Simple_Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libs\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>"
      ],
      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "3          AS6        Z                  12.7                  9.1   \n",
       "4          AS6        Z                  12.1                  8.7   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  \n",
       "3                  11.1                        25           255  \n",
       "4                  10.6                        27           244  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Yasser Arafat\\\\Desktop\\\\FuelConsumptionCo2.csv\") # Importing Data\n",
    "df.head() # Taking a look at the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']] # Inserting part of the dataset into cdf(Prob Common Data Frame)\n",
    "cdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf\n",
    "viz.hist()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kdes6P0DZbreSxxzaV7TlsGABnHuuZyf1u0IBIiI+KelmsnEIARdGxL+WWTGzTmpnMBo85jBfY2PNg4QE553nwDAomnYxSfrZdH8S2QZAu4F7gKWpzKyvLV7s4NBNzbqMVq6Ej3/cwWGQtGpBvBOYAT6YcyyAl3e8RmYd0k5gcNqMzqh++VdnMY2NZUHDQWEwlbajnKRnAF8BDiMLRJ+JiN+T9FzgU8AxwG3AWyLiSUmHAdcAvwj8AHhTROxo9hleB2GNtDPe4DQONmo6uqOcpDdIOio9/l1Jn5P0ohYvewJ4eUS8EFgDvErSKcAfAZdGxInAw8Db0/lvBx6OiOcDl6bzzOalaHBYutTBwayRotNc/2dE/FjSS4FXAlcDlzd7QWQeS08XpVu1W+ozqfxq4Iz0+PT0nHR8Wmq399hG3eRke11L995bXl3MBl3RAFGdl/AaYGNEfAFY3OpFksYkbQUeBL4EfBd4JCKqi+t3A8vS42VkA+Ck448CxxasnxmSN/gx66SiAeJeSVcAbwSuT+MFLV8bEfsjYg2wHDgZ+Lm809J93t99c/4LS5qRtEXSlj179hSsvnVbN9N1S+21GiIcHOpVZ3tVb4tb/vlno6BogHgj8H+BV0XEI2QDzO8u+iHpNTcDpwBLJFVnTy0Hqr3Fu4ETANLxZwE/zHmvTRExFRFTExMTRatgXdTNdN2ewnroFi+Gp556etlTTzlIWPEAcTzwNxHxHUmnAm8Abmn2AkkTkpakx4cDa4G7gZuA16fTziHbzhTguvScdPzLUdYUKytVN9J1t9tqAFi0qHOfP+hqW3j1waGqUbmNjqIB4rPAfknPB64Engt8osVrjgduknQ78E/Al1KK8AuBd0raTjbGcGU6/0rg2FT+TsCZYgdUo+0ji2wrWcR8pi4sWgRPPtmZzx9ks7NZUDjrrIMtPLNGiuZiOhAR+yT9J+DDEfFnkr7Z7AURcTswZypsRHyPbDyivvzfyVomNoAqlayFsGtX9gWUl26hE+m6ly1rfU49fwlmqplWzYoqGiCekvRm4Gzg11OZG+wGHBxzqHYr5QWHTqXrbifZHjg4AKxdW3yv7VrukrOiXUxvA34Z2BAR30+roTeXVy0bJHljDpClWehUum7PVJqfQwkO7pKz0lJtdINTbfRWq53DOrVFqGcqzV/Razc+7j2gR0lHUm1Iujbd3yHp9prbHWnw2UZUkZ3DOjHm0M6OY241ZGZnYeHC4sHhyCMdHCxfqzGIC9L9a8uuiA2OSqX1YGcnxhwWLy7eAhn1wDCfriRnWrVWmgaIiLg/3e8EkPTMVq+x4TY7C5c3zcKVjTls2HDoYw5Fjfpg6nyCg3fLsyIKfdlLOhd4P/A4B9NfBPDTJdXL+lClkgWHZn+tj43Bjh3z/4yxsfbGLTyY6uBg5SnaGvgdYDIiHiqzMtbfLrmkdVfOoWxC78Ho4lpNEKg3Ngb79rU+z6xW0QDxXcD7bY24Viuh57vxzvg4PP548fMbLcQbdpOT7WWrrXUogdtGV9F1EBcD/yjpCkkfqd7KrJj1Xn1G1mOOyT9Pgs2b5xccpPaCw6JFoxccKpVsVtJ8g8P0tAeibX6KtiCuAL4M3AF0YGa79bv61dE7d2ZfzosXP73PX4LzzpvfgPR8ciqN2nhD9d9hPkGxE5MFbLQVDRD7IuKdpdbE+kre6uinnoJjj83mze/ala1zmO8X0Oxse+ePYrdSu11KHmewTisaIG6SNAP8Ndle0wBExJz9Gmw4NBpv+OEP4aFDnKrgwejW5jPe4HEG67SiAeI30/3FNWWe5jrEVqzIupXyyg+Fg0O++eZMgoPdfB5nsE4rNEgdEc/NuTk4DLENG7LZRbUOZXW0k+01dijBYf36bN2Ig4OVoVUupvfUPH5D3bE/KKtS1nvr1mXz7FeuPPSMrG415KvOEms3OGzefDCAOjBYmZpmc5V0W0ScVP8473kvOJvrYHCAmGu+rYbVq2Hbts7Xx0ZLR7K5AmrwOO95fQVOkHSTpLslbZN0QSp/n6R7JW1Nt9NqXnOxpO2S/lnSK1tV3vrbfPaNHoXgMDvr4GCDodUgdTR4nPe83j7gXRFxm6SjgFslfSkduzQi/qT2ZEmrgTOBSWApcIOkn4mIEZvcOBwcGJ7OCfVsELUKEC+U9COy1sLh6THp+TOavTBlgq1mg/2xpLuBZjsKnw58KiKeAL4vaTvZ3tVfa/1jWL9YvDhbL9EOB4e5HBysHzTtYoqIsYh4ZkQcFREL0+Pq88JJliWtAl4EfCMVvSNtPHSVpKNT2TLgnpqX7aZ5QLF5qE+fUal07r2l9oLDqMxUaic4VAegHRysHxTNxTRvko4EPgv8VkT8CNgIPA9YQ9bC+GD11JyXz/n6kDQjaYukLXv27Cmp1sOpmrZh587sS2jnzux5J4LEfNJm2NNNTzsthvWXUgOEpEVkwaESEZ8DiIgHImJ/RBwAPkrWjQRZi+GEmpcvB+6rf8+I2BQRUxExNTExUWb1h05e+oy9e7Py+ZrPQDSMRsuhqLGxbD2DWw3Wb0oLEJIEXAncHREfqik/vua01wF3psfXAWdKOkzSc4ETgVvKqt8oapQ+o1Ua70bmGxhGLThMTzc+tn59lj/J6xmsH5W5fehLgLcAd0jamsreC7xZ0hqy7qMdwLkAEbFN0rXAXWQzoM73DKbOKit9RlGjFhiqbrghf6B6vvtnmHVL04Vy/c4L5dpTn8IbsvQZ7a6Q9hTWp+/oNjaWXVd/2dug6NRCORsinUif4eCQBYeNGw+mH9+/P3vebgpzs37nFoQV5uCQWbgwf28K78dgg6JoC6LMMQgbIu1mYh1mjTYuGrUNjWz4uYtpiMzOZn/dStl9p7o8RnWNw+zswWm81dvatVlLIU+jcrNB5RbEkKifJVPtF4f5D56O8vqG6jhDvRtvhKVL4b45K3S8o5sNH7cghkCl0jidw6ZN83vP+Yw3DEtwgObX7b77simq1RZDdaGbZzHZsHGAGHCzs3DWWY2Pz6dffFQHo2vzVLW6bpddlg1IR3ihmw0vdzENsEbdILXa6Rcf1S6l2Vm4/PLh+FnMOskBYoAV6T4q2i8+ysGhVZCt1yx1htkwcRfTAGvVDTI9XV7XxzAEB2h/jMb7NNgocQtigI2NNQ4SmzcXWyE9quMNVa2C7MqVsGNHV6pi1nfcghgg9Zv9nHpq/nnr1zs4FNVsjGZ8HDZs6F5dzPqNA8SAyNvs52tfy7o82p1u2e4eDosWDWdwgMZjNEce2X6eKrNh4y6mAdFos5/t29vL/+NWw9NVg6kzs5rN5WR9A2LBgvwvawkOHCj2Hu0Gh0WL4Mkn23uNmfU/p/seMo029Slrsx8HBzNzgBgQGzZkg6a1ig6iHn10+9lYHRzMrMw9qU+QdJOkuyVtk3RBKj9G0pckfSfdH53KJekjkrZLul3SSWXVbRDNd7MfCR55pNhnNOrGMrPRVGYLYh/wroj4OeAU4HxJq4GLgBsj4kTgxvQc4NXAiek2A7S5vnX4rVuXzck/cCC7bxYc2p2pFOH9DMzs6UoLEBFxf0Tclh7/GLgbWAacDlydTrsaOCM9Ph24JjJfB5ZIOr6s+g0zz1Qys07oyhiEpFXAi4BvAM+JiPshCyLAs9Npy4B7al62O5WNhE5s9tNuq8HMrJnSA4SkI4HPAr8VET9qdmpO2Zy/bSXNSNoiacuePXs6Vc2empzMEsZVu3iqm/20EyTmGxgGofVQv4K8Uul1jcxGQ6kBQtIisuBQiYjPpeIHql1H6f7BVL4bOKHm5cuBOft2RcSmiJiKiKmJiYnyKt8Fs7PZl95dd+Ufn+9mP0UMygY/eSvIZ2YcJMy6ocxZTAKuBO6OiA/VHLoOOCc9Pgf4Qk352Wk20ynAo9WuqGFUTTPd7Eu6yKDxfLqVBiEwVDVaQX7JJb2pj9koKW0ltaSXAl8F7gCqa33fSzYOcS2wAtgFvCEifpgCyp8DrwL2Am+LiKbLpAd5JfXCha0DwNhY8zQawxwYqjqxgtzMnq7oSurScjFFxD+QP64AMGfLlcgi1fll1adfrF3beP/oes02+xmF4ADZSvGdO/PLzaxcXkndRZOTxYPD6tX5CePms75hUIMDHNoKcjM7NA4QXVKpNB6Mrrd+PWzbNrd8FKewzncFuZkdOqf77oIi+x6XkWZ6kFsOtdatc0Aw6wUHiJIVCQ7gwWgz6z/uYipZkbUM03OG7A9ycDCzXnGAKFmrqayrV8MNN8wtb3cw+vDDHRzMrLMcIEpW3S86z+bNnRmMjpi7mMzM7FA5QJSs0VqG9evzB17bTSGxaFH7dTIzK8IBomSXXZYFg2pLYmwse543W2ntWjjrrOLv3U/bgjqhntnwKS3VRjcMcqqNeoM8GF1NqFfbzTU+7vUKZv2qaKoNtyD6wKAFh/q9K847zwn1zIaRA0QPTU4OXtqM6rqO2r0rHnss/9xdu7pXLzPrPC+U65GxsfaykTZbK9ENlUrWIshLnNeIE+qZDTa3ILps2bL2U1VPT+evleiW2k17inJCPbPB5xZEFy1bBvfN2SOvuV53KUH+pj31pKzFsGtXdr9hgweozQadA0QXDWJwgGJjCeed19lEg2bWe+5i6pLZ2eLnLl3aP8EBmo8lNFvXYWaDrcw9qa+S9KCkO2vK3ifpXklb0+20mmMXS9ou6Z8lvbKsenVbdUpokYyu1e017723/Hq1o9GmPZs3Z1loHRzMhlOZLYiPke0vXe/SiFiTbtcDSFoNnAlMptdcJqlJFqPBUD8ltJmlS4ud1wvetMdsNJW5J/VXJK0qePrpwKci4gng+5K2AycDXyupel1RJNU39Fd3UiPetMds9PRiDOIdkm5PXVBHp7JlwD015+xOZQNp7drsL+0iLYL168uvj5nZfHQ7QGwEngesAe4HPpjK89YT5/5dLWlG0hZJW/bs2VNOLQ/B0UfDjTe2Ps+Du2bW77oaICLigYjYHxEHgI+SdSNB1mI4oebU5UDupNCI2BQRUxExNTExUW6F2zA7m7UaHnmk9bnr13tw18z6X1cDhKTja56+DqjOcLoOOFPSYZKeC5wI3NLNuh2KovtOu9VgZoOktEFqSZ8ETgWOk7Qb+D3gVElryLqPdgDnAkTENknXAncB+4DzI6JP5/QcNDkJd91V/Px9+8qri5lZp5U5i+nNOcVXNjl/AzAw2XvaTZvR62R7Zmbt8krqeahU2gsOS5b0Ntmemdl8OEDMQzsb4UxPw8MPl1cXM7OyOEC0obrvcqu019XB6Ai3HMxscDmba