co2-emission-regression-techniques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "imposed-corrections",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:35.104788Z",
     "iopub.status.busy": "2021-04-19T18:45:35.104130Z",
     "iopub.status.idle": "2021-04-19T18:45:36.174259Z",
     "shell.execute_reply": "2021-04-19T18:45:36.173324Z"
    },
    "papermill": {
     "duration": 1.105825,
     "end_time": "2021-04-19T18:45:36.174444",
     "exception": false,
     "start_time": "2021-04-19T18:45:35.068619",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/co2-emission-by-vehicles/CO2 Emissions_Canada.csv\n",
      "/kaggle/input/co2-emission-by-vehicles/Data Description.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "existing-karen",
   "metadata": {
    "papermill": {
     "duration": 0.027461,
     "end_time": "2021-04-19T18:45:36.230808",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.203347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "hourly-privacy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.293311Z",
     "iopub.status.busy": "2021-04-19T18:45:36.292555Z",
     "iopub.status.idle": "2021-04-19T18:45:36.371709Z",
     "shell.execute_reply": "2021-04-19T18:45:36.371171Z"
    },
    "papermill": {
     "duration": 0.113297,
     "end_time": "2021-04-19T18:45:36.371846",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.258549",
     "status": "completed"
    },
    "tags": []
   },
   "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>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.6</td>\n",
       "      <td>24</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>TSX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>31</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4          AS5   \n",
       "1  ACURA         ILX       COMPACT             2.4          4           M6   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4          AV7   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6          AS6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6          AS6   \n",
       "5  ACURA         RLX      MID-SIZE             3.5          6          AS6   \n",
       "6  ACURA          TL      MID-SIZE             3.5          6          AS6   \n",
       "7  ACURA      TL AWD      MID-SIZE             3.7          6          AS6   \n",
       "8  ACURA      TL AWD      MID-SIZE             3.7          6           M6   \n",
       "9  ACURA         TSX       COMPACT             2.4          4          AS5   \n",
       "\n",
       "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0         Z                               9.9   \n",
       "1         Z                              11.2   \n",
       "2         Z                               6.0   \n",
       "3         Z                              12.7   \n",
       "4         Z                              12.1   \n",
       "5         Z                              11.9   \n",
       "6         Z                              11.8   \n",
       "7         Z                              12.8   \n",
       "8         Z                              13.4   \n",
       "9         Z                              10.6   \n",
       "\n",
       "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                              6.7                               8.5   \n",
       "1                              7.7                               9.6   \n",
       "2                              5.8                               5.9   \n",
       "3                              9.1                              11.1   \n",
       "4                              8.7                              10.6   \n",
       "5                              7.7                              10.0   \n",
       "6                              8.1                              10.1   \n",
       "7                              9.0                              11.1   \n",
       "8                              9.5                              11.6   \n",
       "9                              7.5                               9.2   \n",
       "\n",
       "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "0                           33                  196  \n",
       "1                           29                  221  \n",
       "2                           48                  136  \n",
       "3                           25                  255  \n",
       "4                           27                  244  \n",
       "5                           28                  230  \n",
       "6                           28                  232  \n",
       "7                           25                  255  \n",
       "8                           24                  267  \n",
