Single Linear Regression (SLR) .ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# import library\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport pylab as pl\nimport numpy as np\n%matplotlib inline\n\nprint('imported')\n",
            "execution_count": 1,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "imported\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# download data\n!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv",
            "execution_count": 2,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "--2020-10-11 00:12:56--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 72629 (71K) [text/csv]\nSaving to: \u2018FuelConsumption.csv\u2019\n\n100%[======================================>] 72,629      --.-K/s   in 0.005s  \n\n2020-10-11 00:12:56 (12.9 MB/s) - \u2018FuelConsumption.csv\u2019 saved [72629/72629]\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### The goal is to estimate carbon dioxide emission for a new light-duty vehicles for retail sale from a fuel consumption dataset."
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# read the data\ndf = pd.read_csv(\"FuelConsumption.csv\")\ndf.head()",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 3,
                    "data": {
                        "text/plain": "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n0       2014  ACURA         ILX      COMPACT         2.0          4   \n1       2014  ACURA         ILX      COMPACT         2.4          4   \n2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n\n  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n0          AS5        Z                   9.9                  6.7   \n1           M6        Z                  11.2                  7.7   \n2          AV7        Z                   6.0                  5.8   \n3          AS6        Z                  12.7                  9.1   \n4          AS6        Z                  12.1                  8.7   \n\n   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n0                   8.5                        33           196  \n1                   9.6                        29           221  \n2                   5.9                        48           136  \n3                  11.1                        25           255  \n4                  10.6                        27           244  ",
                        "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>MODELYEAR</th>\n      <th>MAKE</th>\n      <th>MODEL</th>\n      <th>VEHICLECLASS</th>\n      <th>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>TRANSMISSION</th>\n      <th>FUELTYPE</th>\n      <th>FUELCONSUMPTION_CITY</th>\n      <th>FUELCONSUMPTION_HWY</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>FUELCONSUMPTION_COMB_MPG</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014</td>\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>2014</td>\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>2014</td>\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>2014</td>\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>2014</td>\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  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Data Exploration"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# describe dataset\ndf.describe()",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 4,
                    "data": {
                        "text/plain": "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\ncount     1067.0  1067.000000  1067.000000           1067.000000   \nmean      2014.0     3.346298     5.794752             13.296532   \nstd          0.0     1.415895     1.797447              4.101253   \nmin       2014.0     1.000000     3.000000              4.600000   \n25%       2014.0     2.000000     4.000000             10.250000   \n50%       2014.0     3.400000     6.000000             12.600000   \n75%       2014.0     4.300000     8.000000             15.550000   \nmax       2014.0     8.400000    12.000000             30.200000   \n\n       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\ncount          1067.000000           1067.000000               1067.000000   \nmean              9.474602             11.580881                 26.441425   \nstd               2.794510              3.485595                  7.468702   \nmin               4.900000              4.700000                 11.000000   \n25%               7.500000              9.000000                 21.000000   \n50%               8.800000             10.900000                 26.000000   \n75%              10.850000             13.350000                 31.000000   \nmax              20.500000             25.800000                 60.000000   \n\n       CO2EMISSIONS  \ncount   1067.000000  \nmean     256.228679  \nstd       63.372304  \nmin      108.000000  \n25%      207.000000  \n50%      251.000000  \n75%      294.000000  \nmax      488.000000  ",
