car_price_pred.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\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": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the dataset\n",
    "car_dataset = pd.read_csv('archive/train-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              Name    Location  Year   \n",
       "0           0            Maruti Wagon R LXI CNG      Mumbai  2010  \\\n",
       "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
       "2           2                      Honda Jazz V     Chennai  2011   \n",
       "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
       "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine   \n",
       "0              72000       CNG       Manual      First  26.6 km/kg   998 CC  \\\n",
       "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
       "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
       "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
       "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
       "\n",
       "       Power  Seats  New_Price  Price  \n",
       "0  58.16 bhp    5.0        NaN   1.75  \n",
       "1  126.2 bhp    5.0        NaN  12.50  \n",
       "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
       "3  88.76 bhp    7.0        NaN   6.00  \n",
       "4  140.8 bhp    5.0        NaN  17.74  "
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking first 5 data\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6019, 14)"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the numbers of rows and columns\n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6019 entries, 0 to 6018\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         6019 non-null   int64  \n",
      " 1   Name               6019 non-null   object \n",
      " 2   Location           6019 non-null   object \n",
      " 3   Year               6019 non-null   int64  \n",
      " 4   Kilometers_Driven  6019 non-null   int64  \n",
      " 5   Fuel_Type          6019 non-null   object \n",
      " 6   Transmission       6019 non-null   object \n",
      " 7   Owner_Type         6019 non-null   object \n",
      " 8   Mileage            6017 non-null   object \n",
      " 9   Engine             5983 non-null   object \n",
      " 10  Power              5983 non-null   object \n",
      " 11  Seats              5977 non-null   float64\n",
      " 12  New_Price          824 non-null    object \n",
      " 13  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 658.5+ KB\n"
     ]
    }
   ],
   "source": [
    "car_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              0\n",
       "Name                    0\n",
       "Location                0\n",
       "Year                    0\n",
       "Kilometers_Driven       0\n",
       "Fuel_Type               0\n",
       "Transmission            0\n",
       "Owner_Type              0\n",
       "Mileage                 2\n",
       "Engine                 36\n",
       "Power                  36\n",
       "Seats                  42\n",
       "New_Price            5195\n",
       "Price                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there are null values or not\n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display unique values of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel_Type\n",
      "Diesel      3205\n",
      "Petrol      2746\n",
      "CNG           56\n",
      "LPG           10\n",
      "Electric       2\n",
      "Name: count, dtype: int64\n",
      "Transmission\n",
      "Manual       4299\n",
      "Automatic    1720\n",
      "Name: count, dtype: int64\n",
      "Owner_Type\n",
      "First             4929\n",
      "Second             968\n",
      "Third              113\n",
      "Fourth & Above       9\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(car_dataset.Fuel_Type.value_counts())\n",
    "print(car_dataset.Transmission.value_counts())\n",
    "print(car_dataset.Owner_Type.value_counts())\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing the string format data with numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as machine learning model will easily predict and test the data in numerical wise data\n",
    "car_dataset.replace({'Fuel_Type':{'Diesel':0,'Petrol':1,'CNG':2,'LPG':3,'Electric':4}},inplace=True)\n",
    "car_dataset.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)\n",
    "car_dataset.replace({'Owner_Type':{'First':0,'Second':1,'Third':2,'Fourth & Above':3}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              Name    Location  Year   \n",
       "0           0            Maruti Wagon R LXI CNG      Mumbai  2010  \\\n",
