Car_Price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV\n",
    "from hyperopt import hp,fmin,tpe,STATUS_OK,Trials\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('car data.csv')\n",
    "df.head()"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "Car_Name        object\n",
       "Fuel_Type       object\n",
       "Seller_Type     object\n",
       "Transmission    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes[df.dtypes == object]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique items in column Car_Name are: ['ritz' 'sx4' 'ciaz' 'wagon r' 'swift' 'vitara brezza' 's cross'\n",
      " 'alto 800' 'ertiga' 'dzire' 'alto k10' 'ignis' '800' 'baleno' 'omni'\n",
      " 'fortuner' 'innova' 'corolla altis' 'etios cross' 'etios g' 'etios liva'\n",
      " 'corolla' 'etios gd' 'camry' 'land cruiser' 'Royal Enfield Thunder 500'\n",
      " 'UM Renegade Mojave' 'KTM RC200' 'Bajaj Dominar 400'\n",
      " 'Royal Enfield Classic 350' 'KTM RC390' 'Hyosung GT250R'\n",
      " 'Royal Enfield Thunder 350' 'KTM 390 Duke ' 'Mahindra Mojo XT300'\n",
      " 'Bajaj Pulsar RS200' 'Royal Enfield Bullet 350'\n",
      " 'Royal Enfield Classic 500' 'Bajaj Avenger 220' 'Bajaj Avenger 150'\n",
      " 'Honda CB Hornet 160R' 'Yamaha FZ S V 2.0' 'Yamaha FZ 16'\n",
      " 'TVS Apache RTR 160' 'Bajaj Pulsar 150' 'Honda CBR 150' 'Hero Extreme'\n",
      " 'Bajaj Avenger 220 dtsi' 'Bajaj Avenger 150 street' 'Yamaha FZ  v 2.0'\n",
      " 'Bajaj Pulsar  NS 200' 'Bajaj Pulsar 220 F' 'TVS Apache RTR 180'\n",
      " 'Hero Passion X pro' 'Bajaj Pulsar NS 200' 'Yamaha Fazer '\n",
      " 'Honda Activa 4G' 'TVS Sport ' 'Honda Dream Yuga '\n",
      " 'Bajaj Avenger Street 220' 'Hero Splender iSmart' 'Activa 3g'\n",
      " 'Hero Passion Pro' 'Honda CB Trigger' 'Yamaha FZ S '\n",
      " 'Bajaj Pulsar 135 LS' 'Activa 4g' 'Honda CB Unicorn'\n",
      " 'Hero Honda CBZ extreme' 'Honda Karizma' 'Honda Activa 125' 'TVS Jupyter'\n",
      " 'Hero Honda Passion Pro' 'Hero Splender Plus' 'Honda CB Shine'\n",
      " 'Bajaj Discover 100' 'Suzuki Access 125' 'TVS Wego' 'Honda CB twister'\n",
      " 'Hero Glamour' 'Hero Super Splendor' 'Bajaj Discover 125' 'Hero Hunk'\n",
      " 'Hero  Ignitor Disc' 'Hero  CBZ Xtreme' 'Bajaj  ct 100' 'i20' 'grand i10'\n",
      " 'i10' 'eon' 'xcent' 'elantra' 'creta' 'verna' 'city' 'brio' 'amaze'\n",
      " 'jazz']\n",
      "---------------------------------------------------------------------------\n",
      "Unique items in column Fuel_Type are: ['Petrol' 'Diesel' 'CNG']\n",
      "---------------------------------------------------------------------------\n",
      "Unique items in column Seller_Type are: ['Dealer' 'Individual']\n",
      "---------------------------------------------------------------------------\n",
      "Unique items in column Transmission are: ['Manual' 'Automatic']\n",
      "---------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in df.dtypes[df.dtypes == object].index:\n",
    "    print('Unique items in column',col,'are:',df[col].unique())\n",
    "    print('-'*75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Petrol    239\n",
      "Diesel     60\n",
      "CNG         2\n",
      "Name: Fuel_Type, dtype: int64\n",
      "---------------------------------------------------------------------------\n",
      "Dealer        195\n",
      "Individual    106\n",
      "Name: Seller_Type, dtype: int64\n",
      "---------------------------------------------------------------------------\n",
      "Manual       261\n",
      "Automatic     40\n",
      "Name: Transmission, dtype: int64\n",
      "---------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in df[['Fuel_Type','Seller_Type','Transmission']]:\n",
    "    print(df[col].value_counts())\n",
    "    print('-'*75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Owner'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Car_Name'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Current_Year'] = 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Number_Of_Years'] = df['Current_Year'] - df['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Year','Current_Year'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_Of_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Number_Of_Years  \n",
