ML-03 (12-08-2023).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a22c3404",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#import ML evaluation metrics\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics, model_selection\n",
    "\n",
    "#import scikitplot to plot confusion matrix\n",
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1eb6507a",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Location</th>\n",
       "      <th>Color</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Seller Type</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Max Power</th>\n",
       "      <th>Max Torque</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Fuel Tank Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Amaze 1.2 VX i-VTEC</td>\n",
       "      <td>505000</td>\n",
       "      <td>2017</td>\n",
       "      <td>87150</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Grey</td>\n",
       "      <td>First</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>1198 cc</td>\n",
       "      <td>87 bhp @ 6000 rpm</td>\n",
       "      <td>109 Nm @ 4500 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Suzuki</td>\n",
       "      <td>Swift DZire VDI</td>\n",
       "      <td>450000</td>\n",
       "      <td>2014</td>\n",
       "      <td>75000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Ludhiana</td>\n",
       "      <td>White</td>\n",
       "      <td>Second</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1248 cc</td>\n",
       "      <td>74 bhp @ 4000 rpm</td>\n",
       "      <td>190 Nm @ 2000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3995.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>i10 Magna 1.2 Kappa2</td>\n",
       "      <td>220000</td>\n",
       "      <td>2011</td>\n",
       "      <td>67000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Lucknow</td>\n",
       "      <td>Maroon</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1197 cc</td>\n",
       "      <td>79 bhp @ 6000 rpm</td>\n",
       "      <td>112.7619 Nm @ 4000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3585.0</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Glanza G</td>\n",
       "      <td>799000</td>\n",
       "      <td>2019</td>\n",
       "      <td>37500</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Mangalore</td>\n",
       "      <td>Red</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1197 cc</td>\n",
       "      <td>82 bhp @ 6000 rpm</td>\n",
       "      <td>113 Nm @ 4200 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3995.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Innova 2.4 VX 7 STR [2016-2020]</td>\n",
       "      <td>1950000</td>\n",
       "      <td>2018</td>\n",
       "      <td>69000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Grey</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>2393 cc</td>\n",
       "      <td>148 bhp @ 3400 rpm</td>\n",
       "      <td>343 Nm @ 1400 rpm</td>\n",
       "      <td>RWD</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>55.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>XUV500 W8 [2015-2017]</td>\n",
       "      <td>850000</td>\n",
       "      <td>2016</td>\n",
       "      <td>90300</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Surat</td>\n",
       "      <td>White</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>2179 cc</td>\n",
       "      <td>138 bhp @ 3750 rpm</td>\n",
       "      <td>330 Nm @ 1600 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Eon D-Lite +</td>\n",
       "      <td>275000</td>\n",
       "      <td>2014</td>\n",
       "      <td>83000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>White</td>\n",
       "      <td>Second</td>\n",
       "      <td>Individual</td>\n",
       "      <td>814 cc</td>\n",
       "      <td>55 bhp @ 5500 rpm</td>\n",
       "      <td>75 Nm @ 4000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Figo Duratec Petrol ZXI 1.2</td>\n",
       "      <td>240000</td>\n",
       "      <td>2013</td>\n",
       "      <td>73000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Thane</td>\n",
       "      <td>Silver</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1196 cc</td>\n",
       "      <td>70 bhp @ 6250 rpm</td>\n",
       "      <td>102 Nm @ 4000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3795.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>BMW</td>\n",
       "      <td>5-Series 520d Luxury Line [2017-2019]</td>\n",
       "      <td>4290000</td>\n",
       "      <td>2018</td>\n",
       "      <td>60474</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>White</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1995 cc</td>\n",
       "      <td>188 bhp @ 4000 rpm</td>\n",
       "      <td>400 Nm @ 1750 rpm</td>\n",
       "      <td>RWD</td>\n",
       "      <td>4936.0</td>\n",
       "      <td>1868.0</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>Mahindra</td>\n",
       "      <td>Bolero Power Plus ZLX [2016-2019]</td>\n",
       "      <td>670000</td>\n",
       "      <td>2017</td>\n",
       "      <td>72000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Guwahati</td>\n",
       "      <td>White</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1493 cc</td>\n",
       "      <td>70 bhp @ 3600 rpm</td>\n",
       "      <td>195 Nm @ 1400 rpm</td>\n",
       "      <td>RWD</td>\n",
