Carmaprycepred_main_NB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "006f4823",
   "metadata": {},
   "source": [
    "# CAR PRICE PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380078f3",
   "metadata": {},
   "source": [
    "#### importing all essential libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce0272d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35ed3e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cardata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d3a8d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4df4969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check the features & records (rows & columns)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d229e0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "# displaying a categorical feature\n",
    "\n",
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "399f67d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing or NULL values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8991bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37589c61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a40af9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_dataset=df[['year','selling_price','km_driven','fuel','seller_type','transmission','owner', 'mileage', 'engine', 'max_power', 'torque', 'seats']]\n",
    "\n",
    "\n",
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "76993721",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['current_Year'] =  2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb2f3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>current_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_Year  \n",
       "0       Manual      0          2021  \n",
       "1       Manual      0          2021  \n",
       "2       Manual      0          2021  \n",
       "3       Manual      0          2021  \n",
       "4       Manual      0          2021  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "956cbd5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year'] = final_dataset['current_Year'] - final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "179ee526",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>current_Year</th>\n",
       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_Year  no_year  \n",
       "0       Manual      0          2021        7  \n",
       "1       Manual      0          2021        8  \n",
       "2       Manual      0          2021        4  \n",
       "3       Manual      0          2021       10  \n",
       "4       Manual      0          2021        7  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92207dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now after feature engineering, drop unused dataset\n",
    "\n",
    "final_dataset.drop(['Year'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3cd291d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
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       "      <td>27000</td>\n",
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       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
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       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  current_Year  no_year  \n",
       "0       Manual      0          2021        7  \n",
       "1       Manual      0          2021        8  \n",
       "2       Manual      0          2021        4  \n",
       "3       Manual      0          2021       10  \n",
       "4       Manual      0          2021        7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad3fef14",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['current_Year'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c6545b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Dealer</td>\n",
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       "      <td>Dealer</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  no_year  \n",
       "0       Manual      0        7  \n",
       "1       Manual      0        8  \n",
       "2       Manual      0        4  \n",
       "3       Manual      0       10  \n",
       "4       Manual      0        7  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "32e38f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>296</th>\n",