0Gzs3D55a1XPa9eDdu2dadOZmZlcguigEqlWHBYutTBwcyGhwNEC7OzcNZZzYPDypVZZtN+y8JqZnYo3MXUxNq1rdNmrFwJO3Z0pTpmZl3lFkQDlUrr4CB532UzG14OEA20msoqZdtsOgW2mQ0rdzE10Gof5o9/3MHBzIabWxANNNuHef16BwczG34OEA3k7cMM2cpoZ2M1s1HgANFA3j7Mmzd7ZbSZjY6RDRCzs7BwYfblv3Bh9rzeunXZFNYDB7J7dyuZ2SgZyUHq+vUN+/cf3PDH3UdmZpmRa0E0W9+waVN362Jm1s9GLkA0W9+wv+/3sDMz657SAoSkqyQ9KOnOmrJjJH1J0nfS/dGpXJI+Imm7pNslnVRWvZqtbxgbK+tTzcwGT5ktiI8Br6oruwi4MSJOBG5MzwFeDZyYbjPAxrIq1Wx9w8xMWZ9qZjZ4SgsQEfEV4Id1xacDV6fHVwNn1JRfE5mvA0skHV9Gvby+wcysmG6PQTwnIu4HSPfPTuXLgHtqztudyjrO6xvMzIrpl2muyinL3YFB0gxZNxQrmvUXNbFundc0mJm10u0WxAPVrqN0/2Aq3w2cUHPecuC+vDeIiE0RMRURUxMTE6VW1sxslHU7QFwHnJMenwN8oab87DSb6RTg0WpXlJmZ9UZpXUySPgmcChwnaTfwe8AHgGslvR3YBbwhnX49cBqwHdgLvK2sepmZWTGlBYiIeHODQ9M55wZwfll1MTOz9o3cSmozMytG2R/vg0nSHmBnB9/yOOChDr7fsPH1ac7Xpzlfn+a6eX1WRkTLWT4DHSA6TdKWiJjqdT2zOlz/AAAHdElEQVT6la9Pc74+zfn6NNeP18ddTGZmlssBwszMcjlAPJ13hGjO16c5X5/mfH2a67vr4zEIMzPL5RaEmZnlcoAAJO2QdIekrZK29Lo+/aCdDZ9GUYPr8z5J96bfo62STutlHXtF0gmSbpJ0t6Rtki5I5f79oen16bvfH3cxkQUIYCoiPEc7kfQy4DGyfTpekMr+GPhhRHxA0kXA0RFxYS/r2SsNrs/7gMci4k96WbdeS4k4j4+I2yQdBdxKtvfLW/HvT7Pr80b67PfHLQjL1eaGTyOnwfUxsr1eIuK29PjHwN1k+7v494em16fvOEBkAvh7Sbem/SYsX6MNn+ygd6R91a8a1S6UWpJWAS8CvoF/f+aouz7QZ78/DhCZl0TESWR7Y5+fug/M2rUReB6wBrgf+GBvq9Nbko4EPgv8VkT8qNf16Tc516fvfn8cIICIuC/dPwh8Hji5tzXqW402fDIgIh6IiP0RcQD4KCP8eyRpEdmXXyUiPpeK/fuT5F2ffvz9GfkAIemINFCEpCOAXwPubP6qkdVowyfjJ196Va9jRH+PJAm4Erg7Ij5Uc8i/PzS+Pv34+zPys5gk/TRZqwGy/TE+EREbelilvlC74RPwANmGT/8HuBZYQdrwKSJGcqC2wfU5lax7IIAdwLmjuDOipJcCXwXuAA6k4veS9bOP/O9Pk+vzZvrs92fkA4SZmeUb+S4mMzPL5wBhZma5HCDMzCyXA4SZmeVygDAzs1wOEGZmlssBwjpK0v6adMVbJa2S9FZJf1533s2SptLj2nTrWyV9JJV/TNLrcz7jZyRdL2l7Spl8raTnpGMvlXSLpG+n20zN694naa+kZ9eUPVbz+JKUfvn2VI8X19TvuJrzTpX0xfT4rZJC0nTN8delstfX/Kz/LOlbkv6fpP8g6fPpM7ZLerTmZ/+VumvzLEnXSPpuul0j6Vnp2Kr0Of+95rP/XNJbW/wb/U66NnemOp2dyhdL+nD6nO9I+oKk5TWvC0kfr3m+UNKeumuxJ/0c2yR9RtJ4s7pYf3OAsE57PCLW1Nx2FHzdr9a85n80OknSM4C/ATZGxPMj4ufIcthMSPop4BPAeRHxs8BLgXMlvabmLR4C3pXzvr8MvBY4KSJ+AVgL3FOw7neQLXKqOhP4Vt056yLihWRZTP93RLwuItYA/xX4as3P/o91r7sS+F5EPC8ingd8H/jLmuMPAhdIWlykopLOA14BnJzSlL8MUDr8B8BRwM9ExIlkCyM/l1b+Avwb8AJJh6fnrwDurfuIT6efYxJ4EnhTkXpZf3KAsEHzm8DXIuKvqwURcVNE3AmcD3ysJpXyQ8B7gItqXn8V8CZJx9S97/HAQxHxRPW11RxdBXwVOFnSopSA7fnA1gbnfiUdb0nS84FfBH6/pvj9wJSk56Xne4AbOZjCopX3ArPV5HkR8WhEXJ3+0n8b8NsRsT8d+yvgCeDlNa//W6AacN8MfLJB3RcCRwAPF6yX9SEHCOu0w2u6Sz7f+vSfuKnmdb/d5LwXkG2wkmcy59iWVF71GFmQuKDuvL8HTpD0L5Iuk/Qf26h7ADcAryTb8+C6Juf+OlmLo4jVwNbqFzZAeryVp/9MHwDeJWms2Zspyzl2VER8N+fw84FdOVlX66/fp4AzU0vuFziYprrqTZK2krUsjgH+GhtYDhDWabVdTK9LZY3yudSW13YxXTrPz1aDz6ov+whwjqRn/uSEiMfI/lqfIfur/NM1fflF3vNTZF1LZ5L/V3UlfXG+BPid5j/GTzT6eZ5WHhHfB24ha13N5/3a+azbgVVkrYfrc87/dOo6+ymyQPjuFnWyPuYAYd3wA6B+85NjyMYD2rWN7Iu80bGpurJfBO6qLYiIR8jGKmbryvdHxM0R8XvAO4D/nA7V139O3SPiFrLWzXER8S85dVuXgt8ZEVF0bGMb8CJJP/l/mh6/kGwXslp/AFxIk//TqXXwb8oSVNbbDqxMrYxaJ1F3/chaSH9Cg+6l9FlB1nrw3ioDzAHCuuGfgJekQWTSDJ3DKD4IXOsTwK/UDjxLepWknwf+AnirpDWp/Fjgj4A/znmfDwHnkmXwJc0sOrHm+BpgZ3p8M/CWdN4YcBZwU857XkzWx98REbEd+CbwuzXFvwvclo7Vnvttsi/y17Z42z8E/qLaepL0TEkzEfFvZAPoH6p2VaXZTePAl+ve4yrg/RHRqqvspUBed5YNiIW9roANv4h4QNIFwPXpL+DHgDenjVGqbpJU7Wu/PSLOTo+vkPTh9PieiPhlSa8FPpzKnwJuBy5In3MW8NH0l7CAD9cOaNfU6aE0RlId7zgS+DNJS4B9ZH9RV6fI/j6wUdK30nv+HbA55z3/tu2L09rbU722p8/+WirLs4EsoDSzkexn/SdJT5Fdv+rOZReTtQz+RdIB4NvA66Iu5XNE7Ab+tMH7v0lZOusFwG7grS3qY33M6b7NzCyXu5jMzCyXu5jMhpCkvyCbMVXrT9PaBrNC3MVkZma53MVkZma5HCDMzCyXA4SZmeVygDAzs1wOEGZmluv/A0YRi1dBsL2rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of Engine_Size vs Emission\n",