       "9                           31                  212  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../input/co2-emission-by-vehicles/CO2 Emissions_Canada.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "conventional-narrow",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.435634Z",
     "iopub.status.busy": "2021-04-19T18:45:36.434930Z",
     "iopub.status.idle": "2021-04-19T18:45:36.439276Z",
     "shell.execute_reply": "2021-04-19T18:45:36.438734Z"
    },
    "papermill": {
     "duration": 0.037417,
     "end_time": "2021-04-19T18:45:36.439434",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.402017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7385, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "collected-import",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.500737Z",
     "iopub.status.busy": "2021-04-19T18:45:36.500095Z",
     "iopub.status.idle": "2021-04-19T18:45:36.524155Z",
     "shell.execute_reply": "2021-04-19T18:45:36.524702Z"
    },
    "papermill": {
     "duration": 0.0564,
     "end_time": "2021-04-19T18:45:36.524898",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.468498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                  42\n",
       "Model                               2053\n",
       "Vehicle Class                         16\n",
       "Engine Size(L)                        51\n",
       "Cylinders                              8\n",
       "Transmission                          27\n",
       "Fuel Type                              5\n",
       "Fuel Consumption City (L/100 km)     211\n",
       "Fuel Consumption Hwy (L/100 km)      143\n",
       "Fuel Consumption Comb (L/100 km)     181\n",
       "Fuel Consumption Comb (mpg)           54\n",
       "CO2 Emissions(g/km)                  331\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "hazardous-character",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.587129Z",
     "iopub.status.busy": "2021-04-19T18:45:36.586442Z",
     "iopub.status.idle": "2021-04-19T18:45:36.598728Z",
     "shell.execute_reply": "2021-04-19T18:45:36.599272Z"
    },
    "papermill": {
     "duration": 0.044932,
     "end_time": "2021-04-19T18:45:36.599462",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.554530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    0\n",
       "Fuel Consumption Hwy (L/100 km)     0\n",
       "Fuel Consumption Comb (L/100 km)    0\n",
       "Fuel Consumption Comb (mpg)         0\n",
       "CO2 Emissions(g/km)                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "knowing-equilibrium",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.664601Z",
     "iopub.status.busy": "2021-04-19T18:45:36.663916Z",
     "iopub.status.idle": "2021-04-19T18:45:36.676668Z",
     "shell.execute_reply": "2021-04-19T18:45:36.677217Z"
    },
    "papermill": {
     "duration": 0.04845,
     "end_time": "2021-04-19T18:45:36.677404",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.628954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1103"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sharp-referral",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.744934Z",
     "iopub.status.busy": "2021-04-19T18:45:36.743914Z",
     "iopub.status.idle": "2021-04-19T18:45:36.755183Z",
     "shell.execute_reply": "2021-04-19T18:45:36.754595Z"
    },
    "papermill": {
     "duration": 0.047472,
     "end_time": "2021-04-19T18:45:36.755327",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.707855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "outstanding-restaurant",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.821744Z",
     "iopub.status.busy": "2021-04-19T18:45:36.820888Z",
     "iopub.status.idle": "2021-04-19T18:45:36.826432Z",
     "shell.execute_reply": "2021-04-19T18:45:36.826905Z"
    },
    "papermill": {
     "duration": 0.041272,
     "end_time": "2021-04-19T18:45:36.827083",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.785811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Z', 'D', 'X', 'E', 'N'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Fuel Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "functioning-representative",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.894804Z",
     "iopub.status.busy": "2021-04-19T18:45:36.894141Z",
     "iopub.status.idle": "2021-04-19T18:45:36.899470Z",
     "shell.execute_reply": "2021-04-19T18:45:36.899946Z"
    },
    "papermill": {
     "duration": 0.041729,
     "end_time": "2021-04-19T18:45:36.900137",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.858408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AS5', 'M6', 'AV7', 'AS6', 'AM6', 'A6', 'AM7', 'AV8', 'AS8', 'A7',\n",
       "       'A8', 'M7', 'A4', 'M5', 'AV', 'A5', 'AS7', 'A9', 'AS9', 'AV6',\n",
