Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "875ffe1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e14ad222",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "041b6f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03be5126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8396096a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "#To find Unique Values\n",
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa74055e",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check missing or null value\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "efa8ebc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a0185d39",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1fe1df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
    "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "828cb37e",
   "metadata": {},
   "outputs": [
    {
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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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9f74cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current_Year'] = 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "583a7152",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb5aa38e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['No_Of_Years']=final_dataset['Current_Year']-final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d666b590",
   "metadata": {},
   "outputs": [
    {
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       "        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>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_Of_Years</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_Of_Years  \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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "24e0addb",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de4a6bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
       "      <th>Current_Year</th>\n",
       "      <th>No_Of_Years</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",
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       "      <td>2021</td>\n",
       "      <td>7</td>\n",
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       "      <td>6900</td>\n",
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       "      <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_Of_Years  \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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c8c976a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Current_Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad09f70f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Present_Price</th>\n",
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       "      <td>3.35</td>\n",
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       "      <td>27000</td>\n",
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       "      <td>Dealer</td>\n",
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       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</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  No_Of_Years  \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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0750a209",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Dummies\n",
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "be0840fe",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  No_Of_Years  \\\n",
       "0           3.35           5.59       27000      0            7   \n",
       "1           4.75           9.54       43000      0            8   \n",
       "2           7.25           9.85        6900      0            4   \n",
       "3           2.85           4.15        5200      0           10   \n",
       "4           4.60           6.87       42450      0            7   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f6fceb2",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>0.089216</td>\n",
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       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