                        "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>MODELYEAR</th>\n      <th>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>FUELCONSUMPTION_CITY</th>\n      <th>FUELCONSUMPTION_HWY</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>FUELCONSUMPTION_COMB_MPG</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1067.0</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n      <td>1067.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2014.0</td>\n      <td>3.346298</td>\n      <td>5.794752</td>\n      <td>13.296532</td>\n      <td>9.474602</td>\n      <td>11.580881</td>\n      <td>26.441425</td>\n      <td>256.228679</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.0</td>\n      <td>1.415895</td>\n      <td>1.797447</td>\n      <td>4.101253</td>\n      <td>2.794510</td>\n      <td>3.485595</td>\n      <td>7.468702</td>\n      <td>63.372304</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2014.0</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>4.600000</td>\n      <td>4.900000</td>\n      <td>4.700000</td>\n      <td>11.000000</td>\n      <td>108.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2014.0</td>\n      <td>2.000000</td>\n      <td>4.000000</td>\n      <td>10.250000</td>\n      <td>7.500000</td>\n      <td>9.000000</td>\n      <td>21.000000</td>\n      <td>207.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2014.0</td>\n      <td>3.400000</td>\n      <td>6.000000</td>\n      <td>12.600000</td>\n      <td>8.800000</td>\n      <td>10.900000</td>\n      <td>26.000000</td>\n      <td>251.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2014.0</td>\n      <td>4.300000</td>\n      <td>8.000000</td>\n      <td>15.550000</td>\n      <td>10.850000</td>\n      <td>13.350000</td>\n      <td>31.000000</td>\n      <td>294.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2014.0</td>\n      <td>8.400000</td>\n      <td>12.000000</td>\n      <td>30.200000</td>\n      <td>20.500000</td>\n      <td>25.800000</td>\n      <td>60.000000</td>\n      <td>488.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# select features data \ncdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head()",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 5,
                    "data": {
                        "text/plain": "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n0         2.0          4                   8.5           196\n1         2.4          4                   9.6           221\n2         1.5          4                   5.9           136\n3         3.5          6                  11.1           255\n4         3.5          6                  10.6           244",
                        "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>ENGINESIZE</th>\n      <th>CYLINDERS</th>\n      <th>FUELCONSUMPTION_COMB</th>\n      <th>CO2EMISSIONS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>4</td>\n      <td>8.5</td>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.4</td>\n      <td>4</td>\n      <td>9.6</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.5</td>\n      <td>4</td>\n      <td>5.9</td>\n      <td>136</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>11.1</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.5</td>\n      <td>6</td>\n      <td>10.6</td>\n      <td>244</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# visulaize each feature \nviz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\nviz.hist()\n\nplt.show()",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 4 Axes>",
                        "image/png": 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l48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXj/vhtI8304T9v2vpNXk7vfvzN+odL/GZmJePAb2ZWMg78ZmYl48BvZlYyDvxmZiXjwG9mVjIO/GZmJdPz7fibaVduZmbN6/nAb/2hlR9oP/Rl1h2u6jEzKxmX+M3MxkivXBl3pMQv6VBJD0haJumMTqzDrNf4uLd+0fYSv6QtgYuBQ4AVwE8kLYyIn7V7XWa9oh+P+2Lps1bHe2XSbEm8uJ/6+R5VJ6p6DgCWRcSvACR9DZgF9OwJYNYGbTvu3ZLNOk0R0d4FSkcDh0bE+/L48cDrIuKUqvnmAnPz6L7AA23NSPtMAh7tdiZ6TK/ukxdHxAu7seJmjvseP+Z78X/qPDXW0jHfiRK/aqRt8usSEfOB+R1Yf1tJujMipnc7H73E+6Smhsd9Lx/zvfg/dZ46pxM3d1cAexbG9wAe7sB6zHqJj3vrG50I/D8BpkraW9I2wLHAwg6sx6yX+Li3vtH2qp6IWC/pFOB7wJbApRFxf7vXM4Z68tK8y7xPqoyD474X/6fOU4e0/eaumZn1NnfZYGZWMg78ZmYlU+rAL2lPSbdIWiLpfkmn5vSJkm6WtDT/3SWnS9JF+ZH8eyS9urtb0DmStpT0X5K+ncf3lnRH3idfzzcwkbRtHl+Wpw90M982MknLJd0r6W5Jd9aYPqbHuKR9c14qwzpJp1XNMyhpbWGeszqQj0slrZZ0XyGtZhyo8d3ZeZ6lkma3O2+dUOrAD6wH5kXEy4AZwMmS9gPOABZFxFRgUR4HOAyYmoe5wBfHPstj5lRgSWH808Bn8z55Ajgpp58EPBER+wCfzfNZb3tzRLyyTnv0MT3GI+KBnJdXAq8Bnga+WWPWH1bmi4h/7EBWLgMOrUqrFwc2kDQROBt4Henp7bPr/UD0klIH/ohYGRF35c9PkgLd7qRH7S/Ps10OHJk/zwKuiOR2YIKkKWOc7Y6TtAdwOPDlPC7gLcA1eZbqfVLZV9cAB+X5rT918xg/CPhlRPxmjNa3QUTcCjxelVwvDhS9Dbg5Ih6PiCeAm9n0B6TnlDrwF+UqilcBdwCTI2IlpB8HYNc82+7AQ4Wvrchp483ngI8Bz+bxFwBrImJ9Hi9u94Z9kqevzfNbbwrgJkmLcxcS1bp5jB8LXFVn2usl/VTSdyXtP0b5qRcHivoyJjjwA5J2BK4FTouIdSPNWiNtXLWHlXQEsDoiFheTa8waTUyz3nNgRLyaVKVzsqQ3Vk3vyv8z3zN6B/CNGpPvIvVJ8+fA54HrO52fUejL47/0gV/S1qSgvyAirsvJqyqXt/nv6pxehsfyDwTeIWk58DVSFc/nSJf8lQf+itu9YZ/k6Tuz6SWz9YiIeDj/XU2qSz+gapZuHeOHAXdFxKrqCRGxLiKG8+cbga0lTRqDPNWLA0V9GRNKHfhzXfQlwJKIuKAwaSEwOwe/B4G9JQ0DJwDnSZojKYDtK5eCeXkrJA0WxqdK+pqk3+XWCkslfT7XoVdaK6wozD8k6feS9iykHZzzURlfLukZScOF4Qt52jaSzs/5GJb0a0mfrfruwfnz/VXLGJb0B+D0iNgDmEO6tN2GVNc5ARiW9HpgNnBDcV/lz0cDPwg/FdiTJO0gaafKZ+CtwH1Vsy0ETsite2YAa4vHeAe9mzrVPJJ2q9w3knQAKW49NgZ5Kh7bxWO+6HvAWyXtkm/qvjWn9baIKO0AvIF0WXYPcHceZpLqqBcBfyRdZk7M84v0so3VpBZBa4DnF5a3AhjMn/chlXwvAPbIabsCpwHH5vFBYEXh+0OkA3p+Ie1gYHlhfDlwcJ3tORv4D+BFOa8DwAlNfndH0s3tcwt5+x3w7Tz+EuDHwDLS5fi2OX27PL4sT39Jt/+vvTTkff4MMFwY3lP8v1f9/9+XP5+Tj7/i99YU5g1gnzrrnEIq0KwEngR+DpwL7A/8NA+PkLoXfiYfp/8X2LZwjK/L65hdWO4+KWRsGN8fuInUymsNsBiYmafNAW6rsz8Ozp8vy+s4Oh/3O+f0z+X0y/P4V/L4n0jn3S+AI4DjCvvmGdI9qQ37q9YxTyqRL8jreyofs0eQfnRW5n0epHN5EikOLAV+DXw1L2M68OXCMt+bj/9lwPvy/25pXv5y4FJgoDD/EXm9T+V8LCDHiMK+C+CCqn13ZE6/LI8P5PHKNq8C/hXYuuFx2e0To5eH6oOm6h9zG/At4OxCejHwXwl8q8HyB9k08J9NOln3yWmjCfzfJt2nGNX25GlfI53EW9TKm4f2HUP19i2bBv4rR1huzcAPTMzr/Gol2JCqIi4EXpHHP58D0+tJ/XXtnwPRDYXlXJaD0k2FtOrA/yvgo6Srwm1I1YRvyNPm0FzgfwC4tjB9K+C3pCA6p3pZpNL+B0nNPic2sU+L66vsm38HdgO2J11prAOOrtq3jwHvKaR9khxwG/y/F5IKi6/N27IzcDJwUp5+dF7fcXn9u5F+GJYDuxS2d1neD1sVln1d3l+X5fGBnNet8viuwH8xQgyoDKWu6mmDfwA+lNvyVjuYdO9gtH4LfIl04o/W7cCHJX1A0rRmm1VK+nvSSfueiHi20fzW0z5MKjj8TUQsB4iIhyLi1Ii4R9JU4APAcRHxnxGxPlJnckcBh0p6S2FZlwOvkPSm6pXkOva9gS9FxH/n4UcRcdso8/st4MBC2/dDSVfgj9SaOR+fl5KC5ktGua4PkUrGJ0XEIxHxTERcBXwKOL/qfPkMcG7hvlZDuRr1EGBWRPwk79u1EXFxRFySl38+8MmIWJDX/wjpKmE456/iEeBeUnPRyvMCf8EIPb5Gum9zM7Bfo7w68Dd2vaQ1heFvKxMi4m5SKfn0Gt+bROHglXRK/v6wpC81WOc/A28fodlavTz9M+kBquOAO4HfNnqSMNfj/hNwTERUv1noRVXrWZPrhq13HQxcN8IP+EGkkvGPi4kR8RCp4HBIIflp0rHxqRrLeYxUKr1S0pGSJreY39+TgtmxefwE4Ip6M+dAXAmUS0e5rkNIVxfV++ZqYC/gzwpp15FK5nNGsfyDgR/nfVnLvnk9G7Vcyvm5lo33PaT9cEL+fCzpHsMf6q1c0otIPxS3N8qoA39jR0bEhMJQHbTPAt4vabeq9MdIda0ARMQXImICqf5y65FWGBG/A74A1HtCsWaeIuJPuXRxIOlm7KeASyW9rNZCcqntG8DHIz2sU+3hqvVMiIinRsq71VT8oR5NU8R3Vf3o3tLEd15AqquuZ9II01fm6UX/B9hL0mHFxEh1C28mVVGcD6yUdGu+ohitK0g3lHcG3kTt5pozJK0hFabeDbwzItaOcj31tn1lYXpFkK7oz5K0bZPLb2bfU2eeWvv+m8Bg3i8j/SA+mvfNb0n3Da6pM98GDvybKSJ+TiodfKJq0iLgrzZj0f9COrFe02K+nomIi0k33ja59JO0Bake+EcR8fnNyKc1VvyhPpJ0g7LWj//WpJuLFVdX/ei+uYl1bVTgqOHREaZPoep9shHxB+B/5UFV01ZExCkR8VLgxaSgUwlOzW4juXrohcCZpMYEz9T43u15H0yKiBkR8f0RtrGeets+pTC9mK8bSa36aj3oVksz+54689Ta988A3yHtl0kR8aM6y52UC5XPA35EulE/Igf+9jgXOJFUyq44B/hLSRdI2h02lLBrlr6rRcQaUknqY81mQtJpuYno9pK2ytU8O5Fu+FQ7h3TT733NLt/a5kFgktKDg8CGpsUvBja3u4LvA+/MP+y1/ADYMzeL3CA3IZ5BKrBU+3fSTcp31ltprt64GHh5TnqQdKWw4cdC0vNINyBrbeOVwDxGqOZpg+8DR9XYN+8iPX37ixrfORP4n6Sg2szyD6g0167hAVIDkGOKiTk/R1F7319B2i9fabTy/ENxGekp5xGfc3Dgb+xbVW3dN+lAKiJ+TfrH7FBI+wXpRNoD+KmkJ0m/xg+TLiGbcSGpCVuzeXqG9GNRaap3MnBURPyqxjLOJN0ce6RGe/698jwvqjHtqCbzbnVExIOkrkE+LWnHXJXwUVIpuWH9bME2krYrDFuSmg8/H7hc0osBJO2eCyCvyMflvwELJM1Q6oV1f1Id8/drlaQjdcVxDoV7Wbnd+rmS9pG0RQ407y3k/w5S/f0ZOW87AOeR7j3VCvwXkeq4bx3F9o/WZ0n75hKlZwO2k/RuUmD/aK6+2khEDJFusjbsdTPvu5uBb0p6TS587STp7yS9Ny//I8CZkt6TC2i7kfrEen7OX7X/IO2Xhlfl+Tg6nnT+j/ycQ6NmPx48eGh9oH6T4D1J91cqP9LfA/YrTD+HTdvxDwO75ulRY6g0BX0RqeXLIzzXjv9s4Hl5+hakIL6MVFh4iNSKZbvC+i8jtT6h8J372FC9zw6kVj/Lc74eIbWF373wnf3ydj1KamN+DbBnvXVU7Z/bqNGcc4T9PEiD5px5fK+cz8dJVVM/IbXCKX5no6aypJ43N7Sfb5CPbUg1AMvy8n9DCux7FeaZldf7VM7HVVX7pe72UmhWyqbt+NeQfihe2yiffvWimVnJuKrHzKxkHPjNzJok6bga972GJd3f7byNhqt6zMxKpunHkTtp0qRJMTAw0O1s8NRTT7HDDv3zYGq/5Rc6m+fFixc/GhEv7MjC26xXjvl6+vHYapd+2vZWj/meCPwDAwPceecm730ec0NDQwwODnY7G03rt/xCZ/MsqS2v7JO0HalZ4bakc+SaiDhb0t6kzuwmkjriOj4i/js3o7uC9LDdY8BfR+4np55eOebr6cdjq136adtbPeZdx2+2qT8Ab4n0xqdXkjovm4FfOG/jhAO/WZVIhvPo1nkI/MJ5Gyd6oqrHrNfkp2AXk/qgvxj4JU2+cF5S5YXzj1Ytcy6535fJkyczNDTU4a1o3fDwcE/nr5PKsO3jMvAPnPGdUX9n+XmHdyAn1q8i4k/AKyVNIPWSWKuPpUqTuKZeuB0R84H5ANOnT49u1yOPdJ7Mm/Ynzr+tdkes4/1c6ac6/la5qsdsBJE6yxsi9bvkF87buODAb1ZF0gtzSR9J25NesLEEuIX06jzwC+etj43Lqh6zzTSF1LvllqTC0dUR8W1JPwO+JumTpK6uL8nzXwJ8RdIyUkn/2FoLNesVDvxmVSLiHuBVNdJ/BRxQI/33VPWxbtbLHPg3Qys3kWH83xwzs97mOn4zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OSceA3MysZB34zs5Jx4DczKxkHfjOzknHgNzMrGQd+M7OSaRj4Je0p6RZJSyTdL+nUnD5R0s2Slua/u+R0SbpI0jJJ90h6dac3wszMmtdMJ23rgXkRcZeknYDFkm4G5gCLIuI8SWcAZwCnA4cBU/PwOuCL+a+ZjQN+w13/a1jij4iVEXFX/vwk6YUUu7PxC6arXzx9RX5h9e2ktxZNaXvOzcysJaPqllnSAKmf8juAyRGxEtKPg6Rd82wbXjydVV5KvbJqWR178fS8aesbz1RlaGho1C9ZbmU9lXW1Qz++FLof82w23jQd+CXtCFwLnBYR66Ra75dOs9ZIG9MXT89p5VL0uMFRv2S5lfVU1tUO/fhS6H7Mcz9o9d0QVk5NteqRtDUp6C+IiOty8qpKFU7+uzqnb3jxdFZ8KbWZmXVZM616RHqn6JKIuKAwqfiC6eoXT5+QW/fMANZWqoTMzKz7mqnqORA4HrhX0t057RPAecDVkk4CHuS5d47eCMwElgFPAye2NcdmZrZZGgb+iLiN2vX2AAfVmD+AkzczX2Zm1iF+ctesih9atPHOgd9sU5WHFl8GzABOlrQf6SHFRRExFViUx2Hjhxbnkh5aNOtZDvxmVfzQoo13o3qAy6xsxvNDiyOZvH17l9lPD+2V4SFDB36zOsb7Q4sjmTdtPeff277w0K6HFsdCGR4ydFWPWQ1+aNHGMwd+syp+aNHGO1f1mG3KDy3auObAb1bFDy3aeOeqHjOzknHgNzMrGVf1ZANnfId509a3vVmcmVmvcYnfzKxkHPjNzErGgd/MrGQc+M3MSsaB38ysZBz4zcwipsvqAAAHB0lEQVRKxoHfzKxk3I6/TwzUeL6g0XMHy887vJNZMrM+5RK/mVnJOPCbmZWMA7+ZWck48JuZlUzP39ytdVPTzMxa5xK/mVnJNAz8ki6VtFrSfYW0iZJulrQ0/90lp0vSRZKWSbpH0qs7mXkzMxu9Zkr8lwGHVqWdASyKiKnAojwOcBgwNQ9zgS+2J5tmZtYuDQN/RNwKPF6VPAu4PH++HDiykH5FJLcDEyRNaVdmzcxs87V6c3dyRKwEiIiVknbN6bsDDxXmW5HTVlYvQNJc0lUBkydPZmhoqOaK5k1b32IWR2/y9mOzvnrbOpJa+WqU31bW02nDw8M9mS/rrFYaafjJ885pd6se1UiLWjNGxHxgPsD06dNjcHCw5gLH8lWI86at5/x7O9/Qaflxg6P+Tq390Ci/rayn04aGhqj3v+4lki4FjgBWR8TLc9pE4OvAALAceFdEPCFJwIXATOBpYE5E3NWNfJs1o9VWPasqVTj57+qcvgLYszDfHsDDrWfPrGsuw/e2bJxqNfAvBGbnz7OBGwrpJ+TWPTOAtZUqIbN+4ntbNp41rNeQdBUwCEyStAI4GzgPuFrSScCDwDF59htJl7vLSJe8J3Ygz2bdsln3tpq9r9WKdt+bGqv7XSPp1r2gMtyHahj4I+LddSYdVGPeAE7e3EyZ9Zmm7m01e1+rFe2+FzZW97tG0q17VP1yH2pz9HyXDWY9ZJWkKbm073tbHeaWQJ3jLhvMmud7WzYuuMRvVoPvbdl45sBvVoPvbdl45qoeM7OScYm/C/yOATPrJpf4zcxKxoHfzKxkHPjNzErGgd/MrGR8c3cc85OPZlaLS/xmZiXjEr9tpNWmpr5SMOsfLvGbmZWMA7+ZWck48JuZlYwDv5lZyTjwm5mVjFv1mNm44VZpzXGJ38ysZFziN+sx7rbbOs0lfjOzknHgNzMrGVf1mFnpFavX5k1bz5wmqtv6+YawS/xmZiXjEr+ZWQv6udvzjpT4JR0q6QFJyySd0Yl1mPUaH/fWL9pe4pe0JXAxcAiwAviJpIUR8bN2r8usV/i4t2b0ylVCJ6p6DgCWRcSvACR9DZgF+ASwjfTKSdAmPu6tb3Qi8O8OPFQYXwG8rnomSXOBuXl0WNIDHcjLqPw9TAIe7XY+mtVL+dWnm551s/LcYD0vbnW5bdDwuO/FY76eXjq2xlqvbXsnjvlOBH7VSItNEiLmA/M7sP6WSbozIqZ3Ox/N6rf8Qn/muUkNj/tePObrGcf/p4bKsO2duLm7AtizML4H8HAH1mPWS3zcW9/oROD/CTBV0t6StgGOBRZ2YD1mvcTHvfWNtlf1RMR6SacA3wO2BC6NiPvbvZ4O6YvL8IJ+yy/0Z54b6vPjvpZx+X9q0rjfdkVsUv1uZmbjmLtsMDMrGQd+M7OSKVXgl7SnpFskLZF0v6RTa8wzKGmtpLvzcFY38lqVp+WS7s35ubPGdEm6KHcVcI+kV3cjn4X87FvYf3dLWifptKp5em4/l5WkSyWtlnRfIW2ipJslLc1/d+lmHjuhXjwoxbaXqY5f0hRgSkTcJWknYDFwZPGxekmDwEci4oguZXMTkpYD0yOi5kMlkmYCHwRmkh4aujAiNnlorhtyVwa/BV4XEb8ppA/SY/u5rCS9ERgGroiIl+e0zwCPR8R5ud+hXSLi9G7ms93qxQNgDuN820tV4o+IlRFxV/78JLCE9MRlv5tFOmkjIm4HJuSDuhccBPyyGPStt0TErcDjVcmzgMvz58tJAXFcGSEejPttL1XgL5I0ALwKuKPG5NdL+qmk70raf0wzVlsAN0lanB/7r1aru4Be+UE7FriqzrRe28/2nMkRsRJSgAR27XJ+OqoqHoz7bS9lf/ySdgSuBU6LiHVVk+8CXhwRw7kK5Xpg6ljnscqBEfGwpF2BmyX9PJfSKprqJmOs5QeZ3gF8vMbkXtzPVkLV8UCqdTqNL6Ur8UvamvRPXhAR11VPj4h1ETGcP98IbC1p0hhnszpPD+e/q4FvknqCLOrV7gIOA+6KiFXVE3pxP9tGVlWqC/Pf1V3OT0fUiQfjfttLFfiVfsovAZZExAV15tktz4ekA0j76LGxy+Um+dkh33hC0g7AW4H7qmZbCJyQW/fMANZWLlW77N3Uqebptf1sm1gIzM6fZwM3dDEvHTFCPBj/216yVj1vAH4I3As8m5M/AewFEBH/lh+7fz+wHngG+HBE/L8uZBcASS8hlfIhVc19NSI+JenvYEOeBXwBOBR4GjgxIjZp9jmWJD2PdN/hJRGxNqcV89xT+7nMJF0FDJK6I14FnE2qeruadG48CBwTEdU3gPvaCPHgDsb7tpcp8JuZWcmqeszMzIHfzKx0HPjNzErGgd/MrGQc+M3MSsaB38ysZBz4zcxK5v8DhtoJDID9LSsAAAAASUVORK5CYII=\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# visulize each of these features vs the Emission to see their relation  ( Linear Regression)\nx = cdf.FUELCONSUMPTION_COMB\ny = cdf.CO2EMISSIONS\nsize = 50\ncolor = ('red')\n\nplt.scatter(x, y, s =size, c = color)\nplt.xlabel(\"FUELCONSUMPTION_COMB\")\nplt.ylabel(\"Emission\")\nplt.show()",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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xRYPKZwFDc8u4++fTqZZIBXV0hAN+XnopWbmWltB3rj7vZHKP+yzFLMxGmjEjbKM+YgTMmqWpqymJOwZxF/AQ8EtAh+JK/Zo2DS69NHm5AQPgjTd6vj6NYMGC+NtwjxgRgsH116daJQniBojt3H16qjURydXZGb44Vq2C4cPDbJSWlnTfs6OjvOAwfz6cckrP16dRrFoV9kWKY9asdOsiW4g7BnGPmR2Zak1EsrL90VOmwCWXhPtBg0J6muLOksk6+eSw0ZuCQ2mZTNiWZNSocJ/J2SRh+PCwZqEUrYLudRaWH5TIZNYJNAP/BN6Jkt3d35ti3Upqa2vzZbkHj0vt6+wMwSBff3Raffz33guf/jRsStB7+thj2vkzrjlz8m+D3t4eVjYX+zdvagqb62n/pB5lZsvdva1UvlgtCHdvcfcmd39P9Lil0sFB6lSx/ujNm8P1nrTjjnDEEcmCw803KzjEdfPNhc/ImDwZXnwxBP7Fi8N9tiXR3Bye//rXYZqxgkNFxF4oZ2afAw6Onj7o7vekUyVpaMX6ozdsCFs094SHHgozlZKaPDn8RSuFZVdB/+53YaZRMTNmhAHn0aNDuQULtA13FYk7zXUW8L+A7Iqhc8xstLvPSK1m0piy/dH5gkRzc/ji6K5PfAIefjhZmT59wpfdHnt0//3rWddV0KXkBpABA8ICQ6kacQepjwQ+5e7Xuft1wBFRmkjPGj8+9Dvn09QUrnfHtGnJgwOEFdUKDsWVs9vqiBHp1EV6RJKV1ANzHr+vpysiAhTvj168uHtdDgsXljeNdcwYjTnEkXS3VdC01SoXdwziYuAPZvYAYZvugwknw4n0vDT6oz/84TC+kdTJJ8OPf1z++zaSpOdra2ZS1YsVINz9ZjN7kDAOYcB0d38xzYpJg+up/uibboKTTkpeTmMOye25Z7wN9SDMbtJgf9UrGiDMbE93f9rM9ouS1kT3u5rZru6+It3qiXTDTjvBunXllU1yWpwEF19c/AjWvn3D7a674LDDeq9eUrZSYxDTovvZeW7fT7FeIt3zkY+UFxymTg2royW5XXcNi9/yOeww+MEPwr+JgkPNiLWSuqwXNnsP8BugP6Glcru7n2dmuwO3ADsAK4BT3P2fZtYfmA/sD/wdGO/uzxd7D62klrzKWeNw7rnhLALpvhdf1G6rVS7uSuq46yCOBX7h7p1m9g1gP+Db7v6HIsXeBg519/Vm1g9YamY/J7RKLnX3W8zsB8AZwFXR/avuPszMjge+C3RzTqM0nBUrkgeHe+4JW21Iz9Buq3Uj7jTXb0bBYTRwOHAD8INiBTxYHz3tF90cOBS4PUq/ATgqejw2ek50/ZNmZjHrJwL77JP8gJ8RIxQcRAqIGyCyG9V8GrjK3e8CtilVyMz6mNlK4GXgPuDPwGvunh0BXAMMih4PAl4AiK6/Drw/Zv2k2nR2hlPVpk8P93EOgynXpEnhIJknn0xWrrk5+dTMenXTTdCvX/gc+/ULz6XhxV0H8Tcz+yHwn8B3o/GCksHF3TcBI81sILAQ+Ei+bNF9vtbCVgMkZjYRmAiw2267xau99K6lS8MJYZs3hy0zmpvDCubFi8Mah55UbiNz3jw466yerUutGjIE1qx59/nGjWFq8PTp8MILlauXVFzcFsRxwL3AEe7+GmGA+Stx3yQq8yBwIDDQzLKBaTCQ3Rh+DTAEILr+PuB/8rzWPHdvc/e21tbWuFWQ3pJ7fGR2P6UNG95NX7++ePm4Lryw/ODgruCQbeF99rNbBodca9bA7bfnvyYNIW6A2AX4mbuvMrNDgGOBoitizKw1ajlgZtsSWh9PAQ8Ax0TZJhCOMwW4O3pOdP1XntYUK0lPb2zX3a8ffOMb5ZW97bbuv38ty2TC9uYDB8J//VcYoC/mhBN6p15SleIGiDuATWY2DLgW2B0o1Um5C/CAmT0O/B64L9oifDowzcxWE8YYro3yXwu8P0qfBmin2FqS/Yv06qvT3a57993LX8Q2eDAcc0zpfPVq7txwMM+994ZgHedz1ILBhhZ3DGKzu280s6OBy9z9v82s2BRX3P1xYN886c8CW53t6O7/ILRMpNZ0HXMopLvbdT/0EDz/fHllb7utcYNDRweceCIsX568bN/YR8ZIHYr7r/+OmZ0AnAp8Nkrrl06VpKbkjjmU0p3tuqdMgcsvT15um23g7bfLe896MG1aeTvYZt18c8/VRWpO3C6m04GPAxe6+3PRauifpFctqQmZDBx+eOmB5+5u121WXnD43vcaOzh0dHQvODR6l5zE3s31T8CXcp4/B2gj90YW9+SwAw+EM88sf7vu/v2Tl4HG3k8pe+TnHXeUV75v39ByUHBoeKV2c73V3Y8zsz+y5ZoEIyyW1ikqjaijI15waG4OwaHcbbvPPBP++c9kZc46K6xxaDQdHXDaafDEE+UtShwwAH7+855fpyI1rVQL4pzo/jNpV0RqxJIl4YS1OLoz5jBwILz+erIyjdpq6O44w0knhZ1Wu3Mgk9SlomMQ7r42uv+Lu/8FeBXozLlJI1myJIw5FFrnkGVW/pjDtdeG8kmDQ6Oub+jOOMO4cbB2LfzkJwoOklfc3Vy/AFwAvMW7XU0OfCilekm16eyEsWPj5f34x8Nc+6RfOtttB2+9lbxujTiY2t1xhqlTYc6cnq2T1J2401y/DOzt7q+kWRmpYgsWwKZNpfNB+NJKEhwWLoSjjy6vXo20vmHFivA5/fWv5XWnbbst7LtvOPVNR6lKDHEDxJ+BN9OsiFSpzs4QHK6+Gt55p3T+9vZkh8O0tsIrZfzd0UjrGzo74dBDobuHYz37rA7ukUTiBoiZwG/N7HeEg4AAcPcvFS4iNS/uCmkIA9IdHcn+Mi23S2nyZLjyyuTlatHSpWHc581u/n02e7aCgyQWN0D8EPgV8EegxAil1IUkK6QhTJFMEhyOPba84DBwYOMEh4cegn//9+7NzurbN+zIGnf8SCRH3ACx0d2npVoTqS7FdmXN6tcP+vSBu+5KdhD99OnlbSPd0gKvvpq8XC0aPx5uvTV5ufe8J2zI98lPwgEHlL9AUYT4AeKB6KCen7JlF9NW5zVInVi1qni3UrkrpMs9w2H+fDjllPLK1oLsQrfnngvjMn/8Y3mv89xz6kqSHhM3QJwY3c/MSdM013o2fHhYCZ0vSJSzQvrMM8Mah6T69o03OF7Lui50e/HF8l4n6QQBkRLi7sW0e9oVkSozfnz44son6Qrppqby+tG/9z348peTl6sVnZ1