       "0       Manual      0                7  \n",
       "1       Manual      0                8  \n",
       "2       Manual      0                4  \n",
       "3       Manual      0               10  \n",
       "4       Manual      0                7  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual       261\n",
       "Automatic     40\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Encode(df,variable):\n",
    "    encoded_Variable = df[variable].value_counts().to_dict()\n",
    "    df[variable] = df[variable].map(encoded_Variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df[['Fuel_Type','Seller_Type','Transmission']]:\n",
    "    Encode(df,col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_Of_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>7</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>60</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>60</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  \\\n",
       "0           3.35           5.59       27000        239          195   \n",
       "1           4.75           9.54       43000         60          195   \n",
       "2           7.25           9.85        6900        239          195   \n",
       "3           2.85           4.15        5200        239          195   \n",
       "4           4.60           6.87       42450         60          195   \n",
       "\n",
       "   Transmission  Owner  Number_Of_Years  \n",
       "0           261      0                7  \n",
       "1           261      0                8  \n",
       "2           261      0                4  \n",
       "3           261      0               10  \n",
       "4           261      0                7  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x170de204cf8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_corr = df.corr()\n",
    "index = df_corr.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "sns.heatmap(df[index].corr(),annot = True,cmap = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Selling_Price'],axis = 1)\n",
    "y = df['Selling_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_Of_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>60</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>60</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  Owner  \\\n",
       "0           5.59       27000        239          195           261      0   \n",
       "1           9.54       43000         60          195           261      0   \n",
       "2           9.85        6900        239          195           261      0   \n",
       "3           4.15        5200        239          195           261      0   \n",
       "4           6.87       42450         60          195           261      0   \n",
       "\n",
       "   Number_Of_Years  \n",
       "0                7  \n",
       "1                8  \n",
       "2                4  \n",
       "3               10  \n",
       "4                7  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Number_Of_Years'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = .52\n",
    "\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  \n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
    "                colname = corr_matrix.columns[i]  \n",
    "                col_corr.add(colname)\n",
    "    return col_corr\n",
    "\n",
    "correlation(X,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(random_state=101)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=ExtraTreesRegressor(random_state = 101)\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39785798, 0.03835055, 0.14666205, 0.25855293, 0.08085203,\n",
       "       0.00108042, 0.07664404])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "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": [
    "pd.Series(model.feature_importances_, index = X.columns).nlargest(5).plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 36,
