Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "import category_encoders as ce\n",
    "from sklearn.feature_selection import SelectKBest,f_regression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Data/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19109, 36)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand                             2\n",
       "model                             2\n",
       "year                              2\n",
       "mileage                           2\n",
       "engine                           47\n",
       "engine_size                    1249\n",
       "transmission                    105\n",
       "automatic_transmission            2\n",
       "fuel_type                         2\n",
       "drivetrain                        2\n",
       "min_mpg                        3006\n",
       "max_mpg                        3006\n",
       "damaged                         169\n",
       "first_owner                     306\n",
       "personal_using                  189\n",
       "turbo                             2\n",
       "alloy_wheels                      2\n",
       "adaptive_cruise_control           2\n",
       "navigation_system                 2\n",
       "power_liftgate                    2\n",
       "backup_camera                     2\n",
       "keyless_start                     2\n",
       "remote_start                      2\n",
       "sunroof/moonroof                  2\n",
       "automatic_emergency_braking       2\n",
       "stability_control                 2\n",
       "leather_seats                     2\n",
       "memory_seat                       2\n",
       "third_row_seating                 2\n",
       "apple_car_play/android_auto       2\n",
       "bluetooth                         2\n",
       "usb_port                          2\n",
       "heated_seats                      2\n",
       "interior_color                 1157\n",
       "exterior_color                  209\n",
       "price                             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19109 entries, 0 to 19108\n",
      "Data columns (total 36 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   brand                        19107 non-null  object \n",
      " 1   model                        19107 non-null  object \n",
      " 2   year                         19107 non-null  float64\n",
      " 3   mileage                      19107 non-null  float64\n",
      " 4   engine                       19062 non-null  object \n",
      " 5   engine_size                  17860 non-null  float64\n",
      " 6   transmission                 19004 non-null  object \n",
      " 7   automatic_transmission       19107 non-null  float64\n",
      " 8   fuel_type                    19107 non-null  object \n",
      " 9   drivetrain                   19107 non-null  object \n",
      " 10  min_mpg                      16103 non-null  float64\n",
      " 11  max_mpg                      16103 non-null  float64\n",
      " 12  damaged                      18940 non-null  float64\n",
      " 13  first_owner                  18803 non-null  float64\n",
      " 14  personal_using               18920 non-null  float64\n",
      " 15  turbo                        19107 non-null  float64\n",
      " 16  alloy_wheels                 19107 non-null  float64\n",
      " 17  adaptive_cruise_control      19107 non-null  float64\n",
      " 18  navigation_system            19107 non-null  float64\n",
      " 19  power_liftgate               19107 non-null  float64\n",
      " 20  backup_camera                19107 non-null  float64\n",
      " 21  keyless_start                19107 non-null  float64\n",
      " 22  remote_start                 19107 non-null  float64\n",
      " 23  sunroof/moonroof             19107 non-null  float64\n",
      " 24  automatic_emergency_braking  19107 non-null  float64\n",
      " 25  stability_control            19107 non-null  float64\n",
      " 26  leather_seats                19107 non-null  float64\n",
      " 27  memory_seat                  19107 non-null  float64\n",
      " 28  third_row_seating            19107 non-null  float64\n",
      " 29  apple_car_play/android_auto  19107 non-null  float64\n",
      " 30  bluetooth                    19107 non-null  float64\n",
      " 31  usb_port                     19107 non-null  float64\n",
      " 32  heated_seats                 19107 non-null  float64\n",
      " 33  interior_color               17952 non-null  object \n",
      " 34  exterior_color               18900 non-null  object \n",
      " 35  price                        19107 non-null  object \n",
      "dtypes: float64(27), object(9)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=df.columns.str.replace(\"/\",\"\")"
   ]
  },
  {
   "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>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>mileage</th>\n",
       "      <th>engine</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>transmission</th>\n",
       "      <th>automatic_transmission</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>drivetrain</th>\n",
