Created on Skills Network Labs

ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Simple Linear Regression\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Use scikit-learn to implement simple Linear Regression\n",
    "-   Create a model, train,test and use the model\n"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "### Importing Needed packages\n"
   ]
  },
  {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
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    }
   },
   "source": [
    "### Downloading Data\n",
    "\n",
    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
   ]
  },
  {
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   "execution_count": 2,
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     "text": [
      "--2021-03-19 05:50:46--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 72629 (71K) [text/csv]\n",
      "Saving to: ‘FuelConsumption.csv’\n",
      "\n",
      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.06s   \n",
      "\n",
      "2021-03-19 05:50:46 (1.18 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Understanding the Data\n",
    "\n",
    "### `FuelConsumption.csv`:\n",
    "\n",
    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "\n",
    "-   **MODELYEAR** e.g. 2014\n",
    "-   **MAKE** e.g. Acura\n",
    "-   **MODEL** e.g. ILX\n",
    "-   **VEHICLE CLASS** e.g. SUV\n",
    "-   **ENGINE SIZE** e.g. 4.7\n",
    "-   **CYLINDERS** e.g 6\n",
    "-   **TRANSMISSION** e.g. A6\n",
    "-   **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
    "-   **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
    "-   **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
    "-   **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
   ]
  },
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   },
   "source": [
    "## Reading the data in\n"
   ]
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()\n",
    "\n"
   ]
  },
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   "source": [
    "### Data Exploration\n",
    "\n",
    "Lets first have a descriptive exploration on our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets select some features to explore more.