xfPlyxzk//PCD0atXLyMyMtKQ5HSNX3/91fjrX/9qXH/99Y7XN2vWzHjssceMrKwsx3mnTp0yRowYYVStWtUIDw83evbsaXz33Xduzcb6+9//fsXzCmdcXT477NLnLp+9+NlnnzlmUYaHhxsJCQnGjBkznM7ZuXOncffddxsxMTFGSEiIERsba3Tv3t2YM2eO45xJkyYZbdu2NapVq2aEhoYaDRo0MB577DHj2LFjV6wZVweLYbiw3jwAAECAYswOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNdMvKmi32/XTTz8pMjLS5eX0AQCAbxmGodOnT3tkb0PTh52ffvpJderU8XUZAACgDI4cOXLFDWNdYfqwU7hvypEjR1SlShUfVwMAAFyRk5OjOnXqFLv/mbtMH3YKu66qVKlC2AEAIMB4YggKA5QBAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICp+TTsrF+/XgMGDFCtWrVksVi0bNkyp+cNw9Czzz6rWrVqKSwsTF27dtWePXt8UywAAAhIPg07Z86cUYsWLfTaa68V+/wLL7ygl156Sa+99pq2bt2q2NhY9ezZU6dPn67gSgEAQKDy6aKCffr0UZ8+fYp9zjAMvfzyy3rqqad0xx13SJLeeust1axZU4sXL9bDDz9ckaUCAIAA5bdjdjIyMpSVlaVevXo5joWGhqpLly7atGlTia/Ly8tTTk6O0wMAAHcV2A1tPnBcH6X9qM0HjqvAbvi6JJSR324XkZWVJUmqWbOm0/GaNWvq0KFDJb4uOTlZU6ZM8WptAABzW7k7U1OWpyszO9dxLM5mVdKABPVOjPNhZSgLv23ZKXT5nhiGYVxxn4zJkycrOzvb8Thy5Ii3SwQAmMjK3ZkatSjVKehIUlZ2rkYtStXK3Zk+qgxl5bdhJzY2VtJvLTyFjh49WqS151KhoaGOTT/Z/BMA4I4Cu6Epy9NVXIdV4bEpy9Pp0gowfht24uPjFRsbq1WrVjmOnT9/XuvWrVOHDh18WBkAwKxSMk4UadG5lCEpMztXKRknKq4olJtPx+z8+uuv2r9/v+PnjIwMpaWlKSoqSnXr1tX48eM1ffp0NWrUSI0aNdL06dMVHh6uwYMH+7BqAIBZHT1dctApy3nwDz4NO9u2bVO3bt0cP0+YMEGSNGzYMC1YsEBPPPGEzp07p9GjR+vkyZO68cYb9fnnnysyMtJXJQMATCwm0urR8+AfLIZhmLrjMScnRzabTdnZ2YzfAQBcUYHdUKcZa5SVnVvsuB2LpFibVRsndldwUMmTZVB+nvz+9tsxOwAAVLTgIIuSBiRIuhhsLlX4c9KABIJOgCHsAABwid6JcZo9tLVibc5dVbE2q2YPbc06OwHIbxcVBADAV3onxqlnQqxSMk7o6OlcxURa1S4+ihadAEXYAQCgGMFBFrVvGO3rMuABdGMBAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTI+wAAABTq+TrAgAAQOApsBtKyTiho6dzFRNpVbv4KAUHWXxdVrEIOwAAwC0rd2dqyvJ0ZWbnOo7F2axKGpCg3olxPqyseHRjAQAAl63cnalRi1Kdgo4kZWXnatSiVK3cnemjykpG2AEAAC4psBuasjxdRjHPFR6bsjxdBfbizvAdwg4AAHBJSsaJIi06lzIkZWbnKiXjRMUV5QLCDgAAcMnR0yUHnbKcV1EIOwAAwCUxkVaPnldRCDsAAMAl7eKjFGezqqQJ5hZdnJXVLj6qIssqFWEHAAC4JDjIoqQBCZJUJPAU/pw0IMHv1tsh7AAAAJf1TozT7KGtFWtz7qqKtVk1e2hrv1xnh0UFAQCAW3onxqlnQiwrKAMAAPMKDrKofcNoX5fhEsIOAAAmFkh7WHkLYQcAAJMKtD2svIUBygAAmFAg7mHlLYQdAABMJlD3sPIWwg4AACYTqHtYeQthBwAAkwnUPay8hbADAIDJBOoeVt5C2AEAwGQCdQ8rbyHsAABgMoG6h5W3EHYAAB5VYDe0+cBxfZT2ozYfOH7VzPjxN4G4h5W3sKggAMBjWMTOvwTaHlbeYjEMw9SROycnRzabTdnZ2apSpYqvywEA0ypcxO7yL5XCr9WrrTUB5ePJ72+6sQAA5cYidvBnhB0AQLmxiB38GWEHAFBuLGIHf0bYAQCUG4