       "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
       "2           2                      Honda Jazz V     Chennai  2011   \n",
       "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
       "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
       "\n",
       "   Kilometers_Driven  Fuel_Type  Transmission  Owner_Type     Mileage   \n",
       "0              72000          2             0           0  26.6 km/kg  \\\n",
       "1              41000          0             0           0  19.67 kmpl   \n",
       "2              46000          1             0           0   18.2 kmpl   \n",
       "3              87000          0             0           0  20.77 kmpl   \n",
       "4              40670          0             1           1   15.2 kmpl   \n",
       "\n",
       "    Engine      Power  Seats  New_Price  Price  \n",
       "0   998 CC  58.16 bhp    5.0        NaN   1.75  \n",
       "1  1582 CC  126.2 bhp    5.0        NaN  12.50  \n",
       "2  1199 CC   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
       "3  1248 CC  88.76 bhp    7.0        NaN   6.00  \n",
       "4  1968 CC  140.8 bhp    5.0        NaN  17.74  "
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the rows which contain null values in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_dataset = car_dataset.dropna(subset=['Mileage'])\n",
    "car_dataset = car_dataset.dropna(subset=['Engine'])\n",
    "car_dataset = car_dataset.dropna(subset=['Power'])\n",
    "car_dataset = car_dataset.dropna(subset=['New_Price'])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting  the data into training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we storing the columns used for training in X(features), by removing some columns we don't have use and one Price column \n",
    "# because price column is our target.\n",
    "\n",
    "# X is input features and Y is target variable \n",
    "X = car_dataset.drop(['Name','Price','Location'],axis=1)\n",
    "Y = car_dataset['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Toyota Innova Crysta 2.8 GX AT 8S</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2016</td>\n",
       "      <td>36000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.36 kmpl</td>\n",
       "      <td>2755 CC</td>\n",
       "      <td>171.5 bhp</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21 Lakh</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Maruti Ciaz Zeta</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2018</td>\n",
       "      <td>25692</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.56 kmpl</td>\n",
       "      <td>1462 CC</td>\n",
       "      <td>103.25 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.65 Lakh</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Mitsubishi Pajero Sport 4X4</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014</td>\n",
       "      <td>110000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5 kmpl</td>\n",
       "      <td>2477 CC</td>\n",
       "      <td>175.56 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.01 Lakh</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>BMW 3 Series 320d</td>\n",
       "      <td>Kochi</td>\n",
       "      <td>2014</td>\n",
       "      <td>32982</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.69 kmpl</td>\n",
       "      <td>1995 CC</td>\n",
       "      <td>190 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.87 Lakh</td>\n",
       "      <td>18.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                               Name Location  Year   \n",
       "2            2                       Honda Jazz V  Chennai  2011  \\\n",
       "7            7  Toyota Innova Crysta 2.8 GX AT 8S   Mumbai  2016   \n",
       "10          10                   Maruti Ciaz Zeta    Kochi  2018   \n",
       "15          15        Mitsubishi Pajero Sport 4X4    Delhi  2014   \n",
       "20          20                  BMW 3 Series 320d    Kochi  2014   \n",
       "\n",
       "    Kilometers_Driven  Fuel_Type  Transmission  Owner_Type     Mileage   \n",
       "2               46000          1             0           0   18.2 kmpl  \\\n",
       "7               36000          0             1           0  11.36 kmpl   \n",
       "10              25692          1             0           0  21.56 kmpl   \n",
       "15             110000          0             0           0   13.5 kmpl   \n",
       "20              32982          0             1           0  22.69 kmpl   \n",
       "\n",
       "     Engine       Power  Seats   New_Price  Price  \n",
       "2   1199 CC    88.7 bhp    5.0   8.61 Lakh   4.50  \n",
       "7   2755 CC   171.5 bhp    8.0     21 Lakh  17.50  \n",