       "      <td>9.50</td>\n",
       "      <td>11.60</td>\n",
       "      <td>33988</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.90</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>3.35</td>\n",
       "      <td>11.00</td>\n",
       "      <td>87934</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>11.50</td>\n",
       "      <td>12.50</td>\n",
       "      <td>9000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5.30</td>\n",
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       "      <td>5464</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0             3.35           5.59       27000    Petrol      Dealer   \n",
       "1             4.75           9.54       43000    Diesel      Dealer   \n",
       "2             7.25           9.85        6900    Petrol      Dealer   \n",
       "3             2.85           4.15        5200    Petrol      Dealer   \n",
       "4             4.60           6.87       42450    Diesel      Dealer   \n",
       "..             ...            ...         ...       ...         ...   \n",
       "296           9.50          11.60       33988    Diesel      Dealer   \n",
       "297           4.00           5.90       60000    Petrol      Dealer   \n",
       "298           3.35          11.00       87934    Petrol      Dealer   \n",
       "299          11.50          12.50        9000    Diesel      Dealer   \n",
       "300           5.30           5.90        5464    Petrol      Dealer   \n",
       "\n",
       "    Transmission  Owner  no_year  \n",
       "0         Manual      0        7  \n",
       "1         Manual      0        8  \n",
       "2         Manual      0        4  \n",
       "3         Manual      0       10  \n",
       "4         Manual      0        7  \n",
       "..           ...    ...      ...  \n",
       "296       Manual      0        5  \n",
       "297       Manual      0        6  \n",
       "298       Manual      0       12  \n",
       "299       Manual      0        4  \n",
       "300       Manual      0        5  \n",
       "\n",
       "[301 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03ebfb07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion of category features into some other values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "980b0e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset = pd.get_dummies(final_dataset, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c42109f",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        7                 0   \n",
       "1           4.75           9.54       43000      0        8                 1   \n",
       "2           7.25           9.85        6900      0        4                 0   \n",
       "3           2.85           4.15        5200      0       10                 0   \n",
       "4           4.60           6.87       42450      0        7                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8407bcb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_year</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "no_year                     -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                         no_year  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price          -0.236141          0.552339         -0.540571   \n",
       "Present_Price           0.047584          0.473306         -0.465244   \n",
       "Kms_Driven              0.524342          0.172515         -0.172874   \n",
       "Owner                   0.182104         -0.053469          0.055687   \n",