       "       'AS4', 'AM5', 'AM8', 'AM9', 'AS10', 'A10', 'AV10'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Transmission\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unlikely-census",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:36.970126Z",
     "iopub.status.busy": "2021-04-19T18:45:36.969176Z",
     "iopub.status.idle": "2021-04-19T18:45:36.976156Z",
     "shell.execute_reply": "2021-04-19T18:45:36.975224Z"
    },
    "papermill": {
     "duration": 0.044961,
     "end_time": "2021-04-19T18:45:36.976396",
     "exception": false,
     "start_time": "2021-04-19T18:45:36.931435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COMPACT', 'SUV - SMALL', 'MID-SIZE', 'TWO-SEATER', 'MINICOMPACT',\n",
       "       'SUBCOMPACT', 'FULL-SIZE', 'STATION WAGON - SMALL',\n",
       "       'SUV - STANDARD', 'VAN - CARGO', 'VAN - PASSENGER',\n",
       "       'PICKUP TRUCK - STANDARD', 'MINIVAN', 'SPECIAL PURPOSE VEHICLE',\n",
       "       'STATION WAGON - MID-SIZE', 'PICKUP TRUCK - SMALL'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Vehicle Class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "informed-consent",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:37.061997Z",
     "iopub.status.busy": "2021-04-19T18:45:37.061266Z",
     "iopub.status.idle": "2021-04-19T18:45:37.252335Z",
     "shell.execute_reply": "2021-04-19T18:45:37.251612Z"
    },
    "papermill": {
     "duration": 0.244392,
     "end_time": "2021-04-19T18:45:37.252484",
     "exception": false,
     "start_time": "2021-04-19T18:45:37.008092",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe0b0b2b6d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[\"Engine Size(L)\"], data[\"CO2 Emissions(g/km)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "solar-cleaner",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:37.348353Z",
     "iopub.status.busy": "2021-04-19T18:45:37.341736Z",
     "iopub.status.idle": "2021-04-19T18:45:37.472476Z",
     "shell.execute_reply": "2021-04-19T18:45:37.471899Z"
    },
    "papermill": {
     "duration": 0.185963,
     "end_time": "2021-04-19T18:45:37.472621",
     "exception": false,
     "start_time": "2021-04-19T18:45:37.286658",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe0a8afbd90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[\"Fuel Consumption Comb (L/100 km)\"], data[\"CO2 Emissions(g/km)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "catholic-convergence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:37.549462Z",
     "iopub.status.busy": "2021-04-19T18:45:37.548126Z",
     "iopub.status.idle": "2021-04-19T18:45:38.078917Z",
     "shell.execute_reply": "2021-04-19T18:45:38.078046Z"
    },
    "papermill": {
     "duration": 0.571419,
     "end_time": "2021-04-19T18:45:38.079143",
     "exception": false,
     "start_time": "2021-04-19T18:45:37.507724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cylinders', ylabel='CO2 Emissions(g/km)'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data[\"Cylinders\"],data[\"CO2 Emissions(g/km)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "coastal-thesaurus",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:38.174086Z",
     "iopub.status.busy": "2021-04-19T18:45:38.172997Z",
     "iopub.status.idle": "2021-04-19T18:45:38.512152Z",
     "shell.execute_reply": "2021-04-19T18:45:38.512655Z"
    },
    "papermill": {
     "duration": 0.391043,
     "end_time": "2021-04-19T18:45:38.512853",
     "exception": false,
     "start_time": "2021-04-19T18:45:38.121810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fuel Type', ylabel='CO2 Emissions(g/km)'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data[\"Fuel Type\"],data[\"CO2 Emissions(g/km)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "adopted-pocket",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:38.597731Z",
     "iopub.status.busy": "2021-04-19T18:45:38.596690Z",
     "iopub.status.idle": "2021-04-19T18:45:39.485097Z",
     "shell.execute_reply": "2021-04-19T18:45:39.484294Z"
    },
    "papermill": {
     "duration": 0.934929,
     "end_time": "2021-04-19T18:45:39.485249",
     "exception": false,
     "start_time": "2021-04-19T18:45:38.550320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CO2 Emissions(g/km)', ylabel='Vehicle Class'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "order =data.groupby(['Vehicle Class']).mean().sort_values('CO2 Emissions(g/km)',ascending=False).index\n",
    "sns.barplot(data[\"CO2 Emissions(g/km)\"],data[\"Vehicle Class\"],order=order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fatal-marketing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:39.575826Z",
     "iopub.status.busy": "2021-04-19T18:45:39.574943Z",
     "iopub.status.idle": "2021-04-19T18:45:40.855560Z",