       "10  1462 CC  103.25 bhp    5.0  10.65 Lakh   9.95  \n",
       "15  2477 CC  175.56 bhp    7.0  32.01 Lakh  15.00  \n",
       "20  1995 CC     190 bhp    5.0  47.87 Lakh  18.55  "
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Unnamed: 0  Year  Kilometers_Driven  Fuel_Type  Transmission   \n",
      "2              2  2011              46000          1             0  \\\n",
      "7              7  2016              36000          0             1   \n",
      "10            10  2018              25692          1             0   \n",
      "15            15  2014             110000          0             0   \n",
      "20            20  2014              32982          0             1   \n",
      "...          ...   ...                ...        ...           ...   \n",
      "5999        5999  2016              10000          1             0   \n",
      "6002        6002  2011              38000          1             0   \n",
      "6005        6005  2016              37208          0             0   \n",
      "6010        6010  2013              33746          1             0   \n",
      "6014        6014  2014              27365          0             0   \n",
      "\n",
      "      Owner_Type     Mileage   Engine       Power  Seats   New_Price  \n",
      "2              0   18.2 kmpl  1199 CC    88.7 bhp    5.0   8.61 Lakh  \n",
      "7              0  11.36 kmpl  2755 CC   171.5 bhp    8.0     21 Lakh  \n",
      "10             0  21.56 kmpl  1462 CC  103.25 bhp    5.0  10.65 Lakh  \n",
      "15             0   13.5 kmpl  2477 CC  175.56 bhp    7.0  32.01 Lakh  \n",
      "20             0  22.69 kmpl  1995 CC     190 bhp    5.0  47.87 Lakh  \n",
      "...          ...         ...      ...         ...    ...         ...  \n",
      "5999           0  17.57 kmpl  1193 CC    88.7 bhp    5.0   7.77 Lakh  \n",
      "6002           0  16.09 kmpl  1598 CC   103.5 bhp    5.0  11.91 Lakh  \n",
      "6005           0   24.3 kmpl  1248 CC    88.5 bhp    5.0   9.93 Lakh  \n",
      "6010           0   18.5 kmpl  1198 CC    86.8 bhp    5.0   6.63 Lakh  \n",
      "6014           0   28.4 kmpl  1248 CC      74 bhp    5.0   7.88 Lakh  \n",
      "\n",
      "[823 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2        4.50\n",
      "7       17.50\n",
      "10       9.95\n",
      "15      15.00\n",
      "20      18.55\n",
      "        ...  \n",
      "5999     4.00\n",
      "6002     3.25\n",
      "6005     7.43\n",
      "6010     3.20\n",
      "6014     4.75\n",
      "Name: Price, Length: 823, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_Test = train_test_split(X,Y,test_size=0.1,random_state=2)\n",
    "\n",
    "# train_test_split() is a function from a machine learning library, such as scikit-learn, used for data splitting.\n",
    "\n",
    "# test_size=0.1 specifies the proportion of the data to be allocated for testing. In this case, it's set to 0.1,\n",
    "#  which means 10% of the data will be used for testing, and the remaining 90% will be used for training.\n",
    "\n",
    "# random_state=2 sets the random seed to ensure reproducibility. Using the same random state value will \n",
    "# result in the same train-test split every time the code is run."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training sets (X_train and Y_train) will contain 90% of the original data, and the testing sets (X_test and Y_Test) will contain 10% of the original data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****************************************************************************************************************************"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the units of columns data to make it numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Mileage'] = X_train['Mileage'].str.replace(' kmpl', '').str.replace(' km/kg', '').astype(float)\n",
    "X_train['Engine'] = X_train['Engine'].str.replace(' CC', '').astype(int)\n",
    "X_train['Power'] = X_train['Power'].str.replace(' bhp', '').astype(float)\n",
    "X_train['New_Price'] = X_train['New_Price'].str.replace(' Lakh', '').str.replace(' Cr', '').astype(float)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the training data in machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting using linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the average accuracy using R squared Error techique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.7232804687189405\n"
     ]
    }
   ],
   "source": [