       "no_year                 1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel       -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol        0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual  0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual    -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "no_year                               0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4693fe6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81379d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f87f8d3d4c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ekHSWMeaAMeY6SZ8dM63VH0r6m5yUHChSiYWhEkkn3cJQmY7wcLJobqVjUmudy+JTxWxszNz/1AFtWN0+ZZxlIl2HXGIULoubYTLBEr82rkmNxY1r2lVewgUkilMkFpuQfzesbtdwLD7FJ3OLtoH3nUrbELNTS025Yx08v7a8KI6H4pDv3EbbFF5Auw3TlYtrEFcsKG6tvXT06+kz/Pw7HN7+6ikVCpjlMl2IKnFDeWxDL9On40+fN9KBMn4hodPnUREWs7Ex09kT1rd+uU/r39im806bo0X1lTNe8CzRITc+ns5pqdbrz6hnsXBMqr6qVJWlfq1/Y5viVvIZqbLUr/qq0kIXDciJ+soy3bdtv667qE3GSNZK923br7XLmgtaLtoG3ncqbUPMTovqK9XeVJVSB7c3VWlRjh5IyffxUBzyndtom8ILaLdhunJxDeKKzg1jzPmT/dxa+5t8lQXASYmFqNoaqtJuk+6GciZPx2fagYLiMj5mjg9EtLS5Rr9/ZuMp/e0ni6fF89LHMCBJrXMrNb+uX0dDkeR78+vKeeoIRWtxfaU+uvbsGdXfuUTbwPtOpW2I2cnnM1p9VpPa5lXl5bzP9/FQHPKd22ibwgtot2G6cnEN4orODUmfn+RnVtLqfBUEwIh43Gpvd0hdvWE11aSvoE61MsukAwXFZaqYyTT20u2beMJM+HxGf9DeqIaqMnX2hNVSW66Olhoa5ihaPp/Rm89u0n3rL3RdzJPLvY0bHTgVNs/rzuf7ePCufOc22qbwCtptmI5cXIO4onPDWvuHhS4DgJPicatHdh6a0JO6tqM5bQcHlRmmI13MTDf2gGyJx61+9HwXsYdZg5hHLtE2xHTku/1HexMzlc/cRj0NoBjlIre5YkFxY8zbJnsVunzAbPPK0VAy0UgjC6Vt2vK09naHClwyFLu93cQeCoPYw2yTaczH41Z7jvTriZePas+RfsXjPOYMILvyXQdT58MLiFMAxSgXuc0VIzckXTbJz6yk/8pXQYDZLh63er6zN2WhNGkk4RzuC/MEHnKqqzfsGHtdvcQecovYw2yTLubH1vU83QwgH/JdB1PnwwuIUwDFKBe5zRWdG9ba9xW6DABG7O0OadfhPgVLfCkJJ1jiU2N1sIAlw2xQURpwjL2KUn8BS4XZgNjDbNNUE5yyrk/3ZNXSDRdzYwVA1uS7DqbOhxcQpwCKUS5ymyumpUowxjQZY75qjPnh6PfnGGOuK3S5gNmkqzesLdsOaMPqdgVLRlJEsMSnz1y5XK11FQUuHYpdJBabEHufuPQcReOWqVCQU5FYTH/zpjNTYu9v3nSmhmPxKT4JeNPi+kptvnZlSsxvvnalFtdXJrfp6g2rrqJUf/2HS3T96pFXXUWpDveFC1VsAEUo33UwdT68gDgFUIxykdtcMXJjjG9I+rqkvx/9/iVJ90n6aqEKBMw2LbVBXbNqoXzG6KvvXqXu0JCM8anzxIB+uuuwVp/VxFQUOCXxuNUrR0PadyykytKAmmrK1Dq3Uj6fUX1lme7btl/X/+ESNdcEtf/4gO7aulvHByJMhYKcaqgqU0ttqe5512t0LDSsuZUlOjEwpHlVZYUuGjBt8bjV3u6QunrDaqoJanF95YTc6fMZre1o1tINF+twX1iN1RO3a6kN6v/9fpuOhiKKW8lvpP/3+21qrmEkJyaXSQwCCQ1VZaqrCOi2q1coNBRVZTCggaHhnNXB+T4eikc+cxttU3gFdT6mIxd1sNs6N+ZZa7cYY/5Okqy1UWNMrNCFAmaLeNzquc4+3fPYnuTc2p+49Bz1hcP6j1/t15+8tlXNNUGd01JLZYUZcZq/feOadp0+r1L1VaWaW1GmT1x6jrYf6NEN39+RMlRx05antWD9hRqIxNRSG1QsLh3uowGF7Bkctvrbbz2VjM2bLu8odJGAaZvJOhk2zcC46bzPhS0SWKsF02WtdGIwqk8++FwyZjZdcmbaHOS146E4FCK30TaF21HnY7qslfqGYil18Ef+6KxTqoNdNS2VpJAxpl4ji4jLGHOhpJ7CFgmYPZzm1v70Q89pIBLT21e16j9/vV97jw7okZ2HmCIIM+IUY7c/ukvPdfbqpy8e1R/f+bgiUavXLq5zXGTq0RcOa9OWZ/TDHYf0x3c+rnd8+Um99Y7HiUmcsiN9Q7rxwZ0psXnjgzt1pG+owCUDpifdOhl7u0Mp2yUuRt96R/pceiwUUSgS0z2P7dFdW3frS4/tUSgS07FQZNr7wuzxylHnGHzlaGiKT2K2OtI/pM0/fiklZjb/+CUd6c9NHZzv46E45Du30TaFF2Ta7gQSukND+tz/vJgSM5/7nxfVHZp5bnPbyI1Nkh6UdIYx5v8kNUi6urBFAmaPrt6w4w3lBXPK9a8/3a1Lz12g0FBUN/5gJ4uJYkbSxVjcStVBv667qE0vHOrV77c3aFF9ufZ1Dya3C5b4FItLbzt/oW5/dBcL3CKrjvZHHGOze9xNXMDt0uXZw33hlBy5tzukWx95Xtdd1CYz+mDdrY88r6XN1cntwtHYhHx7+6O79NX3rErZPwuPY6x9x0KqqyjV285fmIyt+586oP3HQjqjkXjARCcGhh3z1omB4aI4HopDvnMbbVN4QWJ9tvHnxfh2J5BwLOSc246FZl4Hu6JzwxjzWkm/s9b+xhjz+5L+UtJVkn4k6UBBCzfq3X/xAb169ITjz+bPm6N7v/yv+S0QkANNNUEFS3wpiSZY4tPBE4N6+6pWBXwjTzo53SQBMpEuxipL/aoOluhf/nfkJto9j+3RLeuW6c6tu7Sve1DBEp82rG7Xt365T1e9ZmFGN+6A6agpDzjGZnXQFU0lIGPp8mxjdeo6Gd2hIb19Vavu2LorOSR8w+p2HQsNJXPp0HDcMd9GoqnvZdqhgtmhNliid//eomTHWGIKyppgSaGLBpeqKPU75q2KUn9RHA/FId+5jbYpvKClNuh4XrA+G9IJljjXwYkFxmfCLdNSfUlSovv59RpZUPxuSccl3VOoQo316tETmveWDzq+0nV6AF6zuL5Sm69dmUwqiRsd39l2QHds3aWOBbX6tyf3O94kATLhFGMb17SrvalKn37ouZSnfm94YIfu+JPz9N2/ulDfvu51ilurq16zUFVl/gkVHzGJU1VR4tfGNe0TYrOihBsd8BanPLv52pVaXF+Zsl2p35fs2JBG8u4dW3epxH8yvy6qr3TMt61zU/eV6FAZvx15eXYq8RvHET8lfubehrM5FSWOdfCcitzcNM738VAc8p3baJvCC2JxOZ4XsfgUH8SsVRss0aZLzkzJbZsuOfOUOord0uXrt9YeG/332yXdY629X9L9xpinp/qwMeZrki6VdNhau2z0vbmS7pO0WNJeSddaa49nv+hA8fD5jNZ2NGvuey/Qr/Ye0+J5lXr1xICues1C3f/UAb18uF/HByKON0mATCRi7MzrL9Ir3aFkr/2OAz2OT/0ORGI6PjCcskDZ37zpTH38LUv1mR++kLJoGTGJUzEQiemHz3bqs1ev0GAkqorSgL782MtasaC20EUDpiWRZ5duuFiH+8JqrHZe3Ds0FHOcRmAgEktuc/q8kY6S8YtEnj4vNd8mOlTGb0denp3608RWaExsAWMtbarRK0dDWv/GNsWt5DPSwrpyLW2qKYrjoTjkO7fRNoUXHO5zHr17pD/MVJRwdFZjtQ71hnXb1SsUikRVWRpQid9oaWP1jPfpms4NY0zAWhuVtEbS+jE/y6SM35B0l6R7x7z3MUmPWmv/yRjzsdHvP5ql8gJFy+czqgr6VeI3+tvvPpMytHDZ/Fo9vOFix5skwHS8dLg/5SbYP155ruMaGxWlfr3vG79OeRLkn//3JW1Zf6H++4MX60h/+ht3wHTUVpToLctbJuS9Gp7ihAf5fEZtDVWTTglVUx5wnEZg7HQXmXaUZLodZodMYgsYKxDw6S0dLWqd26NDPWE11wbV0VKrQCA3E03k+3goDvnObbRN4QWZTocKJBzqD2vP0dCEXHpWc7UWB2fWIeaW2vs/JP3MGPN9SYOSHpckY8wSST1Tfdha+5ikY+PevkLSN0f//U1J67JVWKDYDUScFxCNWau2hqqUmxXxuNWeI/164uWj2nOkX/G4LVSx4RFOC8/+3fe269NXLNPGNUvUUhtMPvUbiTnP9z4wHNMZjVW6sG3ehJgEZiJd3hvkSWMUqenGvJ2iek