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       "      <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_Of_Years</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_Of_Years                 -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_Of_Years  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_Of_Years                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_Of_Years                           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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2aab7e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "543cf935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1794ba50448>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": 35,
   "id": "e4763f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8dc26984",
   "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": [
    "corrmat=final_dataset.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#Plot heat map\n",
    "g=sns.heatmap(final_dataset[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4cad74c9",
   "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_Of_Years</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_Of_Years  \\\n",
       "0           3.35           5.59       27000      0            7   \n",
       "1           4.75           9.54       43000      0            8   \n",
       "2           7.25           9.85        6900      0            4   \n",
       "3           2.85           4.15        5200      0           10   \n",
       "4           4.60           6.87       42450      0            7   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8638260",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Independent and dependent features\n",
    "X=final_dataset.iloc[:,1:]\n",
    "Y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad25b7f6",
   "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_Of_Years</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_Of_Years  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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fd282f9e",
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f28b454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature importance\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model=ExtraTreesRegressor()\n",
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f36f0188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.78744626e-01 4.39764140e-02 3.08475464e-04 7.36784779e-02\n",
      " 2.28243276e-01 9.55090710e-03 1.31001105e-01 1.34496718e-01]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8a37ae71",
   "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": [
    "#plot graph of feature of importancefor bewtter visualization\n",
    "feat_importances=pd.Series(model.feature_importances_,index=X.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9a5dd5f5",
   "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": 49,
   "id": "935d9282",
   "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_Of_Years</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>66</th>\n",
       "      <td>23.15</td>\n",
       "      <td>11000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>12.35</td>\n",
       "      <td>135154</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1.47</td>\n",
       "      <td>17000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22.83</td>\n",
       "      <td>80000</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.75</td>\n",
       "      <td>60000</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.55</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.54</td>\n",
       "      <td>8600</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.61</td>\n",
       "      <td>33429</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.75</td>\n",
       "      <td>11500</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>7.60</td>\n",
       "      <td>77632</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",
       "<p>240 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Present_Price  Kms_Driven  Owner  No_Of_Years  Fuel_Type_Diesel  \\\n",
       "66           23.15       11000      0            4                 0   \n",