    "error_score = metrics.r2_score(Y_train,training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the data using scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "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 Price vs Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test['Mileage'] = X_test['Mileage'].str.replace(' kmpl', '').str.replace(' km/kg', '').astype(float)\n",
    "X_test['Engine'] = X_test['Engine'].str.replace(' CC', '').astype(int)\n",
    "X_test['Power'] = X_test['Power'].str.replace(' bhp', '').astype(float)\n",
    "X_test['New_Price'] = X_test['New_Price'].str.replace(' Lakh', '').str.replace(' Cr', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data_prediction = lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.5076563542915116\n"
     ]
    }
   ],
   "source": [
    "error_score = metrics.r2_score(Y_Test,testing_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0FEuWLMHChQtx9uxZjB49Gp6ensjLy8PWrVsxc+ZMLFiwAM7OzliyZAmefvppDB48GOPGjUNeXh7WrFmj13iW999/H/369UNkZCRmzpyJkJAQnD17Ft999512+4yoqCgAwN/+9jeMHz8ezs7OGDVqlNVqtBR9PvuyZcuwZ88e9O7dGzNmzEB4eDiKioqQlZWFH374AUVFRTarn5oRG8zQIrKIUaNGCVdXV1FRUdHgOVOmTBHOzs7aaabXrl0Ts2fPFu3atRMuLi6iffv2YvLkyTrTULdt2ybCw8OFk5NTnamtb7/9tmjXrp2Qy+Wib9++4vDhw3WmgqvVavHGG2+IoKAgIZfLxX333Sd27NghJk+eLIKCgnTqgwFTwd98881Gz9NM99ZMga7vuTunGm/fvl3cf//9ws3NTXh5eYno6GixYcMGnXOOHj0qxo4dK3x9fYVcLhdBQUHiscceE+np6Y3Wo1FaWirc3NwEALFu3bo6zy9ZskRER0cLb29v4ebmJjp37iyWLl0qqqurG72uZir4pk2bGj1PMxX8ypUr9T6/efNm0a9fP+Hu7i7c3d1F586dRUJCgjh58qTOeR9//LEICQkRcrlc9OzZU+zbt6/O976+6dBCCJGTkyPGjBkjvL29haurq7jnnnvEq6++qnPO66+/Ltq1ayccHBzqfK/MWWNDgoKCmpyS39jPoimfvbCwUCQkJIgOHToIZ2dnoVQqxZAhQ8SqVauarJtICCFkQtzRtklERETUjHHMDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSYrkF/FTq9W4dOkSPD09zbrTMxEREVmOEAJlZWVo27YtHBwMa4uRfLi5dOkSOnToYOsyiIiIyAgXLlxA+/btDXqN5MONp6cngNqb4+XlZeNqiIiISB+lpaXo0KGD9ve4ISQfbjRdUV5eXgw3REREzYwxQ0o4oJiIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCRF8isU059UaoHMvCJcLquEv6crokN84OjAzUSJiEhaGG5aiLScfCSl5iK/pFJ7LFDhisRR4YiLCLRhZURERObFbqkWIC0nH7PWZekEGwAoKKnErHVZSMvJt1FlRERE5sdwI3EqtUBSai5EPc9pjiWl5kKlru8MIiKi5ofhRuIy84rqtNjcTgDIL6lEZl6R9YoiIiKyIIYbibtc1nCwMeY8IiIie8dwI3H+nq5mPY+IiMjeMdxIXHSIDwIVrmhowrcMtbOmokN8rFkWERGRxTDcSJyjgwyJo8IBoE7A0TxOHBXO9W6IiEgyGG5agLiIQKRMioRSodv1pFS4ImVSJNe5ISIiSeEifi1EXEQghoYruUIxERFJHsNNC+LoIENMqK+tyyAiIrIou+mWWrZsGWQyGebOnas9VllZiYSEBPj6+sLDwwPx8fEoLCy0XZFERERk9+wi3Bw6dAiffPIJunXrpnN83rx5SE1NxaZNm7B3715cunQJY8eOtVGVRERE1BzYPNyUl5dj4sSJ+PTTT9G6dWvt8ZKSEqxevRrvvPMOBg8ejKioKKxZswa//PILDhw4YMOKiYiIyJ7ZPNwkJCRg5MiRiI2N1Tl+5MgR1NTU6Bzv3LkzOnbsiIyMDGuXSURERM2ETQcUb9y4EVlZWTh06FCd5woKCuDi4gJvb2+d4wEBASgoKGjwmlVVVaiqqtI+Li0tNVu9REREZP9s1nJz4cIFzJkzB+vXr4erq/mW/k9OToZCodB+dejQwWzXJiIiIvtns3Bz5MgRXL58GZGRkXBycoKTkxP27t2L999/H05OTggICEB1dTWKi4t1XldYWAilUtngdRcuXIiSkhLt14ULFyz8SYiIiMie2KxbasiQITh+/LjOsalTp6Jz5854+eWX0aFDBzg7OyM9PR3x8fEAgJMnT+L8+fOIiYlp8LpyuRxyudyitRMREZH9slm48fT0REREhM4xd3d3+Pr6ao9Pnz4d8+fPh4+PD7y8vPDcc88hJiYGffr0sUXJRERE1AzY9QrFK1asgIODA+Lj41FVVYVhw4bh448/tnVZREREZMdkQghh6yIsqbS0FAqFAiUlJfDy8rJ1OURERKQHU35/23ydGyIiIiJzYrghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJcbJ1AURERNT8qNQCmXlFuFxWCX9PV0SH+MDRQWbrsgAw3BAREZGB0nLykZSai/ySSu2xQIUrEkeFIy4i0IaV1WK3FBEREektLScfs9Zl6QQbACgoqcSsdVlIy8m3UWV/YrghIiIivajUAkmpuRD1PKc5lpSaC5W6vjOsh+GGiIiI9JKZV1SnxeZ2AkB+SSUy84qsV1Q9GG6IiIhIL5fLGg42xpxnKQw3REREpBd/T1eznmcpDDdERESkl+gQHwQqXNHQhG8ZamdNRYf4WLOsOhhuiIiISC+ODjIkjgoHgDoBR/M4cVS4zde7YbghIiIivcVFBCJlUiSUCt2uJ6XCFSmTIu1inRsu4kdk5+x5FVAiapniIgIxNFxpt/9vYrghsmP2vgooEbVcjg4yxIT62rqMerFbishONYdVQImI7BHDDZEdai6rgBIR2SOGGyI71FxWASUiskcMN0R2qLmsAkpEZI8YbojsUHNZBZSIyB4x3BDZoeayCigRkT1iuCGyQ81lFVAiInvEcENkp5rDKqBERPaIi/gR2TF7XwWUiMgeMdwQ2Tl7XgWUiMgesVuKiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCTFydYFEBFR86dSC2TmFeFyWSX8PV0RHeIDRweZrcuiForhhoiITJKWk4+k1Fzkl1RqjwUqXJE4KhxxEYE2rIxaKnZLkaSp1AIZZ65hW/ZFZJy5BpVa2LokIklJy8nHrHVZOsEGAApKKjFrXRbScvJtVBm1ZGy5IcniX5NElqVSCySl5qK+PxkEABmApNRcDA1XsouKrIotNyRJ/GuSyPIy84rq/Dd2OwEgv6QSmXlF1iuKCAw3JEFN/TUJ1P41yS4qItNcLms42BhzHpG52DTcpKSkoFu3bvDy8oKXlxdiYmKwc+dO7fOVlZVISEiAr68vPDw8EB8fj8LCQhtWTM0B/5oksg5/T1eznkdkLjYNN+3bt8eyZctw5MgRHD58GIMHD8YjjzyC//znPwCAefPmITU1FZs2bcLevXtx6dIljB071pYlkx1oapAw/5okso7oEB8EKlzR0GgaGWrHuUWH+FizLCLbDigeNWqUzuOlS5ciJSUFBw4cQPv27bF69Wp8+eWXGDx4MABgzZo16NKlCw4cOIA+ffrYomSyMX0GCfOvSSLrcHSQIXFUOGaty4IM0OkK1gSexFHhHExMVmc3Y25UKhU2btyIiooKxMTE4MiRI6ipqUFsbKz2nM6dO6Njx47IyMiwYaVkK/oOEuZfk0TWExcRiJRJkVAqdP9YUCpckTIpkjMTySZsPhX8+PHjiImJQWVlJTw8PLB161aEh4cjOzsbLi4u8Pb21jk/ICAABQUFDV6vqqoKVVVV2selpaWWKp2syNApp/xrksh64iICMTRcyRWKyW7YvOXmnnvuQXZ2Ng4ePIhZs2Zh8uTJyM3NNfp6ycnJUCgU2q8OHTqYsVqyFUMHCfOvSSLrcnSQISbUF4/0aIeYUF8GG7Ipm7fcuLi4oFOnTgCAqKgoHDp0CO+99x7GjRuH6upqFBcX67TeFBYWQqlUNni9hQsXYv78+drHpaWlDDgSYMwgYf41SUTUMtk83NxJrVajqqoKUVFRcHZ2Rnp6OuLj4wEAJ0+exPnz5xETE9Pg6+VyOeRyubXKJSsxdpCw5q9JIiJqOWwabhYuXIjhw4ejY8eOKCsrw5dffokff/wR33//PRQKBaZPn4758+fDx8