     "shell.execute_reply": "2021-04-19T18:45:40.854999Z"
    },
    "papermill": {
     "duration": 1.330848,
     "end_time": "2021-04-19T18:45:40.855706",
     "exception": false,
     "start_time": "2021-04-19T18:45:39.524858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CO2 Emissions(g/km)', ylabel='Transmission'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "order = data.groupby([\"Transmission\"]).mean().sort_values(\"CO2 Emissions(g/km)\", ascending = False).index\n",
    "sns.barplot(data[\"CO2 Emissions(g/km)\"],data[\"Transmission\"],order = order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "popular-length",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:40.958993Z",
     "iopub.status.busy": "2021-04-19T18:45:40.944736Z",
     "iopub.status.idle": "2021-04-19T18:45:40.965139Z",
     "shell.execute_reply": "2021-04-19T18:45:40.964584Z"
    },
    "papermill": {
     "duration": 0.067773,
     "end_time": "2021-04-19T18:45:40.965291",
     "exception": false,
     "start_time": "2021-04-19T18:45:40.897518",
     "status": "completed"
    },
    "tags": []
   },
   "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>Vehicle Class</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COMPACT</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMPACT</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COMPACT</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>COMPACT</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Vehicle Class Transmission Fuel Type  Engine Size(L)  Cylinders  \\\n",
       "0       COMPACT          AS5         Z             2.0          4   \n",
       "1       COMPACT           M6         Z             2.4          4   \n",
       "2       COMPACT          AV7         Z             1.5          4   \n",
       "3   SUV - SMALL          AS6         Z             3.5          6   \n",
       "4   SUV - SMALL          AS6         Z             3.5          6   \n",
       "5      MID-SIZE          AS6         Z             3.5          6   \n",
       "6      MID-SIZE          AS6         Z             3.5          6   \n",
       "7      MID-SIZE          AS6         Z             3.7          6   \n",
       "8      MID-SIZE           M6         Z             3.7          6   \n",
       "9       COMPACT          AS5         Z             2.4          4   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  \n",
       "0                               8.5  \n",
       "1                               9.6  \n",
       "2                               5.9  \n",
       "3                              11.1  \n",
       "4                              10.6  \n",
       "5                              10.0  \n",
       "6                              10.1  \n",
       "7                              11.1  \n",
       "8                              11.6  \n",
       "9                               9.2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[[\"Vehicle Class\",\"Transmission\",\"Fuel Type\",\"Engine Size(L)\",\"Cylinders\",\"Fuel Consumption Comb (L/100 km)\"]]\n",
    "Y = data[[\"CO2 Emissions(g/km)\"]]\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "characteristic-museum",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:41.056889Z",
     "iopub.status.busy": "2021-04-19T18:45:41.056203Z",
     "iopub.status.idle": "2021-04-19T18:45:41.271902Z",
     "shell.execute_reply": "2021-04-19T18:45:41.271149Z"
    },
    "papermill": {
     "duration": 0.264593,
     "end_time": "2021-04-19T18:45:41.272046",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.007453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "cols = [\"Engine Size(L)\",\"Cylinders\",\"Fuel Consumption Comb (L/100 km)\"]\n",
    "X[cols] = sc.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "contained-orlando",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:41.363684Z",
     "iopub.status.busy": "2021-04-19T18:45:41.362613Z",
     "iopub.status.idle": "2021-04-19T18:45:41.388158Z",
     "shell.execute_reply": "2021-04-19T18:45:41.389112Z"
    },
    "papermill": {
     "duration": 0.075538,
     "end_time": "2021-04-19T18:45:41.389351",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.313813",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "oc = OrdinalEncoder()\n",
    "cols = [\"Vehicle Class\",\"Transmission\",\"Fuel Type\"]\n",
    "X[cols] = oc.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "enclosed-treatment",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:41.491477Z",
     "iopub.status.busy": "2021-04-19T18:45:41.485042Z",
     "iopub.status.idle": "2021-04-19T18:45:41.498005Z",
     "shell.execute_reply": "2021-04-19T18:45:41.497383Z"
    },
    "papermill": {
     "duration": 0.065637,