       "      <td>3995.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2059 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make                                  Model    Price  Year  \\\n",
       "0             Honda                    Amaze 1.2 VX i-VTEC   505000  2017   \n",
       "1     Maruti Suzuki                        Swift DZire VDI   450000  2014   \n",
       "2           Hyundai                   i10 Magna 1.2 Kappa2   220000  2011   \n",
       "3            Toyota                               Glanza G   799000  2019   \n",
       "4            Toyota        Innova 2.4 VX 7 STR [2016-2020]  1950000  2018   \n",
       "...             ...                                    ...      ...   ...   \n",
       "2054       Mahindra                  XUV500 W8 [2015-2017]   850000  2016   \n",
       "2055        Hyundai                           Eon D-Lite +   275000  2014   \n",
       "2056           Ford            Figo Duratec Petrol ZXI 1.2   240000  2013   \n",
       "2057            BMW  5-Series 520d Luxury Line [2017-2019]  4290000  2018   \n",
       "2058       Mahindra      Bolero Power Plus ZLX [2016-2019]   670000  2017   \n",
       "\n",
       "      Kilometer Fuel Type Transmission    Location   Color   Owner  \\\n",
       "0         87150    Petrol       Manual        Pune    Grey   First   \n",
       "1         75000    Diesel       Manual    Ludhiana   White  Second   \n",
       "2         67000    Petrol       Manual     Lucknow  Maroon   First   \n",
       "3         37500    Petrol       Manual   Mangalore     Red   First   \n",
       "4         69000    Diesel       Manual      Mumbai    Grey   First   \n",
       "...         ...       ...          ...         ...     ...     ...   \n",
       "2054      90300    Diesel       Manual       Surat   White   First   \n",
       "2055      83000    Petrol       Manual   Ahmedabad   White  Second   \n",
       "2056      73000    Petrol       Manual       Thane  Silver   First   \n",
       "2057      60474    Diesel    Automatic  Coimbatore   White   First   \n",
       "2058      72000    Diesel       Manual    Guwahati   White   First   \n",
       "\n",
       "     Seller Type   Engine           Max Power              Max Torque  \\\n",
       "0      Corporate  1198 cc   87 bhp @ 6000 rpm       109 Nm @ 4500 rpm   \n",
       "1     Individual  1248 cc   74 bhp @ 4000 rpm       190 Nm @ 2000 rpm   \n",
       "2     Individual  1197 cc   79 bhp @ 6000 rpm  112.7619 Nm @ 4000 rpm   \n",
       "3     Individual  1197 cc   82 bhp @ 6000 rpm       113 Nm @ 4200 rpm   \n",
       "4     Individual  2393 cc  148 bhp @ 3400 rpm       343 Nm @ 1400 rpm   \n",
       "...          ...      ...                 ...                     ...   \n",
       "2054  Individual  2179 cc  138 bhp @ 3750 rpm       330 Nm @ 1600 rpm   \n",
       "2055  Individual   814 cc   55 bhp @ 5500 rpm        75 Nm @ 4000 rpm   \n",
       "2056  Individual  1196 cc   70 bhp @ 6250 rpm       102 Nm @ 4000 rpm   \n",
       "2057  Individual  1995 cc  188 bhp @ 4000 rpm       400 Nm @ 1750 rpm   \n",
       "2058  Individual  1493 cc   70 bhp @ 3600 rpm       195 Nm @ 1400 rpm   \n",
       "\n",
       "     Drivetrain  Length   Width  Height  Seating Capacity  Fuel Tank Capacity  \n",
       "0           FWD  3990.0  1680.0  1505.0               5.0                35.0  \n",
       "1           FWD  3995.0  1695.0  1555.0               5.0                42.0  \n",
       "2           FWD  3585.0  1595.0  1550.0               5.0                35.0  \n",
       "3           FWD  3995.0  1745.0  1510.0               5.0                37.0  \n",
       "4           RWD  4735.0  1830.0  1795.0               7.0                55.0  \n",
       "...         ...     ...     ...     ...               ...                 ...  \n",
       "2054        FWD  4585.0  1890.0  1785.0               7.0                70.0  \n",
       "2055        FWD  3495.0  1550.0  1500.0               5.0                32.0  \n",
       "2056        FWD  3795.0  1680.0  1427.0               5.0                45.0  \n",
       "2057        RWD  4936.0  1868.0  1479.0               5.0                65.0  \n",
       "2058        RWD  3995.0  1745.0  1880.0               7.0                 NaN  \n",
       "\n",
       "[2059 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv(\"car details v4.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "885d3f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make                    0\n",
      "Model                   0\n",
      "Price                   0\n",
      "Year                    0\n",
      "Kilometer               0\n",
      "Fuel Type               0\n",
      "Transmission            0\n",
      "Location                0\n",
      "Color                   0\n",
      "Owner                   0\n",
      "Seller Type             0\n",
      "Engine                 80\n",
      "Max Power              80\n",
      "Max Torque             80\n",
      "Drivetrain            136\n",
      "Length                 64\n",
      "Width                  64\n",
      "Height                 64\n",
      "Seating Capacity       64\n",
      "Fuel Tank Capacity    113\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#This is to check if there is any Null Values in the Dataset with sum function it will tell us the number of NULL values)\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f137f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(method=\"ffill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "509db99f",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Location</th>\n",
