Car_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current Year']=2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "      <th>Current Year</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>2022</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",
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       "      <td>2022</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>2022</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>2022</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>2022</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          2022  \n",
       "1       Manual      0          2022  \n",
       "2       Manual      0          2022  \n",
       "3       Manual      0          2022  \n",
       "4       Manual      0          2022  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year']=final_dataset['Current Year']- final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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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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       "      <th>no_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</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>2022</td>\n",
       "      <td>9</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>2022</td>\n",
       "      <td>5</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>2022</td>\n",
       "      <td>11</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>2022</td>\n",
       "      <td>8</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          2022        8  \n",
       "1       Manual      0          2022        9  \n",
       "2       Manual      0          2022        5  \n",
       "3       Manual      0          2022       11  \n",
       "4       Manual      0          2022        8  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
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       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</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>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>6900</td>\n",
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       "      <td>2022</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
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       "      <td>Manual</td>\n",
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       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
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       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current Year  no_year  \n",
       "0       Manual      0          2022        8  \n",
       "1       Manual      0          2022        9  \n",
       "2       Manual      0          2022        5  \n",
       "3       Manual      0          2022       11  \n",
       "4       Manual      0          2022        8  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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          2022        8   \n",
       "1           4.75           9.54       43000      0          2022        9   \n",
       "2           7.25           9.85        6900      0          2022        5   \n",
       "3           2.85           4.15        5200      0          2022       11   \n",
       "4           4.60           6.87       42450      0          2022        8   \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": 17,
     "metadata": {},
     "output_type": "execute_result"
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    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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          2022        8   \n",
       "1           4.75           9.54       43000      0          2022        9   \n",
       "2           7.25           9.85        6900      0          2022        5   \n",
       "3           2.85           4.15        5200      0          2022       11   \n",
       "4           4.60           6.87       42450      0          2022        8   \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": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=final_dataset.drop(['Current Year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        8                 0   \n",
       "1           4.75           9.54       43000      0        9                 1   \n",
       "2           7.25           9.85        6900      0        5                 0   \n",
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       "4           4.60           6.87       42450      0        8                 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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1edf8351c10>"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_dataset.iloc[:,1:]\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>8</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>9</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>5</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>11</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>8</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        8                 0   \n",
       "1           9.54       43000      0        9                 1   \n",
       "2           9.85        6900      0        5                 0   \n",
       "3           4.15        5200      0       11                 0   \n",
       "4           6.87       42450      0        8                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.36026034 0.04116409 0.00055594 0.07327683 0.24025289 0.01284077\n",
      " 0.12478916 0.14685998]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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": 39,
   "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": 40,
   "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": 41,
   "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=   1.3s\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=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.5s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; 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=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; 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=   1.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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=   0.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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\\habib\\anaconda3\\envs\\car_prediction\\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=   0.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   0.8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.2s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.6s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.4s\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=   0.3s\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=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   0.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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=   1.1s\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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.9724006865776302"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\habib\\anaconda3\\envs\\car_prediction\\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": 45,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ed8c6afd00>"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.9043460439560445\n",
      "MSE: 4.121746777110993\n",
      "RMSE: 2.0302085550777766\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant 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
}