    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dRSyvaN2SmsJ++cvgou4eNE99/ev5zx2XNrQ3rVz6x1D2LqUtB7ab2SBBIPqcu99gZreY2TKCJqW7gMvC/XcCa4G9wFHgkhrrJjLL4GD6Rb/VubtqFezeHTweG8vfzNTt1dqiTUhZcwlaTWlQbgnLtDsspcnof3WOYrrH3V/q7r/u7i929z8Oy89z918Ly97WGukUjl663N2fH76utiOpXbRTOs8dwX33wRlnBI+ThmYOpPxFdWs+Q0u07yLvXIKymVOTgkO7cukfmkktpWWlcO71OqSlzs4aCnrffcHPpKGZac1AVc1nWLKk/evxDmTIv+RokSYzmSfyJGzq1U3J+pqTlKiuk6R0TdQhbdGdwcHg9TIpqIseU3T/sgvr5EkZ3vq9i1Kq7v6DkvVJnXohuVqndWh3p+Ce/XqSoiOhir5H0fMnad05xcXvPPIq8zlJsyodxRSOSPr3ZrbNzK5pbZ1XU/pVLyRX67QOWZPQVq1Kfj1aHm/iOvfc5GOqmlRWxYiq1atn95UMDATlZaRNpEsrl/6Rtw/iS8BJwE3AlyObzFO9kFwtTx3a9VGkXbRb5bt3zw4S0VFMSak2br999jFr1qR/M0/79p1WXsXCOps2zR5e+/TTMxfwieuF/iZpQJ52KOCuPPt1e1MfRHP6oQ8iTx03bJjuixgczG7Lj0pbzCfezt/uc+lWH0RU0aVSsz7HrL4c6T3k7IPIGyD+M7A2z77d3BQgmtXJxbUq7VZ8y1pTutPzF1kjOu09y9Sx08+96Htm7a9O6v6TN0DkbWK6ArjBzH5hZo+H289quaWRvjA1Bdu3z8xbtH17bzU9dNpHkdSEtH799O9YpDkt7T3LpLmOzqw+dqx4x/LatcXKsz7Hos1k0kfyRJFe3XQH0Zwqvp13KqvpY9Gi5DouWpTv/Fm/444ds9efbjeENOkuxL37d2JN3EG0uxOT7qPKJqbgfLwR+Itwe0Pe4+rcFCCaU7QdO4+iF5G6mz6yjt+xw314eGb5wEB20Ij3k8TPMTzc/ndP2r+Iqvsg8nxO8fc0U5BoUqUBAvgwcDPwjnC7EfhwnmPr3BQgmlP1HUSZTu+sC13dASLtMxgdnQ50aR24rc9pdDT9HEniwaFMkCjzb9cueGd9TmkBc8GC/HWWalUdIO4BBiLPB4F78hxb56YA0ZyqRzGlXbTaNc00fQeR55t41jmK1rHT38k9uWlswYLy/3Z1B2qpXt4AUSQXUzQLzEnlez1kLqh6ici0jtDjx4NLSbyDGJpfxzjPPIwq5i3UIfiel/68iMsuK1YufSRPFAHeCuwHrgO2Aw8AF+U5ts5NdxBzR9rdQFYzSCdNH1myjs9zF9XEHURWp3cdAwzavafuIHoPNXRSLyfoqL4A+Gd5j6tzU4CYO3bsCDp48wSJvCN+8jQBtQswq1YlH79qVb7j3atvBsvqg8gzka6OAQbtrFmT/H5r1tTzfpKtkgABvCj8eVbSlucN6twUIOaOtAtbni0tSCxenLz/4sXB63na4uNBIhoc8uh0BFCSdqOY0oLswED+z6UO8SCh4NCsqgLEtvDnrQnbLXneoM5NAaJZVY5tTxvtk/eOIknWxbfoCKK84p/Lhg31NYMV/Z3d288A1zyF+aHyJqaiG3Ai8C3gbmA38KGw/HnAN4H7gb8BFoTlJ4TP94avj2e9hwJEc6oexVQ2OLS7mNax3kOWop9LEwEiz+fZ7bxa0l15A0TedN9vNrNnhI//g5l90cxemnHYk8B57v4S4EzgtWZ2DvCnwEfc/TTgp8Cl4f6XAj919xcAHwn3kx61aRMcPTqz7OjR9hlB2+lkVE/asVWkxi6q6s+lqDxpL/J81t2ss/SuvMNc/6O7P25mvwG8hmAk08fbHRAGqiPh0+Fwc+A84PNh+XbgwvDxBeFzwtfXmCmbS69KWqinXXmWTtZLSDt28eL25aOjya+nleeRNlx3//7qUmVH19EeGgqetwQ347NFy9PWrIjr5toe0pvyBojWd67XA1vd/UvAgqyDzGzQzO4CHiGYff194FF3b619dQA4JXx8CvAQQPj6Y0AHf6pSp6rH9yctYpNk8eLp9xgcDNZa2Lkz+eL7858nn6NVftVVMDw887Xh4aA8r/jFetGi9H3dg0BxySXlg0R8He3jx4Pn0SCRZe/efPt1c20P6VF52qGAG4BPEFzglxD0F9yd59jw+CUEHdu/CeyNlJ8KfCd8vBtYEXnt+8BowrnWA7uAXStXrqy8bU7yydPWXaQTO+88CJg+5+jo7FFIRUcIddLRnjbyamgo+3dodYR3s18la03qTvoglIyvv1Bxqo0R4HeB08Lny4FX5zk2co4PAn8AHAaGwrJXAl8NH38VeGX4eCjcz9qdU53UzckaAVRlZ23Rrcp1ChYunHncwoXTr6VdrFsXyayLcZ7PscjnVMXnWOYCXybhoDQrb4DI28S0HPiyu99vZucCbyYYoZQqXMd6Sfh4IXA+sCe8k3hTuNs6guVMAa4PnxO+fkv4i0gPevTR9uVNdtZ20nYebTIygyeemPn6E09Mp/dI6+x2h337Zi/rmeQXvyhWXrennw7qXiRlyhVXwC9/ObPsl78MyqW/5Q0QXwCOm9kLgKsJhqr+dcYxy4Fbzewe4J+AG939BuD9wHvMbC9BH8PV4f5XA6Nh+XuADxT6TeaYqtcAbtexWUbWCKFOF+vpRNm283j7fppW0KiiHyarn6Qf/PjHxcqlj+S5zQDuDH