       "      <th>Color</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Seller Type</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Max Power</th>\n",
       "      <th>Max Torque</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Fuel Tank Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Amaze 1.2 VX i-VTEC</td>\n",
       "      <td>2017</td>\n",
       "      <td>87150</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Grey</td>\n",
       "      <td>First</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>1198 cc</td>\n",
       "      <td>87 bhp @ 6000 rpm</td>\n",
       "      <td>109 Nm @ 4500 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3990.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Suzuki</td>\n",
       "      <td>Swift DZire VDI</td>\n",
       "      <td>2014</td>\n",
       "      <td>75000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Ludhiana</td>\n",
       "      <td>White</td>\n",
       "      <td>Second</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1248 cc</td>\n",
       "      <td>74 bhp @ 4000 rpm</td>\n",
       "      <td>190 Nm @ 2000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3995.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>i10 Magna 1.2 Kappa2</td>\n",
       "      <td>2011</td>\n",
       "      <td>67000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Lucknow</td>\n",
       "      <td>Maroon</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1197 cc</td>\n",
       "      <td>79 bhp @ 6000 rpm</td>\n",
       "      <td>112.7619 Nm @ 4000 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3585.0</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Glanza G</td>\n",
       "      <td>2019</td>\n",
       "      <td>37500</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Mangalore</td>\n",
       "      <td>Red</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1197 cc</td>\n",
       "      <td>82 bhp @ 6000 rpm</td>\n",
       "      <td>113 Nm @ 4200 rpm</td>\n",
       "      <td>FWD</td>\n",
       "      <td>3995.0</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Innova 2.4 VX 7 STR [2016-2020]</td>\n",
       "      <td>2018</td>\n",
       "      <td>69000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Grey</td>\n",
       "      <td>First</td>\n",
       "      <td>Individual</td>\n",
       "      <td>2393 cc</td>\n",
       "      <td>148 bhp @ 3400 rpm</td>\n",
       "      <td>343 Nm @ 1400 rpm</td>\n",
       "      <td>RWD</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make                            Model  Year  Kilometer Fuel Type  \\\n",
       "0          Honda              Amaze 1.2 VX i-VTEC  2017      87150    Petrol   \n",
       "1  Maruti Suzuki                  Swift DZire VDI  2014      75000    Diesel   \n",
       "2        Hyundai             i10 Magna 1.2 Kappa2  2011      67000    Petrol   \n",
       "3         Toyota                         Glanza G  2019      37500    Petrol   \n",
       "4         Toyota  Innova 2.4 VX 7 STR [2016-2020]  2018      69000    Diesel   \n",
       "\n",
       "  Transmission   Location   Color   Owner Seller Type   Engine  \\\n",
       "0       Manual       Pune    Grey   First   Corporate  1198 cc   \n",
       "1       Manual   Ludhiana   White  Second  Individual  1248 cc   \n",
       "2       Manual    Lucknow  Maroon   First  Individual  1197 cc   \n",
       "3       Manual  Mangalore     Red   First  Individual  1197 cc   \n",
       "4       Manual     Mumbai    Grey   First  Individual  2393 cc   \n",
       "\n",
       "            Max Power              Max Torque Drivetrain  Length   Width  \\\n",
       "0   87 bhp @ 6000 rpm       109 Nm @ 4500 rpm        FWD  3990.0  1680.0   \n",
       "1   74 bhp @ 4000 rpm       190 Nm @ 2000 rpm        FWD  3995.0  1695.0   \n",
       "2   79 bhp @ 6000 rpm  112.7619 Nm @ 4000 rpm        FWD  3585.0  1595.0   \n",
       "3   82 bhp @ 6000 rpm       113 Nm @ 4200 rpm        FWD  3995.0  1745.0   \n",
       "4  148 bhp @ 3400 rpm       343 Nm @ 1400 rpm        RWD  4735.0  1830.0   \n",
       "\n",
       "   Height  Seating Capacity  Fuel Tank Capacity  \n",
       "0  1505.0               5.0                35.0  \n",
       "1  1555.0               5.0                42.0  \n",
       "2  1550.0               5.0                35.0  \n",
       "3  1510.0               5.0                37.0  \n",
       "4  1795.0               7.0                55.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,4]\n",
    "x=df.iloc[0:, :2].join(df.iloc[:, 3:])\n",
    "#y.head(4)\n",
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e12573b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Model', 'Price', 'Year', 'Kilometer', 'Fuel Type',\n",
       "       'Transmission', 'Location', 'Color', 'Owner', 'Seller Type', 'Engine',\n",
       "       'Max Power', 'Max Torque', 'Drivetrain', 'Length', 'Width', 'Height',\n",
       "       'Seating Capacity', 'Fuel Tank Capacity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f7776b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Make  Model    Price  Year  Kilometer  Fuel Type  Transmission  \\\n",
      "0        7    106   505000  2017      87150          6             1   \n",
      "1       19    802   450000  2014      75000          2             1   \n",
      "2        8   1030   220000  2011      67000          6             1   \n",