     "end_time": "2021-04-19T18:45:41.498156",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.432519",
     "status": "completed"
    },
    "tags": []
   },
   "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>Vehicle Class</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.851086</td>\n",
       "      <td>-0.876934</td>\n",
       "      <td>-0.854490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.558066</td>\n",
       "      <td>-0.876934</td>\n",
       "      <td>-0.481184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.217362</td>\n",
       "      <td>-0.876934</td>\n",
       "      <td>-1.736851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.247740</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>0.027870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.247740</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>-0.141815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.247740</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>-0.345436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.247740</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>-0.311499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.394251</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>0.027870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.394251</td>\n",
       "      <td>0.206429</td>\n",
       "      <td>0.197555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.558066</td>\n",
       "      <td>-0.876934</td>\n",
       "      <td>-0.616932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Vehicle Class  Transmission  Fuel Type  Engine Size(L)  Cylinders  \\\n",
       "0            0.0          14.0        4.0       -0.851086  -0.876934   \n",
       "1            0.0          25.0        4.0       -0.558066  -0.876934   \n",
       "2            0.0          22.0        4.0       -1.217362  -0.876934   \n",
       "3           11.0          15.0        4.0        0.247740   0.206429   \n",
       "4           11.0          15.0        4.0        0.247740   0.206429   \n",
       "5            2.0          15.0        4.0        0.247740   0.206429   \n",
       "6            2.0          15.0        4.0        0.247740   0.206429   \n",
       "7            2.0          15.0        4.0        0.394251   0.206429   \n",
       "8            2.0          25.0        4.0        0.394251   0.206429   \n",
       "9            0.0          14.0        4.0       -0.558066  -0.876934   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  \n",
       "0                         -0.854490  \n",
       "1                         -0.481184  \n",
       "2                         -1.736851  \n",
       "3                          0.027870  \n",
       "4                         -0.141815  \n",
       "5                         -0.345436  \n",
       "6                         -0.311499  \n",
       "7                          0.027870  \n",
       "8                          0.197555  \n",
       "9                         -0.616932  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "infinite-cancellation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:41.590039Z",
     "iopub.status.busy": "2021-04-19T18:45:41.589260Z",
     "iopub.status.idle": "2021-04-19T18:45:41.740032Z",
     "shell.execute_reply": "2021-04-19T18:45:41.739328Z"
    },
    "papermill": {
     "duration": 0.198533,
     "end_time": "2021-04-19T18:45:41.740179",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.541646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polished-bangkok",
   "metadata": {
    "papermill": {
     "duration": 0.043919,
     "end_time": "2021-04-19T18:45:41.827446",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.783527",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "controlled-draft",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:41.927418Z",
     "iopub.status.busy": "2021-04-19T18:45:41.926205Z",
     "iopub.status.idle": "2021-04-19T18:45:41.936339Z",
     "shell.execute_reply": "2021-04-19T18:45:41.936816Z"
    },
    "papermill": {
     "duration": 0.063705,
     "end_time": "2021-04-19T18:45:41.937040",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.873335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree = 2)\n",
    "x_train_poly = poly.fit_transform(x_train)\n",
    "x_test_poly = poly.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "experimental-webster",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:42.030559Z",
     "iopub.status.busy": "2021-04-19T18:45:42.029812Z",
     "iopub.status.idle": "2021-04-19T18:45:42.177317Z",
     "shell.execute_reply": "2021-04-19T18:45:42.178235Z"
    },
    "papermill": {
     "duration": 0.197383,
     "end_time": "2021-04-19T18:45:42.178517",
     "exception": false,