90qLgtL9NOyT/yKWbC5zOqDpaoprxE1cGSnOeQfB8P3pfv3EYuhRdkOh0qkNDVO+SY27p6h2a8T1c8PmOt/QdjzKOSWiT9yNrk5ZNP0gdnuNsma23n6P47jTGNWSgqULTicau93SF19YZlZVVXUarOnnDy5+HhuPqHohM+88jOQxOmoVjb0cwFAtJKt/Dsr/Ye1w+eOajPvG25ZK1a51bKZ+T4JEhDFU+CILtCQ1HHqQbG5z2gWPRnEPNer+e9Xn6vCg1FHet58inSicettr7Ype0HehS3kt9IyxfWavVZTTk5V/N9PBSHfOc22qbwAkbvYroGIs65bSAy89zmis4NSbLW/tLhvZdyfVxjzHqNToPV2tqa68MBM5KrOI1G49r5ao+OhIbUOxjVx7/3bMqwsHuf2Jfs4AiW+DS3ojTl805P4G/a8rSWbrh40qkwUJymitNEB5rPGMcOi8pSv96+qlX/79tPJePws1edO2F9jY1r2jU4HFU8bmk0YVomi9G5FaWOUw2Mz3tArmWaS7t6w2qqcb6AjEbj2tnZo86esFpqy9XRUjNhupXT6ysdY37sk3Zer+e9Xn43mzSfVpY51vPkU6Sz/1hIu7r6dc9je1Ly0ZKGKi2eN7NzdbIYzcXxUPxykdtom8LtMrkXlcl0qEBCLnKbW6alyoUuY0yLJI1+Pey0kbX2HmvtKmvtqoaGhrwWEMhULuI0Go3rgWcO6u1f/qV+u78n2bEhnRwWds2qhZJGGm0bVrcrEk99UiXdE/hdvWFh9pksThNPz771jsf1D//9nD51WUfK0NUNq9sVi1vdsTV1eOLf3r9dveGorruoTdevXqLrLmrTvU/s04+e69IjOw8xvQimZbIYDUWijsNjQ6fwBAkwE5nm0nd8+Um99Y7HJ+TCZP1+zy/1V9/+jd5+zxN64JmDikZT6+tY3DpPQTlmX+nq+cN93qjnaafkzmRxOjQc09+86cyUev5v3nSmhmJxp10BOpSDKSomi9FcHA/FLxe5jbYp3I57psi2XOQ214zcyIEHJb1H0j+Nfv1+YYsDuMvOV3t0wwM7FB6Oyxg5Xvyf2VStTZecqaFoXPdt2683LKlP2aaiNOD49EpFqT8vvwO8I/H0bF1FqdYua9EXfrZb113UJr9POru5Rl/46W698axGxziMxOK6+ye7k+8FS3yKxcXTt8iq/qGYY/yFhpjXGO6RyUiEnZ0n6/fENjc8sEPtjVVacVpdcl+/Oz7gGPO/Oz6gJU3Vkry/SCTtlMIo8RuVl/i0/o1tilvJZ6TyEp9KGG2JNHoHhx3zUc/gcFEcD8Uh37mNtimAYpSL3FYUIzeMMf8h6QlJZxljDhhjrtNIp8Ylxphdki4Z/R6ARp783H98cMLF/ljBEp8qSvxaNr9GtUG/PrTmTNlxq4lGYjFtWN0+4Qn8YZ7MwziJp2ffdv5C3bF1l/Z1D+run+zWHY/u1t9seVp/sHRkWSSnOPy9tnotqi+XJC2qL9fma1eqLODTn1/cpmMhnrBDdtSUBxzjr7q8mJ8DgddkMpKis8d5m0M9qaMVKsucY76y9GTMt9ZV6PY/Wam73nGebn3bct31zvN0+5+sVGtdRbZ+pZyinVIYg8MxffX/XlHivzlupa/+3ysaHOaGHJxVBf2O+aiqLDcdkfk+HopDvnMbbVMAxSgXua0osqK19h1pfrQmrwUBPGJvd0jGjNwovvTcBaoq8+vmyzv0yQd3psx59/cP7NDxgYg2rmlXeDgqvy81AdVXlum+bft13UVtMkayVrpv236tXdZcoN8MbpV4+jfdKKGFc8oVjsZ00+UdunFMHN50eYc+/6MX9KE1Zyo0NKySgD9lYdgzG6tYewNZYUzcMf6MYeozuEcmIylaassdt2muTR1tMRiJasPq9uR0gIkb/4PDJ4eEHzge0rHQsG76wcnz4sbLOnTgeEiLG6pT9pfJWiD5RjulMCKxmN6+qnVCbEXoVEIaNWUljnVwdbCkKI6H4pDv3EbbFEAxykVuK4rODQDT0x0aUjwe01//wZJkh8bH33KW1r+xTQtqy3WwZzBlMfHbH92l9W9s06L61KHai+sr9dG1Z6fcbN587cqUxUgBaSRWNl+7Uvu7Q4433fYdG5QkPbT9YMpNqH/96W5deu4C/d33ntW9779A7/7aryasyXFOS01yChVgpoz1619/untC/H32qhWFLhqQlMilk9W7HS01umXdsuTUVMESn25Zt0wdLbUp+6osCzje+P/8NSdjvrN3KNmxIY3k3Zt+sFNff+9rUzo3EmuBjC/X2o7mgnZw0E4pjGAgMGENrTu27tLX3/vaApcMbhWJxR3r4H++dmVRHA/FId+5jbYpgGKUi9xG5wYwy8TjVq+eCMtv/Prkg88kG2df/8U+vfv3Fulgz6DueHR3ymfCw3HFrdQbTl3gx+czWtvRrKUbLtbhvrAaq93xpCbcx+czevPZTfrfF7q0cU17cgGpYIlPn7j0HN3z2Mu6bMWC5HRVYyVGe3SHIo6jPl7pDtG5gVN2pH/IMf6O9jP1Gdwjk3rX5zNqqC7VbVevUCgSVWVpQNXl/gl1c7DErz95bWtKPt64pl3BkpPTsnT3O+fd7lAk5b1M1gIpBNophXEizXoGvaxngDSOpq2DI2k+4a3joTjkO7fRNoVXuHH0LtwrF7mNzg1gltnbHdJH79+umy7rUHg4rpbaoN52/kIZM3IT+fVn1Ouex/ZMeLLeZ6SqspGU4VR5sagzprL/+IA+dN/IouLXXdSm1rnl2n9sUPf9ar/eecEiDUSijqM6rB2dBznNwrBjb8QBMzW3stQxvuZWlhawVMBEPp9RW0NV2nr3laMh/eW3fjMhlv/7gxfrjMaTn+kdiOreJ/alPDV17xP7tGLhnOQ2TTVlaabBKks55mRrgYwtZyEufqf6/0L2NVQ5x019Vdkkn8JsFizxp2nj5WaJ0HwfD8Uh37mNtim8IB63+uGOQ/rwd06Okv38NSv1lmWFHb0L98pFbqP2BmaZxA2IOZWl+ujas7TpkjMV8En3P3VA//K/u3S0P6KbL+9IWXxz45p2zass1fzaYHLqibfe8bje8eUn9dY7HtcjOw8pHmfuT0wuEXudPWHd/ZPdOj4Qkd8nvemcZrXNq1RDVak+cek5ExZ+fWj7QX36imUqDZjRp4pTY7OugvmRceqqyvy68bLU3HfjZR2qKqXzDN6y71jIsaNh/7FQyntlJT6VBk5edBojlQaMSgMnLw+aakt18+XLUs6Lmy9fpuba1IuPxuqg48KADVUn1/mg/TB7+H1Wt6xLjZtb1i1TiY+/NZxVlwUc23iJB6u8fjwUh3znNtqm8II9R/r12f95Xtdd1KbrVy/Rn1/cps/+z/Pac6S/0EWDS+Uit1F7A7NMU01Qi+rL9btjAylTUWxY3a5v/XKf/r/vPKObLj1bd7/zPHX3R1ReGlDniQG1zCnX4nlV2tsd0q2PPJ980lOSbn3keS1truapSExq7EK4LbVBGZnkKKFEDD6y44Cuu6hNfp/02sVz1Rce1hUrF2jzj19SacBo0yVnav0b2xS3ks9IC+vKtbSpptC/GoqAkVFteSBlKh+fTzKGJ47gLZVpRrlVlKY2+weGh/VXb1yimx4as1j4pR0KD5+cXuNw77C2bNunz169QoORqMpLA7r3F3vU1lCp1rkn9xXwSzde1jFh4fHAmGsU2g+zRyxuNDAUTamvB4aiisbJp3AW8BstmBNMiZkFc4Iq8ecmZvJ9PBSHfOc22qbwgoMnBvT2Va3J9WgS1/UHTwwwdTQcxeOS39iU3DYQGdapPO9E5wYwyyyur9SnLl+m//ftpyYshnbdRW0j894Zn254YKfedv5CnbugRm86pzk5dUR3aMix8joWGuLmBCa1uL5St151rj56/3a97fyF+uf/fSl9DEq6+0/P04e3PJNyg27zj1/SzVcs06GesE6vr9T5rXUKBBiEiFN3fHBYm8bFW7DEpy+/e1UBSwVMX2WZf8LaRhvXtKuyLPVpqPKSEt300G9S8vBND+3Ut697XXKbQz1hbdvXo237fpvy2UO94dTve4b0xZ+lLgz4xZ/tVtu8FVpUP9I2oP0wewxEovrMD1+YmE/f9ZoClgpudmIwqnuf2Kt3v74tpSP1Q286qyiOh+KQ79xG2xReUBrwJ9t20snr+q+/97UFLhncqrM3rH/+393J6fGtlf7rNwd042XnaKZLitO5Acwy8bjV0HDMccoKY0af7iwLqLMnrK/+fI8eHrcYaKnP51h53fcXF+b194D3+HxG8+cEdd1FbVraXJ02BqXRtTbimrDNvu5B/XrvcX3l8ZHYpGMD2dIfjjrGZGgoWqASATMzOBxTfUXJyNNQQ1FVBgMaCA8rPBxL2S4SjTvG/HDs5Hvzqp3nxK0fNyduKBJ1XBhwIHLy/KH9MHsMpGlnDo57D0gIDUUdO1JzVQfn+3goDvnObbRN4QWhIeIU01NVFtDxgUjKdUOwxHdKU0PSuQHMIvG41X/v6FTniUEtqi/XpecuSN5M/sEzB+UzI9NKHA8NKVji0+ZrV2pxfWXKPo4NRBwrr2MDkXz9GvCw+soyffXne3TLFR3asGZJcujh/U8d0PGBSHLx8Bsv69DR/rDjTTWfkW696twJsQmciqaaMse82MACuPCYhqoy/Xb/CX3yB88lR0hsuuRMzRsXy4lpKsfHfGP1yXUyaisCuunyDt344Mnppm66vENzKlIvIRbNrXTM161zT+Zp2g+zR31lqWNssUYW0mmsdl6oeXze8urxUBzyndtom8ILTq93bgNyrY505pQH9Nmrlmv3kZDiVvIb6YyGSs0pp3MDQAb2dof00fu36+NvWaq/+v0lKXNj33R5h4aGY/riz3br01cs03/8+etUU16qJ1/pVlNNMDktVUWGc3kDThbXV+pL7zpfR/oiKettbFzTrsaaMnX3Dem2q1eoptyvaKxM//i25dp7NKQt20Y6P25Zt0xH+oa0YE5QPh/zzSJ7/D6j//f7S/SpMXnxU5d1KMD82/CYWHxkCr+xIyQ2//glrVnalLJdwK8JbYHx62Qc6x/Wv/40dbqpf/3pbn3myuUp+1o0t0Kfu/pc7Trcn7xIWdJYpUVzK5LbVJQGHG/S0H4oPiU+n2M+LfUz2hLOIrGYbr68Q58c05F68+UdisZjU3/YA8dDcch3bqNtCi84vaFKn79mpT78naeTcfr5a1Yy5SjSisSsjvSn3g/6yB+dpYV1FVN/OA2uJoBZ5NCJsMLDcdVVlukj330m5cbHjQ/u1HUXtWlf96BePtyv+qoy/e1Xnkwmm83XrtTajmY11ZRp0yVnJm+cJJ4IbarhCRJMLR63KvX7dcMDO1Li7/ZHd+n6P1yi2370kj72lrNUP1ymT3x/R8oFZ2t9hayN63fHBjSvqkzxuNXe7pC6Q0Mq9fs0EImldMQB09EbjiYvHqWRuPzUD3bqK8xrDI853Bd2HCFxpD+sMxpPXmge6hlKdmwktrnpBzv1zfddkFwno3/Iebqp/nFTDRw4MaDDfUMTLlIOnBjQ4nkj+2quLdOH3nSmXjl68imtD73pTDXXTmw/RKNx7ezsUWdPWC215epoqWEaQg/pHSKfYnpKfH6VBUzK4qKxeEwBn3/qD3vgeCgO+c5ttE0xU4nr5K7ecM6vj30+oz86p0kL11842m4LqqOllutxpBWKxPS5/3kxJbd97n9ePKXcRucGMEvE41Y+v7Sovlw+M3Etg/BwXGUBn4IlPp09v0bv/fqvU5LNpi1P66wPXqxFcyvUUhvU+je2KW4ln5FaaoNaOGfmvayYHRLTor18pN8x/uJW2rBmiRbMqdDuw306s7FKF5/ZKGOkgycGdWIgorrKMj3w9EEtX1ir5zr7dOsjz09YoDbREUeDCtMxGHGeL3YgwlOc8JbG6qDjCMuGqmDKdn3hYceY7wsPj9lXZlNiHO4bcrxIWb6gNtm5EY+PLFA+ftRefNxU5dFoXA88czDZCR4s8emWdcu0bsUCOjg8gnyK6QqW+vRqz5Buf3RXSn5Y0lRdFMdDcch3biOXYibicatHdh7Spi1P5+X6OB63+umuw9p+oEdxKz3f2asj/UNafVYT1+NwlIvcxhUCMAvE41YvdvXIb4w2XXKWrEZudIwVLPGpbV6l/uHK5WkXL9t/LKT9xwf0ke9u1x2P7tZdW3frjkd36yPf3a79xwfy+BvBixLTosWtc/y1N1bp+08f1AuH+lTi92njm87UQ9sP6q6tu/Wlx/aoMliiu36yS5eeu0DbD/Ro05andem5CyYsULtpy9Pa2x0qxK8ID6spL3GMy5pTmPsTKISAf2T9rEQ8O003JU0W8yfnDvfJ6ENvOlOJGTcSoy38JvVi9cSAc0fJiYGTHSWH+07eSEz8/PZHd+lw31DK53Z29ujOrbt03UVtun71Ev35xW26c+su7ezsmf5/BgqipjxAPsW0HA8NO+aH46HhKT7pjeOhOOQ7t9E2xUzs7Q4lOzak3F8f7z8W0p4jId3z2J7kdfueIyHtP8b1OJxVBZ1zaVXZzEdPFn1WNMbsldQnKSYpaq1lDB9mlXjc6vFdh3VicFgvjy7YU1Xq1z9euVx/971nk735n7j0HJUEfKos8yng9zs+9en3GXX1Ok93cbgvrLaGqrwOgYS3HOoJq66iVMGAT5++YpkOHB9IrqXxmSuX66s/f3nCKIwNq9v1rV/uU2dPWJ9+6Dl97uoVkqSDJwZUV1Gqpc3V+vOL2ySNLEre2RNOiUcgU+UBv/7xbctTpsxZPK9S5ePvCLsY+RfSyHRT9z+1X5+9eoUGI1FVlAb0zV/sUdu8yuR0U5JkrdXH37JUR0ORZMzXV5bKWpvcJhyLOY62mD8ndRRIZalzu6Gi9OT5ExqKqq6iVG87f2FyFMj9Tx3QQCR1iqvu0JDe//rT1T1wslzvf/3pOhZK7QTJFOdF/pUHAp7Pp8iv/jT5ITRuCjyvHg/FId+5rRjapsi/qe7XZNvhviHHtd7OO21OcvQuMFZ5SZrcVkrnxlT+0Fp7tNCFAArh5cP9isWtXj2RenNi0yVn6vY/OU/PHuzRWU3V6jwxoK7esBbXV+rslqA2rmmfMFT7hc5eva5tnuMNjMbqYN6HQMJbKkr9evfvLUqJq09ceo56B4d1tC+s17U1TBiFccfo07t3/2S3wsNxvdjVp688vkf/sG65bvjjpfrwd56Z0BFyfCCieZWsAYPpGRiO6si4NQM2XXKmmj2ynhD5FwmRWEyrlzbrb7+bmh+HY6lDvQeHYxocjqfE/N+86UwNDp/cbjhqHZ9uHj8nbnUwoBsv65iwOHlN8OSlRnNtmd73hsUT1uxqHHeOza0o04uH+id0qNRVTP9c5LwoDK/nU+RfU3XZhDbixjXtaqzOTczk+3goDvnObeRSzERTjfP0pI3VwUk+NXM9g86jd3sGGQkHZ5Fo3DG3za+deYwyLRVQ5F7p7pff75twc2Lzj19SJBrXVx7fo12H+7RwbqW+s+2APv69ZxXw+dU6t0Lr3zgyJcT6N7aposSvr/9in4ZjMW2+dmXKdBebr12pxfWVeR8CCe+Ix636h6IT4vDTDz2n/qGYeodi8vuc14JJPFEXLPHJ2pH3/v6BZ+UzPtVVlCa3u2PrLl2zaqE2rmnXkf4hxeNWQKaicTk+dRSLT/FBl9jbHdKtjzyfMpXPrY88T/6dhWrKSnTftv0psXDftv2qKitJ2a68xK9//t/UmP/n/31JwZKTT031DzlPUzl+QfFQJKov/mx38pjXXdSmL/5st0JjRmUMROKO59hgZOL+nTpUwtHpn4y0SwrD6/kU+TcwHHM87weGc7O2QL6Ph+KQ79xGLsVMLK6vTHu/JhfKS/yOUwyNbU8CY0Vi1jG3Dcdmfv9mNozcsJJ+ZIyxkr5krb2n0AUC8ilY4k97c2IwEh15SqmmTF/86W519oQljdwMaK4J6sWuPklSLC598bE9Oj4QUV1FmU4MDqcsKF4aGLn7nO8hkPCOvd0h9aVZy6W1rlxdfUM677Q5jk+Z2NE1OhIjMxKfe/5Qr952/kLd/ZPdyfcW1JbrXx7dpcHhmJpqgsQdMhbK8CauW3WHhhyndTsWGuI8mGX6hoYdY6F/KPUJuuNp1snoGbNORmJ+8fF5eeyIDEnqD8cUiZ68IDFGikSt+sMnbxQe7R9yPN7R/tTppvqGpl7oXBpZeHxnZ486e8JqqS1XR0vNhAXH07VLunppl+SS1/Mp8i9dzISGctPZkO/joTjkO7eRSzETPp/R2o5mLd1wsQ73hdVYndspOf0+Oc764edReqSRbo3fU8lts6Fz4w3W2leNMY2SfmyMecFa+1jih8aY9ZLWS1Jra2uhyghM6lTitKoskOxNH39zYlF9heZUlOrGB3cmOzaCJT4NDcd1/X/8dsJw7c3XrpTfJ13/77+dsK+HN1yc9yGQcJfJ4rSrN6wSv3GMj4M9g7rj0d1aVF+um69Ypk9+f0cy5m5Zt1zlJT6tf2Nbcu2NxOdicWnsmrbBEp/2Hx/U8YGIYnFpX3eIudWRYrIYbagqc4zPeVXeGPpf6vc5Tut23/oLC1wyTNeptk1L/X7HWLj3fRekbNdYnSbmx0zLUlHi16ZLzpwwlVTFuKfx6qtKHad4qa8sTW5TVebcUVJVlno5Uju6gOr47WrHLHQejcb1wDMHdcMDY+uLZVq3YkFKB0dFqfMxK05hTl+MKOZ8ivxLLC46PmYqT2Fx0cliNBfHQ/HLRW4jlyIXfD6jtoaqrDzIMVW7tKI0oMpSf8rDr5WlflWUzIbbzZiJtNcgp5Dbir4vzVr76ujXw5K+J+mCcT+/x1q7ylq7qqGhoRBFBKZ0KnEaszHFZfWRPzorZWjiR/7oLMWs1YmBIV2zaqGuX71EG9cs0cffslS/Oz6gzp6w7n1in9a/sU2fvWq57nnXKr357Kbkgs1jJUZnLK6v1F3vPE8b1ixJ7u+ud56XsyGQcJfJ4rSpJqgv/my3br68IyUON65p13e2HZAk7ese1N0/2aWvvfe1uuud5+nr732tSgNGNeUlWragVscHImqpDWrDmiX6zJXLtWxBjdobK3X96iVaVF+uDavb9eSeI9p87UqVl/gUisS09cUupqdC0mQxatPkyZEBoO43EIk55uaBCE+hes2ptk1DkTRPJI9buHtZS60+fcWylJj/9BXLtLylNrnN0VBETdWluu3qFbr1bct12zUr1FRdqqMDkZR9Dcec1+YYHpN/S/xGG9e0T6gDSvypHdBl/pEOlLHbbbrkTJWOeQTwuc4e3Tm6JlNi6q07t+7Sc509KfuKxGLasDr1mCPrj8xsTo943GrPkX498fJR7TnSP6vrl8nzadzT+RT5ZyTH8/5Unk+ZLEZzcTwUv1zktmJum6I4TNUuDfidE2cgQEKFs1zktqLuSjPGVEryWWv7Rv/9Zkk3F7hYevdffECvHj0x4f358+bo3i//a/4LhKIVj1v97tiQSvxGAWNSetPnVJQoHo/L5/OlLOTz6SuW6btPjdxs7uwJ645Hd+v61Uv0yQd3JkdnLKov16XnLkg+Nf+DZw4mR2dEojZlf5uvXVmg3x5usri+Uu+/6Ax97ecv67NXr1A4EtX8OeX6yHe3J0djSCPxE4nGFY1Z/XJPt7ZsO6DjAxHdfMUy/dufX6AXDvXr0w89lzLVyg+eOagNa86Ujcd05fmnpSwcu3FNu5Y0VGnxPKYfweT6h4ZV6velTrnn9ynkkaH/jdWMnMOI1rmVjrHQOjf1QYPSUr+uOHe+Tp9Xqa7esJpqgjp3fq1Kx4xqaKwu04uhYX3iv55JaSecPm7R3aP9Q6qrKNXbzl+YbBvc/9SBlCmnSgJGzbXBlHOsuTY44aK4e2BIc4IB3Xb1CoWGoqoMBjQQHtbxgZP7OppmGrbuUGqnS31lWXL9EWMka6X7tu3X2mXNKdvF41Z7u0PJ/wenUX8sTp650FDU0/kU+Vfi92lO+eh5H4mqsjSggciwSnI0r0m+j4fikO/cFvJ42xSzw8Hjg/rCz/Yk24CxuPSFn+3RTbUd6phf6NLBjXKR24q6c0NSk6TvmZGrrICkf7fWPlLYIkmvHj2heW/54MT3f3hnAUqDYvbK0ZA+/r1n9eV3r9JHvrt9wo2OL/zp+bpz3NQVn/j+Dl3/h0v0luUtWlhXocFIVNXlJaqrKNW+7pDe0DZPH1zdPmEqiNa6irQLdy7dcDFzW89yybk/m6t1LDSkEn+5BofjOj7m6d+W2qDe/XuL9FfffioZW5+49Bzd96v9+uT3d+jL716V7NiQTk61ct1Fbfr77z2rr7/3tXrfN3494cnh81vr8tK5kcnNMbhXVWmJ/uHhiVPuffv9F0zyKfdgvlsknD6vUp+/ZqU+/J2TN+E/f81KnT5v4ijK0lK/Vi2em3ZfiXbB+HbCvePOi4Vzyh2npVowp/zkRtbo3365V+9+fZsGh6KqKAvom7/Yo4+/9ZyUfTVWB7X7cEif/MHJjuxNl5ypcxacHFFSURpwnHrrG+97bcq+FtdX6qNrz57QITF2RGmmnRaFaONksq6IG1V6PJ8i/ypL/TLGp//vu6kdqbmaQi7fx0NxyHduI5fCC+ory3R8IJJcB1MaidO5Y6YmBcbKRW4r6s4Na+0eSSsKXQ6gEOJxq+c7exUejutIn/Minr/93Qld85rTNDgcV3XQrwVzKvTK0ZCWLajV/u6QPjKmwb/pkjO1q6tPfp8v2bGR2M8ND+zQ+a11LCiOSfl8RovrK/XCoT5t2vK06ipKU27GXrNq4YRpTT790HO680/O0ycf3Knu/ohjfBkz8vV3xwYcnxweiOT+6Sae6PW+o6GI85Pn454Ed6vOnrB++GynPnv1Cg1GoqooDejLj72s81rnMHJpFiorSR2tWVYyszzU1evcfjjcm7oIeCQWd5yWatWiuuQ2PeGIVi9t1t+OaVtsWN2u3nELhQcD/uQaH4l9bf7xS1p91kXJbQbTTMM2OG4aNp/P6E1nNerb171Oh3rDaqkJavn82pS8/MrRkG595Pnk6A5JuvWR53VWU7XOaDx57uS7jZPpuiJu5PV8ivwLRWKOHanfvi43N3HzfTwUh3znNnIpvCBmrTasbp8wmtYyfRrSyEVuK+rODWA2e+VoSAeODyhY4lNDVanjFBWxuNQyN6g7tu7S21e1pnRmbFzTrrqK0uQaG5t//JKu/8Ml2rbvWNqLexYUx1TGPvk6dl2XZS21Oj7o3HnREx7W+16/SE01zgtPndVUrUX15aqtcF7QNh9rvjBqyfvqKkoc46euomTqD7tAS21Qb1neknLjeOOadjXXkH9nm73dIV3/7xOfhnrYIR9FIjFtf7Un5cb/2GmpmtPk3aaa8dNSOefvsdNElZdkNtriSLoprkJDWqJqSZlPvRWNxvXgs69O2kHwas+A3nnBIv3z/55cNP1v3nSmOnsGUjo38t3G2dnZ4/gwSXtjlVacVjfFpwvL6/kU+XdiYNgxh5wYGE7zCW8dD8Uh37mNXAovKAv4HacAfcOS+kIXDS6Vi9zm7sd+PO7df/EBvenKd054vfDSrkIXDUUuHrfac7Rf5SV+ffwtS9UbjuoTl54zYUHNh7Yf1L5jA7r03AUTbjjc/uguve38hcl9hofjCkfjilsl95OQuLhfXF+pzdeuTDnO+OkfMHvF41b7ukMpF5OJdV1CkagaqsocY2v/sQGd2Vyjr//fy44Lw972oxf0V29cosM9Yccnh/Ox3utkT/TCG4wxjvFjjDdG3sTiciz/DNdNhodlmo8ikZge2P6q/uyrT+r6f/+t/vSrT+qB7a8qMmb0Q/OcMt18eUdK3r358g41z0nt3KgJBhzzd1XZyeeo+sPOC533h1NH1zVWl+ndv7dIX/35Ht21dbe+8vgevfv3Fqmh6uQxF82t0GeuXJ5Srs9cuVyL5lak7CtdB8HOMQuPBwP+ZMdGYpt//t+XVBZInZ4m322cxMMlY4WH4zrU4/56xev5FPlXW17imENqy3NzE7cmzfFqcnQ8FId85zZyKbwgEovp7atak+22r/58j96+qlXDXIQgjVzkNkZu5FC6tTUiOz9QgNJgNnnlaEjBEr/+8ZEXdP0fLlF3KKL/231Em69dqRcO9SoWH+lNv/4P27X5xy/pqtcsTDvdT0KwxCdrpYe2H9StV52rj96/fcL81cl1FTZcrMN94WSHB9PyIDFtU9xaxydfX+kO6fzWObpl3bKUJ2w3rG7Xt365T6fPq9SPnjuqZw/267NXr9Duw32KxaVv/XKfOnvCuumhnbrpsg7HOD7SH055+jYXGLXkfUf7naffGbsgspsd7nO+EZqP+Ie7pFtcvqEqNR9tf7VHnxw3Lcsnv79DbfMqk+twHDgW1vOvntA33neBjvSF1VAd1P88e1Ct9RU6re5kXJWX+h3XfKkcMwokcTNxfLnG30w8PjDseMHz2jFrgxzsGVBdRUD3vOs1Oh4aVl1liWLxuA72DGhR/clyTdZBsOK0ke9PDDo/wd0zmPoEd77bOC215Y7/X8217q9XvJ5PkX9xax1zSNzm5gkVm+Z4NkfHQ3HId24jl2Km8rkWZH1lmePIjbXLmnNyPHhfLnIbnRtZ8PxzO/WmK9854f0XXtqli95SgAJh1tt3LJScjzocjev+pw7ova9frFsfeV6XnrtAfp/0sbVna05FILmgs9MFdKL+S0zR8O+/2qePrj1bbz67ScsX1Dpe3CfWVZBGnh6VRAcHktM2fehN7br58g598sGdEzow4lZqrCpNzhNv7UjnxfGBiA4eH5Q0cqPqpa4+3bV1d8r+w8NxVY4+OTzVDb10TqURmHiid7JFa+Fucyudp++rq/DGYnh0sBVOPi8gM5Hp4vKH0ozwONR7cmRAS21QjbUVeu/XfzXpdGf1VaWaPyeYss7H/DlBza06ef5Ulvm16ZIzk+tpJNbzqhy3gG8mIxZODAzrUG9EN/3gZF1y42Udmls5rEVjZkFI20Ewpvw1QedOl+rgxCe4fT6jtoaqU55uMJOYObupWjdfsSzZARUs8enmK5bp7KaaUzp2Png9nyL/TgwO694n9qXcHLv3iX06I0dTe+b7eCgO+c5t5FLMRDxutfXFLm0/0KO4lfxGWr6wVqvPaspJ+3RxfaU+cek5yeMFfNInLj2H62CklYvcRudGFgxbHyM04CpVZQFVlp6cIuL4QETf+MVe/enrWrVgTrmaa4PqDUdljNFH/ugs3fvE3gmLQH32qnO1YE65zjttjuorSxWJxXXJOU3JC/B0F/csrAwniWlS+odiamtw7sCwVvrCz/bofW9YnHLz66bLO9QzENH1q5eovMSnJY3V2rBmieJ2ZB72zp6wgiU+za8JTrhx5nRDz8mpxi2jlryvvMSvT13WoU+NuVn6qcs6VFHin/rDLkAHW2G4sc5LrGc0/qbd+MXl69Osx1VfefLCIt10Z28+J/VpvOGo1eYfv6RLz10gY0Y+t/nHL+mr7z65nkY4Elcw4EvpAAkGfBM6MjJZ52MgEkt2bCTKddMPdurr701dv2NORUA3Xd6hG8d0qN90eYfmVJ7suGiqKXPsDBq/rki2ZHrT4UDPoLb8ep8+e/UKDUaiKi8N6N5f7NGqRXUz6lzJZyec1/Mp8q+ltlzHByK6+ycnH14Z6YjMzXlYEyxxPF51kNsjSC/fuY1cipnYfyykXV39uuexPSntmiUNVSntwGyKRG3K8TZfuzInx0FxyEVuo/YGilBFqV+H+8L6u7VLNRSN6ROXnqNPP/ScfrTzkN7xukV63zd+PaYTY7ne83uLFYnFddvVKxSJxXVaXbles2jujC56WVgZThJPld//1AGdd1qtqsoCyU6IRfXl2nztSu0+3K+rXrNQDz3zqj539Qq92NWnuRUlKi/168YHU286fWfbAR0fiGjD6nbdt22/1r/xDP1q3zF98xdT39Bzko24zdYTvSiUuOoqSnTb1SsUikRVWRpQid9IxhvzxdLBVhhurPOaaoKON+3Gj+Kpqwjoxss6Jox+qKs4eXmQ6XRn+48PaF/3YMoxJel3xwe0pGlkEfCe8LC+8LM9yYXCY/GRDu3PXLks5TPLW2r16SuW6RNjRix8+oplWt5Sm9ymOzT1AuYj5R9SLB5P6VCJxeM60jeUnL5q4ZwKLawrT9lmYV25Fs5JXb9Dyk4HQaY3Hbp6w9q2r0fb9v123O8UnnZs5bsTLhaPqa4iMC6fSjEbm/rDmJXOaa6ZMDXpLeuW6Zwx5302NTs8ELPpkjMnjEoDxsp/bvN22xSF0dU75PhgyvmtdTnp3HBjWxhul/3cRucGUESi0bh2vtqj350Y1ILaoHbHQtr8v7tUVzHypPzrTq/Xdd/8dUrF87f3P6svves1+vXe43r+UJ8e2n5QX3/vBTO+2J1sIVMqt9lrcX2lvvSu8zU0bFUa8KmlNqiNa9rV1lClo/1DKTdcNq5p14HjA/rK43v0r396vj7wb7+Z0Di77qI23f2T3bpj6y7ddvUK/e74gEKRWEY39JwQt4hEpY33PZ0SB8ESn771/gsKWKrpoYMt/9yYOxL5tm8wptBQVJXBgKqD/gmjeJbMq9H+Y+GUC4vSEp+WNJyc9ijT6c4So0XHb1dRevJSY9HcSscc3To3tVylpX7NqShJ6WyYU1Gi0jHTVzVVpxndUZ36lHeZ36ebH3p+wnZb1l+Y/H7/8QHd9qMXU0ad3PajF9Uxvzblb5itDoJMbzpkc6q5fN94iMWNNt73zISy3+uhfIr8CgR8Wrdigdobq3SoJ6zm2qA6WmoVCGQw/HYGFtVXqq2hMiXPtDVUahGjHTGJfOe2YmibIv9Ckah+7/S5eu9Fp+t4aFhzK0v09Z+/ooFINCfHc2NbGO6Wi9xG5wZQJKLRuB59sUtGRhUlfoVj8eTTSJ09Yd3x6G7delW5Y8XT1RPWXVt3K1ji013vPE/WSk+8fHRGTyUy7zucxONW4eG4Aj6jnoGo4nGrJQ1Vqijza+N/PjfhJs+/XLtSd7/zfO3u6neM2cRi9+HhuIbjcd37xD5JmjC9WibT8sTjVhVpbswRt7PH4T7nhc0O93ln0Ua3rf0wG7ixzkvk27HCw3HF4zYlHnw+I5+RXjrcl7y5d+7C2pRtMp3urKmmTB9/y1IdDUWSUy3VV5amTO10+jznfZ0+L3Vfe7tD+uB//HbC/+nDY27EN9WWTVi/6ebLO9RUm9q5cbR/6hEeXb1hx1En4y/Ks9VBEIpEHcs0/qZDpv/3mZz3+b7xkC6fHvFQPkX+BQI+rTitTitOy/2xfD6j1Wc1qW1eFaMdkbF857ZiaJsi/85oqNT7L16sgG9kDdUSv0/vv3jxhIdJssWNbWG4Wy5yG50bQJHYdaRXzbWlGhqWDvcOKRKLT0gY6W7gnja3Ql9/7yqdPq9Sz3X26Y/vfHzGTyUy7zucvNDVq/6hqELhqHxGWlRfpcHhmPrCzjd5fD6jvUdDOrul2jFmrT357/KAX52jC81+65f7tP6NbTrvtDlaVF855YVq4kncWx95fkYdIyge6dYfmFvpjUUb3bj2w2zgxjpv15FeDURieuVoKNnRsHhepXYd6dXZLXOS2+3tDulHO17VuvNbdbRvSA3VZfreb/arbV7q6J/KMl/K6I7KsolPUi+cU6G6ylIdHdNpUFdZmjK1k89n9Oazm3Tf+gvV2RNWS225OlpqZnQjfuGcSi2cO6B73vUaHR8YVl1FiQL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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8c870ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0faf78da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting this in form a heatmap\n",
    "\n",
    "corrmat = final_dataset.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize = (20,20))\n",
    "\n",
    "# heatmap\n",
    "\n",
    "g = sns.heatmap(final_dataset[top_corr_features].corr(), annot=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73969aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        7                 0   \n",
       "1           4.75           9.54       43000      0        8                 1   \n",
       "2           7.25           9.85        6900      0        4                 0   \n",
       "3           2.85           4.15        5200      0       10                 0   \n",
       "4           4.60           6.87       42450      0        7                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b2f52143",
   "metadata": {},
   "outputs": [],
   "source": [
    "# independent feature\n",
    "X=final_dataset.iloc[:,1:]\n",
    "\n",