      "3       30    492   799000  2019      37500          6             1   \n",
      "4       30    548  1950000  2018      69000          2             1   \n",
      "...    ...    ...      ...   ...        ...        ...           ...   \n",
      "2054    18    999   850000  2016      90300          2             1   \n",
      "2055     8    414   275000  2014      83000          6             1   \n",
      "2056     6    455   240000  2013      73000          6             1   \n",
      "2057     1     13  4290000  2018      60474          2             0   \n",
      "2058    18    138   670000  2017      72000          2             1   \n",
      "\n",
      "      Location  Color  Owner  Seller Type  Engine  Max Power  Max Torque  \\\n",
      "0           56      7      1            1       7        312           5   \n",
      "1           40     15      3            2       9        289          77   \n",
      "2           39      8      1            2       6        301          11   \n",
      "3           41     13      1            2       6        305          15   \n",
      "4           45      7      1            2      60         78         167   \n",
      "...        ...    ...    ...          ...     ...        ...         ...   \n",
      "2054        67     15      1            2      55         66         162   \n",
      "2055         1     15      3            2     103        247         264   \n",
      "2056        68     14      1            2       5        283           1   \n",
      "2057        12     15      1            2      47        144         199   \n",
      "2058        25     15      1            2      23        281          83   \n",
      "\n",
      "      Drivetrain  Length   Width  Height  Seating Capacity  Fuel Tank Capacity  \n",
      "0              1  3990.0  1680.0  1505.0               5.0                35.0  \n",
      "1              1  3995.0  1695.0  1555.0               5.0                42.0  \n",
      "2              1  3585.0  1595.0  1550.0               5.0                35.0  \n",
      "3              1  3995.0  1745.0  1510.0               5.0                37.0  \n",
      "4              2  4735.0  1830.0  1795.0               7.0                55.0  \n",
      "...          ...     ...     ...     ...               ...                 ...  \n",
      "2054           1  4585.0  1890.0  1785.0               7.0                70.0  \n",
      "2055           1  3495.0  1550.0  1500.0               5.0                32.0  \n",
      "2056           1  3795.0  1680.0  1427.0               5.0                45.0  \n",
      "2057           2  4936.0  1868.0  1479.0               5.0                65.0  \n",
      "2058           2  3995.0  1745.0  1880.0               7.0                65.0  \n",
      "\n",
      "[2059 rows x 20 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "categorical_columns = ['Make', 'Model', 'Fuel Type',\n",
    "       'Transmission', 'Location', 'Color', 'Owner', 'Seller Type', 'Engine',\n",
    "       'Max Power', 'Max Torque', 'Drivetrain']\n",
    "\n",
    "# Apply label encoding to each categorical column\n",
    "label_encoder = LabelEncoder()\n",
    "for col in categorical_columns:\n",
    "    df[col] = label_encoder.fit_transform(df[col])\n",
    "\n",
    "print(df)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming you have already performed label encoding on your DataFrame 'df'\n",
    "\n",
    "# Select the columns you want to scale (excluding the label-encoded columns)\n",
    "numerical_columns = ['Price', 'Year','Length', 'Width', 'Height',\n",
    "       'Seating Capacity', 'Fuel Tank Capacity']\n",
    "\n",
    "# Create a MinMaxScaler object\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Apply Min-Max scaling to the numerical columns\n",
    "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4734570",
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Location</th>\n",
       "      <th>Color</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Seller Type</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Max Power</th>\n",
       "      <th>Max Torque</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Length</th>\n",
       "      <th>Width</th>\n",
       "      <th>Height</th>\n",
       "      <th>Seating Capacity</th>\n",
       "      <th>Fuel Tank Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>0.170858</td>\n",
       "      <td>87150</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>312</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.646051</td>\n",
       "      <td>-0.641325</td>\n",
       "      <td>-0.634749</td>\n",
       "      <td>-0.368437</td>\n",
       "      <td>-1.121660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>802</td>\n",
       "      <td>-0.721270</td>\n",
       "      <td>75000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>289</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.634784</td>\n",
       "      <td>-0.530844</td>\n",
       "      <td>-0.267515</td>\n",
       "      <td>-0.368437</td>\n",
       "      <td>-0.657763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1030</td>\n",
       "      <td>-1.613398</td>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>301</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.558651</td>\n",
       "      <td>-1.267382</td>\n",
       "      <td>-0.304238</td>\n",