   "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=   1.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.1s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.2s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   2.0s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   2.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   3.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.5s\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=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.3s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.9s\n",
      "[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=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   0.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   3.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.9s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\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=   2.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   3.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   1.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.3s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.2s\n"
     ]
    },
    {
     "data": {
      "text/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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSMGfQlL1pG5744bkc9q4Qfxu4RYN3CaShkIJfjPbaGbLzWyJmS0Mo4ZUtDSFT+q2d82po9m4ez8LNuwJuxQR6aEwj/jPcfep7l4aYg0p5dfzNlCYk8nlIQy/3FOfnzKcwtxMHn9PJ3lF0o26elLEjuo6nl9WzlWloynISf1BU/Oy40NJvLC8nKoDjWGXIyI9EFbCOPCymTlwn7vPbr+Cmd0E3AQwZkzqnujsLY/M30SzOzecWRJ2Kd12TekYHpm/mYtv/RHFtesPuq+4qJD77rk7pMpE5FDCCv4Z7r7NzIYCr5jZh+7+ZtsVgheD2QClpaV9+gxiXWMzjy7YzLnHDWPM4H5hl9NtU0b2J6+hkv2jT+Os06466LzEWw/dGWJlInIooXT1uPu24PtO4PfA9DDqSBXPLt3G7toGbpxREnYpPWJmDN23jt21DZRVHgi7HBHppqQHv5nlm1lh6zLwZ8CKZNeRKtydB9/ewKRhhZxx7OCwy+mxotot5GXFWFq2N+xSRKSbwjjiHwa8bWZLgXeB5939xRDqSAnvrNvNh9truPGskpS/hLMzGbQwZWR/1lfU6iSvSJpIeh+/u68HTkr2flPVL95YS3FhDrOmpv4lnF359MgBLNxUybKyvcycWBx2OSJyGLqcM0SLNlUyb+1ubv7MeHKzYmGXc8QKc7OYUFzAym3VGq5ZJA0o+EN0z2sfUdQviy+n8Lg83TV19EDqm1pYVV4ddikichip/0mhPmrF1ipeX13B310wib/9znepqKzpsE46XQs/fEAuQwtzWLx5L1NGpO44QyKi4A/NPa+tpTA3k+vOGMsNv6ph5g23dVgnna6FNzOmjxvEc8vKWbOj44uYiKQOdfWEYPX2Gl5cuZ2/OLMkJSZS7y3jh+QzpCCbdzfuoU9/4k4kzSn4Q/CjFz+kMCeTv5gxLuxSepWZMb1kEJX7G6nMGxV2OSLSBQV/ks1bu4vXPtzJNz83gaL87LDL6XUThhYwKD+b7QOOp6VFx/0iqUjBn0TNLc4Pn1/FyIF5aTUYW0+0HvXXZfXnxZXbwy5HRDqh4E+ipxaVsaq8mu99/ri0vm7/cCYOKyCnsZq7XllDk67rF0k5Cv4kqa1v4icvr2bq6IFccuLwsMtJqAwzRlSt5KOd+3h8oSZqEUk1upwzSX7x+lp21tQzcO1LXPnnDxx039Lly5nZyWOWLHmfK75yY4f2rtZPJQMPbGN6ySDuemUNl540gsI+dPWSSLpT8CfBh9urmf3megbVbuTzX/7LDve/961rOn1cYwudXt/f1fqpxIA7vnA8l/3HPH72x4/4p4snh12SiATU1ZNgzS3ObU8tp39eFqP2Lg+7nKQ6afRArp0+hl/P28CKrVVhlyMiAQV/gv1y7jqWbNnL9y+ZTGZLQ9jlJN33LjiOon7Z3P775TrRK5IiFPwJtGJrFT/74xq+cOJwLj1pRNjlhGJAvyz+96wTWFZWxb1vrAu7HBFBwZ8wNXWN3PLoYgblZ/PDWVPScpKV3nLxiSO45KQR3P3qRyzTTF0ioVPwJ4C7872nlrGl8gA/v/bkPvkJ3Z76wawTGFqYwzfmLKZqv2bqEgmTgj8B7nltLS8s387fXzCJ6eMGhV1OShjYL5t7vnIyO6rr+Pbj79Os4RxEQqPg72V/WLqNf3tlDZdPG8lNnxkfdjkp5eQxRfzzJSfw+uoKfvDcB2GXIxJZuo6/F81dU8F3nlhC6dgi/vWKT0e6X78r150+lk27avnV2xsoLszhm+dMCLskkchR8PeSP63dxV/9dhEThhbywA2n9umxeI7W7Rcdz6599fz4pdVkmPHXZx8bdkkikaLg7wWvf7iTv3pkEWMH9+M3N05nQJ6GJziUjAzjJ1edRIvH5ybYta+eOy46nowMvUMSSQYF/1F6dMFm/umZFRx3TCG//dppDNIVPN2SGcvgrmumMig/mwfe3sD6in389OqpugJKJAl0cvcI1Tc184//tZzbf7+csyYM4fGbz1Do91Asw/j+JZP5wWVTmLd2Nxf9+1vMXVMRdlkifZ6C/wis2FrFZb/4E4/M38zNnxnPA9eXUpCjN09Hwsy47vSxPPnXZ9AvO8b1D77LN+YsYu3OfWGXJtJnKa16oK6xmZ/98SPuf2s9g/Kzuf+rpZw/eVjYZfUJJ44ayPPfmskv567j/jfX8+KK7Vw+bRR/ffaxTBhaEHZ5In1Knw/+m2+5lYrKmg7txUWF3HfP3d3aRmNzC08uKuOe19ayde8Brikdze0XHc+Aflmdbn/1ByuYNHlKh+2kwzj6XUnG3AC5WTG+fd6nuO70sfxy7joeeHMtTy0uI79+F4NrN1K0fysxb+ry99tVe0+e63TWG3/riZYONYYlmb+bPh/8FZU1nY5p/9ZDdx72sdV1jTyzZBuz31zHlj0HmDp6ID++6kTOPHbIIbf/3reuSdtx9LuSzLkBBhfkcMcXJjP/P3/OgM/ewMptWWzOGULZ4FKGD8ijpvq/mHjF1ykuyCHW5kqgrn7v3Xmu+4Kj+VtPlnSoMSzJ/N30+eDvqYamFt7dsIen3y/jheXl1DW2cOKoAfyfS6dw9qRifSgribJa6jllbBEnjxnI9uo61lfUsnF3LRknXMDj720hlmEMLcxhWP9cjumfCwXFtLS4LgsVOYzIB39Tcwtrduzj/S2VvLVmF2+v3cW++iYKcjK54uRRfOnU0Xx65AAFfojMjOED8hg+II8ZE4bw07//Opd89y62V9dRXlXH8q1VLNmyl9i5t/Ifb6yjKD+Lwfk5DC7IZnBBNvWxfnpBEGkjMsHf0NRCTV0jNXVNVO5vYHPRNK765Z9Yua2a/Q3NAAwfkMulU0dwzqShzJgwmH7Zkfn1pJe6GiYOK2TisEIgPsvZ7tp65tx7F6dc8Zfs3lfPtqoDrN4R9JeO+DwnfP8lxg3JZ3xxPuOH5DO+uIDxxfmMG5Kv+YAlckJJNjO7ELgbiAG/cveEdPDdN3cdHww7jxVz19HQdPDsT7G8kZQ4XF06mmljBjJ19EDGDOqnI/s0FO/yycXLlnDWhE/Ov9Q3NbOntoH5Lz/DeZd8kfW79rGsrIoXlpfTdnDQoYU5H78ojByYx4jga+TAPIb1zyU7M7Wvem5ucQ40NtOYkUN13SdDXrf+JTfE8iivOgBAhlnwFf+9ZWQYMbP4cpv2o/0/cHdaPF5bizvu0OJOs2VS19iMB+u4Q2aG0UIG7h7p/z8n3gPR1OZ35kBLAq66T3rwm1kM+AVwPlAGvGdmz7p7rw/XmJsVI6e5lpJjxlCQm0lhThaFuZkM7JfFwkd/wpM/frC3dykpJCczxvABeQyp3cg/X/LJZO/1Tc1s3r2fdRW1bNhVy/qKfazfVcsrH+xg176Dp8c0g+KCHAblZzMoP5ui/GyK+mUxqF82A/tl0y87Rm5WjNysDHIyY+RkZZCbFSOjkwBzd5panLrGZuobW6hvaqG+qZm6xk++1zU2U9fYzIHGZg40xL8ffLuFAw1NH9+ua2yhoXVKy5EXs3zexo6/iBEXcca/vtaj350ZxOzgFwYLXhQAWlriwd7iHny1tjmHHHF71CyWvrm+Y/voyzn29heC32WM3MwMcrNj5GbG6JcdIy87/r1fdmZ8Oau1PbPd/THysjI/WQ6en9afJTMj/rPEgp+rs+6/1hek5tafrSX+cza1OPVN8ecu/jy1e96C5fhz08SBhhb2NzbFn7eGZva3PocfL3/yPB5oaKZ21BW838ksdcfmFvfoueuOMI74pwNr3X09gJn9JzAL6PXgv/7MEp75xTvMvPizHe6L7nGF5GTGDuoqaquusZnyqjq27T3A1soDbN17gO1VdezZ30BlbQOryquprG1g74FGPEFTCuRmZZCXFSMvK0Zuduzj5fycTAYXfHK7NdTiyxn8ds5jfOrMCzDiR4qtPpr333zj6zfEjx7bhHTr0Xhzi8dDruXgo/Su2o34eZcMM2IZ8XcR1u7dQobx8TuIjDbvJh6e8wTHTv8cZhb/H7T4dte99wZXXnFZ/MWsTYDWNTSzv6GZ6romdlTXsb81OIMXxaPV9gWuWy9cPZSdmRF/EQqey37B8zkgL4vh/XPJC16c8rJiPP/8C4w/+Swyg9+XGRjGzm3VvVdQwDxRf71d7dDsi8CF7v714PZ1wGnufku79W4CbgpuTgJWt9vUEGBXgss9EqlaF6RubaqrZ1RXz0S5rrHu3uEtQxhH/J0dbHd49XH32cDsLjdittDdS3uzsN6QqnVB6tamunpGdfWM6uoojLNWZcDoNrdHAdtCqENEJJLCCP73gIlmNs7MsoEvAc+GUIeISCQlvavH3ZvM7BbgJeKXcz7o7iuPYFNddgOFLFXrgtStTXX1jOrqGdXVTtJP7oqISLhS+5MpIiLS6xT8IiIR0yeC38z+l5m5mQ05/NqJZ2Y/NrMPzWyZmf3ezAaGXM+FZrbazNaaWcdxX0NgZqPN7HUzW2VmK83s1rBrasvMYmb2vpk9F3YtbZnZQDN7Mvj7WmVmZ4RdE4CZ/W3wPK4ws8fMLDekOh40s51mtqJN2yAze8XMPgq+F6VIXaHlRNoHv5mNJj78w+awa2njFWCKu58IrAH+IaxC2gyR8XlgMnCtmU0+9KOSogn4rrsfD5wOfDNF6mp1K7Aq7CI6cTfworsfB5xECtRoZiOBbwGl7j6F+EUbXwqpnIeAC9u13Qa86u4TgVeD28n2EB3rCi0n0j74gbuAv6eTD4GFxd1fdvem4OZ84p9VCMvHQ2S4ewPQOkRGqNy93N0XB8s1xANsZLhVxZnZKOALwK/CrqUtM+sPfAZ4AMDdG9x9b6hFfSITyDOzTKAfIX02x93fBPa0a54FPBwsPwxclsyaoPO6wsyJtA5+M7sU2OruS8Ou5RBuBP47xP2PBLa0uV1GigRsKzMrAaYBC0IupdXPiB9MtBxmvWQbD1QAvw66oX5lZvlhF+XuW4GfEH/XXQ5UufvL4VZ1kGHuXg7xAw5gaMj1dCapOZHywW9mfwz6Ddt/zQLuAP45BetqXecO4l0ac8KosbWMTtpS5t2RmRUATwHfdvfeH42q5/VcDOx090Vh19KJTOBk4F53nwbUEk63xUGCPvNZwDhgBJBvZn8eblXpI4ycSPmZRtz9vM7azezTxP/QlgZjeI8CFpvZdHffHlZdbeq7HrgYONfD/bBEyg6RYWZZxEN/jrs/HXY9gRnApWZ2EZAL9DezR9w9FYKsDChz99Z3Rk+SAsEPnAdscPcKADN7GjgTeCTUqj6xw8yGu3u5mQ0HdoZdUKuwciLlj/i74u7L3X2ou5e4ewnxf4qTkxH6hxNMNPM94FJ33x9yOSk5RIbFX60fAFa5+0/DrqeVu/+Du48K/qa+BLyWIqFP8Le9xcwmBU3nkoDhzI/AZuB0M+sXPK/nkgInndt4Frg+WL4eeCbEWj4WZk6kbfCnuHuAQuAVM1tiZr8Mq5Dg5FHrEBmrgCeOcIiM3jYDuA74XPA7WhIcZcuh/Q0wx8yWAVOB/xduORC8A3kSWAwsJ54roQxHYGaPAe8Ak8yszMy+BtwJnG9mHxG/AjAhM/4dQV2h5YSGbBARiRgd8YuIRIyCX0QkYhT8IiIRo+AXEYkYBb+ISMQo+EVEIkbBL32Cmd0RDAu8LLgm+rRDrPuQmX0xWH7DzEqD5Rd6c2hcMzvbzKqCcXVWmdn3u1iv1Mz+vbf2K3I4KT9kg8jhBGPSX0z8k9v1Fp+XIbun23H3RHyA7C13vzgYTG2JmT3XdhwgM8t094XAwgTsW6RTOuKXvmA4sMvd6wHcfZe7bzOzU8xsrpktMrOXgnFaumRmG81siJmVBEfo9wfvIl42s7xgnVODdxXvBBNprDjUNlu5ey2wCDjWzP7FzGab2cvAb4J3Bs8F2y8ws1+b2fJgP1cG7X8W7HOxmf0uGNxO5Igo+KUveBkYbWZrzOw/zOyzwQBwPwe+6O6nAA8C/7cH25wI/MLdTwD2AlcG7b8G/srdzwCau7sxMxtMfMKZ1uEyTgFmufuX2636T8SHNf50MEHHa8E7mH8EznP3k4m/O/hOD34WkYOoq0fSnrvvM7NTgJnAOcDjwA+BKcTHQYH4rFDlPdjsBndfEiwvAkqC/v9Cd/9T0P4o8S6mQ5lpZu8TH9v/TndfaWZXAc+6+4FO1j+PNrNXuXtlMFT0ZGBe8LNkEx/3ReSIKPilT3D3ZuAN4A0zWw58E1gZHJkfifo2y81AHp3PbXA4b7l7Zy8OtV2sb3ScL8GAV9z92iPYv0gH6uqRtGdmk8xsYpumqcRHIi0OTvxiZllmdsLR7MfdK4EaMzs9aErEvLIvEx9NFfh4kpP5wAwzmxC09TOzTyVg3xIRCn7pCwqAh83sg2C44snEZ2b7IvAjM1sKLCE+OcjR+how28zeIX4kXtUL22zrh0CRxWdzWwqcE0xwcgPwWPDzzQeO6+X9SoRoWGaRHjCzAnffFyzfBgx391tDLkukR9THL9IzXzCzfyD+v7OJ+JG4SFrREb/IUTKzC4AftWve4O6Xh1GPyOEo+EVEIkYnd0VEIkbBLyISMQp+EZGIUfCLiETM/wDGtwJJtDxazQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(y_test - y_pred,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21972345448>"
      ]
     },
     "execution_count": 39,
     "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,y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': [25], 'max_features': ['sqrt'], 'min_samples_leaf': [1, 3], 'min_samples_split': [0, 1, 2, 3, 4], 'n_estimators': [900, 1000, 1100]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [rf_random.best_params_['max_depth']],\n",
    "    'max_features': [rf_random.best_params_['max_features']],\n",
    "    'min_samples_leaf': [rf_random.best_params_['min_samples_leaf'], \n",