++D/j98PG38xxb5zZXm5iqnmNQxTrGcVlNHUXz/VTVvNTJLOUik/Xyfq6dNAmV+dw7/fzKqPp8Uj8q7oP4JkHCvnuB54Vl9+Y5ts5trgaIqpOp1bGofNZFodt9EFXMUi5zIc1KjJfVx1B1gEj7v5Onc7ps+ou6ZqRLffIGiLxNTJcQdChvdvcHzOx5wI7q7mMkqurmmSYmjFWdDjxLmbbzshYunH68ejWsWBH8jitWBM+jqhhKG7VhQ/vytBTo5503c3jwc587c581a+Cmm8rV6aqrYEFs0PuCBeV/R+kheaJIr25z9Q6i6m9keRK4FVV1s0InuZjS3rPo55jnW3b0M8t7l9RuCGiZTKdZdy1JuaCqbLJMomGu/YWKkvV9Lvz5HYJV5Vrbd9CKcrWpOkAsWpR8vkWLytex6gDRSXBIe8+iF9+879W6IOdZcjTrYllH/1BcHes/SH/LGyAs2DeZmS1394NmNpZy91EysUI1JiYmfNeuXU1WoRYDA8GfcJxZvqGTdZ+vdWyaNv+lUj3jGXDkSPZ+Rd5zaCi5GW1wEI4dy79/2vFpn2vcyEh681rROkIw2mrbtuC4wcEg1ciWLenvX8e/v/Q3M7vD3Sey9mvbB+HuB8Of+8Ng8FPg8cgmNcizlGWT56tDHcM6i/a95M0H1To+7+fXbv5H0TqWSbXRjX//qodlS4/Ic5sBvBP4EbCPYLnRB4Af5Dm2zm2uNjFVvah8FW3lcb3WxNTaogv6lBm9FW3fT9taxyd9rkU/l6J1LPM7VT1sutvnl+pR8TDX+4Glefbt5jaXA0TVqQuyLv5F37NMgCi7fnTZIFGmfT8aINI6raPHx3+ntAEBaRfwonUsG5jr7ERWH0f/qTpAfAUYybNvN7e5GiCa+IOrOidQXCdLb5bZWrJG/ERlLXuadXyZz6VoHeuY09Kpbq9xLZ3LGyDadlK3hIsDXUswYe7JSPPUv62srasEdVJXp2inc579p6aCtvcHHwx+p6R29bGxYP5C1St/5PhvPUsVHe+LFyf3pyxa1FknfMsZZ8B9980uX7UKdu/u/PxljI8nrwPS+reV3lNJJ3XEJ4BbgG8Ad0Q2qUF8olNWeS+amgo6fffvDy6uaZ2uZRcY6lXxBIVZ5UV973vFyrshbXLe5s3N1EeqkzdAHHP397j7te6+vbXVWrN5LJ5BNKu8ClWvrpaUzTVJJ0uNplm1qvpz5pV2p9HuDqTICKAmZsVn6faseemevAHiVjNbb2bLzezk1lZrzeaxtGakOsesV50uIe+dQdUXtk6aWvKs51y1qalghbnWnVbWinNVr+RXlcnJ6RTn3Up5IvXLGyD+FXAl8I9MNy/Nvcb/OST+rXTjxvbfUicn4ZprZn4LvOaa8n/oeS9Ynaz/HNXqGo0HhyLfzi+7rFh5FYqupZA2V6OTNb1FUuXpye7Vba6OYio7lLElz/j8gYHOhjpm1THvaKM8WU3zbEnDN8uMzy8yoqjM59Lp/lXUsQ7KxdRfqCgX0/sij98ce+1P8rxBnZsCRLK86zvXmYspbT5ApwEla4tOKGxiuHA3AkSv0US5/pM3QGQ1MV0UeXxl7LXXVnQTIzGddhjnbf+vc9WyIv0lA3kbOnN46qnp5pkmVrUr+m9X9eCAJjS5vKzUK+tP01IeJz2f+aLZqWZ2q5ntMbPdZnZFWP5HZvZDM7sr3NZGjrnSzPaa2ffM7DWFfpM5ZL7l1w9uSKvTWuqyiRxURf/t5sK/dZPLy0rN2t1eEC41Gn+c9Dzh2OXAWeHjZwD/B1gF/BHw3oT9VwF3AycQrHn9fWCw3XvM1SYm987adPM2x8TXg6gyF1OetRXq3Fq/TxNNH0X/7fq9/V6pNvoPOZuYhjLix0vM7GcEdwsLw8eEz0/MCDwHgVY22MfNbA9wSptDLgA+6+5PAg+Y2V7g5cDtGXWUkt75zunHrYltraaC/funR8aUGclU9V1BGa16t2Zzr1wZTN6qewjm5GSx9yi6f6/ZvHnm/x3QRLk5I08U6XQDxoEHgWcS3EHsI1h46BrgWeE+/w14W+SYq4E3tTvvXL2D2LFj9jdws/zfLLO+XSeNfCn6LTDr23tabqdu3kHMV03ckfT7XdB8Q5W5mDphZouBvydYz/qLZvYc4DDgwH8Clrv7O8zsvwO3u/uO8LirgZ3u/oXY+dYD6wFWrlx59v65lqsBOOGEoLM1bsECePLJ2eVxZXIKFc3/lPUeS5dO9wU0oRfuYJoQvxOE9gsWyfxUdS6mspUYBr4ATLn7FwHc/Ufuftzdnwb+iqAZCeAAcGrk8BXAw/Fzuvs2d59w94lly5bVWf3GJAWHduVVqLpD9yc/KV8XKU8jiqRKtQUIMzOCZqI97v6XkfLlkd1+B7g3fHw9cJGZnWBmzwNOA75VV/1kpqoTrp2sRCyN0IgiqVKddxCrgbcD58WGtP6ZmX3HzO4Bfht4N4C77wY+B9xHsP7E5e7eYAqyucksWAc5vkRltxKuDQzUm9tovuuH5WWlf2SNYirN3b9G8lyJnW2O2Qxo7EPNWusYQ/vF7ts58UT4xS+SyyG9ick96NOoM0j00ySzqmlEkVSp1j4IaUbedNfbtk0/npqCiy+emVX04ovTk9v9yq+0L2/qm+zwcH9NMquaUm9LlRQgalAkg2gdklYcSxJNtf3Od84erfT00zPnSuR5j1b52rXJr6eVd2Lx4umL4bXX6mKo1NtSldqamOarqiecxQ0M1DPhKy0vU9l8TTtTGhLTyjvxxBP1rpUhMl/pDqJidQ8zbDX/rF/f+Z1Jnf0A3RxN0+RqaiJzmQJExbp1Yawi6NQ5maybfRBNr6YmMlcpQFSsmxfGXh7bnjWvosqLulZTE6mHAkTFutk528tj27NG05x+evlzt4LL4CBs2FB+qK6ItFd7LqY6TUxM+K5dvbU0dloOotFROHw43zny9A20y69TpG+h9c9fdS6mLEND5foO8uajEpF0PZGLaT5KS1BXVeK6usa2X3ZZsfJOle1Y/s3frLYeIpJOw1z7TF3DOVvNNNu2BRfvwcGgbb+u5pvBwXJB4rbbKq+KiKTQHURDNm4MmlnSciM1YcsWOHYsaCI6dqzetv2yHcsa0irSPbqDqJhZelt+y8aN07mQoJrcSN2W5/dsJ37HkpeGtIp0j+4gSmiXSiOtgzZaHs2BFJVW3i1F7mp+9VeLlSeJ3rE897n5jtGQVpHu0R1EQVWk0kj7xtxk80nRu5o9e5LPk1aeZXi4/et194mIyGwa5lrQ+HgQFOLGxoLEaHmGf6YN8RwcDL5RdzqEtMww16w6FXmPMv+lqj6fiKTTMNeapC2BXWRp7HPPLVZel4ULpx83fVeT1regPgeR5ii1YuHrAAAP50lEQVRAFFTFheyuu4qV1+WJJ6YfF/29Fi8uVp6l6QAlIrPVuSb1qWZ2q5ntMbPdZnZFWH6ymd1oZveHP58VlpuZfczM9prZPWZ2Vl1160QVF7K6J9OVkdb5m1b+8Y8HzVJRQ0NBeRljY8XKRaR+dd5BHAP+nbv/KnAOcLmZrQI+ANzs7qcBN4fPAV4HnBZu64Gts0/ZvH64kC1ZUvyYLVuCvEZ58xxNTsJ1183MtXTddeVnd3czh5WI5FNbgHD3g+5+Z/j4cWAPcApwAbA93G07cGH4+ALgUx74BrDEzJbXVb+y+uFC9uij5Y4rOlGuypXLurnAkIjk05U+CDMbB14KfBN4jrsfhCCIAM8OdzsFeChy2IGwrKekXbC2bQvmRUg53VxgSETyqf2SZmaLgS8A73L3n7XbNaFs1gBHM1tvZrvMbNehQ4eqqmZuaaOVjh/vznDMpta5rls319EQkXxqDRBmNkwQHKbc/Yth8Y9aTUfhz0fC8gPAqZHDVwAPx8/p7tvcfcLdJ5YtW1Zf5VM0PeyyyiVHe0nWAkMi0n11jmIy4Gpgj7v/ZeSl64F14eN1wJci5ReHo5nOAR5rNUX1kl4ZdlnlOtdltUs5UtTkJKxbN7OTfN26alOai0gxdd5BrAbeDpxnZneF21rgw8CrzOx+4FXhc4CdwA+AvcBfAT2Q37S3pbXPL1qU7/giM67jWilH9u+v5q5maioYItsKwMePB8/n0l2SSL9Rqo2COrmotj7qrLQSed+jld4j7vzz4eabs49ftAiOHMn3XnFZKUeKOvHE5JXiTjgBfvGL4ucTkXRKtTHHtWufv+WWfOf4+c/Lv3/Vo47SlhHV8qIizVGA6DN5lhztxk2hRh2JzH1K991n6lpytKjNm2emPQeNOhKZa3QHMQd10k+S1+RkcBcTTbXR7q4my5o1xcpFpH7qpC6o6U7qKteDGB2Fw4fz7dsN8c71NWvgppuaq4/IXKVO6nksb+LAt7yl3noU9cIXzpwH8cIXNlsfkflOAWIOSpqVnORTn6q/Lnm1ljyNzoPYurX9utgiUi8FiDko3j+QppNhrlXbtq1YuYjUTwFijoqm4u4HWlFOpPcoQDQgLRVGq7zqET1pdxHdGO0kIv1LAaJLoqu8ZV2w9+5Nfj2tXESkDgoQXXLSSdOP0/IftcrT1pxIK8+SNjS2l0Y498NSriLzjQJEl2hltPa0HoRI71GASLBxIwwNBU0+Q0PVDLU8+eTOzzGXVT0zW0Q6p1xMMa3x+C2t8fgAW7Y0U6dOrVmTnP6719JYTE4qIIj0Et1BxGSNxx8dLXfen/yk3HFVuOmm2cFAaSxEJIsCREzWePyy6SmaToN9001Bp3RrU3AQkSx1rkl9jZk9Ymb3Rsr+yMx+GFuCtPXalWa218y+Z2avqate0H4t5VYuoLhW+c6dxd9veHhmZ2vWMNcFC5JfTysXEalDnXcQ1wGvTSj/iLufGW47AcxsFXARcEZ4zBYzS7lUdyZrLeX165OPa5WXGY0UDwhZw06vuWb2MWZBuYhIt9QWINz9H4C8Le8XAJ919yfd/QFgL/DyOuq1adPMRW4geL5pU/B4yxbYsGFmVtENG6Y7qNOaigYHg4t40h3IU09Nnx+yx/xPTsKnPz1zRM+nP60OXBHprib6IP6Nmd0TNkE9Kyw7BXgoss+BsKxyedZSXr0aVqwILs4rVgTPW9LG62/fHuQ9Sst9FD1/njH/0VxK+/YpOIhI93U7QGwFng+cCRwE/ktYntQqn9gQY2brzWyXme06dOhQ4QqkpcFulWc1QSWN11+3LrhDGBgItiTRO488Y/7b9ZPk0enxIiK4e20bMA7cm/UacCVwZeS1rwKvzDr/2Wef7UUNDETH8kxvAwPB62Njya8PDrqbBa/v2DF9vh073EdGko9pbSMjM4/JknTOIufYscN9wYKZxy9YUKwOIjJ3Abs8xzW81iVHzWwcuMHdXxw+X+7uB8PH7wZe4e4XmdkZwF8T9Ds8F7gZOM3d2yZ7LrPkaNZyngMD2TmKRkamv/GPjyfnSBocDJqHVq4Mmo6KNBGlnXNsLGhuyrJ0Kfz4x7PLe22JURFpRuNLjprZZ4DbgdPN7ICZXQr8mZl9x8zuAX4beDeAu+8GPgfcB3wFuDwrOJSVNYw1z3yFaKd2Wp9Gqz+iTP9Bnn6SdpKCQ6u8yvQhIjK31TmK6a3uvtzdh919hbtf7e5vd/dfc/dfd/c3tu4mwv03u/vz3f10d/9fddUraxjr2rXJr8e1LtZpAaWTiXFpeZuqyuek5TxFJI95N5M6axhr3olwrQCQFlDyBpo65E0HouU8RaSdeRcgIAgGx44FfQ3Hjs1MwpenGSc6JDUtoJSZcd2Slrcpbz6nq64KZm9n0XKeItLOvAwQ7WRNhIsPSe20v6BIHfI2W01OwrXXTg+jTZPWHyMiAgoQs2RNhIt3Ope9mLebp1DF4jnRiXZpab3PPTf/+URk/lGAiCm6cE2Zi3mZyXidLJ6jNa5FpAwFiARF0lyUuZhn5YOqWh3NYCIy9ylAlBBvHoJieZOSJsFFy7PuMIqqYyiuiMx9ChAFVXHxzpqsV/UdRhV9GiIy/yhAFFTFxTtr1bqqm4Sq7tMQkflhqOkK9JsqLt5jY+m5liBo+kl6vZMmoclJBQQRKUZ3EAVV0Z6f1eSjJiER6QUKEAVVNUehXZOPmoREpBfUmu67bmXSfVdhairoc3jwwXLpvEVEmtR4uu+5rIrlQLNWfNOKcCLSNAWIChS9mGcNla16HoSISBlqYupQ62IeHfoaXXEuSdaKcZ2uKCci0k7eJiYFiA6VuZinLWtqFjRbZb0uItKJxvsgzOwaM3vEzO6NlJ1sZjea2f3hz2eF5WZmHzOzvWZ2j5mdVVe9qlZmXkTWUFmlxhCRXlBnH8R1wGtjZR8Abnb304Cbw+cArwNOC7f1wNYa61WpMhdzzYMQkX5Q55rU/wDE10C7ANgePt4OXBgp/5QHvgEsMbPlddWtSmUu5poHISL9oNupNp7j7gcB3P2gmT07LD8FeCiy34Gw7GCX61dY66JddF5EVuoLpcYQkab1Si6mpIUxE3vPzWw9QTMUK3ukUV4XcxGZi7o9D+JHraaj8OcjYfkB4NTIfiuAh5NO4O7b3H3C3SeWLVtWa2VFROazbgeI64F14eN1wJci5ReHo5nOAR5rNUWJiEgzamtiMrPPAOcCS83sAPBB4MPA58zsUuBB4M3h7juBtcBe4ChwSV31EhGRfGoLEO7+1pSX1iTs68DlddVFRESKUy4mERFJ1NepNszsEJCQ6CK3pcDhiqpTF9WxGqpjNVTHajRdxzF3zxzl09cBolNmtitPPpImqY7VUB2roTpWox/qCGpiEhGRFAoQIiKSaL4HiG1NVyAH1bEaqmM1VMdq9EMd53cfhIiIpJvvdxAiIpJiXgaIpMWMeomZnWpmt5rZHjPbbWZXNF2nODM70cy+ZWZ3h3X8UNN1SmNmg2b2bTO7oem6pDGzfWb2HTO7y8yaXSYxhZktMbPPm9l3w/+br2y6TlFmdnr4+bW2n5nZu5quV5yZvTv8m7nXzD5jZic2Xac087KJycx+CzhCsAbFi5uuT1yYyHC5u99pZs8A7gAudPf7Gq7a/2dmBixy9yNmNgx8DbgiXM+jp5jZe4AJ4Jnu/oam65PEzPYBE+7es+P3zWw78L/d/ZNmtgAYcfdHm65XEjMbBH4IvMLdO5krVSkzO4Xgb2WVuz9hZp8Ddrr7dc3WLNm8vINIWcyoZ7j7QXe/M3z8OLCHYH2MnhEu7nQkfDocbj33bcPMVgCvBz7ZdF36mZk9E/gt4GoAd3+qV4NDaA3w/V4KDhFDwEIzGwJGSMlc3QvmZYDoJ2Y2DrwU+GazNZktbLq5iyBt+43u3nN1BD4KvA94uumKZHDg78zsjnDNk17zK8Ah4Nqwue6TZrao6Uq1cRHwmaYrEefuPwT+giBZ6UGCzNV/12yt0ilA9DAzWwx8AXiXu/+s6frEuftxdz+TYP2Ol5tZTzXXmdkbgEfc/Y6m65LDanc/i2B99svDZtBeMgScBWx195cCP2d6TfmeEjZ/vRH4H03XJc7MnkWwxPLzgOcCi8zsbc3WKp0CRI8K2/W/AEy5+xebrk87YVPDbcBrG65K3GrgjWH7/meB88xsR7NVSubuD4c/HwH+Fnh5szWa5QBwIHKX+HmCgNGLXgfc6e4/aroiCc4HHnD3Q+7+S+CLwD9vuE6pFCB6UNgBfDWwx93/sun6JDGzZWa2JHy8kOA//nebrdVM7n6lu69w93GCJodb3L3nvq2Z2aJwMAJhs82rgZ4aYefu/xd4yMxOD4vWAD0zaCLmrfRg81LoQeAcMxsJ/87XEPQx9qR5GSDCxYxuB043swPhAka9ZDXwdoJvvK0he2ubrlTMcuBWM7sH+CeCPoieHUba454DfM3M7ga+BXzZ3b/ScJ2S/D4wFf6bnwn8ScP1mcXMRoBXEXwz7znhHdjngTuB7xBcg3t2VvW8HOYqIiLZ5uUdhIiIZFOAEBGRRAoQIiKSSAFCREQSKUCIiEgiBQiZN8zseCzbZ+mZwGb2j1XWLXbuCTP7WF3nF8lLw1xl3jCzI+6+uOl6iPQL3UHIvBeuxfAhM7szXJPhRWH5MjO7MSz/hJntN7Ol4WtHwp/nmtltkXUSpsIZspjZ2Wb292ECvq+Gadzj7/3mcF2Au83sHyLnvCF8vDNyx/OYma0LkyT+uZn9k5ndY2bv7NZnJfOLAoTMJwtjTUz/MvLa4TBZ3lbgvWHZBwnSc5xFkB9pZcp5Xwq8C1hFkPV0dZhL678Cb3L3s4FrgM0Jx/4h8Bp3fwlBgrkZ3H1tmBDxUmA/8D/Dx4+5+8uAlwG/Z2bPy/8xiOQz1HQFRLroifBim6SVmuEO4HfDx78B/A6Au3/FzH6acuy33P0AQJj+fBx4FHgxcGN4QzFIkN457uvAdeHCMYnpIcK7lk8Db3H3x8zs1cCvm9mbwl1OAk4DHkipn0gpChAigSfDn8eZ/ruwgsdGjzdgt7u3XZbT3S8zs1cQLGp0l5nNCGDhymifBf7Y3VsJ/Az4fXf/as76iZSiJiaRdF8D3gIQfmt/VoFjvwcss3DdZjMbNrMz4juZ2fPd/Zvu/ofAYeDU2C4fBu5x989Gyr4KbAibsTCzF/b44j3Sp3QHIfPJwrAJqOUr7t5uqOuHgM+EfRV/T9BE9HieN3L3p8ImoI+Z2UkEf2sfBXbHdv1zMzuN4K7gZuBu4F9EXn8vsDtS7z8kWD51HLgz7BA/BFyYp14iRWiYq0gKMzsBOO7ux8I7ga1t+jBE5hzdQYikWwl8zswGgKeA32u4PiJdpTsIERFJpE5qERFJpAAhIiKJFCBERCSRAoSIiCRSgBARkUQKECIikuj/AXmwBqvUValuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of Engine Size vs Emissions\n",
    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plot of number of cylinders vs Emission\n",
    "plt.scatter(cdf.CYLINDERS,cdf.CO2EMISSIONS,color = 'blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting dataset , 80% for training and 20% rest for testing!\n",
    "mask = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[mask]\n",
    "test = cdf[~mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Checking with train dataset the scatter of Engine_Size vs Emission!\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.84438153]]\n",