       "      <th>...</th>\n",
       "      <th>leather_seats</th>\n",
       "      <th>memory_seat</th>\n",
       "      <th>third_row_seating</th>\n",
       "      <th>apple_car_playandroid_auto</th>\n",
       "      <th>bluetooth</th>\n",
       "      <th>usb_port</th>\n",
       "      <th>heated_seats</th>\n",
       "      <th>interior_color</th>\n",
       "      <th>exterior_color</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda</td>\n",
       "      <td>CX-9 Touring</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>6580.0</td>\n",
       "      <td>2.5L I4 16V GDI DOHC Turbo</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6-Speed Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Snowflake White Pearl</td>\n",
       "      <td>36789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alfa</td>\n",
       "      <td>Romeo Stelvio 4DR SUV AWD</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>62717.0</td>\n",
       "      <td>Twin Turbo Premium Unleaded V-6 2.9 L/176</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Rosso Competizione Tri-Coat</td>\n",
       "      <td>39993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 1500 Limited LT</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>15463.0</td>\n",
       "      <td>5.3L V8 16V GDI OHV</td>\n",
       "      <td>5.3</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Jet Black</td>\n",
       "      <td>Silver Ice Metallic</td>\n",
       "      <td>46986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Land</td>\n",
       "      <td>Rover Range Rover P400 SE</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>1281.0</td>\n",
       "      <td>3.0L I6 24V GDI DOHC Turbo</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ebony</td>\n",
       "      <td>Fuji White</td>\n",
       "      <td>141999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander Sport 2.0 SE</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>36727.0</td>\n",
       "      <td>2.0L I4 16V MPFI DOHC</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Automatic CVT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Front-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>21595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19070</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>RC 350 Base</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>77886.0</td>\n",
       "      <td>3.5L V6 24V PDI DOHC</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8-Speed Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Rear-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Obsidian Black</td>\n",
       "      <td>30023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19071</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Camaro 2SS</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>30921.0</td>\n",
       "      <td>6.2L V8 16V GDI OHV</td>\n",
       "      <td>6.2</td>\n",
       "      <td>10-Speed Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Rear-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jet Black</td>\n",
       "      <td>Black</td>\n",
       "      <td>42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19072</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Palisade Calligraphy</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>25490.0</td>\n",
       "      <td>3.8L V6 24V GDI DOHC</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8-Speed Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Becketts Black</td>\n",
       "      <td>43999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19073</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Accord Sport</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>30840.0</td>\n",
       "      <td>1.5L I4 16V GDI DOHC Turbo</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Automatic CVT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Front-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gray</td>\n",
       "      <td>White</td>\n",
       "      <td>26998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19074</th>\n",
       "      <td>MINI</td>\n",
       "      <td>Countryman Cooper S ALL4</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>110107.0</td>\n",
       "      <td>1.6L I4 16V GDI DOHC Turbo</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6-Speed Automatic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>Four-wheel Drive</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Carbon Black</td>\n",
       "      <td>Absolute Black Metallic</td>\n",
       "      <td>15382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19075 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            brand                      model    year   mileage  \\\n",
       "0           Mazda               CX-9 Touring  2022.0    6580.0   \n",
       "1            Alfa  Romeo Stelvio 4DR SUV AWD  2018.0   62717.0   \n",
       "2       Chevrolet  Silverado 1500 Limited LT  2022.0   15463.0   \n",
       "3            Land  Rover Range Rover P400 SE  2022.0    1281.0   \n",
       "4      Mitsubishi     Outlander Sport 2.0 SE  2021.0   36727.0   \n",