       "      <td>-0.368437</td>\n",
       "      <td>-1.121660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>492</td>\n",
       "      <td>0.765609</td>\n",
       "      <td>37500</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>305</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.634784</td>\n",
       "      <td>-0.162575</td>\n",
       "      <td>-0.598025</td>\n",
       "      <td>-0.368437</td>\n",
       "      <td>-0.989118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>548</td>\n",
       "      <td>0.468234</td>\n",
       "      <td>69000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>78</td>\n",
       "      <td>167</td>\n",
       "      <td>2</td>\n",
       "      <td>1.032684</td>\n",
       "      <td>0.463482</td>\n",
       "      <td>1.495206</td>\n",
       "      <td>2.078700</td>\n",
       "      <td>0.203760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make  Model      Year  Kilometer  Fuel Type  Transmission  Location  Color  \\\n",
       "0     7    106  0.170858      87150          6             1        56      7   \n",
       "1    19    802 -0.721270      75000          2             1        40     15   \n",
       "2     8   1030 -1.613398      67000          6             1        39      8   \n",
       "3    30    492  0.765609      37500          6             1        41     13   \n",
       "4    30    548  0.468234      69000          2             1        45      7   \n",
       "\n",
       "   Owner  Seller Type  Engine  Max Power  Max Torque  Drivetrain    Length  \\\n",
       "0      1            1       7        312           5           1 -0.646051   \n",
       "1      3            2       9        289          77           1 -0.634784   \n",
       "2      1            2       6        301          11           1 -1.558651   \n",
       "3      1            2       6        305          15           1 -0.634784   \n",
       "4      1            2      60         78         167           2  1.032684   \n",
       "\n",
       "      Width    Height  Seating Capacity  Fuel Tank Capacity  \n",
       "0 -0.641325 -0.634749         -0.368437           -1.121660  \n",
       "1 -0.530844 -0.267515         -0.368437           -0.657763  \n",
       "2 -1.267382 -0.304238         -0.368437           -1.121660  \n",
       "3 -0.162575 -0.598025         -0.368437           -0.989118  \n",
       "4  0.463482  1.495206          2.078700            0.203760  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,4]\n",
    "x=df.iloc[0:, :2].join(df.iloc[:, 3:])\n",
    "#y.head(4)\n",
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05cbd383",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, Y_train, y_test = train_test_split(x, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fa5c47a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = LogisticRegression()\n",
    "#model = RandomForestClassifier()\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()\n",
    "#from catboost import CatBoostClassifier as CBC\n",
    "#model = LinearSVC()\n",
    "#model = DecisionTreeClassifier()\n",
    "#model = SVC()\n",
    "#model  = CBC()\n",
    "#model = DummyRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc08169b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARDRegression\n",
      "AdaBoostRegressor\n",
      "BaggingRegressor\n",
      "BayesianRidge\n",
      "CCA\n",
      "DecisionTreeRegressor\n",
      "DummyRegressor\n",
      "ElasticNet\n",
      "ElasticNetCV\n",
      "ExtraTreeRegressor\n",
      "ExtraTreesRegressor\n",
      "GammaRegressor\n",
      "GaussianProcessRegressor\n",
      "GradientBoostingRegressor\n",
      "HistGradientBoostingRegressor\n",
      "HuberRegressor\n",
      "IsotonicRegression\n",
      "KNeighborsRegressor\n",
      "KernelRidge\n",
      "Lars\n",
      "LarsCV\n",
      "Lasso\n",
      "LassoCV\n",
      "LassoLars\n",
      "LassoLarsCV\n",
      "LassoLarsIC\n",
      "LinearRegression\n",
      "LinearSVR\n",
      "MLPRegressor\n",
      "MultiOutputRegressor\n",
      "MultiTaskElasticNet\n",
      "MultiTaskElasticNetCV\n",
      "MultiTaskLasso\n",
      "MultiTaskLassoCV\n",
      "NuSVR\n",
      "OrthogonalMatchingPursuit\n",
      "OrthogonalMatchingPursuitCV\n",
      "PLSCanonical\n",
      "PLSRegression\n",
      "PassiveAggressiveRegressor\n",
      "PoissonRegressor\n",
      "QuantileRegressor\n",
      "RANSACRegressor\n",
      "RadiusNeighborsRegressor\n",
      "RandomForestRegressor\n",
      "RegressorChain\n",
      "Ridge\n",
      "RidgeCV\n",
      "SGDRegressor\n",
      "SVR\n",
      "StackingRegressor\n",
      "TheilSenRegressor\n",
      "TransformedTargetRegressor\n",
      "TweedieRegressor\n",
      "VotingRegressor\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import all_estimators\n",
    "estimators = all_estimators(type_filter='regressor') #regressor\n",
    "for name, get_model in estimators:\n",
    "    try:\n",
    "         print(name)\n",
    "    except:\n",