      "Intercept:  [126.46566787]\n"
     ]
    }
   ],
   "source": [
    "# Using sklearn to model data!\n",
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[[\"ENGINESIZE\"]])\n",
    "train_y = np.asanyarray(train[[\"CO2EMISSIONS\"]])\n",
    "regr.fit(train_x,train_y)\n",
    "print ('Coefficients: ', regr.coef_) # i.e Theta from intercept + coeff*x = y\n",
    "print ('Intercept: ',regr.intercept_)# i.e intercept from intercept + coeff*x = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Plotting the fit line of the linear regression\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r') # Here y = train_x*coeff + intercept..like we learnt\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.80\n",
      "Residual sum of squares (MSE): 893.00\n",
      "R2-score: 0.72\n",
      "220.0 compared with 225 \n",
      "262.0 compared with 239 \n",
      "309.0 compared with 354 \n",
      "282.0 compared with 288 \n",
      "371.0 compared with 361 \n",
      "290.0 compared with 336 \n",
      "328.0 compared with 354 \n",
      "282.0 compared with 290 \n",
      "243.0 compared with 285 \n",
      "204.0 compared with 209 \n",
      "204.0 compared with 209 \n",
      "297.0 compared with 281 \n",
      "360.0 compared with 356 \n",
      "243.0 compared with 212 \n",
      "297.0 compared with 317 \n",
      "297.0 compared with 338 \n",
      "243.0 compared with 253 \n",
      "243.0 compared with 255 \n",
      "204.0 compared with 209 \n",
      "243.0 compared with 278 \n",
      "181.0 compared with 212 \n",
      "220.0 compared with 189 \n",
      "204.0 compared with 228 \n",
      "204.0 compared with 225 \n",
      "266.0 compared with 248 \n",
      "266.0 compared with 294 \n",
      "367.0 compared with 382 \n",
      "367.0 compared with 377 \n",
      "266.0 compared with 283 \n",
      "266.0 compared with 269 \n",
      "181.0 compared with 177 \n",
      "181.0 compared with 163 \n",
      "332.0 compared with 380 \n",
      "332.0 compared with 344 \n",
      "332.0 compared with 362 \n",
      "332.0 compared with 380 \n",
      "313.0 compared with 419 \n",
      "224.0 compared with 221 \n",
      "220.0 compared with 244 \n",
      "220.0 compared with 235 \n"
     ]
    }
   ],
   "source": [
    "# Chechking the accuracy of oor Predicted values using our test data and Mean Squared Error!\n",
    "from sklearn.metrics import r2_score\n",
    "test_x = np.asanyarray(test[[\"ENGINESIZE\"]])\n",
    "test_y = np.asanyarray(test[[\"CO2EMISSIONS\"]])\n",
    "test_results = regr.predict(test_x)\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_results - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_results - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_results , test_y))\n",
    "for i in range(int(len(test_results)*0.2)):\n",
    "    print(\"{} compared with {} \".format(round(test_results[i][0],0),test_y[i][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Therefore the Accuracy of our trained model is 72%"
   ]
  }
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