Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=df[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['Current Year']=2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset['no_year']=final_dataset['Current Year']- final_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "      <th>no_year</th>\n",
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       "  </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>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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset.drop(['Year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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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       "      <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>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",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>6900</td>\n",
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       "      <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>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
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       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</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>2020</td>\n",
       "      <td>6</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          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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=pd.get_dummies(final_dataset,drop_first=True)"
   ]
  },
  {
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   "execution_count": 81,
   "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": 81,
     "metadata": {},
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    "final_dataset.head()"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 82,
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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": 82,
     "metadata": {},
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    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_dataset=final_dataset.drop(['Current Year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.head()"
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  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "    </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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2dbeffe7a88>"
      ]
     },
     "execution_count": 89,
     "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": 90,
   "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": [
    "\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": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_dataset.iloc[:,1:]\n",
    "y=final_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 105,
     "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": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37587921 0.03735726 0.00097047 0.08064828 0.22844919 0.00780328\n",
      " 0.13445871 0.13443361]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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": 120,
   "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": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": 116,
   "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": 117,
   "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": 124,
   "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": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.4s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.5s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.5s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.6s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.9s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   1.6s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=100, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.7s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=15, total=   0.6s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=20, total=   1.0s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.6s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.6s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.5s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.6s\n",
      "[CV] n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=1000, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=   1.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.7s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.4s\n",
      "[CV] n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=300, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=   0.5s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   1.0s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.1s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   1.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   53.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.983773356232129"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dbf66939c8>"
      ]
     },
     "execution_count": 129,
     "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": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2dbf6610708>"
      ]
     },
     "execution_count": 131,
     "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": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8849978021977988\n",
      "MSE: 3.9544237722813156\n",
      "RMSE: 1.9885733007061408\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": 136,
   "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",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}