wxRXdWwUNMGoULFqk4CA9ruhKajP7as7jY7tcuyitSkkVyK6EbmkJLQYob1fWM88sLzi413dwWLQIdtih/JPx+vWDz30urIR+5BEFB0lFqRbE8cAl0eOZQO5+BkcAX0ujUlIlRo8OK3YXLAgnwQ0blmzMYcqU8rqVvvOd5GVqyZw5cO655Zd/+OEwBiSSslIBwgo8zvd8y4tmQ4D5wM6EqbHz3P1yMzsfOAtYF2X9mrsvjsrMBM4ANgFfcvd74/wQkqIBA8rbjbXcwehttoGvf728stUse6rbE090b4HfcccpOEivKXVgkBd4nO95VxuBc939I8CBwGQz2yu6dqm7j4xu2eCwF6HFsjehdTLXzPrE+SGkitx0U/nB4Tvfqc/V0dOmwYgR4cjPpD/fkCGha2+PPeCxx0JrTqSXlGpBfMzM3iC0FraNHhM9f0+xgtFOsNndYDvN7ClgUJEiY4Fb3P1t4DkzW004u/rh0j+GxJbdOmPVqjBTafz4MK7QEwYMKL3iupB63aq7O7utakM9qbBS2333cff3unuLu/eNHmefxz6T2syGAvsCv4uSzjazx83sOjPbPkobBLyQU2wNxQOKJLV0aVhENWUKXHJJuB80KKR3l1l5wWH69PoNDhDWNiQ1alQY81FwkAqLeyZ12cxsAHAHMMXd3wCuAvYARhJaGLOzWfMU3+qbw8wmmtkyM1u2bt26PEUkr9ytM7Jf5Bs2vJte6lzpQr71re51Kc2q85Nrn3suWf5Fi8KsJO22KlUg1QBhZv0IweFGd78TwN1fcvdN7r4ZuJrQjQShxTAkp/hgINP1Nd19nru3uXtba2trmtWvL8W2zti8uby+7f794dvfLq8+9ToY3dXuCZYQzZ6tPZOkqqQWIMzMgGuBp9x9Tk76LjnZxgFPRI/vBo43s/5mtjswHHg0rfo1nGJbZ2zYELo0kvjWt5KfF51Vr4PR+Vx/fek8xx0X1jMUWpgoUiFxt9oox0HAKcAfzWxllPY14AQzG0noPnoe+AKAuz9pZrcCfyLMgJrs7jFPqJGSSm2dMWxY/Nf61rfKbznUy3hD9kS3p5+GPfeEiy+GXXfdOt+HPxwGmwsNVLe3w6RJ6dZVpEzmNfwftq2tzZd19xCVRtHZGQak823f3dISvvDiLIArd7zhm9+ECy4or2y1mTs3nEnRVbEv+z//GY4/Hp58MuyA+6lPhdfRCmipADNb7u5tJfMpQDSQrgcANTeHfZIWLw6rpkspNzjU8O/YVjKZEGgLWbtWX/pS9eIGiDS7mKTadGfrjH32Ke896yk4QOhWKmbGjHjjDiI1QAGinsTpFy9n64xyWg710KWUyYQuo1/+EjZtgr33Lr31+DPP9E7dRHqBAkS96LoB3KOPhkN2ujsIWk5wmDcPzjqr/PesBvnGGZYvL11uxIh06iNSARqDqAeLFsG4cYWvl9MvPnYs3H138roMHFj7x4KWGmcoRmMQUgPijkGkvpJaUtbRAUcfXTzPjBnJXtOsvOAwb15tB4fOTrjmmmTna+fSiW5SZ9TFVMsKTbfsKm6/+KRJcNVV5dWlhluiZDLw+c/DffeFWV0bN5Yus9NOMGZM+GxHjAhbhig4SJ1RgKhVmUy84ADx+sX79Cm8FUcx++4LK1YkL1ctugbZuJ/BsGGarSR1T11MtarUdMtcpTbEmzSpvOAAtR0ckgTZrubP79m6iFQhBYha9fTT8fLNnl2862PKlMbsVoJkQTbX1KnabVUagrqYaknuYT99Shy2ZwYLFxbfHbTcldFQ+8EB4gXZPn3eve29N9xyi4KDNAwFiFrRdZuM7bYrnn/VqsJfZLNnw5e/XF49vvOd+tmme889w3qRYrbbLv4+VSJ1RgGiFuQe9pP15puF87e3Fw4O5Q5G9+8P//hH8nLV7OKLi48lDBgQ9qlScJAGpTGIWlDssJ/ttoNPfAIOPBAmTAgLtQqtnDYrLzjcdlv9BQcI25C0t+e/dtJJ4bOMs4mhSJ1SC6IWFDvs58034eCDw1/DxRxzTHnvfdtt5ZetBZMmhYWGM2ZoTYNIFwoQtaC7h/3cey/ccUfy9x08uL6DQ9bOO2tNg0ge6mKqBePHhxW++TQ1heuFDBoERxyR/D1vuw1eeCF5ORGpG2meST3EzB4ws6fM7EkzOydK38HM7jOzVdH99lG6mdkVZrbazB43s/3SqlvNaWkJg6UtLaHFAOE+m55vEHX69DDmkMkke69ttglTWBuh5SAiRaXZxbQRONfdV5hZC7DczO4DTgPud/dZZjYDmAFMB8YAw6PbKOCq6F4g2WE/5a5vmD8fTjmle/UUkbqRWoBw97XA2uhxp5k9BQwCxgKHRNluAB4kBIixwHwP+48/YmYDzWyX6HXqX08c9nPCCWEhVznqYeGbiPSoXhmkNrOhwL7A74APZL/03X2tme0UZRsE5HZ6r4nS6j9AXHTRlovPyjnspzuroqdOLb9sb8hdQT58eGg5tbRUulYidS/1AGFmA4A7gCnu/oYV/iLLd2GrP2vNbCIwEWC33XbrqWpWRiYDn/40rFyZ//rkyWEKZqkplyecUH4dmprCaXTVqusK8uZmmDYtjL1ojYJIqlKdxWRm/QjB4UZ3vzNKfsnMdomu7wK8HKWvAYbkFB8MbDXC6u7z3L3N3dtaW1vTq3za5s4NM4wKBYesUof9nHlm+d1KU6eGs5arVe4K8uwU3w0b3k1fv76y9ROpc2nOYjLgWuApd8/9E/VuYEL0eAJwV076qdFspgOB1+t2/CHJNtPFDvsxg2uvTf7+2ZlK1dxygOIryDdvDtdFJDVptiAOAk4BDjWzldHtSGAW8CkzWwV8KnoOsBgh/xo/AAAPJUlEQVR4FlgNXA3E7HyvMR0d8LGPxc9f6LCfcsccrrgC3n67vLK9rdgK8g0bwmwuEUlNmrOYlpJ/XAHgk3nyO1Dm6S014uyzC+/9U0jXw35OPRV+/OPy3r/WZip1dwW5iHSLVlL3lptuSh4cLrpoywFqs/KCwze/WXvBAbq3glxEuk0Bojd0dMDJJycrc9FFW554duqp5b33vHlwwQXlla20claQi0iP0WZ9aZs7N9m5x6NGwaJFW7Ycyl0AN3AgnHVW8nLVJMkKchHpUQoQaUoyW6m1FR5/fOs1D+UORs+bV/vBIavUCnIRSYW6mNKU20VUysMPbxkc2tvLDw7u9RMcRKRi1IJI09NPx8s3efKWR4S+733wxhvJ32+HHeDvf09eTkQkD7Ug0rTnnsWvm4WxhSuvfDdtypTygsM99yg4iEiPMq/F6Y+RtrY2X7ZsWaWrUVgmE7bTKGT16i1bDitWwP77J3+fajgWVBvqidQMM1vu7m2l8qkFkaZddy289qG9fcvgMGZMecGhGo4FXbo0BMIpU+CSS8L9oEEhXURqlloQveHFF8Ome888E7bOmDVrywHp7beH115L/rqVajnknl2xxx7w05/m3zivpSXk1ZRUkaoStwWhQeresPPOcP31W6evWBHWPWzcmPw1KxXYu67rePTRwnmzG+ppiqpITVIXU6UcemjoUkoaHL73vcoEh85O+P73ky3604Z6IjVNLYje9tBDcMghhbexLqbroHZvyR7a89ZbycppQz2RmqYA0ZsOPRQeeKC8slOnViY45B7ak5Q21BOpaQoQveWhh8oLDu99bxirqERwgOKH9nTVt2/oMmtuDsFBG+qJ1DQFiN4yZkzyMtWwMrrYoT1dffe7sG6dNtQTqRMKEGnLZOCcc+J/yWZ95CPwpz+lU6ckih3ak6u9HSbV5yGAIo0qzTOprzOzl83siZy0883sb12OIM1em2lmq83sGTM7PK169aq5c8OCsdtvj1+mb9+wcV81BAcofWjPiSfC2rUKDiJ1KM1prtcDR+RJv9TdR0a3xQBmthdwPLB3VGaumfVJsW7pS7LVd1ZbG7zzDhx4YDp1KkexQ3t+/Wu48cattygXkbqQ5pnUvzGzoTGzjwVucfe3gefMbDVwAPBwStVLV0cHHHRQsjIPP1xdgSGXDu0RaUiVGIM428xOBZYB57r7q8Ag4JGcPGuitNpz+un5V00Xs3Zt9f8VrkN7RBpOb6+kvgrYAxgJrAVmR+n5TsbJu1zYzCaa2TIzW7Zu3bp0almOTCZMRU0aHNrbqz84iEhD6tUA4e4vufsmd98MXE3oRoLQYhiSk3UwkCnwGvPcvc3d21pbW9OtcFzZwehnn41f5rjjNLgrIlWtVwOEme2S83QckJ3hdDdwvJn1N7PdgeFAkV3gqsSKFTB0aPLB6Pb20J+vloOIVLHUxiDM7GbgEGBHM1sDnAccYmYjCd1HzwNfAHD3J83sVuBPwEZgsrtvSqtuPeLoo2HhwmRlWlvh8ccVGESkJqQ5i+mEPMnXFsl/IXBhWvXpUdddlzw4QJippOAgIjVC230n1dkJZ56ZvNzpp1duPyURkTIoQCTR2Qlnn538PIbTTgutDhGRGqK9mOJasgTGjoV//jN+md13h/vuU8tBRGqSWhBxLFkChx8O//hHvK2vP/e5MIX12WcVHESkZqkFUUpHR7KtuseNgzvvTK8+IiK9RC2IYubMgREj4rUazOBHP1JwEJG6oQBRyKJFcO65pfM1NcGECfDGG2EwWkSkTihA5NPZCcceGy/vNtvAlVdqZ1MRqTsKEPkkOYf5rrsUHESkLilA5LNqVekA0dQE994Lhx3WO3USEellmsWUz/DhsN128OabhfN0dGgKq4jUNbUg8hk/HvoUOfF09mwFBxGpe40bIDKZMPto1Khwn8k5fiL3HObttgtpTU3Qt2+Y3TRtWmXqLCLSi8yT7itURdra2nzZsmXJC86Zk38Ka3v7lgf4rF+vc5hFpO6Y2XJ3byuZr+ECxKJFYbVzIbVwPrSISDfEDRCN1cUUZ33DjBm9UxcRkSrXWAEizvqGZ57pnbqIiFS51AKEmV1nZi+b2RM5aTuY2X1mtiq63z5KNzO7wsxWm9njZrZfKpWKs75hxIhU3lpEpNak2YK4HjiiS9oM4H53Hw7cHz0HGAMMj24TgatSqVF2fUMxs2al8tYiIrUmtQDh7r8B/qdL8ljghujxDcBROenzPXgEGGhmu/R4peKsb9AAtYgI0PtjEB9w97UA0f1OUfog4IWcfGuitJ6l9Q0iIrFVy1Yblict7/xbM5tI6IZit912S/5Oo0eHRXFa3yAiUlRvB4iXzGwXd18bdSG9HKWvAYbk5BsMZLYqDbj7PGAehHUQZdViwAA444yyioqINIre7mK6G5gQPZ4A3JWTfmo0m+lA4PVsV5SIiFRGai0IM7sZOATY0czWAOcBs4BbzewM4K9AdtXaYuBIYDXwJnB6WvUSEZF4UgsQ7n5CgUufzJPXgclp1UVERJJrrJXUIiISW01v1mdm64C/9OBL7gi80oOvV2/0+RSnz6c4fT7F9ebn80F3by2VqaYDRE8zs2VxdjhsVPp8itPnU5w+n+Kq8fNRF5OIiOSlACEiInkpQGxpXqUrUOX0+RSnz6c4fT7FVd3nozEIERHJSy0IERHJSwEiYmbPm9kfzWylmSU86Lr+JDnwqREV+HzON7O/Rb9DK83syErWsVLMbIiZPWBmT5nZk2Z2TpSu3x+Kfj5V9/ujLqaImT0PtLm75mkDZnYwsJ5wTsc+UdolwP+4+ywzmwFs7+7TK1nPSinw+ZwPrHf371eybpUWbcS5i7uvMLMWYDnh7JfT0O9Psc/nOKrs90ctCMkr4YFPDafA5yOEs17cfUX0uBN4inC+i35/KPr5VB0FiHc5sMTMlkdnTsjWCh34JO86OzpX/bpG7ULJZWZDgX2B36Hfn610+Xygyn5/FCDedZC770c4H3ty1IUgksRVwB7ASGAtMLuy1aksMxsA3AFMcfc3Kl2fapPn86m63x8FiIi7Z6L7l4GFwAGVrVFVeil7VniXA58EcPeX3H2Tu28GrqaBf4fMrB/hy+9Gd78zStbvTyTf51ONvz8KEICZNUeDRZhZM3AY8ETxUg2p0IFPwr++9LLG0aC/Q2ZmwLXAU+4+J+eSfn8o/PlU4++PZjEBZvYhQqsBwhkZN7n7hRWsUsXlHvgEvEQ48GkRcCuwG9GBT+7ekAO1BT6fQwjdAw48D3yhEU9GNLPRwEPAH4HNUfLXCP3sDf/7U+TzOYEq+/1RgBARkbzUxSQiInkpQIiISF4KECIikpcChIiI5KUAISIieSlAiIhIXgoQ0qPMbFPOdsUrzWyomZ1mZld2yfegmbVFj3O3Wl9pZldE6deb2TF53uPDZrbYzFZHWybfamYfiK6NNrNHzezp6DYxp9z5Zvamme2Uk7Y+5/HXo+2XH4/qMSqnfjvm5DvEzO6JHp9mZm5mn8y5Pi5KOybnZ33GzB4zs/9nZiPMbGH0HqvN7PWcn/0TXT6b95nZfDP7c3Sbb2bvi64Njd7n/+S895VmdlqJf6MvR5/NE1GdTo3StzGzy6L3WWVmd5nZ4JxybmY/znne18zWdfks1kU/x5NmdruZbVesLlLdFCCkp73l7iNzbs/HLPcfOWW+VCiTmb0H+BlwlbsPc/ePEPawaTWznYGbgC+6+57AaOALZvbpnJd4BTg3z+t+HPgMsJ+7fxT4T+CFmHX/I2GRU9bxwGNd8pzk7h8j7GL6PXcf5+4jgTOBh3J+9t92KXct8Ky77+HuewDPAdfkXH8ZOMfMtolTUTP7IvAp4IBom/KDAYsuXwS0AB929+GEhZF3Rit/ATYA+5jZttHzTwF/6/IWC6KfY2/gn8D4OPWS6qQAIbXmROBhd/9pNsHdH3D3J4DJwPU5Wym/AnwVmJFT/jpgvJnt0OV1dwFecfe3s2Wz+3PF8BBwgJn1izZgGwasLJD3N9H1ksxsGLA/8O2c5AuANjPbI3q+Drifd7ewKOVrwKTs5nnu/rq73xD9pX86MNXdN0XXfgS8DRyaU/7nQDbgngDcXKDufYFm4NWY9ZIqpAAhPW3bnO6ShaWz/8sDOeWmFsm3D+GAlXz2znNtWZSetZ4QJM7pkm8JMMTMOsxsrpn9e4K6O/BL4HDCmQd3F8n7WUKLI469gJXZL2yA6PFKtvyZZgHnmlmfYi9mYb+xFnf/c57Lw4C/5tl1tevndwtwfNSS+yjvblOdNd7MVhJaFjsAP0VqlgKE9LTcLqZxUVqh/Vxy03O7mC4t872twHt1TbsCmGBm7/1XBvf1hL/WJxL+Kl+Q05cf5zVvIXQtHU/+v6pvjL44DwK+XPzH+JdCP88W6e7+HPAooXVVzuslea/HgaGE1sPiPPkXRF1nOxMC4VdK1EmqmAKE9Ia/A10PP9mBMB6Q1JOEL/JC19q6pO0P/Ck3wd1fI4xVTOqSvsndH3T384Czgf8dXepa/63q7u6PElo3O7p7R566nRQFv6PcPe7YxpPAvmb2r/+n0eOPEU4hy3URMJ0i/6ej1sEGC5tTdrUa+GDUysi1H10+P0IL6fsU6F6K3ssJrQedq1LDFCCkN/weOCgaRCaaodOf+IPAuW4CPpE78GxmR5jZvwHtwGlmNjJKfz/wXeCSPK8zB/gCYfdeoplFw3OujwT+Ej1+EDglytcHOBl4IM9rziT08fcId18N/AH4Rk7yN4AV0bXcvE8Tvsg/U+JlLwbas60nM3uvmU109w2EAfQ52a6qaHbTdsCvurzGdcAF7l6qq2w0kK87S2pE30pXQOqfu79kZucAi6O/gNcDJ0QHo2Q9YGbZvvbH3f3U6PEPzeyy6PEL7v5xM/sMcFmU/g7wOHBO9D4nA1dHfwkbcFnugHZOnV6Jxkiy4x0DgP82s4HARsJf1Nkpst8GrjKzx6LX/AXwkzyv+fPEH05pZ0T1Wh2998NRWj4XEgJKMVcRftbfm9k7hM8ve3LZTELLoMPMNgNPA+O8y5bP7r4GuLzA64+3sJ11E7AGOK1EfaSKabtvERHJS11MIiKSl7qYROqQmbUTZkzlujxa2yASi7qYREQkL3UxiYhIXgoQIiKSlwKEiIjkpQAhIiJ5KUCIiEhe/x/zpmWCoGb/UwAAAABJRU5ErkJggg==\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, s = size, color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, s = size, color='green')\nplt.xlabel(\"Cylinderse\")\nplt.ylabel(\"Emission\")\nplt.show()",
            "execution_count": 13,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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GzB4ApgDvuntLXPlJbtA8s0RRR7lDJd08Kc61mEYBi8yskMyRyoPu/kszezIoHga8CHwl2L4BmA40AruBL8SYm+SI00eezvLm5Vnj+TTPLOHUUS49sRUId18NnB0yfn6W7R0In4yWQeuWC25h8ers3bDyaZ5ZJNfoTmpJ1ehjR0d2ysrXqQSRXKACIanqXus/zMJVC/NqxU6RXKMCIanSip0iuatf5yCCk8pfAsb2fI+7fzGetCRfaMVOkdzV35PUvwD+C1gKdMaXjuSb6hHVDCkcwp7Ow6/xH1I4JK9W7CwpKGFvV/jidJD5PkSS1N8CMdTd5/S9mciRmT5+Ol/q/FJobE/nHi467aKEM0qPhS4mIJKe/p6D+KWZTY81E8lLDY0NkWv+P7busYQzSs++rn2R8WxLX4vEpb8F4kYyRWKPmbUFj51xJib5Ye2WtZFr/r+89eWEM0pPcWFxZDxbIRWJS7+mmNy9Iu5EJD81vdsUGd+0Y1NkfDAx1xST5JZ+30ltZpcAHw1e/sbdfxlPSpJPVm9Z/b7ig0lfi/GFncgXiVO/ppjM7FYy00wvB48bgzGR92XbH7dFxrfu3ppQJiLSW3+PIKYDk9wzdzSZ2SLgBSB/1mKWWBRY9N8oRQVxricpIlGO5E7qYT2eHzfQiUh+yqzRmF22u6xFJH79/fPsFuAFM3uKzDLdHyXTGU7kfemrAHR1qUCIpKW/VzH9xMx+Q6ZNqAFz3D1/ekFKbEYOHcn2PduzxivL86droGH44V12DyjQ0mmSsMjfODM7Pfg5mUwDoCZgEzA6GBN5X04oOyEyfuLQExPKJH1Di4dGx0ui4yIDra8jiNnADODfQ2IOhDb/EemvZZuWRcZ/t+l3CWWSvmyLFnbT0ueStMgC4e4zgp9/fqQfbGZDgKeB0mA/D7n7N81sHPAAMBxYBXze3feaWSmwGDgH2AbUuvvGI92vHF32sz867tFxEYlPf++DuMLMKoLn/8fMHjazw9qJ9tIBnO/uZwGTgAvNbCrwr8B8d68G3gGuDba/FnjH3ccD84PtZJDra4E6LWAnkp7+nvX6e3dvM7NpwCeBRcB/Rr3BM7qPiYuDR/e01EPB+CLgsuD5pcFrgvgFZqZ/HQa5CcMnRMY/OPKDCWUiIr31t0B094C4CLjb3X8B9LlymJkVmtmLwBbgceB1YIf7gXmDJqAqeF5F5gQ4QfxdYEQ/85Oj1JbdWyLjb+96O6FM0qejKck1/S0Qb5nZ94DPAg3B+YI+3+vune4+CRgDfBgI+3Ow+7q+sN/+w675M7MZZrbCzFa0trb2M33JVe/ueTcyvmPPjoQySV/UJa79iYsMtP4WiM8CS4AL3X0HmRPMf9vfnQTv+Q0wFRhmZt0nx8cAzcHzJuBkgCB+HHDYBfLuvsDda9y9prIyf66RH6w6+2hQ2FdcROLT3wIxCnjM3deb2ceAK4DlUW8ws0ozGxY8Pwb4OPAK8BTwmWCzOjLtTAEeDV4TxJ/0vtZhOMqt27qO8xaex6hvj+K8heexbuu6tFMSETmgvwXiZ0CnmY0H7gXGAT/u4z2jgKfMbDXwPPB4sET4HGC2mTWSOcdwb7D9vcCIYHw2g3whwNlLZjPhzgk8+9azbG7fzLNvPcuEOycwe8nstFMTEQH6vxZTl7vvN7PLgdvc/btm9kLUG9x9NXDYpbDu/gaZ8xG9x/eQOTIZ9NZtXcf8ZfNDY/OXzWfmuTM5dfipCWclInKo/h5B7DOzzwFXA92NgqL7I0pW1/z8msj41Y9cnUwiIiIR+lsgvgD8KXCzu28I7ob+UXxpDW4bdmyIjG/csTGZRCSnFFsfPalNPaklWf0qEO7+srt/1d1/Erze4O7qKPceVVVURcZHV4xOKBPJJdefe31k/IYP35BQJiIZfa3m+mDw8yUzW93j8VJw8lnegzHHjomMn3zcyQllIrnkxbdfjIy/sCXytJ/IgOvrJPWNwc+L404kn7TsaomOt0XHZXD67R9+Gxl/