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot each of these fearues:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets plot each of these features vs the Emission, to see how linear is their relation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3EFqVt4UwY4azmfaqXEEhIj4n6X7gX5IEhQ9GxP8tsmJm7dTqHghOpzA9IyONA4ME117rYNDLGnYfSfrx9PbNJJvm7AWeAualZWY9zwGhcxp1By1aBHfc4YDQ65q1FH4NWAl8rMZzAbyt7TUya5NWuoucsqI9Kl/4GzYkLYaRkSRQOBD0j9zrFFp+Yel1wNeA40mCz59FxIclnQZ8niTj6i7gPRHxYnrOGuAakqR774+Iv2r0Hl6nYPW0EhCcQsGGTTvWKbxb0snp/d+S9EVJR221WeVV4G0R8UZgKXCRpH8N3ABsiYglwJb0MZLGgOXAOMmU15vT/Z3NWpY3IMyb54BglpV3Sup/iYjvS3oLcCFwO3BLoxMisT99OCv9CeDS9HzS23el9y8F7oyIVyPiSWAncF7eX8QMkoR2rYwhPP10cXUx60d5g0JlPsE7gPURcRdwXLOTJI1I2gY8D9wbEd8AzoyIZwHS2zPSw+eTDGJX7E3LzHKRvCmO2bHKGxSelnQr8B7gy5KOz3NuRByMiKXAAuA8SW9ocHitv++O+m8raaWkrZK27tu3L1/treM6mfpaaq11MGuWA0K14447fB2l5LENp7xB4T3AXwEXRcRLwGnAb+Z9k/Sc+0nGCp6TNBcgvX0+PWwvsDBz2gLgqJ7hiNgQERMRMTE6Opq3CtZBnUx9PZ3ppt7c/UjHHQevvXZk2WuvOTAMq7xBYS7wFxHxHUkXAO8GHmx0gqRRSaem908AJoFvA3cDV6WHXUWy1Sdp+XJJx0s6G1jS7D2sN3Ui9XWrrQNIWgiWyLbkqgNCRb1yG2x501x8AZiQ9Hrg0yRf4J+lcVK8ucDt6QyiGcCmiLhH0gPAJknXAHtIAgwRsV3SJpIsrAeA6yLCORT7UL2tFfNsuZhHq8EAkoDgFkLzVNZmeYPCoYg4IOnfATdFxB9K+majEyLiEeCoaasR8Y/AsjrnrAPalAvTOqlcTloCe/bU38i+Hamv509j6oG/ABOVDKZmjeQNCq9Juhz4JeDn0zI3xg04PIZQ6TKqFRDalfq6lYR24IBQMTmZb+/pLHe3Dae8Ywq/DPw0sC4inkz7/DcWVy3rJ7XGECBJcdCu1NetjiFEOCBUTDcguLttOBWW5qITnOaiu0qlwzluamnX9plOaHds8ly/2bO9J/IwmXaai3TgF0mPSnok8/OopEeKqKz1hzw7bLVjDKGVnbncOkhkt8OcmaOD+KSTHBDssGYfmevT20uKroj1j3K5+YBlO8YQjjsuf0tj2INBvS6iZpveOBmgVWsYFDLpKHYDSDql2Tk22CpTGhtZtCgJCMc6hpDXsA+ITmfMAJKd5RwQrFquL3hJq4DfBn7A4dQTAfxoQfWyHlQuN5/jPjICu3ZN/z1GRlobh/CA6PQDgrcatVry/tX/G8B4RLxQZGWst61d27yb5lg2YveAcn7NBvmrjYzAgQPF1skGQ96g8A+A96Uacs1WJB9L/3QrAaHe4rhBNz7eWhbYrGMJ1jZc8gaFNcDfSvoGyeY5AETE+wuplfWMPCuVpWTv3emMIUwnf9Ewdhe1spNcNY8dWCvyBoVbga8CjwJtmHlu/SDPSmUJrr22MwEBhjMglMvTCwjtGPC34ZM3KByIiF8rtCbWcxqtVD50KFmHMN0vnVKpteOHscuo1e4ijxtYO+QNCvdJWgn8OUd2H323kFpZT6g3hnDo0LGtVPaAcnPTGT/wuIG1Q96g8Ivp7ZpMmaekDrizzko2yKlVPl0OCLVNd61BxdiYxw2sPXIlxIuIs2v8OCAMuHXrkpXJWdNdqdxqQrsTTnBAyGNkJJn1tX17e+tkw6tZ7qMPZO6/u+q53y2qUtYbVqxI5sIvWnRs2U6n0zqoNZYxiMrl1gPCokWwcWNynQ4ccAvB2qthllRJD0fEm6vv13rcDc6S2h/cZXS0UgluvbX1sZmxMbcK7NhNO0sqoDr3az2uftOFku6T9Lik7ZKuT8s/IulpSdvSn4sz56yRtFPSE5IubFI363HT2Ud5WALC+vUOCNabmg00R537tR5XOwD8ekQ8LOlk4CFJ96bPfSIiPpo9WNIYsBwYB+YBmyWd632a+5ODwZGmM27g/ETWDc2CwhslfY+kVXBCep/08esanZhmWK1kWf2+pMeBRjvsXgrcGRGvAk9K2gmcBzzQ/NewXuKAcCQHBOsnDbuPImIkIk6JiJMjYmZ6v/I4d8JiSYuBNwHfSIvel27Wc5ukOWnZfOCpzGl7aRxEbBrKZVi8OFkMtnhx8rhdvGVmba0EhNmzk0FkBwTrlrx7NE+bpJOALwC/EhHfA9YD5wBLSVoSH6scWuP0o74yJK2UtFXS1n379hVT6QFVSVuxe3fyZbx7d/K4HYFhOikr7Egnnugd0Kz7Cg0KkmaRBIRyRHwRICKei4iDEXEI+BRJFxEkLYOFmdMXAEdlfImIDRExERETo6OjRVZ/4NRKW/HKK0n5dE1nMBmGo4WQV2Wtwf79DgjWfYUFBUkCPg08HhEfz5TPzRx2GfBYev9uYLmk4yWdDSwBHiyqfsOoXtqKZimx65luMBi2gLBsWf3nVq/2WgPrLUVurXk+cCXwqKRtadmHgMslLSXpGtoFrAKIiO2SNgE7SGYuXeeZR+1VRNqKVgxbMKjYvLn2YLP3R7Ze1HDxWq/z4rXWVKfChmRgs9V+bO+BcOTOZyMjyXX1F7z1i2NZvGYDpB1pK6Yz3XQQA8L69YdTeR88mDxuNR24WS9yS8Fy8/qDxMyZtfd28H4G1i8atRSKHFOwAdLq+oNBVm+zn2HbBMgGk7uPBkiplPwVKyW37erOGNY1CKXS4Sm3lZ/JyaRFUEu9crN+4pbCgKie3VLp54ZjGwAd1i6jyrhBtS1bYN682nsme+czGwRuKQyARjn5N2yY3msOe8qKRtftmWeS6aSVlkFl8ZlnH9kgcFDoc6USXHFF/een0889rK2DbF6oZtft5puTQWVvdGODxt1HfaxeF0dWK/3cw5quolSCW24ZjN/F7Fg5KPSxPF1Defu5hzkgNAus1RqlrTDrd+4+6mPNujiWLSuuW2MQAgK0PubifQ5s0Lml0MdGRuoHho0b861UHtYWQkWzwLpoEeza1ZGqmPUEtxT6SPUGORdcUPu41auLCQiDNsMIGo+5zJ4N69Z1ri5mvcBBoU/U2iDngQeS7oxWp0a2Ot101qzBCwYV9cZcTjrJG97YcHL3UZ+ot0HOzp2t5dsZ1umm9VQCqDOemiWcEK9PzJhR+wtagkOH8r2GU16bGTh19kCotxFOURvkOCCYDScHhT6xbl0y8JmVdyB0zpzWU1Y4IJgNpyL3aF4o6T5Jj0vaLun6tPw0SfdK+k56OydzzhpJOyU9IenCourWj6a7QY4EL72U7z3qdVGZ2fAobExB0lxgbkQ8LOlk4CHgXcDVwHcj4kZJNwBzIuKDksaAzwHnAfOAzcC5jfZpHqYxhVZ5QNnM6unKmEJEPBsRD6f3vw88DswHLgVuTw+7nSRQkJbfGRGvRsSTwE6SAGEtckAws+nqyJiCpMXAm4BvAGdGxLOQBA7gjPSw+cBTmdP2pmVDoR0b5LS6/sDMrFrhQUHSScAXgF+JiO81OrRG2VF/w0paKWmrpK379u1rVzW7anz82DeCn24w6IdWQvVK7nK52zUyG1yFBgVJs0gCQjkivpgWP5eON1TGHZ5Py/cCCzOnLwCO2t8qIjZExERETIyOjhZX+Q4olZIvuh07aj8/3Q1y8uiXlBW1VnKvXOnAYFaUImcfCfg08HhEfDzz1N3AVen9q4C7MuXLJR0v6WxgCfBgUfXrtkrK5kZfzHk2yJlOl1E/BIOKeiu5167tTn3MBl2RaS7OB64EHpW0LS37EHAjsEnSNcAe4N0AEbFd0iZgB3AAuK7RzKN+l6cV0GyDnEEOBhV79rRWbmbHprCgEBF/Q+1xAoCa25RExDpgoPNSTk7W30+5WqMNcoYhIECyYnv37trlZtZ+XtHcQePj+QPC2FjtpGytdhf1y9hBPceyktvMWueg0CHlcv0B5WqrV8P27UeXD+N00+mu5Daz6XHq7A7Isw9wESmb+7mFkLVihYOAWac4KBQs78bwjfZEGJbxAzPrPncfFSzPLKNlNYfdEw4IZtZJDgoFa7bWYGwMNm8+urzVAeUTTnBAMLNj56BQsEZrDTZubM+AcsTRC7zMzKbDQaFg9dYarF5de/C01fQNs2a1Xiczs3ocFAp2881JAKi0GEZGkse1ZhlNTsIVV+R/7V7aMtNJ68wGQ2Gb7HTCIG2y088DypWkddkurNmzvZ7ArFd1ZZMdy6/fAkL13g/XXuukdWaDwkGhi8bH+y9lRWXdRXbvh/37ax/rpHVm/ceL17pkZAQOHcp/fKO1DJ1QLid/+ddKTlePk9aZ9R+3FDps/vykddBqQKi1lqFTshvd5OWkdWb9yS2FDpo/H545ai+5xrrdXQS1N7qpJiUtgz17ktt16zzIbNaPHBQ6qB8DAuQbG7j22vYm8zOz7nD3UYeUSvmPnTevdwICNB4baLTuwsz6T5F7NN8m6XlJj2XKPiLpaUnb0p+LM8+tkbRT0hOSLiyqXp1Wmb6ZJ1PqjBlJMHj66eLr1Yp6G91s3Jhkd3VAMBscRbYUPgNcVKP8ExGxNP35MoCkMWA5MJ6ec7OkJjsU977q6ZuNzJuX77hu8EY3ZsOjyD2avyZpcc7DLwXujIhXgScl7QTOAx4oqn6dkCdtNvRWV1E93ujGbDh0Y0zhfZIeSbuX5qRl84GnMsfsTcv60uRk8hd1nr/8V68uvj5mZnl1OiisB84BlgLPAh9Ly2ut663597OklZK2Stq6b9++Qip5LObMgS1bmh/nAVoz60UdDQoR8VxEHIyIQ8CnSLqIIGkZLMwcugCoOYEzIjZExERETIyOjhZb4RaUSknr4KWXmh+7erUHaM2sN3U0KEiam3l4GVCZmXQ3sFzS8ZLOBpYAD3aybsci7z7Mbh2YWa8rbKBZ0ueAC4DTJe0FPgxcIGkpSdfQLmAVQERsl7QJ2AEcAK6LiB6di