    "         print('Unable to import', name) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a4a68bfd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to import ARDRegression\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import AdaBoostRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import BaggingRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import BayesianRidge\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import CCA\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import DecisionTreeRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import DummyRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import ElasticNet\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import ElasticNetCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import ExtraTreeRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\cross_decomposition\\_pls.py:245: FutureWarning: As of version 0.24, n_components(2) should be in [1, min(n_features, n_samples, n_targets)] = [1, 1]. n_components=1 will be used instead. In version 1.1 (renaming of 0.26), an error will be raised.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to import ExtraTreesRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import GammaRegressor\n",
      "Some value(s) of y are out of the valid range for family GammaDistribution\n",
      "Unable to import GaussianProcessRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import GradientBoostingRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import HistGradientBoostingRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import HuberRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import IsotonicRegression\n",
      "Isotonic regression input X should be a 1d array or 2d array with 1 feature\n",
      "Unable to import KNeighborsRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_huber.py:332: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), Lars())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LarsCV())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to import KernelRidge\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import Lars\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LarsCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import Lasso\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LassoCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LassoLars\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LassoLarsCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LassoLarsIC\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import LinearRegression\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LassoLars())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsCV())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unable to import LinearSVR\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import MLPRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import MultiOutputRegressor\n",
      "__init__() missing 1 required positional argument: 'estimator'\n",
      "Unable to import MultiTaskElasticNet\n",
      "For mono-task outputs, use ElasticNet\n",
      "Unable to import MultiTaskElasticNetCV\n",
      "For mono-task outputs, use ElasticNetCVCV\n",
      "Unable to import MultiTaskLasso\n",
      "For mono-task outputs, use ElasticNet\n",
      "Unable to import MultiTaskLassoCV\n",
      "For mono-task outputs, use LassoCVCV\n",
      "Unable to import NuSVR\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import OrthogonalMatchingPursuit\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import OrthogonalMatchingPursuitCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import PLSCanonical\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuitCV())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_omp.py:419: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.\n",
      "  out = _cholesky_omp(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_omp.py:419: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.\n",
      "  out = _cholesky_omp(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_omp.py:419: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.\n",
      "  out = _cholesky_omp(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_omp.py:419: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.\n",
      "  out = _cholesky_omp(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_omp.py:419: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met.\n",
      "  out = _cholesky_omp(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\cross_decomposition\\_pls.py:245: FutureWarning: As of version 0.24, n_components(2) should be in [1, min(n_features, n_samples, n_targets)] = [1, 1]. n_components=1 will be used instead. In version 1.1 (renaming of 0.26), an error will be raised.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\link.py:90: RuntimeWarning: overflow encountered in exp\n",
      "  return np.exp(lin_pred)\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\link.py:93: RuntimeWarning: overflow encountered in exp\n",
      "  return np.exp(lin_pred)\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:132: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return -2 * (y - y_pred) / self.unit_variance(y_pred)\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:323: RuntimeWarning: invalid value encountered in add\n",
      "  dev = 2 * (xlogy(y, y / y_pred) - y + y_pred)\n",
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:323: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unable to import PLSRegression\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import PassiveAggressiveRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import PoissonRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import QuantileRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import RANSACRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import RadiusNeighborsRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import RandomForestRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import RegressorChain\n",