vYwZ8RdgAA5cYidvBnhB0AQLmxiB38mV+Hnfz8fP31r39VfHy8wsLC1KBBA02dOlV2u93XpQEALsMiduZipsUh/XpRwRkzZmjOnDl666231LRpU23btk3333+/bDabxo0b5+vyAACXYRE7czDb4pB+vahg//79VbNmTc2dO9dx7M4771R4eLgWLlzo0jVYVBAAANf5y+KQV82igp06ddLq1au1b98+SdLOnTu1ceNG9e3bt8TX5OXlKScnx+kBAPA8M3Vz4CKzLg7p191YEydOVHZ2tho3bqzg4GAVFBTo+eef1z333FPia5KTkzVlypQKrBIArj5m6+bARe4sDtm+YXTFFVZOft2ys2TJEi1atEiLFy9Wamqq3nrrLf3jH//QW2+9VeJrJk+erOzsbMfjyJEjFVgxAJhfYTfH5V+KWdm5GrUoVSt3Z/qoMpSXWReH9OuWnb/85S+aNGmSBg0aJElq1qyZDh06pOTkZA0bNqzY14SGhio0NLQiywSAq0Zp3RwWXezm6JkQy6DkAGTWxSH9umXn7NmzCgpyLjE4OJip5wDgI+yBZW5mXRzSr8POgAED9Pzzz+vTTz/VDz/8oA8//FAvvfSSfv/73/u6NAC4Kpm1mwMXmXVxSL8OOzNnztRdd92l0aNHq0mTJnr88cf18MMP67nnnvN1aQBwVTJrNwd+Y8bFIf16nR1PYJ0dAPCcAruhTjPWKCs7t9hxOxZd/FLcOLF7wP32D2cFdsOni0N68vvbrwcoAwD8S2E3x6hFqbJIToEnkLs5UFRwkCWgppdfiV93YwEA/I8ZuzlgbrTsAADcxh5YCCSEHQBAmZipmwPmRjcWAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtUq+LgAAAl2B3VBKxgkdPZ2rmEir2sVHKTjIYtr3BQINYQcAymHl7kxNWZ6uzOxcx7E4m1VJAxLUOzHOdO8LBCK6sQCgjFbuztSoRalOgUOSsrJzNWpRqlbuzjTV+wKBirADAGVQYDc0ZXm6jGKeKzw2ZXm6CuzFnRF47wsEMsIOAJRBSsaJIi0rlzIkZWbnKiXjhCneFwhkhB0AKIOjp0sOHGU5z9/fFwhkhB0AKIOYSKtHz/P39wUCGWEHAMqgXXyU4mxWlTTR26KLs6PaxUeZ4n2BQEbYAYAyCA6yKGlAgiQVCR6FPycNSPD4uje+el8gkBF2AKCMeifGafbQ1oq1OXcZxdqsmj20tdfWu/HV+wKBymIYhqnnJ+bk5Mhmsyk7O1tVqlTxdTkATIgVlAHP8+T3NysoA0A5BQdZ1L5h9FXzvkCgoRsLAACYGmEHAACYGmEHAACYmt+HnR9//FFDhw5VdHS0wsPD1bJlS23fvt3XZQEAgADh1wOUT548qY4dO6pbt25asWKFYmJidODAAVWtWtXXpQEAgADh12FnxowZqlOnjubPn+84Vr9+fd8VBAAAAo5fd2N9/PHHatu2rf7whz8oJiZGrVq10ptvvunrsgAAQAApc9g5f/689u7dq/z8fE/W4+TgwYOaPXu2GjVqpP/+978aOXKkxo4dq7fffrvE1+Tl5SknJ8fpAQAArl5uh52zZ8/qgQceUHh4uJo2barDhw9LksaOHau//e1vHi3ObrerdevWmj59ulq1aqWHH35YDz30kGbPnl3ia5KTk2Wz2RyPOnXqeLQmAAAQWNwOO5MnT9bOnTu1du1aWa2/7cvSo0cPLVmyxKPFxcXFKSEhwelYkyZNHAGrpPqys7MdjyNHjni0JgAAEFjcHqC8bNkyLVmyRDfddJMslt/2YElISNCBAwc8WlzHjh21d+9ep2P79u1TvXr1SnxNaGioQkNDPVoHAAAIXG637Pzyyy+KiYkpcvzMmTNO4ccTHnvsMW3ZskXTp0/X/v37tXjxYr3xxhsaM2aMR98HAACYl9th54YbbtCnn37q+Lkw4Lz55ptq37695yr7v/f68MMP9e677yoxMVHPPfecXn75ZQ0ZMsSj7wMAAMzL7W6s5ORk9e7dW+np6crPz9crr7yiPXv2aPPmzVq3bp3HC+zfv7/69+/v8esCgKsK7IZSMk7o6OlcxURa1S4+SsFBnm3JBuA9boedDh066KuvvtI//vEPNWzYUJ9//rlat26tzZs3q1mzZt6oEQB8ZuXuTE1Znq7M7FzHsTibVUkDEtQ7Mc6HlQFwlcUwDMPXRXhTTk6ObDabsrOzVaVKFV+XAyCArNydqVGLUnX5X5KFbTqzh7Ym8ABe4snvb7fH7Hz22Wf673//W+T4f//7X61YsaJcxQCAvyiwG5qyPL1I0JHkODZleboK7Kb+fREwBbfDzqRJk1RQUFDkuGEYmjRpkkeKAgBfS8k44dR1dTlDUmZ2rlIyTlRcUQDKxO2w8/333xdZ6E+SGjdurP3793ukKADwtaOnSw46ZTkPgO+4HXZsNpsOHjxY5Pj+/fsVERHhkaIAwNdiIq2ln+TGeQB8x+2wc9ttt2n8+PFOqyXv379ff/7zn3Xbbbd5tDgA8JV28VGKs1lV0gRziy7OymoXH1WRZQEoA7fDzt///ndFRESocePGio+PV3x8vJo0aaLo6Gj94x//8EaNAFDhgoMsShpwscv+8sBT+HPSgATW2wECQJmmnhuGoVWrVmnnzp0KCwtT8+bNdfPNN3ujvnJj6jmA8mCdHcA3PPn9zTo7AFAKVlAGKp4nv79dWkH51Vdf1Z/+9CdZrVa9+uqrVzx37Nix5SoIAPxNcJBF7RtG+7oMAGXkUstOfHy8tm3bpujoaMXHx5d8MYul2JlavkTLDgBaZoDAU+EtOxkZGcX+OwD4O8bcAHBrNtaFCxfUoEEDpaene6seAPCYwr2tLl8JOSs7V6MWpWrl7kwfVQagIrkVdkJCQpSXlyeLheZfAP6Nva0AFHJ7nZ1HH31UM2bMUH5+vjfqAQCPYG8rAIVcGrNzqa+//lqrV6/W559/rmbNmhXZImLp0qUeKw4Ayoq9rQAUcjvsVK1aVXfeeac3agEAj2FvKwCF3A478+fP90YdAOBRhXtbZWXnFjtuxyIplr2tgKuCy2N27Ha7/v73v6tjx45q166dnnzySeXm0vwLwD+xtxWAQi6HnRkzZmjSpEmKiIhQXFycXnrpJVZLBuDXeifGafbQ1oq1OXdVxdqsmj20NevsAFcJl/fGuv766zVu3DiNHj1akrRy5UoNHDhQ586d8+up6KygDIAVlIHA45ONQK1Wq/bt26e6detKurjzudVq1cGDB1W7du1yFeFNhB0AAAKPJ7+/Xe7GOn/+vMLCwhw/WywWVa5cWXl5eeUqAAAAwJvcmo319NNPKzw83PHz+fPn9fzzz8tmszmOvfTSS56rDgAAoJxcDjs333yz9u7d63SsQ4cOTruc+/PYHQAAcHVyOeysXbvWi2UAAAB4h9t7YwEAAAQSwg4AADA1wg4AADA1wg4AADA1wg4AADA1l2ZjffPNNy5fsHnz5mUuBgAAwNNcCjstW7aUxWKRYRilrqVTUFDgkcIAAAA8waVurIyMDB08eFAZGRn64IMPFB8fr1mzZmnHjh3asWOHZs2apYYNG+qDDz7wdr0AAABucallp169eo5//8Mf/qBXX31Vffv2dRxr3ry56tSpo6effloDBw70eJEAAABl5fYA5V27dik+Pr7I8fj4eKWnp3ukKAAAAE9xO+w0adJE06ZNU25uruNYXl6epk2bpiZNmni0OAAVq8BuaPOB4/oo7UdtPnBcBXbD1yUBQLm5teu5JM2ZM0cDBgxQnTp11KJFC0nSzp07ZbFY9Mknn3i8QAAVY+XuTE1Znq7M7N9+kYmzWZU0IEG9E+N8WBkAlI/FMAy3f3U7e/asFi1apO+++06GYSghIUGDBw9WRESEN2osl5ycHNlsNmVnZ6tKlSq+LgfwSyt3Z2rUolRd/pdB4dzL2UNbE3gAVChPfn+XKewEEsIOcGUFdkOdZqxxatG5XJzNqo0Tuys46MpLTwCAp3jy+7tMKygvXLhQnTp1Uq1atXTo0CFJ0j//+U999NFH5SoGgHdcaSxOSsaJKwYdScrMzlVKxglvlwkAXuF22Jk9e7YmTJigPn366OTJk45FBKtVq6aXX37Z0/UBKKeVuzPVacYa3fPmFo17L033vLlFnWas0crdmZKkrJwrB51Crp4HAP7G7bAzc+ZMvfnmm3rqqadUqdJv45vbtm2rXbt2ebQ4AOVTOBbn8pabrOxcjVqUqpW7M3Xi1zyXruXqeQDgb9wOOxkZGWrVqlWR46GhoTpz5oxHigKudp6YAl5gNzRleXqRQceSHMemLE9X1bAQl64XFVHZ7RoAwB+4PfU8Pj5eaWlpTqsqS9KKFSuUkJDgscKAq5WnpoCXNhbH0MWxOKfOXXDperG2MJffGwD8idth5y9/+YvGjBmj3NxcGYahlJQUvfvuu0pOTta///1vb9