       "77           12.35      135154      0           17                 0   \n",
       "114           1.47       17000      0            6                 0   \n",
       "78           22.83       80000      0           11                 0   \n",
       "190           0.75       60000      0           13                 0   \n",
       "..             ...         ...    ...          ...               ...   \n",
       "166           0.55        1000      0            5                 0   \n",
       "158           0.54        8600      0            4                 0   \n",
       "7             8.61       33429      0            6                 1   \n",
       "124           1.75       11500      0            9                 0   \n",
       "222           7.60       77632      0            7                 1   \n",
       "\n",
       "     Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "66                  1                       0                    0  \n",
       "77                  1                       0                    0  \n",
       "114                 1                       1                    1  \n",
       "78                  1                       0                    0  \n",
       "190                 1                       1                    1  \n",
       "..                ...                     ...                  ...  \n",
       "166                 1                       1                    1  \n",
       "158                 1                       1                    1  \n",
       "7                   0                       0                    1  \n",
       "124                 1                       1                    1  \n",
       "222                 0                       0                    1  \n",
       "\n",
       "[240 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39b0a81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "638423f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b4580a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hyper parameter tuning\n",
    "import numpy as np\n",
    "#number of terrs 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 each split\n",
    "max_features  = ['auto','sqrt']\n",
    "#maximum number of levels at trees\n",
    "max_depth = [int(x) for x in np.linspace(5,30,num=6)]\n",
    "#minimum number of samples required to split a node\n",
    "min_samples_split=[2,5,10,15,100]\n",
    "#minimum samples required at each leaf node\n",
    "min_samples_leaf=[1,2,5,10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fffd4169",
   "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": [
    "#Create random grid\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",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "deb0d842",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use the random grid to search for best hyperparameters\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e2c358f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\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": 74,
   "id": "e60ae42d",
   "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=   0.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.2s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.1s\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=   0.3s\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=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; 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=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; 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=   1.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.1s\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=   0.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.3s\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=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   0.7s\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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "007c9323",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bbd613ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5394 ,  5.25729,  5.77355,  0.49711,  4.8421 ,  0.52326,\n",