     "start_time": "2021-04-19T18:45:41.981134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "several-being",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:42.320370Z",
     "iopub.status.busy": "2021-04-19T18:45:42.319310Z",
     "iopub.status.idle": "2021-04-19T18:45:42.328439Z",
     "shell.execute_reply": "2021-04-19T18:45:42.329469Z"
    },
    "papermill": {
     "duration": 0.07053,
     "end_time": "2021-04-19T18:45:42.329742",
     "exception": false,
     "start_time": "2021-04-19T18:45:42.259212",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9780428330316173"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "communist-intermediate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:42.473372Z",
     "iopub.status.busy": "2021-04-19T18:45:42.472275Z",
     "iopub.status.idle": "2021-04-19T18:45:42.484177Z",
     "shell.execute_reply": "2021-04-19T18:45:42.485252Z"
    },
    "papermill": {
     "duration": 0.07541,
     "end_time": "2021-04-19T18:45:42.485538",
     "exception": false,
     "start_time": "2021-04-19T18:45:42.410128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9794464379667018"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test_poly,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intermediate-secretary",
   "metadata": {
    "papermill": {
     "duration": 0.056579,
     "end_time": "2021-04-19T18:45:42.623052",
     "exception": false,
     "start_time": "2021-04-19T18:45:42.566473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "perfect-passage",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:42.723685Z",
     "iopub.status.busy": "2021-04-19T18:45:42.722489Z",
     "iopub.status.idle": "2021-04-19T18:45:42.859909Z",
     "shell.execute_reply": "2021-04-19T18:45:42.859212Z"
    },
    "papermill": {
     "duration": 0.191368,
     "end_time": "2021-04-19T18:45:42.860076",
     "exception": false,
     "start_time": "2021-04-19T18:45:42.668708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=5, random_state=5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt_reg = DecisionTreeRegressor(random_state = 5, max_depth = 5)\n",
    "dt_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "effective-regular",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:42.959116Z",
     "iopub.status.busy": "2021-04-19T18:45:42.958403Z",
     "iopub.status.idle": "2021-04-19T18:45:42.967460Z",
     "shell.execute_reply": "2021-04-19T18:45:42.966768Z"
    },
    "papermill": {
     "duration": 0.062439,
     "end_time": "2021-04-19T18:45:42.967602",
     "exception": false,
     "start_time": "2021-04-19T18:45:42.905163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9835169463357659"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "informal-header",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.071116Z",
     "iopub.status.busy": "2021-04-19T18:45:43.069199Z",
     "iopub.status.idle": "2021-04-19T18:45:43.078981Z",
     "shell.execute_reply": "2021-04-19T18:45:43.078383Z"
    },
    "papermill": {
     "duration": 0.064591,
     "end_time": "2021-04-19T18:45:43.079132",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.014541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9785721631327842"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incomplete-chambers",
   "metadata": {
    "papermill": {
     "duration": 0.047643,
     "end_time": "2021-04-19T18:45:43.173118",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.125475",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "sorted-stewart",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.273859Z",
     "iopub.status.busy": "2021-04-19T18:45:43.273114Z",
     "iopub.status.idle": "2021-04-19T18:45:43.343474Z",
     "shell.execute_reply": "2021-04-19T18:45:43.342696Z"
    },
    "papermill": {
     "duration": 0.121973,
     "end_time": "2021-04-19T18:45:43.343652",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.221679",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=3, random_state=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_reg = RandomForestRegressor(n_estimators = 3, random_state = 0)\n",
    "rf_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "conscious-evans",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.446351Z",
     "iopub.status.busy": "2021-04-19T18:45:43.445282Z",
     "iopub.status.idle": "2021-04-19T18:45:43.455501Z",
     "shell.execute_reply": "2021-04-19T18:45:43.454879Z"
    },
    "papermill": {
     "duration": 0.065114,
     "end_time": "2021-04-19T18:45:43.455638",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.390524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9980971358116845"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_reg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "recovered-colon",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.563009Z",