      "__init__() missing 1 required positional argument: 'base_estimator'\n",
      "Unable to import Ridge\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import RidgeCV\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import SGDRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import SVR\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import StackingRegressor\n",
      "__init__() missing 1 required positional argument: 'estimators'\n",
      "Unable to import TheilSenRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import TransformedTargetRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import TweedieRegressor\n",
      "Found input variables with inconsistent numbers of samples: [515, 412]\n",
      "Unable to import VotingRegressor\n",
      "__init__() missing 1 required positional argument: 'estimators'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zaucc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:323: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "\n",
    "\n",
    "from sklearn.utils import all_estimators\n",
    "estimators = all_estimators(type_filter='regressor')\n",
    "\n",
    "model_name = []\n",
    "model_r2_score = []\n",
    "ms = []\n",
    "ma = []\n",
    "\n",
    "for name, get_model in estimators:\n",
    "    try:\n",
    "        model = get_model()\n",
    "        model.fit(X_train, Y_train)\n",
    "        pred_y = model.predict(x_test)\n",
    "        r2_score = sm.r2_score(y_test, prediction)\n",
    "        model_r2_score.append(r2_score)\n",
    "        model_name.append(name)\n",
    "        mse = mean_squared_error(y_test,prediction)\n",
    "        ms.append(mse)\n",
    "        mae = mean_squared_error(y_test,prediction)\n",
    "        ma.append(mae)\n",
    "    except Exception as e:\n",
    "        print('Unable to import', name)\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a18196ee",
   "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>Model Name</th>\n",
       "      <th>Model precision</th>\n",
       "      <th>Mean Abso Error</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARDRegression</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoostRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BayesianRidge</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCA</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DecisionTreeRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DummyRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ElasticNetCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ExtraTreeRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ExtraTreesRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GaussianProcessRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GradientBoostingRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HistGradientBoostingRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HuberRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KernelRidge</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lars</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LarsCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LassoCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LassoLars</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LassoLarsCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LassoLarsIC</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LinearSVR</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MLPRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NuSVR</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>OrthogonalMatchingPursuit</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>OrthogonalMatchingPursuitCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PLSCanonical</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PLSRegression</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PassiveAggressiveRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PoissonRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>QuantileRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RANSACRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RadiusNeighborsRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RidgeCV</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SGDRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SVR</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TheilSenRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TransformedTargetRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>TweedieRegressor</td>\n",
       "      <td>0.997956</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>2.282765e+06</td>\n",
       "      <td>1510.882242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model Name  Model precision  Mean Abso Error  \\\n",
       "0                   ARDRegression         0.997956     2.282765e+06   \n",
       "1               AdaBoostRegressor         0.997956     2.282765e+06   \n",
       "2                BaggingRegressor         0.997956     2.282765e+06   \n",
       "3                   BayesianRidge         0.997956     2.282765e+06   \n",
       "4                             CCA         0.997956     2.282765e+06   \n",
       "5           DecisionTreeRegressor         0.997956     2.282765e+06   \n",
       "6                  DummyRegressor         0.997956     2.282765e+06   \n",
       "7                      ElasticNet         0.997956     2.282765e+06   \n",