    "# selling_price == dependent feature\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f3af99d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>no_year</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0        7                 0   \n",
       "1           9.54       43000      0        8                 1   \n",
       "2           9.85        6900      0        4                 0   \n",
       "3           4.15        5200      0       10                 0   \n",
       "4           6.87       42450      0        7                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c9fd3d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8bca0f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### feature importance\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "351db24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37766688 0.04705731 0.00044038 0.07323177 0.22203492 0.0161451\n",
      " 0.11963687 0.14378677]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f884b267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the feature importance data for better visualisation\n",
    "\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6cd75fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a496932c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9e24b1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_random = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a5551b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "# hyper-parameters\n",
    "import numpy as np\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7872a360",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "10156993",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0f9c7baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# creating random grid\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fcdc401f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the random grid to search for best hyperparameters\n",
    "# creation of model to tune it\n",
    "\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7bdbf441",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "068a6838",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a0482f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b117bad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.44722708, 17.41094362,  0.41971388, 21.98172485, 22.01932135,\n",
       "        4.05423565,  0.34662319,  6.51700517, 13.06947596,  0.48497447,\n",
       "        9.84346824,  9.88730559,  0.45767692,  3.23772318,  0.40593651,\n",
       "        7.94765162,  0.42016507,  9.78384965,  0.30328096,  3.00166475,\n",
       "        0.26005982,  7.41978331,  6.9789521 ,  0.70105373,  0.61230315,\n",
       "        5.3511003 ,  2.82751922,  0.66376307,  4.68337633,  5.98584464,\n",
       "        4.36209844,  5.45683804,  4.64121107,  9.37092757,  9.21178564,\n",
       "        0.26387228,  4.48920916,  4.54798286,  0.59520011,  2.70941748,\n",
       "        4.48837449,  1.99484085, 22.04201151, 10.01275431,  2.78563085,\n",
       "       10.67537655,  5.43216601,  3.0151912 ,  2.87270767,  0.41825717,\n",
       "        4.46483209,  4.84152226,  7.7691786 ,  0.2619386 ,  4.0185865 ,\n",
       "       22.04201151,  4.29223458,  7.82273951,  3.95878536,  7.23142088,\n",
       "        3.95708893])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b0b73b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(y_test-predictions)\n",
    "\n",
    "# sns.distplot(y_test-predictions) | distplot is deprecated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d2d2d6cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f87f9a9f730>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e31799bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# info dumping in pickle file (model)\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d56ec5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "eea9282ec93a5e891ded56c755fc38718dc44576bee598d53efb0940530d829e"
  },
  "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": "3.9.5"
  },
  "metadata": {
   "interpreter": {
    "hash": "eea9282ec93a5e891ded56c755fc38718dc44576bee598d53efb0940530d829e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}