     "iopub.status.busy": "2021-04-19T18:45:43.559582Z",
     "iopub.status.idle": "2021-04-19T18:45:43.569265Z",
     "shell.execute_reply": "2021-04-19T18:45:43.568583Z"
    },
    "papermill": {
     "duration": 0.065126,
     "end_time": "2021-04-19T18:45:43.569416",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.504290",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9958800608857328"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "three-scanner",
   "metadata": {
    "papermill": {
     "duration": 0.049397,
     "end_time": "2021-04-19T18:45:43.667955",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.618558",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "american-auckland",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.772167Z",
     "iopub.status.busy": "2021-04-19T18:45:43.771452Z",
     "iopub.status.idle": "2021-04-19T18:45:43.789325Z",
     "shell.execute_reply": "2021-04-19T18:45:43.790376Z"
    },
    "papermill": {
     "duration": 0.074113,
     "end_time": "2021-04-19T18:45:43.790651",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.716538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=5)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_reg = Ridge(alpha = 5)\n",
    "ridge_reg.fit(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "excellent-beijing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:43.945838Z",
     "iopub.status.busy": "2021-04-19T18:45:43.944741Z",
     "iopub.status.idle": "2021-04-19T18:45:43.956556Z",
     "shell.execute_reply": "2021-04-19T18:45:43.957573Z"
    },
    "papermill": {
     "duration": 0.075379,
     "end_time": "2021-04-19T18:45:43.957852",
     "exception": false,
     "start_time": "2021-04-19T18:45:43.882473",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9779962436701151"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "respective-value",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:44.110075Z",
     "iopub.status.busy": "2021-04-19T18:45:44.108960Z",
     "iopub.status.idle": "2021-04-19T18:45:44.121634Z",
     "shell.execute_reply": "2021-04-19T18:45:44.122756Z"
    },
    "papermill": {
     "duration": 0.075989,
     "end_time": "2021-04-19T18:45:44.123049",
     "exception": false,
     "start_time": "2021-04-19T18:45:44.047060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9793635572189293"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(x_test_poly,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "durable-export",
   "metadata": {
    "papermill": {
     "duration": 0.055857,
     "end_time": "2021-04-19T18:45:44.271494",
     "exception": false,
     "start_time": "2021-04-19T18:45:44.215637",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "macro-point",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:44.376786Z",
     "iopub.status.busy": "2021-04-19T18:45:44.375956Z",
     "iopub.status.idle": "2021-04-19T18:45:44.410717Z",
     "shell.execute_reply": "2021-04-19T18:45:44.411679Z"
    },
    "papermill": {
     "duration": 0.091056,
     "end_time": "2021-04-19T18:45:44.411970",
     "exception": false,
     "start_time": "2021-04-19T18:45:44.320914",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "la_reg = Lasso()\n",
    "la_reg.fit(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "silent-timing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:44.568880Z",
     "iopub.status.busy": "2021-04-19T18:45:44.567724Z",
     "iopub.status.idle": "2021-04-19T18:45:44.580147Z",
     "shell.execute_reply": "2021-04-19T18:45:44.581332Z"
    },
    "papermill": {
     "duration": 0.080079,
     "end_time": "2021-04-19T18:45:44.581641",
     "exception": false,
     "start_time": "2021-04-19T18:45:44.501562",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9661927396018358"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_reg.score(x_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "every-briefing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-19T18:45:44.734719Z",
     "iopub.status.busy": "2021-04-19T18:45:44.733632Z",
     "iopub.status.idle": "2021-04-19T18:45:44.744961Z",
     "shell.execute_reply": "2021-04-19T18:45:44.745969Z"
    },
    "papermill": {
     "duration": 0.074668,
     "end_time": "2021-04-19T18:45:44.746228",
     "exception": false,
     "start_time": "2021-04-19T18:45:44.671560",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9664755565360988"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_reg.score(x_test_poly,y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 17.386534,
   "end_time": "2021-04-19T18:45:45.548637",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-04-19T18:45:28.162103",
   "version": "2.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}