csOTCWUikhF5BOHuLcHPP7j7H8gsrtfW4yHvwekjT4+MTxgZvT6RiEgS+rua65fN7G1gNbAyeKyIM7HB7JYLbomM3/pxnd4RkfT19z6IrwMT3X1rnMmI5LPSwlI6OjuyxocUDkkwG5H+X+b6OrA7zkTyydd//fXI+E1LbkooE8klZcVl0fGS6LjIQOvvEcQ84L/N7DkyjYAAcPevxpLVIPds07OR8WVN0W04ZXA6+6SzeWLjE1nj55x0ToLZiPS/QHwPeBJ4CeiKL538UFIYfcNTX3EZnNZti16s8dVtryaUiUhGfwvEfnfXKnIDpKKkIjJeXlKeUCaSS95uj26OtHnX5oQyEcno7zmIp4JGPaPMbHj3I9bMBrFOj+5x0KWDtLxUWlQaGR9SpJPUkqz+HkH8z+DnvB5jDnxgYNPJD31NIfW1Jo8MTqPLR/Pa9teyxvtaokVkoPWrQLj7uLgTySdm0b2FCwsKE8pEckmf/bn7mIISGWh9rcX0jR7Pr+gV+5e4khrsNrwTvZpr4zuNCWUiuSTqHoj+xEUGWl/nIK7s8Xxer9iFA5xL3tjZsTMy3tahVUzyUXlx9MUJfV3cIDLQ+ioQluV52OtDg2Ynm9lTZvaKma01sxuD8X8ws7fM7MXgMb3He+aZWaOZvWZmnzyi/yVHkb7+Etyzf09CmUgu+WDlB6PjI6PjIgOtr3MQnuV52Ove9gM3ufsqM6sAVprZ40Fsvrt/u+fGZvYnZI5YJgKjgaVmdpp7H5f8iAwSn/mTz0Su6PpXE/8qwWxE+j6COMvMdppZG3Bm8Lz79Yei3ujuLe6+KnjeBrwCRF2GcSnwgLt3uPsGoJGQ3tWDwdDCoZHxskItqZCP6s6qY2hx+O/G0OKh1J1Vl3BGku/6Wu670N2PdfcKdy8Knne/7ve1mGY2FjgbeC4Y+pug8dD3zez4YKwK2NTjbU1EF5Sj1tjjx0bGx43QRWP5qKK0gm994luhsW994lu6gVIS198b5d4zMysHfgZ8zd13AncDpwKTgBbg37s3DXn7YdNYwQ17K8xsRWtra0xZx6uvyxm37IqOy+DU1tHG3KVzQ2Nzl85l195dCWck+S7WAmFmxWSKw/3u/jCAu7/t7p3u3gXcw8FppCagZ6/NMUBz78909wXuXuPuNZWVlXGmH5ttu7dFxlt3H52FT96f+rX1dHn4XfRd3kX9mvqEM5J8F1uBsMzdYPcCr7j7f/QYH9Vjs08Da4LnjwJXmlmpmY0DqoHlceWXpr6W0tBSG/lp/bb1tO9rD42172uncbvuj5Fk9XepjffiI8DngZfMrLsb+98BnzOzSWSmjzYCXwZw97Vm9iDwMpkroGbqCibJJ9UjqikrLgstEmXFZYwfPj6FrCSfxVYg3P0Zws8rNES852bg5rhyyhWFFNJJ9tpXFGvdllxVO7GW2UvCF00usAJqz6hNOCPJd7GfpJbDDS2Kvsz1mKJjEsoEmnc2U/dIHVPumULdI3U07zzstI8kpKK0goarGqgoqTjQXa6suIyKksy4rmKSpOlP1RS07Y9eSqOv+EC56/m7mNkw88Dr5c3LWbx6MXdOv5Prz70+kRzkUNNOmUbzTc3Ur6mncXsj44ePp/aMWhUHSYUKRJ5q3tl8SHHoaWbDTC7/4OWcVH5SwlkJZBpGXTv52rTTENEUU76a90TvtRcPle16fBHJHyoQeerVrdH9jV/bmr1xjYjkBxWIPHX6yNMj4xNGTkgoExHJVSoQeerGKTdGxmdPDb/cUkTyhwpEnrrlmVsi4//89D8nlImI5CoViDz15MYn31dcRAY/FYg81bFfXe1EJJoKRJ4aVT4qMj66YnRCmYhIrlKByFMTR06MjJ9ReUYieRT08SvYV1xE4qP/+vJU+/7wZaW77dqXTHOaf7vg3yLjt33ytkTyEJHDqUDkqcKCwsh4kSWzCsuPXvpRZPwHL/wgkTxE5HAqEHnq4tMujoxfNOGiRPJY07omMv5S60uJ5CEih1OByFN1Z9UxtDh82fGhxUOpO6sukTyytdjsb1xE4qMCkacqSitY8tdLKC8pp7SwFIDSwlLKS8oPjCdhxNARkfGRZSMTyUNEDhfbRLOZnQwsBk4CuoAF7n67mQ0H6oGxZFqOftbd3wl6WN8OTAd2A9e4+6q48pNM74GWm1pS7T3w1Slf5e+f+vus8a9N+VpiuYjIoczd4/lgs1HAKHdfZWYVwErgMuAaYLu732pmc4Hj3X2OmU0HbiBTIKYAt7v7lKh91NTU+IoVK2LJP072j2GdWA/l34zn/5dc07yzmar5VVnjLTe1qC+FyAAzs5XuXtPXdrFNMbl7S/cRgLu3Aa8AVcClwKJgs0VkigbB+GLPWAYMC4qMDGINjQ0MKRoSGhtSNITH1j2WcEYi0i2RcxBmNhY4G3gOONHdWyBTRIATgs2qgE093tYUjMkgtn7b+qzLeuzZv4fG7Y0JZyQi3WIvEGZWDvwM+Jq774zaNGTssHkWM5thZivMbEVra+tApSkpqR5RTVlxWWisrLiM8cPHJ5yRiHSLtUCYWTGZ4nC/uz8cDL/dPXUU/NwSjDcBJ/d4+xigufdnuvsCd69x95rKysr4kpdE1E6spcDCfw0LrIDaM2oTzkhEusVWIIKrku4FXnH3/+gRehTovsi+DvhFj/GrLWMq8G73VJQMXhWlFVw3+brQ2HWTr0v0iioROVScRxAfAT4PnG9mLwaP6cCtwCfMbD3wieA1QAPwBtAI3ANcH2NukiOadzYzf9n80Nj8ZfPZvGtzwhmJSLfY7oNw92cIP68AcEHI9g7MjCsfyU3XPxb9d8D1j13Pw7UPR24jIvHQndSSqqUblkbGH3/j8YQyEZHeVCAkVZ1dndHxzui4iMRHBUJSNbEyunHRxBOi4yISHxUISdWP/+rHkfEHPvNAQpmISG8qEJKq00aexqyps0Jjs6bO4tThpyackYh0U4GQ1GW7W1p3UYukSwVCUtW8s5mZDeFXN89smKn7IERSpAIhqZr3xLzI+NylcxPKRER6U4GQVK1tXRsZf7n15YQyEZHeVCAkVSUFJZHx4oLihDIRkd5UICRVk0ZNioxPHjU5oUxEpDcVCEnV5FGTsx5FlBSUMOmk6AIiIvFRgZBU1U6spbSoNDRWWlSqfhAiKVKBkFRVlFbQcFUDFSUVBzrLlRWXUVGSGVc/CJH0xLbct0h/TTtlGs03NVO/pp7G7Y2MHz6e2jNqVRxEUqYCITmhvKScaydfm3YaItKDpphERCRUnD2pv29mW8xsTY+xfzCzt3q1IO2OzTOzRjN7zcw+GVdeuaDIog/cik3X/otI+uI8grgPuDBkfL67TwoeDQBm9ifAlcDE4D13mVlhjLmlauywsZHxccePSyYREZEIsRUId38a2N7PzS8FHnD3DnffADQCH44rt3Vb13HewvMY9e1RnLfwPNZtXRfXrkJ1eVdkvNPVRU1E0pfGOYi/MbPVwRTU8cFYFbCpxzZNwdiAm71kNhPunMCzbz3L5vbNPPvWs0y4cwKzl8yOY3ehji09NjJ+XOlxCWUiIpJd0gXibuBUYBLQAvx7MG4h23rYB5jZDDNbYWYrWltbj2jn67auY/6y+aGx+cvm8/r214/o896rM088MzL+oRM/lEgeIiJREi0Q7v62u3e6exdwDwenkZqAk3tsOgZozvIZC9y9xt1rKisrj2j/1/z8msj41Y9cfUSf915dd/Z1kfGvnPOVRPIQEYmSaIEws1E9Xn4a6L7C6VHgSjMrNbNxQDWwfKD3v2HHhsj4xh0bB3qXoWYtCW+x2e2GhhsSyUNEJEpsN8qZ2U+AjwEjzawJ+CbwMTObRGb6aCPwZQB3X2tmDwIvA/uBme4Df6Z23LBxbG7P3qGsr6uLBsrardE9EPqKi4gkIbYC4e6fCxm+N2L7m4Gb48oH4I7pd3DOPedkjd990d1x7v4A87BTLv2Pi4gkIa/upF61eVXWm9SKrIjnm59PJI9sq5f2Ny4ikoS8KhDrt61nv+8Pje33/TRub0wkj2w59DcuIpKEvCoQ1SOqDywp3VtZcRnjh49PJI9jS6Lvg6gorUgkDxGRKHlVIGon1lJg4f+TC6yAcGc3AAAHyUlEQVQgseY0Xzz7i5HxL036UiJ5iIhEyasCkSvNafq6U/rYY6KPMEREkpB3/SByoTnN0g1Lo+OvL2X2nya39IeISJi8KxCQfnOajv0dkfE9nXsSykREJLu8mmLKFW0dbdHxPdFxEZEkqECkYOsft76vuIhIElQgUtDX+Y7y0uTOh4iIZKMCkYJrzromMv6FSV9IJhERkQgqECn4cs2XKS0MX06jtLCUGefMSDgjEZHDqUCkoKK0gqVXL2Vo8VAs6JVkGEOLh7L06qWJXnIrIpKNCkRKVr+9mt37duNB4zzH2b1vN6vfXp1yZiIiGSoQKWje2czMhpmhsZkNM9m8K3vPChGRpKhApODrv/56ZPymJTcllImISHYqECl4tunZyPiypmUJZSIikl1sBcLMvm9mW8xsTY+x4Wb2uJmtD34eH4ybmX3HzBrNbLWZTY4rr1xQVhK+5Hi3oSVDE8pERCS7OI8g7gMu7DU2F3jC3auBJ4LXAJ8CqoPHDCCZ3p8p0X0QInI0iK1AuPvTwPZew5cCi4Lni4DLeowv9oxlwDAzGxVXbmnTfRAicjRI+hzEie7eAhD8PCEYrwI29diuKRgblLrvgygvLj/QI7vIiigvLtd9ECKSM3JluW8LGfPQDc1mkJmG4pRTTokzp1hNO2UaLV9vSbUvhYhIlKQLxNtmNsrdW4IppC3BeBNwco/txgDNYR/g7guABQA1NTWhReRokXZfChGRKElPMT0K1AXP64Bf9Bi/OriaaSrwbvdUlIiIpCO2Iwgz+wnwMWCkmTUB3wRuBR40s2uBN4Ergs0bgOlAI7Ab0GU8IiIpi61AuPvnsoQuCNnWgfC1J0REJBW6k1pEREJZ5o/3o5OZtQJ/SDuPATASUJ/RDH0XB+m7OJS+j4Pe73fxP9y9sq+NjuoCMViY2Qp3r0k7j1yg7+IgfReH0vdxUFLfhaaYREQklAqEiIiEUoHIDQvSTiCH6Ls4SN/FofR9HJTId6FzECIiEkpHECIiEkoFImVmVmhmL5jZL9POJW1mNszMHjKzV83sFTP707RzSouZzTKztWa2xsx+YmZD0s4pSUfScGywy/JdfCv472S1mT1iZsPi2LcKRPpuBF5JO4kccTvwK3c/HTiLPP1ezKwK+CpQ4+5nAIXAlelmlbj76H/DscHuPg7/Lh4HznD3M4F1wLw4dqwCkSIzGwNcBCxMO5e0mdmxwEeBewHcfa+770g3q1QVAceYWREwlCyrGw9WR9hwbFAL+y7c/dfuvj94uYzMCtgDTgUiXbcB3wC60k4kB3wAaAV+EEy5LTSz6Obdg5S7vwV8m8yCli1kVjf+dbpZ5YRsDcfy3ReB/xfHB6tApMTMLga2uPvKtHPJEUXAZOBudz8baCd/phAOEcytXwqMA0YDZWb21+lmJbnIzP43sB+4P47PV4FIz0eAS8xsI/AAcL6Z/SjdlFLVBDS5+3PB64fIFIx89HFgg7u3uvs+4GHgvJRzygVvd/eq79VwLC+ZWR1wMXCVx3S/ggpEStx9nruPcfexZE5APunueftXortvBjaZ2YRg6ALg5RRTStObwFQzG2pmRua7yMsT9r1kaziWd8zsQmAOcIm7745rP7nSk1oE4AbgfjMrAd4gTxtHuftzZvYQsIrM9MEL5NldxEfYcGxQy/JdzANKgcczf0OwzN2/MuD71p3UIiISRlNMIiISSgVCRERCqUCIiEgoFQgREQmlAiEiIqFUICQvmdlJZvaAmb1uZi+bWYOZnZZl2491r7ZrZpeY2RHd4W1m95nZZwYib5Ek6T4IyTvBzWePAIvc/cpgbBJwIpmVMbNy90fJ3LAVZ35FPRZiE0mNjiAkH/05sM/d/7N7wN1fBGaY2aXdY2Z2v5ld0vONZnaNmd0RPL/PzL5jZv9tZm90HyVYxh3Bkclj9FhUzszOMbPfmtlKM1vSY+mI35jZv5jZb4EbzeyKoBfE783s6WCbwqAPwPNBH4Avx/cViegIQvLTGUDYIokLgVnAL8zsODLrH9UB0yI+a1QQP53MkcVDwKeBCcCHyByVvAx838yKge8Cl7p7q5nVAjeTWY0TYJi7/xmAmb0EfNLd3+rRDOZaMiu7nmtmpcDvzOzX7r7hPX0LIn1QgRAJuPtvzexOMzsBuBz4mbvvD5YyyObn7t4FvGxmJwZjHwV+4u6dQLOZPRmMTyBTnLqXRygks5x3t/oez38H3GdmD5JZrA/gL4Aze5zPOA6oBlQgJBYqEJKP1gLZThr/ELiKzAKKX8yyTU8dPZ73rCRha9gYsNbds7VSbT/wZvevmNkUMg2lXgzOkRhwg7sv6UdeIu+bzkFIPnoSKDWzL3UPmNm5ZvZnZNo7fg3A3de+x89/GrgyOGcwisw5D4DXgMruXttmVmxmE8M+wMxOdffn3P3/AluBk4ElwP8Kpqows9PytamSJENHEJJ33N3N7NPAbcElq3uAjcDX3P1tM3sF+Pn72MUjwPnAS2SuivptsN+9wfTQd4JzHEVkugqGFaJvmVk1maOGJ4DfA6uBscCq4EqsVvKk7aakQ6u5ivRgZkPJ/MM+2d3fTTsfkTRpikkkYGYfB14FvqviIKIjCBERyUJHECIiEkoFQkREQqlAiIhIKBUIEREJpQIhIiKhVCBERCTU/weWh1KLC2k3WwAAAABJRU5ErkJggg==\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Train/Test Split"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Randomly split dataset into  70/30 Train/Test ratio\nmsk = np.random.rand(len(df)) < 0.7\ntrain = cdf[msk]\ntest = cdf[~msk]",
            "execution_count": 14,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Single Regression Model"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Train Data between the independent(features) X and the dependent(target) y by the lineqr regression."
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Training Data distribution\nplt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
            "execution_count": 15,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Modeling using sklearn package\nfrom sklearn import linear_model\n\nregr = linear_model.LinearRegression()\ntrain_x = np.asanyarray(train[['ENGINESIZE']])\ntrain_y = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit (train_x, train_y)\n\n# The coefficients\nprint ('Coefficients: ', regr.coef_)\nprint ('Intercept: ',regr.intercept_)",
            "execution_count": 16,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Coefficients:  [[38.53210249]]\nIntercept:  [126.87579179]\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\nplt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")",
            "execution_count": 17,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 17,
                    "data": {
                        "text/plain": "Text(0, 0.5, 'Emission')"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Evaluate"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# compare the actual values and predicted values to calculate the accuracy of a regression model that provides areas that require improvment.\nfrom sklearn.metrics import r2_score\n\ntest_x = np.asanyarray(test[['ENGINESIZE']])\ntest_y = np.asanyarray(test[['CO2EMISSIONS']])\ntest_y_ = regr.predict(test_x)",
            "execution_count": 18,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "#### - Mean absolute error (MAE): It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it\u2019s just average error."
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))",
            "execution_count": 19,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Mean absolute error: 24.03\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "#### - Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It\u2019s more popular than Mean absolute error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n#### - Root Mean Squared Error (RMSE). "
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))",
            "execution_count": 20,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Residual sum of squares (MSE): 1070.53\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "#### - R-squared is not error, but is a popular metric for accuracy of your model. It represents how close the data are to the fitted regression line. The higher the R-squared, the better the model fits your data. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse)."
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )",
            "execution_count": 21,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "R2-score: 0.76\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "",
            "execution_count": null,
            "outputs": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.6",
            "language": "python"
        },
        "language_info": {
            "name": "python",
            "version": "3.6.9",
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