       "...           ...                        ...     ...       ...   \n",
       "19070       Lexus                RC 350 Base  2017.0   77886.0   \n",
       "19071   Chevrolet                 Camaro 2SS  2020.0   30921.0   \n",
       "19072     Hyundai       Palisade Calligraphy  2021.0   25490.0   \n",
       "19073       Honda               Accord Sport  2019.0   30840.0   \n",
       "19074        MINI   Countryman Cooper S ALL4  2016.0  110107.0   \n",
       "\n",
       "                                          engine  engine_size  \\\n",
       "0                     2.5L I4 16V GDI DOHC Turbo          2.5   \n",
       "1      Twin Turbo Premium Unleaded V-6 2.9 L/176          2.9   \n",
       "2                            5.3L V8 16V GDI OHV          5.3   \n",
       "3                     3.0L I6 24V GDI DOHC Turbo          3.0   \n",
       "4                          2.0L I4 16V MPFI DOHC          2.0   \n",
       "...                                          ...          ...   \n",
       "19070                       3.5L V6 24V PDI DOHC          3.5   \n",
       "19071                        6.2L V8 16V GDI OHV          6.2   \n",
       "19072                       3.8L V6 24V GDI DOHC          3.8   \n",
       "19073                 1.5L I4 16V GDI DOHC Turbo          1.5   \n",
       "19074                 1.6L I4 16V GDI DOHC Turbo          1.6   \n",
       "\n",
       "             transmission  automatic_transmission fuel_type  \\\n",
       "0       6-Speed Automatic                     1.0  Gasoline   \n",
       "1               Automatic                     1.0  Gasoline   \n",
       "2               Automatic                     1.0  Gasoline   \n",
       "3               Automatic                     1.0  Gasoline   \n",
       "4           Automatic CVT                     1.0  Gasoline   \n",
       "...                   ...                     ...       ...   \n",
       "19070   8-Speed Automatic                     1.0  Gasoline   \n",
       "19071  10-Speed Automatic                     1.0  Gasoline   \n",
       "19072   8-Speed Automatic                     1.0  Gasoline   \n",
       "19073       Automatic CVT                     1.0  Gasoline   \n",
       "19074   6-Speed Automatic                     1.0  Gasoline   \n",
       "\n",
       "              drivetrain  ...  leather_seats  memory_seat  third_row_seating  \\\n",
       "0       Four-wheel Drive  ...            1.0          0.0                1.0   \n",
       "1       Four-wheel Drive  ...            0.0          1.0                0.0   \n",
       "2       Four-wheel Drive  ...            0.0          0.0                0.0   \n",
       "3       Four-wheel Drive  ...            1.0          1.0                0.0   \n",
       "4      Front-wheel Drive  ...            0.0          0.0                0.0   \n",
       "...                  ...  ...            ...          ...                ...   \n",
       "19070   Rear-wheel Drive  ...            1.0          1.0                0.0   \n",
       "19071   Rear-wheel Drive  ...            1.0          1.0                0.0   \n",
       "19072   Four-wheel Drive  ...            1.0          1.0                1.0   \n",
       "19073  Front-wheel Drive  ...            0.0          0.0                0.0   \n",
       "19074   Four-wheel Drive  ...            1.0          0.0                0.0   \n",
       "\n",
       "       apple_car_playandroid_auto  bluetooth  usb_port  heated_seats  \\\n",
       "0                             1.0        0.0       0.0           1.0   \n",
       "1                             1.0        1.0       0.0           0.0   \n",
       "2                             0.0        0.0       0.0           1.0   \n",
       "3                             0.0        0.0       0.0           1.0   \n",
       "4                             1.0        1.0       1.0           1.0   \n",
       "...                           ...        ...       ...           ...   \n",
       "19070                         0.0        1.0       1.0           1.0   \n",
       "19071                         1.0        1.0       1.0           0.0   \n",
       "19072                         0.0        1.0       0.0           1.0   \n",
       "19073                         1.0        1.0       1.0           0.0   \n",
       "19074                         0.0        1.0       1.0           1.0   \n",
       "\n",
       "       interior_color               exterior_color   price  \n",
       "0               Black        Snowflake White Pearl   36789  \n",
       "1               Black  Rosso Competizione Tri-Coat   39993  \n",
       "2           Jet Black          Silver Ice Metallic   46986  \n",
       "3               Ebony                   Fuji White  141999  \n",
       "4               Black                        White   21595  \n",
       "...               ...                          ...     ...  \n",
       "19070           Black               Obsidian Black   30023  \n",
       "19071       Jet Black                        Black   42500  \n",