    "                         rf_random.best_params_['min_samples_leaf']+2],\n",
    "    'min_samples_split': [rf_random.best_params_['min_samples_split'] - 2,\n",
    "                          rf_random.best_params_['min_samples_split'] - 1,\n",
    "                          rf_random.best_params_['min_samples_split'], \n",
    "                          rf_random.best_params_['min_samples_split'] +1,\n",
    "                          rf_random.best_params_['min_samples_split'] + 2],\n",
    "    'n_estimators': [rf_random.best_params_['n_estimators'] - 100, \n",
    "                     rf_random.best_params_['n_estimators'], \n",
    "                     rf_random.best_params_['n_estimators'] + 100]\n",
    "}\n",
    "\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:921: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      " 0.85338519 0.85357987 0.8522987  0.84774591 0.84721947 0.84961762\n",
      " 0.84642452 0.84505249 0.84490387        nan        nan        nan\n",
      "        nan        nan        nan 0.80167577 0.79847343 0.80294206\n",
      " 0.79853623 0.80204447 0.80066106 0.79523415 0.80142545 0.79937624]\n",
      "  category=UserWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [25], 'max_features': ['sqrt'],\n",
       "                         'min_samples_leaf': [1, 3],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4],\n",
       "                         'n_estimators': [900, 1000, 1100]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestRegressor()\n",
    "grid_search=GridSearchCV(estimator=rf,param_grid=param_grid,cv=3,n_jobs=-1,verbose=2)\n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [25], 'max_features': ['sqrt'],\n",
       "                         'min_samples_leaf': [1, 3],\n",
       "                         'min_samples_split': [0, 1, 2, 3, 4],\n",
       "                         'n_estimators': [900, 1000, 1100]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Count'>"
      ]
     },
     "execution_count": 43,
     "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": [
    "y_pred=grid_search.predict(X_test)\n",
    "sns.histplot(y_test - y_pred, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2197379ddc8>"
      ]
     },
     "execution_count": 44,
     "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, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'max_depth': hp.quniform('max_depth', 10, 1200, 10),\n",
    "        'max_features': hp.choice('max_features', ['auto', 'sqrt','log2', None]),\n",
    "        'min_samples_leaf': hp.uniform('min_samples_leaf', 0, 0.5),\n",
    "        'min_samples_split' : hp.uniform ('min_samples_split', 0, 1),\n",
    "        'n_estimators' : hp.choice('n_estimators', [10, 50, 300, 750, 1200])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': <hyperopt.pyll.base.Apply at 0x21974998608>,\n",
       " 'max_features': <hyperopt.pyll.base.Apply at 0x21974998b88>,\n",
       " 'min_samples_leaf': <hyperopt.pyll.base.Apply at 0x21974998f48>,\n",
       " 'min_samples_split': <hyperopt.pyll.base.Apply at 0x21974996348>,\n",
       " 'n_estimators': <hyperopt.pyll.base.Apply at 0x21974996988>}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████| 100/100 [01:09<00:00,  1.44trial/s, best loss: 2.513295255713382]\n"
     ]
    }
   ],
   "source": [
    "seed=2\n",
    "def objective(params):\n",
    "    est = int(params['n_estimators'])\n",
    "    md = int(params['max_depth'])\n",
    "    msl = int(params['min_samples_leaf'])\n",
    "    mss = int(params['min_samples_split'])\n",
    "    model = RandomForestRegressor(n_estimators=est,max_depth=md,min_samples_leaf=msl,min_samples_split=mss)\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred_hyperopt = model.predict(X_test)\n",
    "    score = mean_squared_error(y_test,y_pred_hyperopt)\n",
    "    return score\n",
    "\n",
    "def optimize(trial):\n",
    "    params={'n_estimators':hp.uniform('n_estimators',100,500),\n",
    "           'max_depth':hp.uniform('max_depth',5,20),\n",
    "           'min_samples_leaf':hp.uniform('min_samples_leaf',1,5),\n",
    "           'min_samples_split':hp.uniform('min_samples_split',2,6)}\n",