cHXl5eeO655xATE8OZUi2QZpBwQUllveNuZKjtcuIgYSIismm4uXz5Mp588knk5+dDoVCgW7du+P777zF06FAAwIoVK+Dg4ID4+HhUVVVh2LBh+Pjjj21ZMtmIMYOEVWrBLikiohZIJoSQ9Br0paWlUCgUKCkpgZeXl63LIRPpuxkmN80kImreTPn9zXBDzU5TLTKa9XAa+sGeFxuG2YPDDG7FYUsQEZH1MNw0guGmZVGpBfot393otHEAUHq5YvHD+rfisCWIiMi6TPn9bfN1bojMqan1cDQKSnVXNW6MvisjExGRfWC4oXo1tTmlvTJ0M8yk1NxGP1tTKyPrcw0iIrIuu1vnhmyvOXfBGLIZ5u2rGje0Fo4hKyNzPR0iIvvAlhvS0dy7YJraNLM+jbX2GLMyMhER2RbDDWlJoQtGsx6OIRpr7TF2ZWQiIrIdhhvSMnRzSnul3TTTq/FtOGSo7W5rbFXjplqC9LkGERFZF8MNaUmpCyYuIhA/vzIE82Lvrvf5hlY1vtPtLUF3nqXvNYiIyLoYbkhLal0wjg4yzIkNw8pJkQhU6NasVLgiZVKkXgOktS1BJlyDiIish7OlSEuqm1PGRQRiaLjSpNWFzXENIiKyDoYb0jJmc8rmwtFBZvJUbXNcg4iILI/dUqSDXTBERNTcseWG6mAXDBERNWcMN1SvltgFw12/iYikgeGGCM17ywkiItLFMTfU4jX3LSeIiEgXww21aFLYcoKIiHQx3FCLJpUtJ4iI6E8MN9SiSWnLCSIiqsVwQy2a1LacICIihhtq4bjrNxGR9DDcUIvGXb+JiKSH4YZaPG45QUQkLVzEjwjccoKISEoYboj+pyVuOUFEJEUMNy0E900iIqKWguGmBeC+SURE1JIYNaD4n//8J/r27Yu2bdvi3LlzAIB3330X27ZtM2txZDrum0RERC2NweEmJSUF8+fPx4gRI1BcXAyVSgUA8Pb2xrvvvmvu+sgE9rxvkkotkHHmGrZlX0TGmWvcu4mIiMzG4G6pDz74AJ9++ilGjx6NZcuWaY/37NkTCxYsMGtxZBpD9k2y5kBadpMREZElGdxyk5eXh/vuu6/OcblcjoqKCrMUReZhj/smsZuMiIgszeBwExISguzs7DrH09LS0KVLF3PURGZib/sm2XM3GRERSYfB3VLz589HQkICKisrIYRAZmYmNmzYgOTkZHz22WeWqJGMpNk3qaCkst5AIUPtKrzW2jfJXrvJiIhIWgwON0899RTc3Nzw97//HTdu3MDjjz+Otm3b4r333sP48eMtUSMZSbNv0qx1WZABOgHHlH2TjF0zxx67yYiISHqMWudm4sSJmDhxIm7cuIHy8nL4+/ubuy4yE82+SXcO4FUaOYDXlMHA9tZNRkRE0iQTQhg0wCEvLw+3bt1CWFiYzvHTp0/D2dkZwcHB5qzPZKWlpVAoFCgpKYGXl5ety7EZc6xQrBkMfOcPjOYqTW0yqVIL9Fu+u8lusv0vD+bqyURELZwpv78NHlA8ZcoU/PLLL3WOHzx4EFOmTDH0cmQlmn2THunRDjGhvkZ1RZk6GFjTTQb8GYg0TOkmIyIiup3B4ebo0aPo27dvneN9+vSpdxYVSYMhg4Ebo+kmUyp0u56UCtcmW36IiIj0YfCYG5lMhrKysjrHS0pKtKsVk/SYczBwXEQghoYruZEnERFZhMHhZsCAAUhOTsaGDRvg6OgIAFCpVEhOTka/fv3MXiDZB2MHAzc01kfTTWYs7nJOREQNMTjcLF++HAMGDMA999yD/v37AwB++uknlJaWYvfu3WYvkOyDMWvmGDuzqqngwu0biIioMQbPlgKAS5cu4cMPP8SxY8fg5uaGbt26Yfbs2fDxsc5icIbgbCnz0cyWAupfM+f2MTPGzqxqKriYOmOLiIiaB1N+fxsVbpoThhvz0qfVRDPlu6EByA1N+W4quHz0eCRe/y7X4OsSEVHzY8rvb726pX799VdERETAwcEBv/76a6PnduvWzaACqHnRZzCwMdssNDXVXAbg1W05uFZRbdB1iYio5dEr3PTo0QMFBQXw9/dHjx49IJPJUF+Dj0wm44ypFqCpwcDGzKzSJxA1FmyMeX8iIpImvcJNXl4e/Pz8tP8maowxM6vMGUi4fQMRUcumV7gJCgoCANTU1CApKQmvvvoqQkJCLFoYNV/GzKzSN5D4uDvjekWNXexyTkRE9smgFYqdnZ2xefNmS9VCzZRKLZBx5hq2ZV9ExplrAGDwNguaQNTQMGAZagcuL3kkwqDrEhFRy2Pw9gujR4/Gt99+a4FSqDlKy8lHv+W7MeHTA5izMRsTPj2Afstr1zsyZJsFffedGtGtLbdvICKiRhk8FXzJkiV4++23MWTIEERFRcHd3V3n+eeff96sBZqKU8EtR581ZwzdZkHfBfq4QjERkbRZdZ2bxsbayGQy/PHHHwYVYGkMN5Zh7Fo2+l6bwYWIqGWz+Do3t+NsKQKMW8tGX6buO0VERC2bQeHmwIEDSE1NRXV1NYYMGYK4uDhL1UV2zpy7hBMREZmT3uHmm2++wbhx4+Dm5gZnZ2e88847WL58ORYsWGDJ+shOGbtLOBERkaXpPVsqOTkZM2bMQElJCa5fv44lS5bgjTfesGRtZMf0nbrNNWeIiMja9A43J0+exIIFC+Do6AgAeOGFF1BWVobLly9brDiyX/pO3eZAYCIisja9w82NGzd0Riu7uLjA1dUV5eXlFimM7F9cRCDXnCEiIrtj0IDizz77DB4eHtrHt27dwtq1a9GmTRvtMXtb54YsS59dwomIiKxJ73VugoODIZM1/guL69wQERGROVhlnZuzZ88aWhcRERGR1Rm8txQRERGRPbNpuElOTkavXr3g6ekJf39/jB49GidPntQ5p7KyEgkJCfD19YWHhwfi4+NRWFhoo4qJiIjI3tk03OzduxcJCQk4cOAAdu3ahZqaGjz44IOoqKjQnjNv3jykpqZi06ZN2Lt3Ly5duoSxY8fasGoiIiKyZwZvnGlJV65cgb+/P/bu3YsBAwagpKQEfn5++PLLL/GXv/wFAPDbb7+hS5cuyMjIQJ8+fZq8JgcUExERNT9W3TjTkkpKSgAAPj61q9oeOXIENTU1iI2N1Z7TuXNndOzYscFwU1VVhaqqKu3j0tJSC1dNTeEu30REZE16hRtDAoKxrSNqtRpz585F3759ERERAQAoKCiAi4sLvL29dc4NCAhAQUFBvddJTk5GUlKSUTWQ+aXl5CMpNVdnB/FAhSsSR4VzkT8iIrIIvcKNt7d3k2vcaKhUKqMKSUhIQE5ODvbv32/U6zUWLlyI+fPnax+XlpaiQ4cOJl2TjJOWk49Z67JwZ79nQUklZq3L4irGRERkEXqFmz179mj/ffbsWbzyyiuYMmUKYmJiAAAZGRn44osvkJycbFQRs2fPxo4dO7Bv3z60b99ee1ypVKK6uhrFxcU6rTeFhYVQKpX1Xksul0MulxtVB5mPSi2QlJpbJ9gAgEDt/lNJqbkYGq5kFxUREZmVXuFm4MCB2n+/9tpreOeddzBhwgTtsYcffhhdu3bFqlWrMHnyZL3fXAiB5557Dlu3bsWPP/6IkJAQneejoqLg7OyM9PR0xMfHA6jdwPP8+fPaYEX2KTOvSKcr6k4CQH5JJTLzihAT6mu9woiISPIMngqekZGBnj171jnes2dPZGZmGnSthIQErFu3Dl9++SU8PT1RUFCAgoIC3Lx5EwCgUCgwffp0zJ8/H3v27MGRI0cwdepUxMTE6DVTimznclnDwcaY84iIiPRlcLjp0KEDPv300zrHP/vsM4PHtqSkpKCkpASDBg1CYGCg9uurr77SnrNixQo89NBDiI+Px4ABA6BUKrFlyxZDyyYr8/d0bfokA84jIiLSl8Hr3PzrX/9CfHw8OnXqhN69ewMAMjMzcfr0aWzevBkjRoywSKHGainr3NjbdGuVWqDf8t0oKKmsd9yNDIBS4Yr9Lw/mmBsiIqrDquvcjBgxAqdOnUJKSgp+++03AMCoUaPwzDPPcFaSjdjjdGtHBxkSR4Vj1rosyACdgKOJMomjwhlsiIjI7OxqhWJLkHrLTUPTrTWRIWVSJIaGK23WqmOPwYuIiOyfKb+/jQo3P/30Ez755BP88ccf2LRpE9q1a4d//vOfCAkJQb9+/Qy9nEVJOdxoun4ampUkA+DdyhlyJwcUlP65arO1w4W9dZkREZH9M+X3t8EDijdv3oxhw4bBzc0NWVlZ2q0OSkpK8MYbbxh6OTKBPtOtr9+o0Qk2wJ+L6KXl5Fu4wlqODjLEhPrikR7tEBPqy2BDREQWZXC4WbJkCVauXIlPP/0Uzs7O2uN9+/ZFVlaWWYujxhk7jVrTVJeUmguVWtK9kkRE1AIZHG5OnjyJAQMG1DmuUChQXFxsjppIT6ZMo759ET0iIiIpMTjcKJVK/P7773WO79+/H3fddZdZiiL9RIf4IFDhClM6ebiIHhERSY3B4WbGjBmYM2cODh48CJlMhkuXLmH9+vVYsGABZs2aZYkaqQGa6dYAjA44XESPiIikxuB1bl555RWo1WoMGTIEN27cwIABAyCXy7FgwQI899xzlqiRGhEXEYiUSZH1Tre+WaNCyY2aRhfRiw7xsVqtRERE1mD0OjfV1dX4/fffUV5ejvDwcHh4eJi7NrOQ8lTw29U33XpXbgFmrasd5F3fInopkyK51gwREdklq04FnzZtGsrKyuDi4oLw8HBER0fDw8MDFRUVmDZtmqGXIzOpb7q1plVHqdDtelIqXBlsiIhIsgxuuXF0dER+fj78/f11jl+9ehVKpRK3bt0ya4GmaiktN43hInpERNTcWGVvqdLSUgghIIRAWVkZXF3/bA1QqVT417/+VSfwkH3QtOoQERG1BHqHG29vb8hkMshkMtx99911npfJZEhKSjJrcURERESG0jvc7NmzB0IIDB48GJs3b4aPz5+zbFxcXBAUFIS2bdtapEgiIiIifekdbgYOHAgAyMvLQ8eOHSGTccwGERER2R+DZ0vt3r0b33zzTZ3jmzZtwhdffGGWooiIiIiMZXC4SU5ORps2beoc9/f3567gREREZHMGh5vz588jJCSkzvGgoCCcP3/eLEURERERGcvgcOPv749ff/21zvFjx47B15fTje2dSi2QceYatmVfRMaZa1CpjVqgmoiIyG4ZvLfUhAkT8Pzzz8PT0xMDBgwAAOzduxdz5szB+PHjzV4gmU9aTn69e1AljgrnasVERCQZBq9QXF1djSeeeAKbNm2Ck1NtNlKr1XjyySexcuVKuLi4WKRQY3GF4lppOfmYtS6rziaa3GeKiIjskSm/v43eOPPUqVM4duwY3Nzc0LVrVwQFBRlzGYtjuKntiuq3fLdOi83tNDuE7395MLdlICIiu2CV7RfudPfdd9e7UjHZn8y8ogaDDVC7Y3h+SSUy84q4TQMRETV7eoWb+fPn4/XXX4e7uzvmz5/f6LnvvPOOWQoj87lc1nCwMeY8IiIie6ZXuDl69Chqamq0/24IVy22T/6erk2fZMB5RERE9kyvcLNnz556/03NQ3SIDwIVrigoqawzoBj4c8xNdIhPPc8SERE1Lwavc0PNj6ODDImjwgH8OTtKQ/M4cVQ4BxMTEZEk6NVyM3bsWL0vuGXLFqOLIcuJiwhEyqTIOuvcKLnODRERSYxe4UahUGj/LYTA1q1boVAo0LNnTwDAkSNHUFxcbFAIIuuLiwjE0HAlMvOKcLmsEv6etV1RbLEhIiIp0SvcrFmzRvvvl19+GY899hhWrlwJR0dHAIBKpcKzzz7bYteRaU4cHWSc7k1ERJJm8CJ+fn5+2L9/P+655x6d4ydPnsT999+Pa9eumbVAU3ERPyIioubHlN/fBg8ovnXrFn777bc6x3/77Teo1WpDL0dERERkVgavUDx16lRMnz4dZ86cQXR0NADg4MGDWLZsGaZOnWr2AomIiIgMYXC4eeutt6BUKvH2228jPz8fABAYGIgXX3wRL7zwgtkLJCIiIjKE0RtnArX9YQDseiwLx9wQERE1P1YdcwPUjrv54YcfsGHDBu2WC5cuXUJ5ebkxlyMiIiIyG4O7pc6dO4e4uDicP38eVVVVGDp0KDw9PbF8+XJUVVVh5cqVlqiTiIiISC8Gt9zMmTMHPXv2xPXr1+Hm5qY9PmbMGKSnp5u1OCIiIiJDGdxy89NPP+GXX36Bi4uLzvHg4GBcvHjRbIURERERGcPglhu1Wg2VSlXn+H//+194enqapSgiIiIiYxkcbh588EG8++672scymQzl5eVITEzEiBEjzFkbERERkcEMngp+4cIFxMXFQQiB06dPo2fPnjh9+jTatGmDffv2wd/f31K1GoVTwYmIiJofU35/G7XOza1bt/DVV1/h2LFjKC8vR2RkJCZOnKgzwNheSDHcqNSCO3sTEZGkWS3c1NTUoHPnztixYwe6dOlicKG2ILVwk5aTj6TUXOSXVGqPBSpckTgqHHERgTasjIiIyHystoifs7MzKisrmz6RLCItJx+z1mXpBBsAKCipxKx1WUjLybdRZURERPbD4AHFCQkJWL58OW7dumWJeqgBKrVAUmou6mtm0xxLSs2FSm30bhpERESSYPA6N4cOHUJ6ejr+/e9/o2vXrnB3d9d5fsuWLWYrjv6UmVdUp8XmdgJAfkklMvOKEBPqa73CiIiI7IzB4cbb2xvx8fGWqIUacblMv+5Afc8jIiKSKoPDzZo1ayxRBzXB39PVrOcRERFJld5jbtRqNZYvX46+ffuiV69eeOWVV3Dz5k1L1ka3iQ7xQaDCFQ1N+JahdtZUdIiPNcsiIiKyO3qHm6VLl+Kvf/0rPDw80K5dO7z33ntISEiwZG10G0cHGRJHhQNAvQFHABjfq6NVayIiIrJHeq9zExYWhgULFuDpp58GAPzwww8YOXIkbt68CQcHgyddWU1LWOfmdlzzhoiIpMAq69ycP39eZ++o2NhYyGQyXLp0yaA3JMOp1AIZZ65hW/ZFKNxcsPfFBzAvNqzec7nmDRERtXR6Dyi+desWXF11B6s6OzujpqbG7EXRn+prqVF6yVF5S13v+QK13VZJqbkYGq7ktgxERNTi6B1uhBCYMmUK5HK59lhlZSWeeeYZnbVuuM6N+WhWJL6z37CgtKrR13HNGyIiasn0DjeTJ0+uc2zSpElmLYb+1NiKxPrimjdERNQS6R1uuL6NdR04c63RFYn1wTVviIioJbLfaU4tWFpOPhK+zDL69VzzhoiIWjKDVygmy2ponI2+NMOHE0eFczAxERG1SAw3dkSlFli83bRxNkquc0NERC2cTbul9u3bh1GjRqFt27aQyWT49ttvdZ4XQmDRokUIDAyEm5sbYmNjcfr0adsUawUf7j6NglLDx9m8OrIL3hvfAxtm9MH+lwcz2BARUYtm03BTUVGB7t2746OPPqr3+X/84x94//33sXLlShw8eBDu7u4YNmwYKiulNwsoLScfK34wPLgFKlwxpW8IHunRDjGhvuyKIiKiFs+m3VLDhw/H8OHD631OCIF3330Xf//73/HII48AAP7v//4PAQEB+PbbbzF+/HhrlmpRmmnfxrhZo8Ku3AK21hAREf2P3c6WysvLQ0FBAWJjY7XHFAoFevfujYyMjAZfV1VVhdLSUp0ve5eZV2T0tO/iGzXcboGIiOg2dhtuCgoKAAABAQE6xwMCArTP1Sc5ORkKhUL71aFDB4vWaQ6mLrYnACzcchwqtSlDkYmIiKTBbsONsRYuXIiSkhLt14ULF2xdUpPMsdje9Rs1+HC3dAdbExER6ctuw41SqQQAFBYW6hwvLCzUPlcfuVwOLy8vnS97Fx3ig0CFK0wdCrzm57NQqYXOLuIZZ66xRYeIiFoUu13nJiQkBEqlEunp6ejRowcAoLS0FAcPHsSsWbNsW5yZOTrIkDgqHLPWZUEG6Kxzc+fjxhTfrG292Xjogs4YnkCufUNERC2ITVtuysvLkZ2djezsbAC1g4izs7Nx/vx5yGQyzJ07F0uWLMH27dtx/PhxPPnkk2jbti1Gjx5ty7ItIi4iECmTIqFU6HZRKRWu+Pjx++Dt5qzXdVb8cLrO4OSCkkoOOiYiohbDpi03hw8fxgMPPKB9PH/+fAC1O5CvXbsWL730EioqKjBz5kwUFxejX79+SEtLg6urdDaEVKkFMvOKcLmsEv6ertj74gM4cu669nF0iA8cHWQ4fbkCK344ZdR7CNS2ACWl5mJouJJr4RARkaTJhBCSHpBRWloKhUKBkpISuxt/k5aTj6TU3Ea7kDThp6DkJl7d9h+UV90y6T03zOiDmFBfk65BRERkaab8/rbbMTdS19AGmZoupJRJkQBQJ/yYytRp50RERPaO4cYGNCsS19dkpulCWrjlOK7fqDH7e5tj2jkREZE9Y7ixgQN/XGu0NUYAZg82MtQOTo4O8THrdYmIiOyN3a5zI1VpOflIWJ9l1ffUDB9OHBXOwcRERCR5bLmxoobG2Zibgwy4fd0+Jde5ISKiFoThxkoaG2djTjIAH06IRGt3lzrTyYmIiFoChhsrMXTn71YujrhRrTLoPTzkjnjr0e5soSEiohaNY26sxJAp2N6tnDGz/10Gv8eTMcEMNkRE1OIx3FiJIVOwi2/UoFewD7xb6bflgkbfTm0MLYuIiEhyGG6s5HpFFQwZ9nK1ogrLxnbV+/zWrZzR5y6uPExERMRwYwVpOflI+PKozgymphi62F7y2K4cNExERASGG4szZpZUoMIVUUGtkZSa2+S5Si85Vk6K5FgbIiKi/+FsKQszdJYUAES088KRc9f1et3bj/XgWBsiIqLbsOXGwozZqDL9xGXkF9/U69yr5VUGX5+IiEjKGG4s7OzVCoNfoxbA0QvX9TqXG2ESERHpYrixoLScfKz44bRRrz1zuRyyJsYHO8iAqKDWRl2fiIhIqhhuLESlFnhly3GjX//LH0UQTYxCVgvgyDn9WniIiIhaCoYbC/lw9+8ovlFj8fcxZkwPERGRlDHcWIBKLbDm5zyrvBfH3BAREeniVHALyMwrQvFNy7bayAAoFbU7fhMREdGf2HJjAdboKhIAEkeFc1ViIiKiOzDcWIA1uormxYZxVWIiIqJ6MNxYQHSID3zcXSz6HsFt3C16fSIiouaK4cYCHB1keKS7ZVtVOJCYiIiofhxQbCHtW7eyyHU5kJiIiKhxDDdmoFILZOYV4XJZJfw9a4OHj4fc7O+jGTrMgcREREQNY7gxUVpOPpJSc3V28A5UuGJ8r45mfy+lwhWJo8I5kJiIiKgRDDcmSMvJx6x1Wbhzl4SCkkq8+8MpuMsdUVGlMvl9Zj/QCX07tUF0iA9bbIiIiJrAcGMklVogKTW3TrABoD0m1E1sDtUEzfiaeUPvZqghIiLSE2dLGSkzr0inK6o+N2rUJr