3PY+Djs2JH/+AMHiquLmVk7FDn76PIaxZ9ucPw6oG+y5bSasqLbCe3MzPLwiuZpKJdbCwinntrdhHZmZnk5KExDK5vHLFsGL75YXF3MzNrJQaEFlX2Im6WQrgwoR7iFYGb9xVlScyqV4JZbmq8+HhuD7ds7Uyczs3ZzSyGHcjlfQJg3zwHBzPqbg0ITpRJccUXjgLBoUZIxtNeym5qZtcrdRw1MTjZPWbFoEeza1ZHqmJkVzi2FOsrl5gFB8j7EZjZYHBTqaDbtVEq2oHQ6aTMbJO4+qqPZvsR33OGAYGaDxy2FOhrtS7x6tQOCmQ0mB4U6au1LDMkKZWc5NbNB5aBQR619iTdu9AplMxtsQxsUSiWYOTP5wp85M3lcbcWKZLrpoUPJrbuMzGzQDeVAc/X6g4MHD2+S464hMxtmQ9dSaLT+YMOGztbFzKzXDF1QaLT+4GDP7/VmZlaswoKCpNskPS/psUzZaZLulfSd9HZO5rk1knZKekLShUXVq9H6g5GRot7VzKw/FNlS+AxwUVXZDcCWiFgCbEkfI2kMWA6Mp+fcLKmQr+hG6w9WriziHc3M+kdhQSEivgZ8t6r4UuD29P7twLsy5XdGxKsR8SSwEziviHp5/YGZWX2dHlM4MyKeBUhvz0jL5wNPZY7bm5a1ndcfmJnV1ytTUlWjrOYOBpJWAisBzmrUF9TAihVec2BmVkunWwrPSZoLkN4+n5bvBRZmjlsAPFPrBSJiQ0RMRMTE6OhooZU1Mxs2nQ4KdwNXpfevAu7KlC+XdLyks4ElwIMdrpuZ2dArrPtI0ueAC4DTJe0FPgzcCGySdA2wB3g3QERsl7QJ2AEcAK6LCK8aMDPrsMKCQkRcXuepZXWOXwd4HzMzsy4auhXNZmZWnyJqTvLpC5L2Abvb+JKnAy+08fUGja9PY74+jfn6NNbJ67MoImrO1OnroNBukrZGxES369GrfH0a8/VpzNensV65Pu4+MjOzKQ4KZmY2xUHhSN5RoTFfn8Z8fRrz9WmsJ66PxxTMzGyKWwpmZjbFQQGQtEvSo5K2Sdra7fr0glY3SRo2da7PRyQ9nX6Otkm6uJt17CZJCyXdJ+lxSdslXZ+W+zNEw+vT9c+Qu49IggIwERGeQ52S9FZgP/CnEfGGtOz3ge9GxI2SbgDmRMQHu1nPbqlzfT4C7I+Ij3azbr0gTXg5NyIelnQy8BDJ/ilX489Qo+vzHrr8GXJLwWpqcZOkoVPn+lgqIp6NiIfT+98HHifZI8WfIRpen65zUEgE8NeSHkr3a7Da6m2SZIe9T9IjaffSUHaNVJO0GHgT8A38GTpK1fWBLn+GHBQS50fEm4F/C1yXdg2YtWo9cA6wFHgW+FhXa9MDJJ0EfAH4lYj4Xrfr02tqXJ+uf4YcFICIeCa9fR74EgXtDz0A6m2SZEBEPBcRByPiEPAphvxzJGkWyRdeOSK+mBb7M5SqdX164TM09EFB0onpQA+STgR+Dnis8VlDq94mScbUl1zFZQzx50iSgE8Dj0fExzNP+TNE/evTC5+hoZ99JOlHSVoHkOwv8dl0b4ehlt0kCXiOZJOk/wlsAs4i3SQpIoZysLXO9bmApNkfwC5gVaX/fNhIegvwdeBR4FBa/CGSfvOh/ww1uD6X0+XP0NAHBTMzO2zou4/MzOwwBwUzM5vioGBmZlMcFMzMbIqDgpmZTXFQMDOzKQ4K1laSDmbS/m6TtFjS1ZL+qOq4+yVNpPezqcu3SfqDtPwzkn6hxnucK+nLknamqYc3STozfe4tkh6U9O30Z2XmvI9IekXSGZmy/Zn7a9M0xo+k9fhXmfqdnjnuAkn3pPevlhSSlmWevywt+4XM7/qEpG9J+l+SfkzSl9L32CnpnzK/+89UXZsfkvSnkv4h/flTST+UPrc4fZ//nHnvP5J0dZN/o99Ir81jaZ1+KS0/TtJN6ft8R9JdkhZkzgtJd2Qez5S0r+pa7Et/j+2S/kzS7EZ1sd7joGDt9oOIWJr52ZXzvJ/NnPP+egdJeh3wF8D6iHh9RPwLknwxo5J+BPgscG1E/DjwFmCVpHdkXuIF4NdrvO5PA5cAb46InwQmgady1v1RkkVHFcuBb1UdsyIi3kiSGfS/R8RlEbEU+I/A1zO/+99Wnfdp4P9ExDkRcQ7wJPAnmeefB66XdFyeikq6Fng7cF6a8vutgNKnfxc4GTg3IpaQLFb8Yrr6FuBl4A2STkgfvx14uuotPp/+HuPAPwPvzVMv6x0OCtZvfhF4ICL+vFIQEfdFxGPAdcBnMimJXwA+ANyQOf824L2STqt63bnACxHxauXcSk6sHL4OnCdplpIEZ68HttU59mvp801Jej3wU8DvZIp/G5iQdE76eB+whcOpI5r5EFCqJKeLiH+KiNvTv+h/GfjViDiYPvc/gFeBt2XO/wpQCbKXA5+rU/eZwInAiznrZT3CQcHa7YRMV8iXmh8+5b7Meb/a4Lg3kGxIUst4jee2puUV+0kCw/VVx/01sFDS30u6WdK/aaHuAWwGLiTZL+DuBsf+PEnLIo8xYFvlSxogvb+NI3+nG4FflzTS6MWU5Pg6OSL+ocbTrwf21MhkWn397gSWpy22n+RwuueK90raRtKCOA34c6yvOChYu2W7jy5Ly+rlUsmWZ7uPPjHN91ad96ou+wPgKkmnTB0QsZ/kr/KVJH99fz7TN5/nNe8k6TZaTu2/nsvpl+X5wG80/C0Oq/f7HFEeEU8CD5K0oqbzeq281yPAYpJWwpdrHP/5tFvsR0iC3282qZP1GAcF64R/BKo3CzmNpH+/VdtJvrzrPTdRVfZTwI5sQUS8RDL2UKoqPxgR90fEh4H3Af8+faq6/kfVPSIeJGnFnB4Rf1+jbivSgPeuiMg7VrEdeJOkqf+n6f03kuzUlfW7wAdp8H86bQW8rCQJZLWdwKK0NZH1ZqquH0lL6KPU6TpK3ytIWgnem6TPOChYJ/wdcH46EEw6s+Z48g/kZn0W+Jns4LGkiyT9BPDHwNWSlqblPwz8N+D3a7zOx4FVJJlxSWcELck8vxTYnd6/H7gyPW4EuAK4r8ZrriHps2+LiNgJfBP4rUzxbwEPp89lj/02yZf3JU1e9veAP660kiSdImllRLxMMgj+8Uo3VDoraTbw1arXuA347Yho1g32FqBWV5X1sJndroANvoh4TtL1wJfTv3T3A5enG4lU3Cep0nf+SET8Unr/Vkk3pfefioiflnQJcFNa/hrwCHB9+j5XAJ9K/+IVcFN2UDpTpxfSMY/K+MVJwB9KOhU4QPKXc2U66+8A6yV9K33NvwQ21njNr7R2ZXK5Jq3XzvS9H0jLallHEkQaWU/yu/6dpNdIrl9ld681JC2Av5d0CPg2cFlUpVKOiL3AJ+u8/nuVpIWeAewFrm5SH+sxTp1tZmZT3H1kZmZT3H1kNoAk/THJTKesT6ZrD8zqcveRmZlNcfeRmZlNcVAwM7MpDgpmZjbFQcHMzKY4KJiZ2ZT/D4SFrJqSvFbvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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frbSVh4ZscSauF18MJnL3wOz0jW/ku3aUp57K1y+6m3nppxRmGbDDzPoJlM+N7n6zmX3WzM4nMA0dAK4AcPe9ZnYjsA84Blzp7ioQKDpGf3/yJN9w0I6OTi8gPzyczYRUR1WzhrknLda/YSaDYqUdk1ZSSlExO6ky+uhed3+Vu7/S3V/h7r8f9r/T3X8m7L/M3Q9Hxmxx97Pd/Tx3//uqZBOiQdSxnOWpf98+WLly6nPWqmad2m8QpeGPyBrrXzQjaZJpLalfdDfa0SwKk1aVrNtlaHYsN0gL24ymqm4OpUzia1/LLlcrTj45vn9gINkRnFVx5fVZiB4laQPDbGjavFYf3bDZqF0Z0jaOFUnnnHdM3vOLFqPJssGu8b3zUuR3EvVCi81rSognCtENCczalaHVk717+vE48kYw5b1HkQipZhorpGaiq4s8FPmdRL20HX0URhL9BzPbHm44u87MritXTDGb6IYEZu3KkLYxbHQ0/nhzf9SEtXBh/JiyNnoViZBqZtWqqf0KDfr6gv4iJG1uS+oX3U1Wn8IXgZcBu4C/izQxR+mGBGZZZGjlc0grOL9370wF0Bx91Jzm4plnZk64q1cnP4EnPWUn9ZdRjGbz5pmhsCdOTBW8iaMb/EeiQyTZlaINuDvLeZ1u8inUx2zwKWSRccOGKd9Cf3/+QvFJpSiz/i6d8ilEyVtCNO13TPPNiO6DFj6FrErhPwNrspzbySalUC/tTqhl0KrWcJYay+1c3z173eSkexaRsSpFVlRGOZpnH62UQlbz0VXAzWb2YzN7Omw/rGTpImYFExNBgrVonqAdO7rLrNCuz6HZNBRXxjKruSzpnlnqQDcT3eF87Fh+5/CaNfn6037HvCYw0eUkaYvZ0LRSqI8ynsLbJc2ssWhRvIyLFmW7fpbvuHOn+/z56SuF/v7k1UanV1x1rRTSVl2ic9Cu+Si4BpcBHw/bm7KOq7JJKdRHXrt0FvJOGlWbNbKM37nTfWAgXSkkKa648QMDrb973Pl5KNunkPV3ar6vmRRDXbStFICrgd3Ae8J2G3B1lrFVNimF+ih7pVDEcZ02uXVCKST9Do2VQZITtvE7DQ3FHx8aipcpSQHlUQxF/u1aKewsv1PSamr+/Oxyi/IoQyncC/RFPvcD92YZW2WTUqiPsqOP0ibXuJVDN6wU2lVMeWVs9zu5x5u85s8v/m+XZeVRhtyiPFophTy5j06OvH9ZYSeG6AnKLp+Y5Mw8fjyYOuKcvEWctGWTtleijH0FVRA82yV/zsP73pevX3Q5Sdoi2oC3AweB64EdwEPA2ixjq2xaKfQOWeL940wc7Zo1WpFlfLv29qpWCq2c11UECaQ5y7VS6C4oydG8jMDZfDnwr7KOq7JJKfQOO3e69/VlUwxZI3WymDVaKZXR0fjxo6MzZS+6VyLvZJnFp5C2wa2KIIE0Vq+Ov+fq1dXdUyRTWCkAPxW+vjqutRrbiSal0DskTWRZWpJiWLw4/vzFi4PjWWzrzYqhWSGkUUbkTjNp0UdJyrWvL9vvUhXNikEKoT7aUQrbw9evxrSvtBrbiSalUC9lxp0nRelkXTnEkTbh5o38yUrz77JhQ3UmrjjSrtlqF7b2EMwNSjEf5W3ASQQ1lu8B9gIfC/tPJQhpfTB8PSUyZhOwH3gAuDTtHlIK9VF29FFRhdBqAq2iXkIaeX+XOpRClt+z03msRGdppRQy1VMws7cCt7r702b2H0Pz0R+4+7dajDFgkbs/Y2YDwNcJ0mX8GvCUu19tZh8OlcLvmtko8DngQuB0goys53qLOs2qp1AfZddTSKoTkIWitQqqqAOQ93epQoa+vvixZkE21Ky/dSdrY4jO0nY9BeA/hQrhF4FLCSKQPtlqQKiQngk/DoTNCRzVO8L+HcCbw/eXAze4+/Pu/hDBiuHCjPKJDpNUrD5LEfs42qk3kDR28eLW/UND8ceT+rOQFFp78GB5aaejdaXnzQs+R0lSJo3+iy7Kdp9O1sYQ3UNWpdB4rngjsM3dvwjMTxtkZv1mdjfwBHCbu38TOM3dDwOEry8PTz8DeCQy/FDYJ7qQsuPv4wq/xLF48fRaxKtXwy23xE+4P/pR/DUa/ddcE9Q2jjIwEPTnITpJt3q6dw+Uw7vfXVwxNNeVPn48+NysGFqxf3+28zpZG0N0EUl2pWgDbgb+EvguwSa2BcA9WcaG408mcE6/Avh+07Hvha9/Abwj0n8t8G9irrUe2APsWbFiRXlGNpGLLLbwPI7orPsUYOqaQ0Mzo4eK5ORpx1leNGqq4cwu20+Sds2sqb7z+hSU7G52QQlpLgYJfAHnhJ+XAa/PMjZyjY8AHyRwIi+LXOeB8P0mYFPk/C8Br2l1TTma6yMtcqdMh2veVmae/4ULp49buHD68VZRU2kTcJbfMc/vVMZvWWRSL5LUT9RLK6WQ1dF8NnDI3Z83s4uAVwKfcffvtxizFHjR3b9vZguBLwP/FfgV4EmfcjSf6u4fMrOVwF8z5WjeHSohOZq7kLQC8mU6XPPScKgWceJu3Bik62jliF24EJ59dupere6Rdnzx4ngz16JFQWnPZrJ8p3Z+ywzTwQyWLIEnn5zZPzQER48Wl0VURxmO5s8Dx83sJwnMOmcRTOCtWAZ81czuBf6FwKdwM0HG1UvM7EHgkvAz7r4XuBHYB9wKXNlKIfQ6ZdfETXNO5iWtgHy7BW7aoagtvNlen8Rzz029b9e3kub3mA3EKYRW/aLLSVpCRBtwV/j6IeC3wvffyjK2ytar5qOy9wCUUde3mTQzRt78OmWZjtrZLZxnA13W3zbtGnllrNp8VISyryeqhxJ8Ct8kSIp3H3BW2HdflrFVtl5VCmUnLKuisHraRNBpn0IZu4WLTp6tksGl+QyqUAp5nPbRVjTtRFU7w0V1tFIKWc1H7wZeA2xx94fM7CxgZ3nrFRGlbNNLmqmnCspOrZ3GiROBr6Kq60dZuHD651WrYPny4HsuXx58blBW2GuDDRvS++NSijdMhxCYtk4/ffrx1ath165iMl1zDcxvClCfP7/4dxQ1k6QtZkPr1ZVC2U9eaQnSilC2yaCd3EdJ98z7O2YJ12z+zbKsiFqFaxbJHpqlpnP0nkNDM6ODyk5joZDU2QVFVwpmdmP4+m0zuzfSvh06kMUsoPnJNq2/DqpYtZx/fr5+9/Rrnjgx3Um/efNUJFKDZ5+FdeumggQgWMXErWbOPTf+Pkn9AFu3BhFe7sHr1q0zzxkfn7rn4sXw4oszZdy8OfkeeYner1MrNlENLUNSzWyZux82s+G44+5eMKlBOfRqSGpa7pq6r9cYm0SWybWZl7wkPgQzK3H3TAubzXp+q/FJv22UwcFk01leGWF62Gx/f5DmI04xNKji31/MbgqHpPpUOoqDoQL4HvB0pIkKSCvxWPf1qqCKEMy8vpSs+Zei47P8hq2eyvPKWCTNRSf+/csOoRY1kmRXijbgCuBx4ABBKc6HgP+bZWyVrVd9CmUXVm/X7h1H2T6FdvwJ0RYtglMk6ipqr09q0fFxv22e3yWvjEW+U9khzp2+vigfSghJfRBYkuXcTrZeVgplpw1oNekXuV8RpVC0nnJRxVBkf0ZUKSQ5npvHR79XFkXSfL88MhZVxlU6gquo+SyqpQylcCswmOXcTrZeVQqd/iMrEu2Ud3JqpyxlkdYgS6RO9NxW10wbX+R3yStjFXtO2qWOms+iPVophay5j14FfJpgE9vzEdPTb5dmxyqAHM3lUMRpnGXMxERgS3/44eA7xdnJG7mQysx9FJUhD2U4z/PmMsrLypWwb9/M/tFR2Lu3/esXoeyCS6J6ysh99JfAV4B/Bu6MNFEBzRuP0vq7kYmJwHF78GAwoSY5TosW5elWmsNT0/rz8sAD+fo7QdxmucHBoF/MPrIqhWPu/jvu/ml339FolUo2