    "    best=fmin(fn=objective,space=params,algo=tpe.suggest,trials=trial,max_evals=100,rstate=np.random.RandomState(seed))\n",
    "    return best\n",
    "\n",
    "trial=Trials()\n",
    "best=optimize(trial)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 13.426229652625118,\n",
       " 'min_samples_leaf': 2.984442880960474,\n",
       " 'min_samples_split': 2.557614790947238,\n",
       " 'n_estimators': 306.79316178551005}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'state': 2, 'tid': 0, 'spec': None, 'result': {'loss': 3.1632944633230804, 'status': 'ok'}, 'misc': {'tid': 0, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [0], 'min_samples_leaf': [0], 'min_samples_split': [0], 'n_estimators': [0]}, 'vals': {'max_depth': [6.44924027262962], 'min_samples_leaf': [3.2557038491408083], 'min_samples_split': [3.705893814587132], 'n_estimators': [310.68170422696016]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 4, 3, 18, 40, 7, 79000), 'refresh_time': datetime.datetime(2021, 4, 3, 18, 40, 7, 621000)}\n",
      "{'state': 2, 'tid': 1, 'spec': None, 'result': {'loss': 3.5589819711361543, 'status': 'ok'}, 'misc': {'tid': 1, 'cmd': ('domain_attachment', 'FMinIter_Domain'), 'workdir': None, 'idxs': {'max_depth': [1], 'min_samples_leaf': [1], 'min_samples_split': [1], 'n_estimators': [1]}, 'vals': {'max_depth': [13.330641077198102], 'min_samples_leaf': [4.1346696482442695], 'min_samples_split': [3.3480498788350173], 'n_estimators': [446.25537837458296]}}, 'exp_key': None, 'owner': None, 'version': 0, 'book_time': datetime.datetime(2021, 4, 3, 18, 40, 7, 625000), 'refresh_time': datetime.datetime(2021, 4, 3, 18, 40, 8, 521000)}\n"
     ]
    }
   ],
   "source": [
    "for t in trial.trials[:2]:\n",
    "    print (t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "TID = [t['tid'] for t in trial.trials]\n",
    "Loss = [t['result']['loss'] for t in trial.trials]\n",
    "maxd = [t['misc']['vals']['max_depth'][0] for t in trial.trials]\n",
    "nest = [t['misc']['vals']['n_estimators'][0] for t in trial.trials]\n",
    "min_ss = [t['misc']['vals']['min_samples_split'][0] for t in trial.trials]\n",
    "min_sl = [t['misc']['vals']['min_samples_leaf'][0] for t in trial.trials]\n",
    "\n",
    "hyperopt_rfr = pd.DataFrame({'tid':TID,'loss':Loss,\n",
    "                          'max_depth':maxd,'n_estimators':nest,\n",
    "                          'min_samples_split':min_ss, 'min_samples_leaf':min_sl})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>tid</th>\n",
       "      <th>loss</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3.163294</td>\n",
       "      <td>6.449240</td>\n",
       "      <td>310.681704</td>\n",
       "      <td>3.705894</td>\n",
       "      <td>3.255704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.558982</td>\n",
       "      <td>13.330641</td>\n",
       "      <td>446.255378</td>\n",
       "      <td>3.348050</td>\n",
       "      <td>4.134670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.970991</td>\n",
       "      <td>12.294777</td>\n",
       "      <td>441.121102</td>\n",
       "      <td>5.352586</td>\n",
       "      <td>1.369119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.627654</td>\n",
       "      <td>11.468520</td>\n",
       "      <td>148.041302</td>\n",
       "      <td>2.798398</td>\n",
       "      <td>1.919946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.586665</td>\n",
       "      <td>15.271833</td>\n",
       "      <td>266.679634</td>\n",
       "      <td>2.268145</td>\n",
       "      <td>2.775302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tid      loss  max_depth  n_estimators  min_samples_split  min_samples_leaf\n",
       "0    0  3.163294   6.449240    310.681704           3.705894          3.255704\n",
       "1    1  3.558982  13.330641    446.255378           3.348050          4.134670\n",
       "2    2  2.970991  12.294777    441.121102           5.352586          1.369119\n",
       "3    3  2.627654  11.468520    148.041302           2.798398          1.919946\n",
       "4    4  2.586665  15.271833    266.679634           2.268145          2.775302"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperopt_rfr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainedforest = RandomForestRegressor(max_depth = best['max_depth'],  \n",