       "19072           Black               Becketts Black   43999  \n",
       "19073            Gray                        White   26998  \n",
       "19074    Carbon Black      Absolute Black Metallic   15382  \n",
       "\n",
       "[19075 rows x 36 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(df[df[\"price\"]==\"ot Priced\"].index)\n",
    "df.price=df.price.fillna(\"o\")\n",
    "df=df.drop(df[df['price']==\"o\"].index)\n",
    "df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['price']=df['price'].astype('Int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_mpg                        15.743119\n",
       "min_mpg                        15.743119\n",
       "engine_size                     6.537353\n",
       "interior_color                  6.007864\n",
       "first_owner                     1.588467\n",
       "exterior_color                  1.069463\n",
       "personal_using                  0.975098\n",
       "damaged                         0.870249\n",
       "transmission                    0.513761\n",
       "engine                          0.235911\n",
       "third_row_seating               0.000000\n",
       "automatic_emergency_braking     0.000000\n",
       "stability_control               0.000000\n",
       "leather_seats                   0.000000\n",
       "memory_seat                     0.000000\n",
       "brand                           0.000000\n",
       "apple_car_playandroid_auto      0.000000\n",
       "remote_start                    0.000000\n",
       "bluetooth                       0.000000\n",
       "usb_port                        0.000000\n",
       "heated_seats                    0.000000\n",
       "sunroofmoonroof                 0.000000\n",
       "navigation_system               0.000000\n",
       "keyless_start                   0.000000\n",
       "backup_camera                   0.000000\n",
       "power_liftgate                  0.000000\n",
       "model                           0.000000\n",
       "adaptive_cruise_control         0.000000\n",
       "alloy_wheels                    0.000000\n",
       "turbo                           0.000000\n",
       "drivetrain                      0.000000\n",
       "fuel_type                       0.000000\n",
       "automatic_transmission          0.000000\n",
       "mileage                         0.000000\n",
       "year                            0.000000\n",
       "price                           0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.isnull().sum()/df.shape[0])*100).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing value analysis and imputations\n",
    "data=[]\n",
    "for i in df.columns.to_list():\n",
    "    if df[i].dtype==\"O\":\n",
    "        data.append([i,df[i].unique(),df[i].nunique()])\n",
    "uniques=pd.DataFrame(data=data,columns=[\"colname\",\"unique\",\"nunique\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>colname</th>\n",
       "      <th>unique</th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>brand</td>\n",
       "      <td>[Mazda, Alfa, Chevrolet, Land, Mitsubishi, FIA...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>model</td>\n",
       "      <td>[CX-9 Touring, Romeo Stelvio 4DR SUV AWD, Silv...</td>\n",
       "      <td>3494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>engine</td>\n",
       "      <td>[2.5L I4 16V GDI DOHC Turbo, Twin Turbo Premiu...</td>\n",
       "      <td>1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>transmission</td>\n",
       "      <td>[6-Speed Automatic, Automatic, Automatic CVT, ...</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fuel_type</td>\n",
       "      <td>[Gasoline, Hybrid, E85 Flex Fuel, Electric, Di...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>drivetrain</td>\n",
       "      <td>[Four-wheel Drive, Front-wheel Drive, Rear-whe...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>interior_color</td>\n",
       "      <td>[Black, Jet Black, Ebony, Gray, Saddle, Carbon...</td>\n",
       "      <td>957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>exterior_color</td>\n",
       "      <td>[Snowflake White Pearl, Rosso Competizione Tri...</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          colname                                             unique  nunique\n",
       "0           brand  [Mazda, Alfa, Chevrolet, Land, Mitsubishi, FIA...       25\n",
       "1           model  [CX-9 Touring, Romeo Stelvio 4DR SUV AWD, Silv...     3494\n",
       "2          engine  [2.5L I4 16V GDI DOHC Turbo, Twin Turbo Premiu...     1162\n",
       "3    transmission  [6-Speed Automatic, Automatic, Automatic CVT, ...      189\n",
       "4       fuel_type  [Gasoline, Hybrid, E85 Flex Fuel, Electric, Di...       10\n",
       "5      drivetrain  [Four-wheel Drive, Front-wheel Drive, Rear-whe...        5\n",
       "6  interior_color  [Black, Jet Black, Ebony, Gray, Saddle, Carbon...      957\n",
       "7  exterior_color  [Snowflake White Pearl, Rosso Competizione Tri...     1941"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj=df.select_dtypes(include=\"object\").columns.to_list()\n",