       "8                    ElasticNetCV         0.997956     2.282765e+06   \n",
       "9              ExtraTreeRegressor         0.997956     2.282765e+06   \n",
       "10            ExtraTreesRegressor         0.997956     2.282765e+06   \n",
       "11       GaussianProcessRegressor         0.997956     2.282765e+06   \n",
       "12      GradientBoostingRegressor         0.997956     2.282765e+06   \n",
       "13  HistGradientBoostingRegressor         0.997956     2.282765e+06   \n",
       "14                 HuberRegressor         0.997956     2.282765e+06   \n",
       "15            KNeighborsRegressor         0.997956     2.282765e+06   \n",
       "16                    KernelRidge         0.997956     2.282765e+06   \n",
       "17                           Lars         0.997956     2.282765e+06   \n",
       "18                         LarsCV         0.997956     2.282765e+06   \n",
       "19                          Lasso         0.997956     2.282765e+06   \n",
       "20                        LassoCV         0.997956     2.282765e+06   \n",
       "21                      LassoLars         0.997956     2.282765e+06   \n",
       "22                    LassoLarsCV         0.997956     2.282765e+06   \n",
       "23                    LassoLarsIC         0.997956     2.282765e+06   \n",
       "24               LinearRegression         0.997956     2.282765e+06   \n",
       "25                      LinearSVR         0.997956     2.282765e+06   \n",
       "26                   MLPRegressor         0.997956     2.282765e+06   \n",
       "27                          NuSVR         0.997956     2.282765e+06   \n",
       "28      OrthogonalMatchingPursuit         0.997956     2.282765e+06   \n",
       "29    OrthogonalMatchingPursuitCV         0.997956     2.282765e+06   \n",
       "30                   PLSCanonical         0.997956     2.282765e+06   \n",
       "31                  PLSRegression         0.997956     2.282765e+06   \n",
       "32     PassiveAggressiveRegressor         0.997956     2.282765e+06   \n",
       "33               PoissonRegressor         0.997956     2.282765e+06   \n",
       "34              QuantileRegressor         0.997956     2.282765e+06   \n",
       "35                RANSACRegressor         0.997956     2.282765e+06   \n",
       "36       RadiusNeighborsRegressor         0.997956     2.282765e+06   \n",
       "37          RandomForestRegressor         0.997956     2.282765e+06   \n",
       "38                          Ridge         0.997956     2.282765e+06   \n",
       "39                        RidgeCV         0.997956     2.282765e+06   \n",
       "40                   SGDRegressor         0.997956     2.282765e+06   \n",
       "41                            SVR         0.997956     2.282765e+06   \n",
       "42              TheilSenRegressor         0.997956     2.282765e+06   \n",
       "43     TransformedTargetRegressor         0.997956     2.282765e+06   \n",
       "44               TweedieRegressor         0.997956     2.282765e+06   \n",
       "\n",
       "            MSE          RMSE  \n",
       "0   2.282765e+06  1510.882242  \n",
       "1   2.282765e+06  1510.882242  \n",
       "2   2.282765e+06  1510.882242  \n",
       "3   2.282765e+06  1510.882242  \n",
       "4   2.282765e+06  1510.882242  \n",
       "5   2.282765e+06  1510.882242  \n",
       "6   2.282765e+06  1510.882242  \n",
       "7   2.282765e+06  1510.882242  \n",
       "8   2.282765e+06  1510.882242  \n",
       "9   2.282765e+06  1510.882242  \n",
       "10  2.282765e+06  1510.882242  \n",
       "11  2.282765e+06  1510.882242  \n",
       "12  2.282765e+06  1510.882242  \n",
       "13  2.282765e+06  1510.882242  \n",
       "14  2.282765e+06  1510.882242  \n",
       "15  2.282765e+06  1510.882242  \n",
       "16  2.282765e+06  1510.882242  \n",
       "17  2.282765e+06  1510.882242  \n",
       "18  2.282765e+06  1510.882242  \n",
       "19  2.282765e+06  1510.882242  \n",
       "20  2.282765e+06  1510.882242  \n",
       "21  2.282765e+06  1510.882242  \n",
       "22  2.282765e+06  1510.882242  \n",
       "23  2.282765e+06  1510.882242  \n",
       "24  2.282765e+06  1510.882242  \n",
       "25  2.282765e+06  1510.882242  \n",
       "26  2.282765e+06  1510.882242  \n",
       "27  2.282765e+06  1510.882242  \n",
       "28  2.282765e+06  1510.882242  \n",
       "29  2.282765e+06  1510.882242  \n",
       "30  2.282765e+06  1510.882242  \n",
       "31  2.282765e+06  1510.882242  \n",
       "32  2.282765e+06  1510.882242  \n",
       "33  2.282765e+06  1510.882242  \n",
       "34  2.282765e+06  1510.882242  \n",
       "35  2.282765e+06  1510.882242  \n",
       "36  2.282765e+06  1510.882242  \n",
       "37  2.282765e+06  1510.882242  \n",
       "38  2.282765e+06  1510.882242  \n",
       "39  2.282765e+06  1510.882242  \n",
       "40  2.282765e+06  1510.882242  \n",
       "41  2.282765e+06  1510.882242  \n",
       "42  2.282765e+06  1510.882242  \n",
       "43  2.282765e+06  1510.882242  \n",
       "44  2.282765e+06  1510.882242  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model_precision = precision_score(y_test, prediction)\n",
    "\n",
    "results=pd.DataFrame({\"Model Name\":model_name , \"Model precision\":model_r2_score, \"Mean Abso Error\": mae, \"MSE \": mse,\n",
    "                     \"RMSE\": rmse})\n",
    "results"
   ]
  }
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