0HF+ojIiIyHMONkayxCjEX6iMiIjIcw42RrLHODBfqIyIiMhzDjZGiQ3wQqLBswDl7tcKi1yciIpIihhsjOTrIkDgq3KLvsSHzPFQmDkomIiJqaRhuTBAXEYh5sWEWu35BaRUy84osdn0iIiIpYrgx0ezBYVB6Nd09ded8J+9Wznpd3xoDl4mIiKSE4cZEjg4yLH44HDLUDTC303QuTe8bjPXTeyNhUCe9rs8NMomIiAwjE0JIelBHaWkpFAoFSkpK4OXlZbH3+dev+fj7thwUVVQ3eI4MgKKVM1ydHFFQ2niLjGYBv/0vD+Y6N0RE1OKY8vubKxSbQVpOPl7/LrfRYAPUtt4U36gBUNPoedwgk4iIyHgMNyZqaH8pU3CDTCIiIuMx3Jigsf2ljPXqyC6Y0jeELTZERERG4oBiE+izv5Sh2njKGWyIiIhMwHBjAktM0+bsKCIiItOwW8oE5gwimtlR0SE+ZrsmERFRS8SWGxNo9pcytROJs6OIiIjMh+HGBJr9pUwdUKxUuCJlUiRnRxEREZkBu6VMFBcRiGl9g/H5z2eNej1nRxEREZkXW27MYGi40ujXNjY7SqUWyDhzDduyLyLjzDXuEE5ERKQHttyYgWbsTUFJpcFdVFfLqqBSizoBJy0nH0mpuTpTzQO5uB8REVGT2HJjBqaMvXn9uxPot3w30nLytcc0qx7fuYZOQUklZq3L0jmXiIiIdDHcmElcRCDmxYYZ9drbQ0tjqx5rjiWl5rKLioiIqAEMN2YU3MbdqNfdHloOnLnW6KrHAkB+SSUy84qMei8iIiKpY7gxI1MW9dOElow/rup1viVWRyYiIpIChhszMs+ifvq9mts0EBER1Y/hxow0A4tNERPq22hAkqF21hS3aSAiIqofw42ZDQ1XYm5sGNzljga9ThNa+tzlqw1IdwYcbtNARETUNIYbM0rLyUe/5bux4ofTqKhS6f26O0NLXEQgUiZFQqnQ7XriNg1ERERN4yJ+ZqJZm8aYCdrKehbni4sIxNBwJTLzinC5rBL+nrVdUWyxISIiahzDjRk0tjZNY7zdnPHRxEj0ucu33tDi6CBDTKiveYokIiJqIRhuzCAzr6jRtWnupIkxy+K7om+nNpYpioiIqIViuDEDQ9ecqa8bioiIiMyjWQwo/uijjxAcHAxXV1f07t0bmZmZti5JhyFrzrw6sgv2vzyYwYaIiMhC7D7cfPXVV5g/fz4SExORlZWF7t27Y9iwYbh8+bKtS9OKCmqt99TvNp5yDgomIiKyILsPN++88w5mzJiBqVOnIjw8HCtXrkSrVq3w+eef27o0ALWzpAa+uUfvqd9cWZiIiMiy7DrcVFdX48iRI4iNjdUec3BwQGxsLDIyMup9TVVVFUpLS3W+LEUz/VufwcRcWZiIiMg67DrcXL16FSqVCgEBATrHAwICUFBQUO9rkpOToVAotF8dOnSwSG3GTP/mysJERESWZ9fhxhgLFy5ESUmJ9uvChQsWeR9Dpn87yICZA0I4iJiIiMgK7HoqeJs2beDo6IjCwkKd44WFhVAqlfW+Ri6XQy6XW7w2Q6Z/CwGs2peH+zq2ZsAhIiKyMLtuuXFxcUFUVBTS09O1x9RqNdLT0xETE2PDyoCzVyv0PlfTdZWUmguV2pgNGoiIiEhfdh1uAGD+/Pn49NNP8cUXX+DEiROYNWsWKioqMHXqVJvVlJaTjxU/nDboNQJAfkklMvOKLFMUERERAbDzbikAGDduHK5cuYJFixahoKAAPXr0QFpaWp1BxtaiGUhsLENXMyYiIiLD2H24AYDZs2dj9uzZti4DgOH7SN3palkVVGrBWVNEREQWYvfdUvbG1JaX1787gX7LdyMtJ99MFREREdHtGG4MZI4VhgtKKjFrXRYDDhERkQUw3BgoOsQHgQpXmNKpxNlTRERElsNwYyBHBxkSR4WbfB3OniIiIrIMhhsjxEUE4qPHI2GOMcGcPUVERGReDDdGau3uAnP0KHGXcCIiIvNqFlPB7ZGpLS4yAEruEk5ERGR2bLkxkiEtLnf2Xmkec5dwIiIi82O4MVJTs6ZkAAIVrvj48UgoFbpBSKlwRcqkSG6iSUREZAHsljKSZtbUrHVZkOHP6d2AbstMXEQghkUokZlXhMtllfD3rO2KYosNERGRZciEEJJeaKW0tBQKhQIlJSXw8vIy+/XTcvKRlJqrsyVDoMJVG2yIiIjIcKb8/mbLjYniIgIxNLy2Zaag5CaKKqrh4yGHws2Fe0gRERHZAMONGTg6yFBysxr/+P4kW3CIiIhsjAOKzSAtJx+z1mXV2S2ce0gRERFZH8ONkVRqgYwz17A167/469Yc1DdwiXtIERERWR+7pYxQ3yDihty+h1RMqK/liyMiImrh2HJjoIa6oJryQ26BhSoiIiKi2zHcGEClFkhKza23C6opq38+y7E3REREVsBwY4DMvCKDW2xux7E3RERElsdwYwBTN8vUjL0hIiIiy2G4MYAhm2U2xNSARERERI1juDHA9Yoqk69hjoBEREREDeNUcD2p1AKvf3fC6NfLULsbeHSIj/mKIiIiojrYcqMnQwYT37mb1O27hHOvKSIiIstiuNGTvmNlpvYNhlKh2/WkVLgiZVIk95giIiKyAnZL6UnfsTLbsi9iySMRaO0ux+WySvh71nZFscWGiIjIOthyo6foEB8EKlzrdDndqaiiBglfHkXJzWo80qMdYkJ9GWyIiIisiOFGT44OMiSOCgdQd0xNfbhgHxERkW0w3BggLiIQKZMi0drdpdHzbt8sk4iIiKyL4cZAcRGBeHVkF73O5YJ9RERE1sdwYwSlwk2v87hgHxERkfUx3BihqcHFMgCBXLCPiIjIJhhujNDY4GIu2EdERGRbDDdG0gwu5oJ9RERE9oWL+JkgLiIQQ8OVyMwr4oJ9REREdoLhxkSODjLEhPraugwiIiL6H3ZLERERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpEh+hWIhBACgtLTUxpUQERGRvjS/tzW/xw0h+XBTVlYGAOjQoYONKyEiIiJDlZWVQaFQGPQamTAmEjUjarUaly5dgqenJ2Sypje0LC0tRYcOHXDhwgV4eXlZoULS4L23Hd572+G9tx3ee9vR594LIVBWVoa2bdvCwcGwUTSSb7lxcHBA+/btDX6dl5cXf9hthPfednjvbYf33nZ4722nqXtvaIuNBgcUExERkaQw3BAREZGkMNzcQS6XIzExEXK53NaltDi897bDe287vPe2w3tvO5a+95IfUExEREQtC1tuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYbu7w0UcfITg4GK6urujduzcyMzNtXZKkJCcno1evXvD09IS/vz9Gjx6NkydP6pxTWVmJhIQE+Pr6wsPDA/Hx8SgsLLRRxdK1bNkyyGQyzJ07V3uM995yLl68iEmTJsHX1xdubm7o2rUrDh8+rH1eCIFFixYhMDAQbm5uiI2NxenTp21YsTSoVCq8+uqrCAkJgZubG0JDQ/H666/r7FfEe28++/btw6hRo9C2bVvIZDJ8++23Os/rc6+LioowceJEeHl5wdvbG9OnT0d5eblhhQjS2rhxo3BxcRGff/65+M9//iNmzJghvL29RWFhoa1Lk4xhw4aJNWvWiJycHJGdnS1GjBghOnbsKMrLy7XnPPPMM6JDhw4iPT1dHD58WPTp00fcf//9NqxaejIzM0VwcLDo1q2bmDNnjvY4771lFBUViaCgIDFlyhRx8OBB8ccff4jvv/9e/P7779pzli1bJhQKhfj222/FsWPHxMMPPyxCQkLEzZs3bVh587d06VLh6+srduzYIfLy8sSmTZuEh4eHeO+997Tn8N6bz7/+9S/xt7/9TWzZskUAEFu3btV5Xp97HRcXJ7p37y4OHDggfvrpJ9GpUycxYcIEg+pguLlNdHS0SEhI0D5WqVSibdu2Ijk52YZVSdvly5cFALF3714hhBDFxcXC2dlZbNq0SXvOiRMnBACRkZFhqzIlpaysTISFhYldu3aJgQMHasMN773lvPzyy6Jfv34NPq9Wq4VSqRRvvvmm9lhxcbGQy+Viw4YN1ihRskaOHCmmTZumc2zs2LFi4sSJQgjee0u6M9zoc69zc3MFAHHo0CHtOTt37hQymUxcvHhR7/dmt9T/VFdX48iRI4iNjdUec3BwQGxsLDIyMmxYmbSVlJQAAHx8fAAAR44cQU1Njc73oXPnzujYsSO/D2aSkJCAkSNH6txjgPfekrZv346ePXvi0Ucfhb+/P+677z58+umn2ufz8vJQUFCgc+8VCgV69+7Ne2+i+++/H+np6Th16hQA4NixY9i/fz+GDx8OgPfemvS51xkZGfD29kbPnj2158TGxsLBwQEHDx7U+70kv3Gmvq5evQqVSoWAgACd4wEBAfjtt99sVJW0qdVqzJ07F3379kVERAQAoKCgAC4uLvD29tY5NyAgAAUFBTaoUlo2btyIrKwsHDp0qM5zvPeW88cffyAlJQXz58/HX//6Vxw6dAjPP/88XFxcMHnyZO39re//P7z3pnnllVdQWlqKzp07w9HRESqVCkuXLsXEiRMBgPfeivS51wUFBfD399d53snJCT4+PgZ9PxhuyGYSEhKQk5OD/fv327qUFuHChQuYM2cOdu3aBVdXV1uX06Ko1Wr07NkTb7zxBgDgvvvuQ05ODlauXInJkyfbuDpp+/rrr7F+/Xp8+eWXuPfee5GdnY25c+eibdu2vPcSxm6p/2nTpg0cHR3rzAwpLCyEUqm0UVXSNXv2bOzYsQN79uxB+/bttceVSiWqq6tRXFyscz6/D6Y7cuQILl++jMjISDg5OcHJyQl79+7F+++/DycnJwQEBPDeW0hgYCDCw8N1jnXp0gXnz58HAO395f9/zO/FF1/EK6+8gvHjx6Nr16544oknMG/ePCQnJwPgvbcmfe61UqnE5cuXdZ6/desWioqKDPp+MNz8j4uLC6KiopCenq49plarkZ6ejpiYGBtWJi1CCMyePRtbt27F7t27ERISovN8VFQUnJ2ddb4PJ0+exPnz5/l9MNGQIUNw/PhxZGdna7969uyJiRMnav/Ne28Zffv2rbPkwalTpxAUFAQACAkJgVKp1Ln3paWlOHjwIO+9iW7cuAEHB91fdY6OjlCr1QB4761Jn3sdExOD4uJiHDlyRHvO7t27oVar0bt3b/3fzOTh0BKyceNGIZfLxdq1a0Vubq6YOXOm8Pb2FgUFBbYuTTJmzZolFAqF+PHHH0V+fr7268aNG9pznnnmGdGxY0exe/ducfjwYRETEyNiYmJsWLV03T5bSgjee0vJzMwUTk5OYunSpeL06dNi/fr1olWrVmLdunXac5YtWya8vb3Ftm3bxK+//ioeeeQRTkc2g8mTJ4t27dppp4Jv2bJFtGnTRrz00kvac3jvzaesrEwcPXpUHD16VAAQ77zzjjh69Kg4d+6cEEK/ex0XFyfuu+8+cfDgQbF//34RFhbGqeCm+uCDD0THjh2Fi4uLiI6OFgcOHLB1SZICoN6vNWvWaM+5efOmePbZZ0Xr1q1Fq1atxJgxY0R+fr7tipawO8MN773lpKamioiICCGXy0Xnzp3FqlWrdJ5Xq9Xi1VdfFQEBAUIul4shQ4aIkydP2qha6SgtLRVz5swRHTt2FK6uruKuu+4Sf/vb30RVVZX2HN5789mzZ0+9/4+fPHmyEEK/e33t2jUxYcIE4eHhIby8vMTUqVNFWVmZQXXIhLhtmUYiIiKiZo5jboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYboiIiEhSGG6IqFmTyWT49ttvzX7d4OBgvPvuu2a/LhFZHsMNEeklIyMDjo6OGDlypMGvtWVQmDJlCmQyGWQyGVxcXNCpUye89tpruHXrVqOvO3ToEGbOnGmlKonInBhuiEgvq1evxnPPPYd9+/bh0qVLti7HIHFxccjPz8fp06fxwgsvYPHixXjzzTfrPbe6uhoA4Ofnh1atWlmzTCIyE4YbImpSeXk5vvrqK8yaNQsjR47E2rVr65yTmpqKXr16wdXVFW3atMGYMWMAAIMGDcK5c+cwb948bQsKACxevBg9evTQuca7776L4OBg7eNDhw5h6NChaNOmDRQKBQYOHIisrCyD65fL5VAqlQgKCsKsWbMQGxuL7du3A6ht2Rk9ejSWLl2Ktm3b4p577gFQt7WpuLgYTz/9NAICAuDq6oqIiAjs2LFD+/z+/fvRv39/uLm5oUOHDnj++edRUVFhcK1EZDqGGyJq0tdff43OnTvjnnvuwaRJk/D555/j9m3pvvvuO4wZMwYjRozA0aNHkZ6ejujoaADAli1b0L59e7z22mvIz89Hfn6+3u9bVlaGyZMnY//+/Thw4ADCwsIwYsQIlJWVmfR53NzctC00AJCeno6TJ09i165dOoFFQ61WY/jw4fj555+xbt065ObmYtmyZXB0dAQAnDlzBnFxcYiPj8evv/6Kr776Cvv378fs2bNNqpOIjONk6wKIyP6tXr0akyZNAlDbxVNSUoK9e/di0KBBAIClS5di/PjxSEpK0r6me/fuAAAfHx84OjrC09MTSqXSoPcdPHiwzuNVq1bB29sbe/fuxUMPPWTw5xBCID09Hd9//z2ee+457XF3d3d89tlncHFxqfd1P/zwAzIzM3HixAncfffdAIC77rpL+3xycjImTpyIuXPnAgDCwsLw/vvvY+DAgUhJSYGrq6vBtRKR8dhyQ0SNOnnyJDIzMzFhwgQAgJOTE8aNG4fVq1drz8nOzsaQIUPM/t6FhYWYMWMGwsLCoFAo4OXlhfLycpw/f96g6+zYsQMeHh5wdXXF8OHDMW7cOCxevFj7fNeuXRsMNkDt52vfvr022Nzp2LFjWLt2LTw8PLRfw4YNg1qtRl5enkG1EpHp2HJDRI1avXo1bt26hbZt22qPCSEgl8vx4YcfQqFQwM3NzeDrOjg46HRtAUBNTY3O48mTJ+PatWt47733EBQUBLlcjpiYGJ0uJX088MADSElJgYuLC9q2bQsnJ93/9bm7uzf6+qY+X3l5OZ5++mk8//zzdZ7r2LGjQbUSkekYboioQbdu3cL//d//4e2338aDDz6o89zo0aOxYcMGPPPMM+jWrRvS09MxderUeq/j4uIClUqlc8zPzw8FBQUQQmgHGWdnZ+uc8/PPP+Pjjz/GiBEjAAAXLlzA1atXDf4c7u7u6NSpk8Gv0+jWrRv++9//4tSpU/W23kRGRiI3N9ek9yAi82G3FBE1aMeOHbh+/TqmT5+OiIgIna/4+Hht11RiYiI2bNiAxMREnDhxAsePH8fy5cu11wkODsa+fftw8eJFbTgZNGgQrly5gn/84x84c+YMPvroI+zcuVPn/cPCwvDPf/4TJ06cwMGDBzFx4kSjWolMNXDgQAwYMADx8fHYtWsX8vLysHPnTqSlpQEAXn75Zfzyyy+YPXs2srOzcfr0aWzbto0DiolshOGGiBq0evVqxMbGQqFQ1HkuPj4ehw8fxq+//opBgwZh06ZN2L59O3r06IHBgwcjMzNTe+5rr72Gs2fPIjQ0FH5+fgCALl264OOPP8ZHH32E7t27IzMzEwsWLKjz/tevX0dkZCSeeOIJPP/88/D397fsh27A5s2b0atXL0yYMAHh4eF46aWXtK1R3bp1w969e3Hq1Cn0798f9913HxYtWqTTlUdE1iMTd3Z6ExERETVjbLkhIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJ+X8qZAaU6AY/jgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_Test,testing_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Price vs Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "********************************************************************************************************"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model using Lasso, a machine learning algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "lass_reg_model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" 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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" 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": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lass_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_prediction = lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.718329241713954\n"
     ]
    }
   ],
   "source": [
    "error_score = metrics.r2_score(Y_train,training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "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 Price vs Predicted Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data_prediction = lass_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.48128953976661903\n"
     ]
    }
   ],
   "source": [
    "error_score = metrics.r2_score(Y_Test,testing_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "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,testing_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Price vs Predicted Price\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}