    "flo=df.select_dtypes(include=\"float\").columns.to_list()\n",
    "inte=df.select_dtypes(include=\"int\").columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "si_obj=SimpleImputer(strategy=\"most_frequent\")\n",
    "df[obj]=si_obj.fit_transform(df[obj])\n",
    "si_float=SimpleImputer(strategy=\"median\")\n",
    "df[flo]=si_float.fit_transform(df[flo])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand                          0\n",
       "model                          0\n",
       "year                           0\n",
       "mileage                        0\n",
       "engine                         0\n",
       "engine_size                    0\n",
       "transmission                   0\n",
       "automatic_transmission         0\n",
       "fuel_type                      0\n",
       "drivetrain                     0\n",
       "min_mpg                        0\n",
       "max_mpg                        0\n",
       "damaged                        0\n",
       "first_owner                    0\n",
       "personal_using                 0\n",
       "turbo                          0\n",
       "alloy_wheels                   0\n",
       "adaptive_cruise_control        0\n",
       "navigation_system              0\n",
       "power_liftgate                 0\n",
       "backup_camera                  0\n",
       "keyless_start                  0\n",
       "remote_start                   0\n",
       "sunroofmoonroof                0\n",
       "automatic_emergency_braking    0\n",
       "stability_control              0\n",
       "leather_seats                  0\n",
       "memory_seat                    0\n",
       "third_row_seating              0\n",
       "apple_car_playandroid_auto     0\n",
       "bluetooth                      0\n",
       "usb_port                       0\n",
       "heated_seats                   0\n",
       "interior_color                 0\n",
       "exterior_color                 0\n",
       "price                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "flo=[i for i in flo if df[i].nunique() > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year', 'mileage', 'engine_size', 'min_mpg', 'max_mpg']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[flo].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iqr(col):\n",
    "    qt_25=df[col].quantile(0.25)\n",
    "    qt_75=df[col].quantile(0.75)\n",
    "    iqr=qt_75-qt_25\n",
    "    low=qt_25-1.5*iqr\n",
    "    upp=qt_75+1.5*iqr\n",
    "    return (low,upp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "low,upp=iqr('mileage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['mileage']>=upp].index,inplace=True)\n",
    "df.drop(df[df['mileage']<=low].index,inplace=True)\n",
    "df=df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['mileage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "low,upp=iqr('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop(df[df['price']>=upp].index,inplace=True)\n",
    "df.drop(df[df['price']<=low].index,inplace=True)\n",
    "df=df.reset_index(drop=True)\n",
    "sns.violinplot(df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[flo].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj=df.select_dtypes(include=\"object\").columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tar=[]\n",
    "ohe=[]\n",
    "for i in obj:\n",
    "    if(df[i].nunique()>10):\n",
    "        tar.append(i)\n",
    "    else:\n",
    "        ohe.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['brand',\n",
       " 'model',\n",
       " 'engine',\n",
       " 'transmission',\n",
       " 'interior_color',\n",
       " 'exterior_color']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fuel_type', 'drivetrain']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"price\",axis=1)\n",
    "y=df[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.30,random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct=ColumnTransformer([(\"targetEncoder\",ce.TargetEncoder(),['brand','model','engine','transmission','interior_color','exterior_color']),(\"ohe\",ce.OneHotEncoder(),['fuel_type', 'drivetrain'])],remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelPipe=Pipeline([(\"transformer\",ct),(\"scaler\",StandardScaler()),(\"kbest\",SelectKBest(f_regression,k=39)),(\"model\",XGBRegressor(n_estimators=1000))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;targetEncoder&#x27;,\n",
       "                                                  TargetEncoder(),\n",
       "                                                  [&#x27;brand&#x27;, &#x27;model&#x27;, &#x27;engine&#x27;,\n",
       "                                                   &#x27;transmission&#x27;,\n",
       "                                                   &#x27;interior_color&#x27;,\n",
       "                                                   &#x27;exterior_color&#x27;]),\n",
       "                                                 (&#x27;ohe&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;fuel_type&#x27;,\n",
       "                                                   &#x27;drivetrain&#x27;])])),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kbest&#x27;,\n",