       "        3.99159,  5.6387 ,  0.59595, 12.67595,  0.32397,  0.27679,\n",
       "        3.53964,  2.62388,  4.42937,  3.60893,  3.17957, 21.00205,\n",
       "        6.43709,  0.54938, 12.39771,  8.74039,  5.05061,  3.17067,\n",
       "       18.14551,  0.54225,  7.87854,  8.67902,  4.0563 ,  0.52584,\n",
       "        2.74097,  7.29761,  7.01535, 20.83885,  0.72261,  6.02817,\n",
       "        4.50062,  1.11387,  0.60999,  0.94497,  1.42134,  3.59508,\n",
       "        2.63393,  1.10777,  1.45102,  0.57412,  9.29833,  5.3602 ,\n",
       "        9.37801,  5.8873 , 14.89984,  0.9656 ,  0.9603 ,  9.3293 ,\n",
       "        3.4426 ,  5.01548,  6.2149 ,  6.8509 ,  0.69004,  5.3748 ,\n",
       "        4.45893])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1f8b6acb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADK\\.conda\\envs\\carprediction\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Density'>"
      ]
     },
     "execution_count": 77,
     "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.distplot(Y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1a415609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17956438508>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAATeklEQVR4nO3df2zc9X3H8dc7xyEuYarDYlByg4UhZLY2K14t1inTlLRbzVp1uOmohrSJSdXSP4oE22Qt6ZBgVadEc+n2x6ZKqYqaTpRChzGRqOZGwMSKNIqDQw0LHl2XAucoMaJe+XFdjP3eH/e9xD7f77vv3fdz93xIke2Pv+d78xV5+Zv39/P9fMzdBQAIz6ZuFwAAaA4BDgCBIsABIFAEOAAEigAHgEBd0sk327Ztm+/cubOTbwkAwTtx4sQb7j5YOt7RAN+5c6dmZmY6+ZYAEDwz+0m5cVooABAoAhwAAkWAA0CgCHAACBQBDgCB6ugsFADxmprNaWJ6XgtLee0YyGh8dEhjw9lul4WYEOBAj5iazeng5JzyyyuSpNxSXgcn5ySJEO9RBDjQQXFeIU9Mz18I76L88oompucJ8B5FgAMdEvcV8sJSvqFxhI+bmECHVLtCbocdA5mGxhE+AhzokLivkMdHh5RJp9aNZdIpjY8OteXnI3kIcKBD4r5CHhvO6tC+XcoOZGSSsgMZHdq3i/53D6MHDnTI+OjQuh641P4r5LHhLIHdRwhwoEOKwco8bbQLAQ50EFfIaKeaPXAzu9rMnjKzU2b2kpndGY1fYWbHzeyV6OPW+MsFABTVcxPzPUl/6e6/KunDkj5vZr8m6YCkJ9z9eklPRF8DADqkZoC7+xl3fz76/C1JpyRlJd0i6Wh02FFJYzHVCAAoo6FphGa2U9KwpGclXeXuZ6RCyEu6ssJr9pvZjJnNLC4utlguAKCo7gA3s8slPSLpLnf/Wb2vc/cj7j7i7iODgxv25AQANKmuADeztArh/YC7T0bDZ81se/T97ZLOxVMiAKCcemahmKSvSzrl7l9Z861jkm6PPr9d0mPtLw8AUEk988B3S/oTSXNmdjIa+4Kkw5IeNrPPSnpV0q2xVAgAKKtmgLv79yVZhW9/tL3lAADqxWJWABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgECxIw9Qh6nZHFuhIXEIcKCGqdncus2Ic0t5HZyckyRCHF1FCwWoYWJ6ft1O8pKUX17RxPR8lyoCCghwoIaFpXxD40CnEOBADTsGMg2NA51CgAM1jI8OKZNOrRvLpFMaHx3qUkVAATcxgRqKNyqZhYKkIcCBOowNZwlsJA4tFAAIFAEOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BAEeAAEKiaAW5m95vZOTN7cc3YvWaWM7OT0Z+Px1smAKDUJXUc8w1J/yjpmyXjf+/uX257RUCJqdmcJqbntbCU146BjMZHhzQ2nO12WUDX1Qxwd3/azHZ2oBZgg6nZnA5Ozim/vCJJyi3ldXByTpIIcfS9Vnrgd5jZD6MWy9ZKB5nZfjObMbOZxcXFFt4O/Whiev5CeBfll1c0MT3fpYqA5Gg2wL8q6TpJN0o6I+m+Sge6+xF3H3H3kcHBwSbfDv1qYSnf0DjQT5oKcHc/6+4r7r4q6WuSbmpvWUDBjoFMQ+NAP2kqwM1s+5ovPyXpxUrHAq0YHx1SJp1aN5ZJpzQ+OtSlioDkqHkT08welLRH0jYze13SPZL2mNmNklzSaUmfi69E9LPijUpmoQAbmbt37M1GRkZ8ZmamY+8HAL3AzE64+0jpOE9iAkCgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoAhwAAgUAQ4AgSLAASBQBDgABIoAB4BA1bMrPQCgSVOzudjWsyfAASAmU7M5HZycu7Axd24pr4OTc5LUlhAnwJEYcV6pAN0wMT1/IbyL8ssrmpieJ8DRO+K+UgG6YWEp39B4o7iJiUSodqUChGrHQKah8UYR4EiEuK9UgG4YHx1SJp1aN5ZJpzQ+OtSWn0+AIxHivlIBumFsOKtD+3YpO5CRScoOZHRo3y5moaC3jI8OreuBS+29UgG6ZWw4G9t9HAIciVD8H5xZKED9CHAkRpxXKkAvogcOAIEiwAEgUAQ4AASKAAeAQBHgABAoAhwAAkWAA0CgCHAACBQBDgCBIsABIFAEOAAEigAHgEAR4AAQKAIcAAJFgANAoGoGuJndb2bnzOzFNWNXmNlxM3sl+rg13jIBAKXquQL/hqSbS8YOSHrC3a+X9ET0NQCgg2oGuLs/LenNkuFbJB2NPj8qaay9ZQEAamm2B36Vu5+RpOjjlZUONLP9ZjZjZjOLi4tNvh0AoFTsNzHd/Yi7j7j7yODgYNxvBwB9o9kAP2tm2yUp+niufSUBAOrR7K70xyTdLulw9PGxtlWEWEzN5jQxPa+Fpbx2DGQ0PjrEDvBA4GoGuJk9KGmPpG1m9rqke1QI7ofN7LOSXpV0a5xForpa4Tw1m9PByTnll1ckSbmlvA5OzklSXSFO+APJVDPA3f22Ct/6aJtrQRPqCeeJ6fkL3y/KL69oYnq+ZhC3Gv4A4sOTmIGrFs5FC0v5sq+tNN7ozwfQHQR44OoJ5x0DmbLHVBpv9OcD6A4CPHD1hPP46JAy6dS672fSKY2PDrXl5wPoDgI8cPWE89hwVof27VJ2ICOTlB3I6NC+XXX1sMdHh5RO2bqxdMrqCn8A8Wp2GiESYu2NymqzREqPK/aw67oR6TW+BtAVBHgPGBvOxjabZGJ6Xsur6xN7edXrmsECIF60UPpEs7NJuIkJJBcB3ieaDWJuYgLJRYD3iWaDuJUZLADiRYD3iWaDuJUZLADixU3MPlHvbJVKryWwgeQhwAPXyEJTBDHQWwjwgLHQFNDf6IEHjIWmgP5GgAeMOdpAfyPAA8YcbaC/EeABY4420N+4iZlgtWaYtDI1EED4CPCEqneGCVMDgf5FCyWhmGECoBauwFsU147tzDABUAsB3oI4H6TZMZBRrkxYr51hEtcvDwBhoIXSgjjbHLVmmBR/eeSW8nJd/OUxNZtr+b0BhIEAb0GcbY5aqwDSIwdAC6UF9bQ5WlFthgk9cgBcgbeg0QdppmZz2n34SV174HHtPvxkS+0OnsIEQIC3oJHNDtrds+YpTAC0UFpU74M0tXrWjc4m4SlMAObuHXuzkZERn5mZ6dj7Jcm1Bx5XpTOdSafWhXsmnWLbMgAXmNkJdx8pHaeF0gb19LYr9aZTZswmAdAUWigtqudhnqnZnN49/96G15Zeea/FbBIAtXAF3qJave1iwP/03eV1x2xOb7pwA7QcZpMAqIUAb1Gt+djlAl6S8surkphNAqB5tFDqUG3NkVoP81QKeFch3J858BFJzCYB0DgCvIZaPe7x0aF135fWX0FXCnjpYrizpjeAZtBCqaGeNUcuS188jQOZtD79oawmpud17YHH9c7/bbx5WUSfG0AruAKvoVqPu/TqXJLeOf+eHvrBa1peLcz6Xsova5NJqyWTwDPplPbeMKjdh5+kdQKgKQR4DdV63OWuzpdXNj6us+rS1s1pbb70kgthvfeGQT1yIhfLWuIA+gMtlBqqzRJpZK720rvLeubAR/Q/hz+hZw58RE+9vMgDPABa0lKAm9lpM5szs5Nm1pPPyFdbsKqRHnbpsSwHC6BV7Wih7HX3N9rwcxKr3CyRqdlc1RuUpUrndce9ljiA3kcLpQnFm5dL+eXaB0dKfwHwAA+AVrUa4C7pe2Z2wsz2lzvAzPab2YyZzSwuLrb4dslQ6enKRjSyljgAlNNqC2W3uy+Y2ZWSjpvZy+7+9NoD3P2IpCNSYTnZFt8vERrtU2/dnC47zgM8AFrRUoC7+0L08ZyZPSrpJklPV39VclV6ZP7uqTk9+OxrWnFXykyXpTddWMtkrYFMWu+cf2/dVMJ0ynTPJ9/fyf8MAH2i6QA3sy2SNrn7W9HnH5P0xbZV1mGVHpn/zsyreua/37xw3Iq78suuTZLWRngmndK9f1AIatY1AdAJrVyBXyXpUTMr/pxvufu/tqWqLqj0yPza8F7HpOz7MmWDmsAG0AlNB7i7/1jSB9tYS1c12tdedV1YSRAAuoFH6SPVVg0sZ5MVPlZbahYA4sQ88Eiledm7r7ui/AtcuntqTgcn55Rbyst1sW9ebk9MAGg3AjxSaV72A3/2W8qkN56mVUkPPvsa65kA6BpaKGtUmpf98zJTBqXCjJRyWM8EQCf0dYCX619LG6cBVuqPp8zKhjjrmQDohL5toRTnfa/tX9/10End9dDJDT3tvTcMlu2P3/abV7OeCYCu6dsAr3c9k/zyip56ebFsf/xLY7tYzwRA1/RtC6WRPvXCUr5if5z1TAB0S88HeKV52o3M+6anDSCJerqFUq7PXZynPT46pHTKav4MetoAkqqnA7zS+iYT0/MaG85qy6XV/wGSMqOnDSCxejrAa+07+b9VdtTJpFO67zMfJLwBJFZPB3il3nVx/H2Z8hstcOUNIAQ9HeDl1jcxSXtvGCxsSnx+46bE6U3GlTeAIPR0gI8NZ/XpD2W19lalS3roudf05w+dXLdzTtHll11CeAMIQs9PI3zq5UWVxnS54C5aerf+neYBoJt6+gpcanxhKeZ8AwhFzwd4I4HMnG8AIen5AC93I7McZp4ACE3PB3jpRg1bN6eV3rT+CUzmfAMIUc/fxJQ2LjjFPpYAekFfBHgpVhAE0At6voUCAL2KAAeAQBHgABConuiBT83mdO+xl7QUrS64dXNa93zy/fS5AfS04AN8ajan8e+8oOXVi4/H//TdZY3/ywuSRIgD6FnBt1AmpufXhXfR8oprYnq+CxUBQGcEdQVebv52tX0tG10HBQBCEkyAF/e3LG6RllvKX2iTVMLCVAB6WTAtlHL7W1ZbFjadMhamAtDTggnwRtshE3/I2iYAelswAd5oO4TwBtDrggnwvTcM1n2s1T4EAIIXRIBPzeb0wH+8Wvfxl6WD+M8CgJYEkXR//ejchn0tq/n58mpstQBAUiQ+wO+emtM751dqH7gG0wcB9INEB3ijrROJfS0B9I+WAtzMbjazeTP7kZkdaFdRRRPT83W1TrZcmpJJyg5k2NcSQN9o+klMM0tJ+idJvyfpdUnPmdkxd//PdhVXz9zv66/couN/saddbwkAwWjlCvwmST9y9x+7+3lJ35Z0S3vKKqjVy/7jD19DeAPoW60EeFbSa2u+fj0aa5vx0SFl0ql1Y6ZCcJ8+/Al9aWxXO98OAILSymJW5Z6X2dCyNrP9kvZL0jXXXNPQGxR72ewgDwAbtRLgr0u6es3XvyRpofQgdz8i6YgkjYyMNDKdWxI7yANAJa20UJ6TdL2ZXWtml0r6I0nH2lMWAKCWpq/A3f09M7tD0rSklKT73f2ltlUGAKiqpQ0d3P27kr7bploAAA1I9JOYAIDKCHAACJS5NzwxpPk3M1uU9JMGX7ZN0hsxlBMX6o1XSPWGVKtEvXFrpd5fdvcNmyJ0NMCbYWYz7j7S7TrqRb3xCqnekGqVqDducdRLCwUAAkWAA0CgQgjwI90uoEHUG6+Q6g2pVol649b2ehPfAwcAlBfCFTgAoAwCHAAClegAj3vLtnYzs9NmNmdmJ81sptv1lDKz+83snJm9uGbsCjM7bmavRB+3drPGogq13mtmuej8njSzj3ezxrXM7Goze8rMTpnZS2Z2ZzSeuPNbpdZEnl8zu8zMfmBmL0T1/k00nrhzK1Wtt+3nN7E98GjLtv/Smi3bJN3Wzi3b2s3MTksacfdEPlxgZr8j6W1J33T3D0RjfyfpTXc/HP2S3Oruf9XNOqO6ytV6r6S33f3L3aytHDPbLmm7uz9vZr8g6YSkMUl/qoSd3yq1fkYJPL9mZpK2uPvbZpaW9H1Jd0rap4SdW6lqvTerzec3yVfgsW/Z1m/c/WlJb5YM3yLpaPT5URX+InddhVoTy93PuPvz0edvSTqlwg5ViTu/VWpNJC94O/oyHf1xJfDcSlXrbbskB3jsW7bFwCV9z8xORDsRheAqdz8jFf5iS7qyy/XUcoeZ/TBqsSTin8ylzGynpGFJzyrh57ekVimh59fMUmZ2UtI5ScfdPdHntkK9UpvPb5IDvK4t2xJmt7v/hqTfl/T5qA2A9vmqpOsk3SjpjKT7ulpNGWZ2uaRHJN3l7j/rdj3VlKk1sefX3Vfc/UYVdv66ycw+0OWSqqpQb9vPb5IDvK4t25LE3Reij+ckPapCGyjpzkY90WJv9FyX66nI3c9GfzFWJX1NCTu/Ub/zEUkPuPtkNJzI81uu1qSfX0ly9yVJ/6ZCPzmR53attfXGcX6THOBBbdlmZluiG0Iysy2SPibpxeqvSoRjkm6PPr9d0mNdrKWq4l/WyKeUoPMb3bj6uqRT7v6VNd9K3PmtVGtSz6+ZDZrZQPR5RtLvSnpZCTy3UuV64zi/iZ2FIknRNJt/0MUt2/62uxVVZma/osJVt1TY6ehbSavXzB6UtEeFZS3PSrpH0pSkhyVdI+lVSbe6e9dvHlaodY8K//x0Saclfa7YA+02M/ttSf8uaU7SajT8BRV6y4k6v1VqvU0JPL9m9usq3KRMqXDR+bC7f9HMflEJO7dS1Xr/WW0+v4kOcABAZUluoQAAqiDAASBQBDgABIoAB4BAEeAAECgCHAACRYADQKD+HzxT8go8WDRDAAAAAElFTkSuQmCC\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": 81,
   "id": "2785142c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "#open a file where you want ot store data\n",
    "file= open('random_forest_regression_model.pkl','wb')\n",
    "\n",
    "#dump information to that file\n",
    "pickle.dump(rf_random,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab4c7d8b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}