    "                                       min_samples_leaf = round(best['min_samples_leaf']), \n",
    "                                       min_samples_split = round(best['min_samples_split']), \n",
    "                                       n_estimators = int(best['n_estimators'])).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_hyperopt = trainedforest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Selling_Price', ylabel='Count'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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at403V27m8qpKzHLj2P22mBlXjKlk9opNLFq3Lew4IpIlKv4Odv+sVeTHwp+QLV2fPmkQ+THTXr9IhKj4O9DO5gTT3qjh4yMP5ZCyorDjpKW8rIjzRvTnoTdXE2/W2blEokDF34GenL+OTQ1NXDk2t9/U3dMVYyrZWB/nmQXrwo4iIlmg4u9AU2eupLJPCeOOKA87SrucMbwvA3oWa+I2kYhQ8XeQZXX1zFi6gSvHDCaWIxOypSsvZlxaVclLi9ZTs6kh7DgikmH5YQfoKu6btZK8mHHZ6NyakA1g0aL3GHv62W3e3q+8D7fd+UdufW4RD1bX8PXzjsxiOhHJNhV/B4g3J/lzdQ3nHtOPfj2Kw46zl+akM/Hm29u8fer3r6OyTymnDyvnz7Nr+Oo5w3NmGmkR6Xga6ukAT7+zjg31cSaOHRx2lINy5ZjBrN7cyEuL1ocdRUQySMXfAf742nIG9irhjBw5y9aBOm9Ef8rLCrnntRVhRxGRDFLxH6QFa7fy2tKNfPbUIZ1+eKQwP8bEsYN5dmEtKzfoTV6RriqU4jez5Wb2tpnNMbPqMDJ0lLtfWU5JQR5Xjuncwzy7/MPJQ4iZcc9M7fWLdFVh7vGf7e4nuHtViBkOysb6ONPnrGbCSQPpWVoQdpwOcWjPYs4feSj3z1pFYzwRdhwRyQAN9RyE+2atZGdzks+dNjTsKB3qs6cOYUtjE4/onLwiXVJYh3M68JSZOXC7u0/e8w5mdi1wLcDgwbk3jNKUSPLHGSsYN+wQjuzf/UO3jb/4EmrrNrb6uMVLFmcjXrvseZy/A/knfpZ//8Oz/PGHj/H49GnhhRORDhdW8Y9z9zVm1g942swWuvuLLe8Q/DKYDFBVVZVzZwR/av461m7ZwX9dNGqv22rrNrZ53PxNV52V4WTt19px/vNWb+HZhbWsjpeElEpEMiWUoR53XxNc1gIPA2PDyHGg3J3JLy1lcJ9Szj66X9hxMuKoQ7tTnB+jYWCnfQtGRNqQ9eI3s25m1n3XMvAxYF62cxyMlxfXMXfVZr545hGd/hDOthTkxThuUC/ihwxnca1O0iLSlYSxx98feNnM5gKvA4+7+99CyHHAbn1uMYf2KOaS0Z3jZCsH6vjKnpBo4vYXloYdRUQ6UNaL392XuvvxwddId/9BtjMcjNeXbeT1ZRu59iOHU5SfF3acjCotzKdk3Tymz1nN2i2NYccRkQ6iwznb6dfPL+aQboWdfl6edJWuribpcOfLy8KOIiIdRMXfDm/VbObF99bzhTMOp6Swa+/t75K3cwufPLaCP81cyZaGprDjiEgHUPG3wy+fWUSP4nw+c0o09vZ3ue7Mw6mPJ5gyY3nYUUSkA6j40/TqkjqeXVjLF886gu7FXWN6hnSNHNCTc4/pz+9eWsrmhnjYcUTkIKn405BMOj98YgEDe5Xw+XGHhR0nFN/82JFs39nM7S/qCB+Rzk7Fn4aH31zNvNVb+dfzj6K4IBpj+3s6pqIHFx4/gLteWUbtth1hxxGRg6Di34/GeIKfPvUuxw3qyaeOGxB2nFB9/dwjaUo4v3ku9+YbEpH0qfj3446Xl7J2yw6+e8ExxLrop3TTNbS8G5dXVfKn11eyaqNO1CLSWan492HVxgZ+/fxizh95KCcffkjYcXLCV88ZhpnxkyffDTuKiBwgFX8b3J1/nz6PPDNu+tSIsOPkjIqeJXzxI4fz6Nw1zFiyIew4InIAwpqWOedNn7OaF95bz03jRzCg195TE3e2Ofc70j+fNYyH3lzNTY/M44kbzqAgT/sPIp2Jir8Va7c0ctMj8xk9pDeT2ji7Vmebc78jlRTm8b1PjeQLf6jmrleWce1Hjgg7koi0g3bV9pBIOv/y4FwSSefnlx/fZaddPljnjujPOUf34xfPLNIEbiKdjIp/D//3/GJeWbyBm8aPYMgh3cKOk9O+d+FIEknnP6bPxz3nTpImIm1Q8bfwyuI6/veZ97j4hAFcMaYy7Dg5r7JPKd/6+FE8s2Ad981aFXYcEUmTij+wvK6eL937BsP6lfGDCcdipiGedHx+3GGcPqyc//zLOyxdvz3sOCKSBhU/sKk+zjVTZhEz+P1nx9CtSO95pysWM352+fEUFcS44b45xJuTYUcSkf2IfPE3xJv5x7tnsWpTI7/9zGgGH1IadqROp3+PYm759HG8vXoLP31KH+wSyXVdftd2X8fbH9KvP2Uf+ypv1Wzmts+M1qdzW7Fo0XuMPf3sVm/rV96Hx6ZPA+D8UYdy9SlDmPziUob1K+PyKr1HIpKrunzxt3W8fWNTgjsffxVWbuZXE0/k4yMPDSFd7mtOepufV5j6/es+dP2mT41g+YZ6vvPQ2wzqXcJpR5RnI6KItFMkh3q272xm2uwamruVM/mzoxkf8Vk3O0pBXoxfX3USQ8u78c/3vMESvdkrkpMiV/yrNzUy9fWVbN3RRK/50/jo0f3DjtSl9Cwp4K7PjSE/Zlz9+5ksr6sPO5KI7CEyxe/uvLFiE9PerKEwP8blVZUUbtGx55lQ2aeUKZ8fS2NTgstvn8Hi2m1hRxKRFiJR/Fsbm3hk7hpeWlzH4eXduHJMJeVlRWHH6tJGDezJ/dedStLhittf4501W8OOJCKBLl38iaTTMOAk7pm5gjWbGznzyL588tgKivKjefrEbDuyf3ceuO4UCvNjXPbbV3n8rbVhRxIRunjx3zjtLbYf/lEG9irhMycP4YTKXvpEbpYd3reMh750Gkce2p0v/+kN/vMv79CU0Ie8RMLUpQ/nvPrUITx572+58Etfa7Xw93WMOsDKFcsYPOSwVm/r6nPupyPdY/wrepZw/7Wn8sMnFnDnK8uYvXIT/zPhWEYM6JHNuDlnX58x2dfPXsvXNlfs63vJxby5ZF+vHWTm9evSxX/coF4U1y1scy9/X8eoQ2pe/ajOuZ+O9hzjX5gf43sXjqRqaG9uemQ+4299ic+ddhhfP2843YsLshE35+zvnA7pvra5YF/fSy7mzSX7eu0gM69fly5+yT3jjxvA6cPK+cmT73LXq8t4dO5q/nHcYVx96hB6RPQXgEi2dekxfslNvUoL+cGEY5n+pXGMHNCTnzz5LuP+5zl++MQC3n1fh36KZJr2+CU0x1f2YsrnxzJv9RZue2EJd7y8jMkvLuXoQ7tz4QkD+Mjwvoyo6EGsC5wFrTmRZGN9nPXbd1K3PU7dtp3UDxzDy4vrSCSc5mSShDuJhONA2Vlf4Mn57xMzI2apobLC/BhF+Xk09hvJk/Pfp3txPr1KCundrYDepYUUF+hoNUlPKMVvZucDvwTygN+7+y1h5JDcMGpgT35z1UnUbd/J42+tZfqc1fz4b+/y47+9S+/SAk45/BBGDezJyAE9GFHRg77di3Li6KymRJIN2+PUbd+ZKvRtQalv3/nB17bU9Y0NcfY6SdlhZzJn5Wby8oz8mJEXfBmQXz6UNZsbSTok3Yk3J2lOBk9w5Ce47o+z98pTlB+jd2khvUoLdl/2Ki2kd3C9Z4v1RfkxCvJSv0wKg8uCvBjuTsId99Th0IlksOyp5aQ7zYnUZSLpNAfr4j0rWbmxAXcn6akPTDoQMyPeczCvL9tIQZ5RkBfbve2ighglBXmUFOZRmBfLiX/TbEkknYZ4Mw3xBM3FvandtmP365ZMpv7Nk8G/QzK/pMO3n/XiN7M84DfAeUANMMvMHnX3d7KdRXJLeVkRk04byqTThrJu6w5eXVLHK4s3MHPZBv467/3d9ysuiDGwVwkDe5cysFcJ/boX0bOkgJ4lBfQoKaBHcT7FBXkU5MV2l01BfmoZT70pvavEdl3uaErSEE/QEG+mMZ7Yvbx1RzOb6uNsbIizqT7OpoYmNjXE2VgfZ9uO5la/j5KCPMq7F1JeVsTgQ0oZPbQ35WVF9C1LrSvvXkR5WRGXXfJprvrur1otvJuu+hzf+NPfP7QukXTiiSTTfvZtpvxhClt3NLGloYnNjalMmxua2FQfZ3NjE5sb4iyq3c7mYP3uXxqZcuwVPPzm6jZuu5zLb5+xz4fHDEoLU/9uJYUxSgvyKS7MozT4xVDS4rK0MC+4X4vlYP3u++3xmJKCvA+dP7utXzKJpNOUSNKUSNKcCJaTTlNzkuZkknizs6M5QcPOxO7iro83B9dT63Zdr991+84P368+3syOphaHNFddw9TX255FoGdZx08rE8Ye/1hgsbsvBTCz+4CLABW/7Na/RzETThzEhBMHAbClsYkFa7fy7vvbqNnUwOrNjdRsamTe6i1srI9nNEtJQR59un0wpDK4Tyl9uqX2nMvLUiXeNyj68rKitE/kE0vE27WXmxczSmJ55O3cwqiBPdN+nLuzfWczmxua2NzQxJbGJnY2J4g3J4knkrsvm5qTxGKGmZEXDDHFYsFyLLX3nh+LkbdrOc92r7v+a9/g3EnfxCx1mxkYkHR48s4f85tf/eKD7QTb3NGUZEdTgsamBI3x1GVDPJFaF0/Q0JRgRzxB7bYdNMYTwS/nZhqbEh8uzhxSXBCjW2E+pUV5lBbk060oj25F+bt/LkoL8z64DO73o1t+xBmXfoG8WOo1t12vvaVe32dndPwHHy3bJ8k2s0uB8939C8H1q4GT3f36Pe53LXBtcPUooCPO8FEO1HXA82SbcmdfZ83eWXND582ey7mHuHvfPVeGscff2i7OXr993H0yMLlDN2xW7e5VHfmc2aDc2ddZs3fW3NB5s3fG3GEczlkDtDw90yBgTQg5REQiKYzinwUMN7PDzKwQuBJ4NIQcIiKRlPWhHndvNrPrgSdJHc55p7vPz9LmO3ToKIuUO/s6a/bOmhs6b/ZOlzvrb+6KiEi4NGWDiEjEqPhFRCImUsVvZj8xs4Vm9paZPWxmvcLOtC9mdr6ZvWtmi83sxrDzpMvMKs3seTNbYGbzzeyGsDO1h5nlmdmbZvZY2Fnaw8x6mdmfg5/xBWZ2atiZ0mFmXw9+TuaZ2VQzKw47U1vM7E4zqzWzeS3W9TGzp81sUXDZO8yM6YhU8QNPA6Pc/TjgPeDbIedpU4upLT4BjAAmmtmIcFOlrRn4prsfA5wCfLkTZQe4AVgQdogD8Evgb+5+NHA8neB7MLOBwFeBKncfReqAjyvDTbVPdwPn77HuRuBZdx8OPBtcz2mRKn53f8rdd02w8hqpzxDkqt1TW7h7HNg1tUXOc/e17v5GsLyNVAENDDdVesxsEPBJ4PdhZ2kPM+sBfAS4A8Dd4+6+OdRQ6csHSswsHyglhz/X4+4vAnueLusiYEqwPAW4OJuZDkSkin8Pnwf+GnaIfRgItJy5qYZOUp4tmdlQ4ERgZshR0vUL4F+B3JwMpm2HA+uBu4Jhqt+bWbewQ+2Pu68GfgqsBNYCW9z9qXBTtVt/d18LqZ0eoF/IefaryxW/mT0TjBXu+XVRi/t8l9RwxL3hJd2vtKa2yGVmVgZMA77m7lvDzrM/ZjYeqHX3vec8zn35wEnAbe5+IlBPJxhyCMbDLwIOAwYA3czsM+Gm6vq63IlY3P3cfd1uZpOA8cA5ntsfYujUU1uYWQGp0r/X3R8KO0+axgEXmtkFQDHQw8zucffOUEQ1QI277/rL6s90guIHzgWWuft6ADN7CDgNuCfUVO2zzswq3H2tmVUAtWEH2p8ut8e/L8EJYP4NuNDdG8LOsx+ddmoLS801fAewwN1/HnaedLn7t919kLsPJfV6P9dJSh93fx9YZWZHBavOoXNMdb4SOMXMSoOfm3PoBG9K7+FRYFKwPAl4JMQsaelye/z78WugCHg6mAf9NXf/YriRWhfy1BYHaxxwNfC2mc0J1n3H3Z8IL1IkfAW4N9hRWAr8Y8h59svdZ5rZn4E3SA2/vkkOT4FgZlOBs4ByM6sBbgZuAR4ws2tI/SK7LLyE6dGUDSIiEROpoR4REVHxi4hEjopfRCRiVPwiIhGj4hcRiRgVv4hIxKj4pUsws+8GU/u+ZWZzzOzkfdz3bjO7NFj+u5lVBctPdORU3WZ2lpltCebOWWBmN7dxvyoz+1VHbVdkf6L2AS7pgoJ558cDJ7n7TjMrBwrb+zzufkGHh4OX3H18MGHaHDN7rOVcQGaW7+7VQHUGti3SKu3xS1dQAdS5+04Ad69z9zVmNtrMXjCz2Wb2ZDCPSpvMbLmZlZvZ0GAP/XfBXxFPmVlJcJ8xwV8VM4IT+8zb13Pu4u71wGzgCDP7nplNNrOngD8Efxk8Fjx/mZndZWZvB9u5JFj/sWCbb5jZg8EEeCIHRMUvXcFTQKWZvWdm/2dmZwaTxN0KXOruo4E7gR+04zmHA79x95HAZuCSYP1dwBfd/VQgke6TmdkhpE5Ks2vajdHARe5+1R53/Q9SUxMfG5ww6LngL5h/B85195NI/XXwjXZ8LyIfoqEe6fTcfbuZjQbOAM4G7gf+GxjFB/My5ZGa7z1dy9x9TrA8GxgajP93d/dXg/V/IjXEtC9nmNmbpOb3v8Xd55vZZcCj7t7Yyv3PpcUZqNx9UzBd9AjgleB7KQRmtON7EfkQFb90Ce6eAP4O/N3M3ga+DMwP9swPxM4WywmghNbPkbA/L7l7a78c6tu4v7H3eRcMeNrdJx7A9kX2oqEe6fTM7CgzG95i1QmkpvbtG7zxi5kVmNnIg9mOu28CtpnZKcGqTJwb9ing+l1XghOVvAaMM7NhwbpSMzsyA9uWiFDxS1dQBkwxs3fM7C1SwyI3AZcCPzKzucAcUif4OFjXAJPNbAapPfEtHfCcLf030Ds4a9xc4OzgJCWfA6YG399rwNEdvF2JEE3LLNIOZlbm7tuD5RuBCne/IeRYIu2iMX6R9vmkmX2b1P+dFaT2xEU6Fe3xixwkM/s48KM9Vi9z9wlh5BHZHxW/iEjE6M1dEZGIUfGLiESMil9EJGJU/CIiEfP/UHf+u0cJjpgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(y_test - y_pred_hyperopt,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21975aa8908>"
      ]
     },
     "execution_count": 55,
     "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,y_pred_hyperopt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE is: 0.7945220385137702\n",
      "MSE is: 3.0497635406170778\n",
      "RMSE is: 1.7463572202207307\n"
     ]
    }
   ],
   "source": [
    "print('MAE is:',mean_absolute_error(y_test,y_pred_hyperopt))\n",
    "print('MSE is:',mean_squared_error(y_test,y_pred_hyperopt))\n",
    "print('RMSE is:',np.sqrt(mean_squared_error(y_test,y_pred_hyperopt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file = open('regression_rf.pkl','wb')\n",
    "pickle.dump(trainedforest,file)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}