QIXDVKmgJe2O3kzhTwo6ddG2MTdU2o4mzWUmdjtYuPcul6AOBv3A47999/v/Lz8/XEE0/o7NmzGjx4sGrXrq1XXnlFgwYN8kaNwFWhtG4niy52O/VMiHVpCnhMpNWl942tcrHVqLiQpf9736QBCUw7BxCwyjT1/KGHHtKhQ4d09OhRZWVl6ciRI3rggQc8XRtwVXG128nVKeDt4qMUZ7OqpIhi0W8tNr0T4zR7aGvF2ZwDUpzNyoKCAAKe2y073bt319KlS1W1alVVr17dcTwnJ0cDBw7UmjVrPFog4AkFdkMpGSd09HSuYiIvfsH7W0uFq91Orp4XHGRxtNhYJKdWm8JPfmmLTe/EOPVMiPX7+wQA7nK7ZWft2rU6f/58keO5ubnasGGDR4oqSXJysiwWi8aPH+/V94G5lLbOjL9wtdvJ1fMkOVpsYi9rsYktocUmOMii9g2jdXvL2mrfMJqgA8AUXG7Z+eabbxz/np6erqysLMfPBQUFWrlypWrXru3Z6i6xdetWvfHGG2revLnX3gPm48kBv95W2O2UlZ1b4tiZ2DIMFKbFBsDVzuWw07JlS1ksFlksFnXv3r3I82FhYZo5c6ZHiyv066+/asiQIXrzzTc1bdo0r7wHzMfTA369zd1uJ3ev3b5htCfKBICA43I3VkZGhg4cOOCYbp6RkeF4/Pjjj8rJydGIESO8UuSYMWPUr18/9ejRo9Rz8/LylJOT4/TA1cnTA34rgrvdTgCA0rncslO4iKDdbvdaMcV57733lJqaqq1bt7p0fnJysqZMmeLlqhAIPD3gt6LQ7QQAnuX2AOXk5GTNmzevyPF58+ZpxowZHimq0JEjRzRu3DgtWrRIVqtrgzInT56s7Oxsx+PIkSMerQmBwxsDfisKA4UBwHPcDjv/+te/1Lhx4yLHmzZtqjlz5nikqELbt2/X0aNH1aZNG1WqVEmVKlXSunXr9Oqrr6pSpUqOHdcvFRoaqipVqjg9cHVyZ50ZAIB5uR12srKyFBdXdNxAjRo1lJnp2am8t9xyi3bt2qW0tDTHo23bthoyZIjS0tIUHBzs0feDuRQO+JVUbOAxJD3dj5WBAcDs3F5UsE6dOvrqq68UHx/vdPyrr75SrVq1PFaYJEVGRioxMdHpWEREhKKjo4scB4pTOOD38o01Cz33abqCgsTAXwAwMbfDzoMPPqjx48frwoULjinoq1ev1hNPPKE///nPHi8QKK/eiXGy2w2NXryjyHP+uN4OAMCz3A47TzzxhE6cOKHRo0c7VlK2Wq2aOHGiJk+e7PECL7d27VqvvwfMpcBu6LlPvy32OX9cbwcA4FkWwzCKW3OtVL/++qu+/fZbhYWFqVGjRgoNDfV0bR6Rk5Mjm82m7OxsBitfpTYfOK573txS6nnvPnQTC+8BgJ/w5Pe32y07ha655hrdcMMN5XpzoCIE6no7AADPcCns3HHHHVqwYIGqVKmiO+6444rnLl261COFAZ4SyOvtAADKz6WwY7PZZLFYHP8OBBJvbbAJAAgMZR6zEygYswPpt93PpeI32GQ2FgD4F09+f7u9qCAQiNhgEwCuXi51Y7Vq1crRjVWa1NTUchUEeAsbbALA1cmlsDNw4EDHv+fm5mrWrFlKSEhQ+/btJUlbtmzRnj17NHr0aK8UCXhK4QabAICrh0thJykpyfHvDz74oMaOHavnnnuuyDnsMA4AAPyN2wOUbTabtm3bpkaNGjkd//7779W2bVtlZ2d7tMDyYoAyAACBx6cDlMPCwrRx48Yixzdu3CirlXVKAACAf3F7BeXx48dr1KhR2r59u2666SZJF8fszJs3T88884zHCwQAACgPt8POpEmT1KBBA73yyitavHixJKlJkyZasGCB7r77bo8XCAAAUB4sKggAAPyOzxcVPHXqlP7973/rySef1IkTJyRdXF/nxx9/LFcxgK8U2A1tPnBcH6X9qM0HjqvAburfAQDgquJ2N9Y333yjHj16yGaz6YcfftCDDz6oqKgoffjhhzp06JDefvttb9QJeM3K3Zmasjxdmdm/7XoeZ7MqaUACKysDgAm43bIzYcIEDR8+XN9//73T7Ks+ffpo/fr1Hi0O8LbCPbMuDTqSlJWdq1GLUrVyd6aPKgMAeIrbYWfr1q16+OGHixyvXbu2srKyPFIUUBEK7IamLE8vdif0wmNTlqfTpQUAAc7tsGO1WpWTk1Pk+N69e1WjRg2PFAVUhJSME0VadC