       "                 SelectKBest(k=39,\n",
       "                             score_func=&lt;function f_regression at 0x000001DF2...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=None, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;targetEncoder&#x27;,\n",
       "                                                  TargetEncoder(),\n",
       "                                                  [&#x27;brand&#x27;, &#x27;model&#x27;, &#x27;engine&#x27;,\n",
       "                                                   &#x27;transmission&#x27;,\n",
       "                                                   &#x27;interior_color&#x27;,\n",
       "                                                   &#x27;exterior_color&#x27;]),\n",
       "                                                 (&#x27;ohe&#x27;, OneHotEncoder(),\n",
       "                                                  [&#x27;fuel_type&#x27;,\n",
       "                                                   &#x27;drivetrain&#x27;])])),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kbest&#x27;,\n",
       "                 SelectKBest(k=39,\n",
       "                             score_func=&lt;function f_regression at 0x000001DF2...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=None, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;targetEncoder&#x27;, TargetEncoder(),\n",
       "                                 [&#x27;brand&#x27;, &#x27;model&#x27;, &#x27;engine&#x27;, &#x27;transmission&#x27;,\n",
       "                                  &#x27;interior_color&#x27;, &#x27;exterior_color&#x27;]),\n",
       "                                (&#x27;ohe&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;fuel_type&#x27;, &#x27;drivetrain&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">targetEncoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;brand&#x27;, &#x27;model&#x27;, &#x27;engine&#x27;, &#x27;transmission&#x27;, &#x27;interior_color&#x27;, &#x27;exterior_color&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TargetEncoder</label><div class=\"sk-toggleable__content\"><pre>TargetEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ohe</label><div class=\"sk-toggleable__content\"><pre>[&#x27;fuel_type&#x27;, &#x27;drivetrain&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;year&#x27;, &#x27;mileage&#x27;, &#x27;engine_size&#x27;, &#x27;automatic_transmission&#x27;, &#x27;min_mpg&#x27;, &#x27;max_mpg&#x27;, &#x27;damaged&#x27;, &#x27;first_owner&#x27;, &#x27;personal_using&#x27;, &#x27;turbo&#x27;, &#x27;alloy_wheels&#x27;, &#x27;adaptive_cruise_control&#x27;, &#x27;navigation_system&#x27;, &#x27;power_liftgate&#x27;, &#x27;backup_camera&#x27;, &#x27;keyless_start&#x27;, &#x27;remote_start&#x27;, &#x27;sunroof/moonroof&#x27;, &#x27;automatic_emergency_braking&#x27;, &#x27;stability_control&#x27;, &#x27;leather_seats&#x27;, &#x27;memory_seat&#x27;, &#x27;third_row_seating&#x27;, &#x27;apple_car_play/android_auto&#x27;, &#x27;bluetooth&#x27;, &#x27;usb_port&#x27;, &#x27;heated_seats&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=39, score_func=&lt;function f_regression at 0x000001DF23B47940&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('targetEncoder',\n",
       "                                                  TargetEncoder(),\n",
       "                                                  ['brand', 'model', 'engine',\n",
       "                                                   'transmission',\n",
       "                                                   'interior_color',\n",
       "                                                   'exterior_color']),\n",
       "                                                 ('ohe', OneHotEncoder(),\n",
       "                                                  ['fuel_type',\n",
       "                                                   'drivetrain'])])),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('kbest',\n",
       "                 SelectKBest(k=39,\n",
       "                             score_func=<function f_regression at 0x000001DF2...\n",
       "                              feature_types=None, gamma=None, gpu_id=None,\n",
       "                              grow_policy=None, importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_bin=None, max_cat_threshold=None,\n",
       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
       "                              max_depth=None, max_leaves=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=1000,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              predictor=None, random_state=None, ...))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelPipe.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8885720198121061"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=modelPipe.predict(x_test)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./models/modelPipe.pkl\",\"wb\") as fd:\n",
    "    fd.write(pickle.dumps(modelPipe))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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