Car_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To predict the price of car based on given feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n"
     ]
    }
   ],
   "source": [
    "print(df['Seller_Type'].unique())\n",
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Owner'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current Year']=2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
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       "      <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>2020</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>2020</td>\n",
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       "    <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>2020</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>2020</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>2020</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          2020  \n",
       "1       Manual      0          2020  \n",
       "2       Manual      0          2020  \n",
       "3       Manual      0          2020  \n",
       "4       Manual      0          2020  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year']=final_dataset['Current Year']- final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <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>2020</td>\n",
       "      <td>6</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>2020</td>\n",
       "      <td>7</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>2020</td>\n",
       "      <td>3</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>2020</td>\n",
       "      <td>9</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>2020</td>\n",
       "      <td>6</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          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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  Current Year  no_year  \n",
       "0       Manual      0          2020        6  \n",
       "1       Manual      0          2020        7  \n",
       "2       Manual      0          2020        3  \n",
       "3       Manual      0          2020        9  \n",
       "4       Manual      0          2020        6  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
   ]
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   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "   Selling_Price  Present_Price  Kms_Driven  Owner  Current Year  no_year  \\\n",
       "0           3.35           5.59       27000      0          2020        6   \n",
       "1           4.75           9.54       43000      0          2020        7   \n",
       "2           7.25           9.85        6900      0          2020        3   \n",
       "3           2.85           4.15        5200      0          2020        9   \n",
       "4           4.60           6.87       42450      0          2020        6   \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": 16,
     "metadata": {},
     "output_type": "execute_result"
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    "final_dataset.head()"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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   "execution_count": 17,
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   "outputs": [
    {
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       "   Selling_Price  Present_Price  Kms_Driven  Owner  Current Year  no_year  \\\n",
       "0           3.35           5.59       27000      0          2020        6   \n",
       "1           4.75           9.54       43000      0          2020        7   \n",
       "2           7.25           9.85        6900      0          2020        3   \n",
       "3           2.85           4.15        5200      0          2020        9   \n",
       "4           4.60           6.87       42450      0          2020        6   \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  "
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=final_dataset.drop(['Current Year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        6                 0   \n",
       "1           4.75           9.54       43000      0        7                 1   \n",
       "2           7.25           9.85        6900      0        3                 0   \n",
       "3           2.85           4.15        5200      0        9                 0   \n",
       "4           4.60           6.87       42450      0        6                 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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x251eb15d130>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "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": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10916/1113059497.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcorrmat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtop_corr_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcorrmat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m#plot heat map\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtop_corr_features\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"RdYlGn\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "#get correlations of each features in dataset\n",
    "corrmat = df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#plot heat map\n",
    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_dataset.iloc[:,1:]\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>6</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>7</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>3</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>9</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>6</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        6                 0   \n",
       "1           9.54       43000      0        7                 1   \n",
       "2           9.85        6900      0        3                 0   \n",
       "3           4.15        5200      0        9                 0   \n",
       "4           6.87       42450      0        6                 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Feature Importance\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3969875  0.04025492 0.00099523 0.07730746 0.2032134  0.0128887\n",
      " 0.1299543  0.13839849]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(6).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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 the 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",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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,
   "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=   1.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.3s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; 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=   1.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.8s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; 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=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:416: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.053482388902394"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\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": 48,
     "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22f85d77790>"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you want to store the 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,
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}