5lSMrMzlVKxomKKwoA4HFuh53bb79dU6dO1YULFyRJFotFhw8f1qRJk3TnnXd6vEDAW46eLjnolOU8AIB/cjvs/OMf/9Avv/yimJgYnTt3Tl26dNF1112nyMhIPf/8896oEfCKmEjXVvx29TwAgH9yezZWlSpVtHHjRq1Zs0apqamy2+1q3bq1evTo4Y36AK9pFx+lOJtVWdm5xY7bsUiKtVnVLj6qoksDAHiQW2EnPz9fVqtVaWlp6t69u7p37+6tugCvCw6yKGlAgkYtSpVFcgo8lv/7Z9KABAUHWYp5NQAgULjVjVWpUiXVq1dPBQUF3qoHqFC9E+M0e2hrxdqcu6pibVbNHtqadXYAwATc3i5i/vz5+s9//qNFixYpKsr/m/fZLgKuKLAbSsk4oaOncxUTebHrihYdAPAdT35/ux12WrVqpf379+vChQuqV6+eIiIinJ5PTU0tV0GeRtgBACDwePL72+0ByrfffrssFn7jBQAAgYFdzwEAgN/xya7nZ8+e1ZgxY1S7dm3FxMRo8ODBOnbsWLneHAAAwNtcDjtJSUlasGCB+vXrp0GDBmnVqlUaNWqUN2sDAAAoN5fH7CxdulRz587VoEGDJElDhw5Vx44dVVBQoODgYK8VCAAAUB4ut+wcOXJEnTt3dvzcrl07VapUST/99JNXCgMAAPAEl1t2CgoKVLlyZecXV6qk/Px8jxd1NWO9FwAAPMvlsGMYhoYPH67Q0FDHsdzcXI0cOdJprZ2lS5d6tsKryMrdmZqyPF2Z2b/tsh1nsyppQAIr+QIAUEYuh51hw4YVOTZ06FCPFnM1W7k7U6MWpRbZkDIrO1ejFqWydQEAAGXkctiZP3++N+u4qhXYDU1Znl7sztuGLm5KOWV5unomxNKlBQCAm9zaCBTekZJxwqnr6nKGpMzsXKVknKi4ogAAMAm3t4uAe1wZcHz0dMlBpyznAQCA3xB2vMjVAccxkVaXrufqeQAA4Dd0Y3lJ4YDjy7unMv9vwPHK3ZmOY+3ioxRbpeQgY9HFkNQuPqrU9y2wG9p84Lg+SvtRmw8cV4Hd1FufAQBQKlp2vOBKA46li2NwJi/d5RhwvCo9S7n5BcWeW9jhlTQgodTByUxdBwCgKFp2vKC0AceSdPLsBb225ntHC9CpsxeKPa9qeIhL085LaknKKqYlCQCAqwktO17g6kDi+V/9IGtIUIktQJIUWilIPRNir3gdpq4DAFAyWna8wNWBxKfOXVBWTt4Vz8nKySt1yjlT10vGGCYAAC07XtAuPkpVw0J06lzxXVPuKq2liKnrxWMMEwBAomXHK4KDLLq/Y7zHrldaSxFT14tiDBMAoBBhx0se6X6dqoaHXPGcqIgQ2cJKblxzdcp5u/goxdmsKmk0jjtT182gtDFM0sUxTHRpAcDVgbDjJcFBFv3tjmZXPOfEmQvKPpdf7HPuTDkPDrIoaUCC0+vKch2zYAwTAOBShB0v6p0YpzlDWyvO5n73UazN6tZO570T4zR7aGvFXvZe7l7HDBjDBAC4FAOUvaxnQqwirSHafOC47IZd7239n06cOV/i+VXDQ/T6Pa11U8Not1tieifGqWdCbKl7cQUyV/YaYwwTAOBSfh12kpOTtXTpUn333XcKCwtThw4dNGPGDF1//fW+Ls0lxc0GKs2psxcUFGQpc0AJDrKofcPoMr3W37k6u6pwDFNWdm6x43YsutjidbWMYQKAq51fd2OtW7dOY8aM0ZYtW7Rq1Srl5+erV69eOnPmjK9LK1VJs4FcQfdKUe7MrmIMEwDgUn7dsrNy5Uqnn+fPn6+YmBht375dN998s4+qKl1pe2OVhu4VZ2VZIbpwDNPlLUGxrLMDAFcdvw47l8vOzpYkRUWV3P2Ql5envLzfViXOycnxel2Xc2VvrOLQvVI8d2ZXXdqFdzWMYQIAlC5gwo5hGJowYYI6deqkxMTEEs9LTk7WlClTKrCyosrSDUX3SsnKM7vKzGOYAACu8esxO5d65JFH9M033+jdd9+94nmTJ09Wdna243HkyJEKqvA3ZemGuhqniLuK2VUAgPIIiJadRx99VB9//LHWr1+va6+99ornhoaGKjQ0tIIqK54rs4FqVgnVi3e31LFf8+heKQWzqwAA5eHXLTuGYeiRRx7R0qVLtWbNGsXHe26/KW9yZTbQs7c1Vcfrquv2lrXVvgxr6lxNmF0FACgPvw47Y8aM0aJFi7R48WJFRkYqKytLWVlZOnfunK9LKxUrGnsW9xMAUFYWwzD8djdEi6X439Tnz5+v4cOHu3SNnJwc2Ww2ZWdnq0qVKh6szjWurPgL13E/AeDq4Mnvb78es+PHOcxlzAbyLO4nAMBdft2NBQAAUF6EHQAAYGp+3Y1lJow1AQDANwg7FcDV3boBAIDn0Y3lZe7s1g0AADyPsONFpe3WLV3crbvAHvizzgAA8FeEHS9yZ7duAADgHYQdL/oiPcul88qySzoAAHANYcdLVu7O1NyvfnDpXHbrBgDAe5iN5QWFY3VKw27dAAB4Hy07HlZgN7Tgq4wrjtUpZIjdugEA8DZadjyouPV0rmREx/qsswMAgJcRdjykcD0ddyaR90yI9Vo9AADgIsKOB1xpPZ3iMFYHAICKw5gdDyhtPZ1LFY7OYawOAAAVg5YdD3BnnZxY9sQCAKBCEXY8wNV1cp7u10TDO8bTogMAQAWiG8sD2sVHKc5mVUkRxqKLu5wTdAAAqHiEHQ8IDrIoaUCCJBUJPIzRAQDAtwg7HtI7MU6zh7ZWrM25SyvWZtXsoa0ZowMAgI8wZseDeifGqWdCrFIyTujo6VzFRF6cXk6LDgAAvkPYKacCu1Ek3LRvGO3rsgAAwP8h7JRDcdtDxDG1HAAAv8KYnTIq3B7i8sUEs7JzNWpRqlbuzvRRZQAA4FKEnTK40vYQhcemLE9Xgd2dnbIAAIA3EHbKoLTtIQxJmdm5Ssk4UXFFAQCAYhF2ysDV7SHc2UYCAAB4B2GnDFzdHsLV8wAAgPcQdsrA1e0h2sVHVWRZAACgGISdMmB7CAAAAgdhp4zYHgIAgMDAooLlwPYQAAD4P8JOOQUHWdgeAgAAP0Y3FgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMLWACDuzZs1SfHy8rFar2rRpow0bNvi6JAAAECD8PuwsWbJE48eP11NPPaUdO3aoc+fO6tOnjw4fPuzr0gAAQACwGIZh+LqIK7nxxhvVunVrzZ4923GsSZMmGjhwoJKTk0t9fU5Ojmw2m7Kzs1WlShVvlgoAADzEk9/fft2yc/78eW3fvl29evVyOt6rVy9t2rSp2Nfk5eUpJyfH6QEAAK5efh12jh07poKCAtWsWdPpeM2aNZWVlVXsa5KTk2Wz2RyPOnXqVESpAADAT/l12ClksVicfjYMo8ixQpMnT1Z2drbjceTIkYooEQAA+KlKvi7gSqpXr67g4OAirThHjx4t0tpTKDQ0VKGhoRVRHgAACAB+3bJTuXJltWnTRqtWrXI6vmrVKnXo0MFHVQEAgEDi1y07kjRhwgTde++9atu2rdq3b6833nhDhw8f1siRI31dGgAACAB+H3b++Mc/6vjx45o6daoyMzOVmJiozz77TPXq1fN1aQAAIAD4/To75cU6OwAABJ6rZp0dAACA8iLsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU6vk6wICVYHdUErGCR09nauYSKvaxUcpOMji67IAAMBlCDtlsHJ3pqYsT1dmdq7jWJzNqqQBCeqdGOfDygAAwOXoxnLTyt2ZGrUo1SnoSFJWdq5GLUrVyt2ZPqoMAAAUh7DjhgK7oSnL02UU81zhsSnL01VgL+4MAADgC4QdN6RknCjSonMpQ1Jmdq5SMk5UXFEAAOCKCDtuOHq65KBTlvMAAID3EXbcEBNp9eh5AADA+wg7bmgXH6U4m1UlTTC36OKsrHbxURVZFgAAuALCjhuCgyxKGpAgSUUCT+HPSQMSWG8HAAA/QthxU+/EOM0e2lqxNueuqlibVbOHtmadHQAA/AyLCpZB78Q49UyIZQVlAAACAGGnjIKDLGrfMNrXZQAAgFLQjQUAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEzN9CsoG4YhScrJyfFxJQAAwFWF39uF3+PlYfqwc/r0aUlSnTp1fFwJAABw1/Hjx2Wz2cp1DYvhicjkx+x2u3766SdFRkbKYnFvo86cnBzVqVNHR44cUZUqVbxUoXlx/8qH+1c+3L/y4f6VD/ev/LKzs1W3bl2dPHlSVatWLde1TN+yExQUpGuvvbZc16hSpQp/WMuB+1c+3L/y4f6VD/evfLh/5RcUVP7hxQxQBgAApkbYAQAApkbYuYLQ0FAlJSUpNDTU16UEJO5f+XD/yof7Vz7cv/Lh/pWfJ++h6QcoAwCAqxstOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOyWYNWuW4uPjZbVa1aZNG23YsMHXJQWE5ORk3XDDDYqMjFRMTIwGDhyovXv3+rqsgJWcnCyLxaLx48f7upSA8uOPP2ro0KGKjo5WeHi4WrZsqe3bt/u6rICQn5+vv/71r4qPj1dYWJgaNGigqVOnym63+7o0v7R+/XoNGDBAtWrVksVi0bJly5yeNwxDzz77rGrVqqWwsDB17dpVe/bs8U2xfuhK9+/ChQuaOHGimjVrpoiICNWqVUv33XeffvrpJ7ffh7BTjCVLlmj8+PF66qmntGPHDnXu3Fl9+vTR4cOHfV2a31u3bp3GjBmjLVu2aNWqVcrPz1evXr105swZX5cWcLZu3ao33nhDzZs393UpAeXkyZPq2LGjQkJCtGLFCqWnp+vFF18s93LzV4sZM2Zozpw5eu211/Ttt9/qhRde0N///nfNnDnT16X5pTNnzqhFixZ67bXXin3+hRde0EsvvaTXXntNW7duVWxsrHr27OnYt/Fqd6X7d/bsWaWmpurpp59Wamqqli5dqn379um2225z/40MFNGuXTtj5MiRTscaN25sTJo0yUcVBa6jR48akox169b5upSAcvr0aaNRo0bGqlWrjC5duhjjxo3zdUkBY+LEiUanTp18XUbA6tevnzFixAinY3fccYcxdOhQH1UUOCQZH374oeNnu91uxMbGGn/7298cx3Jzcw2bzWbMmTPHBxX6t8vvX3FSUlIMScahQ4fcujYtO5c5f/68tm/frl69ejkd79WrlzZt2uSjqgJXdna2JCkqKsrHlQSWMWPGqF+/furRo4evSwk4H3/8sdq2bas//OEPiomJUatWrfTmm2/6uqyA0alTJ61evVr79u2TJO3cuVMbN25U3759fVxZ4MnIyFBWVpbT90loaKi6dOnC90kZZWdny2KxuN1Sa/qNQN117NgxFRQUqGbNmk7Ha9asqaysLB9VFZgMw9CECRPUqVMnJSYm+rqcgPHee+8pNTVVW7du9XUpAengwYOaPXu2JkyYoCeffFIpKSkaO3asQkNDdd999/m6PL83ceJEZWdnq3HjxgoODlZBQYGef/553XPPPb4uLeAUfmcU931y6NAhX5QU0HJzczVp0iQNHjzY7c1VCTslsFgsTj8bhlHkGK7skUce0TfffKONGzf6upSAceTIEY0bN06ff/65rFarr8sJSHa7XW3bttX06dMlSa1atdKePXs0e/Zswo4LlixZokWLFmnx4sVq2rSp0tLSNH78eNWqVUvDhg3zdXkBie+T8rtw4YIGDRoku92uWbNmuf16ws5lqlevruDg4CKtOEePHi2SzlGyRx99VB9//LHWr1+va6+91tflBIzt27fr6NGjatOmjeNYQUGB1q9fr9dee015eXkKDg72YYX+Ly4uTgkJCU7HmjRpog8++MBHFQWWv/zlL5o0aZIGDRokSWrWrJkOHTqk5ORkwo6bYmNjJV1s4YmLi3Mc5/vEPRcuXNDdd9+tjIwMrVmzxu1WHYnZWEVUrlxZbdq00apVq5yOr1q1Sh06dPBRVYHDMAw98sgjWrp0qdasWaP4+HhflxRQbrnlFu3atUtpaWmOR9u2bTVkyBClpaURdFzQsWPHIssd7Nu3T/Xq1fNRRYHl7NmzCgpy/moIDg5m6nkZxMfHKzY21un75Pz581q3bh3fJy4qDDrff/+9vvjiC0VHR5fpOrTsFGPChAm699571bZtW7Vv315vvPGGDh8+rJEjR/q6NL83ZswYLV68WB999JEiIyMdLWQ2m01hYWE+rs7/RUZGFhnfFBERoejoaMY9ueixxx5Thw4dNH36dN19991KSUnRG2+8oTfeeMPXpQWEAQMG6Pnnn1fdunXVtGlT7dixQy+99JJGjBjh69L80q+//qr9+/c7fs7IyFBaWpqioqJUt25djR8/XtOnT1ejRo3UqFEjTZ8+XeHh4Ro8eLAPq/YfV7p/tWrV0l133aXU1FR98sknKigocHynREVFqXLlyq6/URlniJne66+/btSrV8+oXLmy0bp1a6ZOu0hSsY/58+f7urSAxdRz9y1fvtxITEw0QkNDjcaNGxtvvPGGr0sKGDk5Oca4ceOMunXrGlar1WjQoIHx1FNPGXl5eb4uzS99+eWXxf6dN2zYMMMwLk4/T0pKMmJjY43Q0FDj5ptvNnbt2uXbov3Ile5fRkZGid8pX375pVvvYzEMwyh7JgMAAPBvjNkBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBENAsFouWLVvm8et27dpV48eP9/h1AVQ8wg4Al2zatEnBwcHq3bu326+tX7++Xn75Zc8X5YLhw4fLYrHIYrEoJCREDRo00OOPP64zZ85c8XVLly7Vc889V0FVAvAmwg4Al8ybN0+PPvqoNm7cqMOHD/u6HLf07t1bmZmZOnjwoKZNm6ZZs2bp8ccfL/bcCxcuSLq4905kZGRFlgnASwg7AEp15swZvf/++xo1apT69++vBQsWFDnn448/Vtu2bWW1WlW9enXdcccdki52Bx06dEiPPfaYo4VFkp599lm1bNnS6Rovv/yy6tev7/h569at6tmzp6pXry6bzaYuXbooNTXV7fpDQ0MVGxurOnXqaPDgwRoyZIij66uwjnnz5qlBgwYKDQ2VYRhFurHy8vL0xBNPqE6dOgoNDVWjRo00d+5cx/Pp6enq27evrrnmGtWsWVP33nuvjh075natADyPsAOgVEuWLNH111+v66+/XkOHDtX8+fN16bZ6n376qe644w7169dPO3bs0OrVq9W2bVtJF7uDrr32Wk2dOlWZmZnKzMx0+X1Pnz6tYcOGacOGDdqyZYsaNWqkvn376vTp0+X6PGFhYY4WHEnav3+/3n//fX3wwQdKS0sr9jX33Xef3nvvPb366qv69ttvNWfOHF1zzTWSpMzMTHXp0kUtW7bUtm3btHLlSv3888+6++67y1UnAM+o5OsCAPi/uXPnaujQoZIudgn9+uuvWr16tXr06CFJev755zVo0CBNmTLF8ZoWLVpIutgdFBwcrMjISMXGxrr1vt27d3f6+V//+peqVaumdevWqX///mX6LCkpKVq8eLFuueUWx7Hz589r4cKFqlGjRrGv2bdvn95//32tWrXK8ZkbNGjgeH727Nlq3bq1pk+f7jg2b9481alTR/v27dPvfve7MtUKwDNo2QFwRXv37lVKSooGDRokSapUqZL++Mc/at68eY5z0tLSnMKDpxw9elQjR47U7373O9lsNtlsNv36669ujxn65JNPdM0118hqtap9+/a6+eabNXPmTMfz9erVKzHoSBc/X3BwsLp06VLs89u3b9eXX36pa665xvFo3LixJOnAgQNu1QrA82jZAXBFc+fOVX5+vmrXru04ZhiGQkJCdPLkSVWrVk1hYWFuXzcoKMipK0ySU9eSdHEm1S+//KKXX35Z9erVU2hoqNq3b6/z58+79V7dunXT7NmzFRISolq1aikkJMTp+YiIiCu+vrTPZ7fbNWDAAM2YMaPIc3FxcW7VCsDzaNkBUKL8/Hy9/fbbevHFF5WWluZ47Ny5U/Xq1dM777wjSWrevLlWr15d4nUqV66sgoICp2M1atRQVlaWU+C5fLzMhg0bNHbsWPXt21dNmzZVaGhomQb9RkRE6LrrrlO9evWKBB1XNGvWTHa7XevWrSv2+datW2vPnj2qX7++rrvuOqdHaUEKgPcRdgCU6JNPPtHJkyf1wAMPKDEx0elx1113OWYjJSUl6d1331VSUpK+/fZb7dq1Sy+88ILjOvXr19f69ev1448/OsJK165d9csvv+iFF17QgQMH9Prrr2vFihVO73/ddddp4cKF+vbbb/X1119ryJAhZWpFKq/69etr2LBhGjFihJYtW6aMjAytXbtW77//viRpzJgxOnHihO655x6lpKTo4MGD+vzzzzVixIgiIQ9AxSPsACjR3Llz1aNHD9lstiLP3XnnnUpLS1Nqaqq6du2q//znP/r444/VsmVLde/eXV9//bXj3KlTp+qHH35Qw4YNHWNjmjRpolmzZun1119XixYtlJKSUmTtm3nz5unkyZNq1aqV7r33Xo0dO1YxMTHe/dAlmD17tu666y6NHj1ajRs31kMPPeRYmLBWrVr66quvVFBQoFtvvVWJiYkaN26cbDabgoL4axbwNYtxeac5AACAifArBwAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMLX/Dz5VhSXuPkcsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0aa23f4",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}