h4kmXMvS3y5VVCCLi9+Po2hRnlaMjpZ/zawkrSharTTyRO7UsTs9jU7vXhfVklUpfNXM1pvZMjM7tdEqlWwOk2QiqiqmvIo0BVlXAGVPZu2YUZLMR2WbtqJMTASV2BorqrTKbGVXvCsLbV7rHbIqhX9HUATnn5gyHfWeMb+HaH763Lgx+Wl0fByuu276k95117X3h511kmpnNRKl4d5sVgh5nsLf9758/WVw1VUzdxu/+GLQH0fSXop2alwLMY0kD/RsaL0afVQ07LBBltj5vr72whLTZMwaJZQlW2iWlhRqmzd+Pk8kUJHfpd3zy5CxCpT7aHZB0ZBU4EOR929tOvaHrcZ2okkpxJM1dfKiRdXJmJSEr10lktaim/zqiJ/vhFLoNrR5bfbRSimkmY/WRt5vajr2hpIWK6KJdh2/We35VVb3yuP/6MtqxMzACy9MmV7qqP6W99+uCid/p0lKClhmwj3ROdL+HC3hfdzn6QfNzjSzr5rZ/Wa218yuCvs/amaPmtndYVsTGbPJzPab2QNmdmmub9JDzLX89MHCszwaZSDryPmU99+uF/6t6yy9KiogaQkRrDCCMpzN7+M+x4xdBrw6fP8S4P8Ao8BHgQ/GnD8K3AMsIKgB/V2gv9U9etV85N6ejTarqSVaG6Ds3EdZahNU2RrfqQ6zRt7fcrbb45XmYvZBC/PRvBSd8bNm9kOCVcHC8D3h55NSlM1hoJFl9Wkzux84o8WQy4Eb3P154CEz2w9cCNyeIqMoyBVXBK+NjWYNE8DBg1PRLEUjkMp++i9CQ/bGruoVK4INVVWHS46P57tH3vO7jS1bpv//AW1em9UkaYsyGzACPAy8lGClcAC4F7gOOCU8578D74iMuRb49VbX7dWVws6dM5+0zbI/QaY9RTdHrBR50kt7Sk/Kp9TJlcJcptOrj9m+2plr0G7uo3Yws8XAPxDUd/6CmZ0GHAUc+ANgmbu/x8z+Arjd3XeG464FbnH3zzddbz2wHmDFihUXHOy1PAnAggWBw7SZ+fPh+edn9jeTN4dPkVxLafdYsmTKtl8H3bBSqYvmlR+0LvQj5h5l5D4qeuMB4PPAhLt/AcDdH3f34+5+AvgrAhMRwCHgzMjw5cBjzdd09+3uPubuY0uXLq1S/NqIUwit+tulCofsU08VHyvaQ9FAoh0qUwpmZgQmoPvd/U8j/csip70FuC98fxOw1swWmNlZwDnAHVXJJ6aoIqHZqUqCUhuKBhLtUOVKYRXwTuB1TeGnf2Rm3zaze4HXAh8AcPe9wI3APoL6DVe6e41pvnoTs6AucLR8YycTmvX1VZtLSMyO8quie0mLPiqMu3+d+L0Mt7QYswVQzELFNOr6QuuC76046ST48Y/j+yHZfOQe+CmqVAyzaeNXFSgaSLRDpT4FUQ9ZU0dv3x68TkzAu941PVPnu97VOnncT/xE6/66nlYHBmbXxq8qUCpr0Q5SChWQJzNnFcRV5oqjkbb6iitmRhmdODG1jyHPPRr9a9bEH0/qb4fFi6cmv09/WpMfKJW1KE5l5qO5ShUbwaL09ZW/CSspB1I7uZFuSTASJvW3w3PPVVdrQoi5hlYKJVN1OGDDvLN+ffsrkCrt+p2MgKmz6pgQvYaUQsl0ajIsQ9FUucGrkz6FuquOCdFLSCmUTCcnw26OO0/b+1DmRK6qY0KUh5RCyXTSwdrNcedpETDnnVf82g2F0t8PGzYUD6sVQsyk8txHVTI2NuZ79nRXqeiknD9DQ3D0aLZrZLH1t8plk8dX4F5N7qM05s0r5gvImv9JCJFMbbmP5iJJSeDKSg5XRdx5HQXrizqHf+mXypVDCDEdhaTOMqoIvWyYX7ZvDybr/v7ATl+lWaa/v5hi+NrXShdFCBFBK4Ua2LgxMJ/E5SGqi61b4dixwPRz7Fj1dvqizmGFnwpRLVoplIxZsn0eAgXQyDsE5eQhqoO075lG8+okKwo/FaJatFIoQKs0FklO1kZ/I99QM0n9nSLv6uWnfzpffxzR1cnpp2cbo/BTIapFK4WctJvGIumpuE6zSJHVy/335+tPY2Cg9fFO+DmEEApJzc3ISKAImhkeDhKPpYVqJoVi9vcHT83Qfrhn3pDULDLluUeR/1JlX08IkYxCUkskqSR01lLRF12Ur79KFi4MXrth9ZLkK5APQYjOIqWQk3Ynr7vvztdfJc89F7wW+U6LF+frT6MbFJMQotoazWea2VfN7H4z22tmV4X9p5rZbWb2YPh6SmTMJjPbb2YPmNmlVcnWDu1OXlVvbitCkvO2lVP3k58MzE5R5s0L+oswPJyvXwhRDVWuFI4B/97dfxr4BeBKMxsFPgzsdvdzgN3hZ8Jja4GVwBuArWbWdcaD2TB5nXxyvvO3bg1yCOXJKTQ+DtdfPz230fXXF99l3cmcUUKIZCpTCu5+2N3vCt8/DdwPnAFcDuwIT9sBvDl8fzlwg7s/7+4PAfuBC6uSryizYfL6/vfzjymyea3M6l6dLMojhEimIz4FMxsBXgV8EzjN3Q9DoDiAl4ennQE8Ehl2KOzrKpImqe3bg30LohidLMojhEim8mnMzBYDnwfe7+4/bHVqTN+MYEQzW29me8xsz5EjR8oSMzNJUUbHj3cmdLKuus9V08k6FEKIZCpVCmY2QKAQJtz9C2H342a2LDy+DHgi7D8EnBkZvhx4rPma7r7d3cfcfWzp0qXVCZ9A3SGSZZbj7CbSivIIITpDldFHBlwL3O/ufxo5dBOwLny/DvhipH+tmS0ws7OAc4A7qpKvKN0SIllm3eeitEr3kZfxcVi3brqze9268tKDCyGyUeVKYRXwTuB1ZnZ32NYAVwOXmNmDwCXhZ9x9L3AjsA+4FbjS3btkCu5OkuztixZlG59n53MzjXQfBw+Ws3qZmAjCWRtK9/jx4HMvrYaEmA0ozUVO2plI3bOlc8h6j0ZqjWYuvhh2704fv2gRPPNMtns1k5buIy8nnRRfUW3BAvjxj/NfTwiRjNJc9CCt7O1f+Uq2a/zoR8XvX3a0UFKJTZXeFKKzSCnMMrKU4+zE4k/RQkL0JkqdPcuoohxnEbZsmZ5CHBQtJEQvoJVCD9KO3yMr4+PBaiWa5qLV6iWN1avz9QshqkGO5pzU7Wgus57C0BAcPZrt3E7Q7CBfvRp27apPHiF6FTma5xhZk/O97W3VypGXc8+dvk/h3HPrlUeIuYiUQg8Stzs4js98pnpZstIoCRrdp7BtW3qtaCFEuUgp9CDN9v4k2glJLZvt2/P1CyGqQUqhR4mmtZ4NqPKaEN2BlEKHSUpBEe0vOxInabXQiSglIcTsQkqhQzSqoWWZoPfvjz8nqV8IIcpCSqFDvOxlwWtSrqFof1LNhqT+NJLCWLspGnk2lDkVYi4gpdAhVEGsNaqnIER3IKUQw8aNMG9eYNKZN6+csMhTT23/Gr1M2TukhRDFUO6jJhrx8g0a8fKQrZh9N7J6dXwq7W5LITE+LiUgRN1opdBEWrz80FCx6z71VLFxZbBr10wFoBQSQog4pBSaSIuXL5oaou6U0rt2BY7lRpNCEELEUWWN5uvM7Akzuy/S91Eze7SpPGfj2CYz229mD5jZpVXJBa1rCzdy7zTT6L/llvz3GxiYcphmCUmdPz/+nKR+IYQoiypXCtcDb4jp/zN3Pz9stwCY2SiwFlgZjtlqZgnTc3uk1RZevz5+XKO/SBRRdMLPEh563XUzlYdZ0C+EEFVSmVJw938EslrSLwducPfn3f0hYD9wYRVybd48vTAMBJ83bw7eb90KGzZMz9a5YcOUkznJDNTfH0zccSuNF16Yun6WePzxcfjsZ6dH4nz2s3LCCiGqpw6fwm+a2b2heemUsO8M4JHIOYfCvtLJUlt41SpYvjyYkJcvDz43SIqn37EjyDOUlGuocf2s8fjR3EUHDkghCCE6Q6eVwjbgbOB84DDwJ2F/nKU91tBiZuvNbI+Z7Tly5EhuAZJSSjf608xLcfH069YFK4G+vqDF0VhhZI3Hb+X3yEK744UQcxR3r6wBI8B9aceATcCmyLEvAa9Ju/4FF1zgeenri8bgTLW+vuD48HD88f5+d7Pg+M6dU9fbudN9cDB+TKMNDk4fk0bcNfNcY+dO9/nzp4+fPz+fDEKI3gXY4wnzaqXlOM1sBLjZ3V8Rfl7m7ofD9x8Aft7d15rZSuCvCfwIpwO7gXPcvWXi5CLlONNKXfb1pecEGhycerofGYnPSdTfH5h+VqwITEN5zD9J1xweDkxJaSxZAk8+ObO/28pvCiHqoZZynGb2OeB24DwzO2Rm7wX+yMy+bWb3Aq8FPgDg7nuBG4F9wK3AlWkKoShpIadZ9hNEHdNJPoqGf6GIPyCL36MVcQqh0V9m6g4hRO9RZfTR2919mbsPuPtyd7/W3d/p7j/j7q9098saq4bw/C3ufra7n+fuf1+VXGkhp2vWxB9vpjFBJymRdjarJeVJKit/kkpdCiGSmHM7mtNCTrNuTmtM+klKJKtyqYKsqThU6lII0cycUwoQKIBjxwLfwbFj0xPdZTHRRENIk5RIkZ3PDZLyJGXNn3TNNcEu6jRU6lII0cycVAqtSNuc1hxC2q79P48MWU1S4+Pw6U9Phb0mkeRfEULMXaQUmkjbnNbsOC4ygaftISij4Ex081tSiuyLLsp+PSHE3EBKoYm8xV7yTuBpm+OKyJCGaj4LIbIipRBDnhQTeSfwtNxLVVCFiUsI0ZtIKRSg2fwD2ZVI3Ka05v4sq4k8VBE2K4ToTaQUctLuhJ22eQ7KX02U4aMQQswNpBRy0u6EnVbZDco395TtoxBC9C7z6hZgttHuhD08nJzXqMGKFfHntGPuGR+XEhBCpKOVQk7atc9nMeXI3COEqAsphZy0O2FnMeXI3COEqItKU2dXTZHU2WUwMRH4EB5+uFhqbCGEqJNaUmf3Mu2WysxSFU2V04QQdSClUAJ5JvAsIa1l71MQQoisyHzUJo0JPBqmGq3M1kyWqmrtVl4TQohWtDIfSSm0Sd4JPKncp1lgjsp6jhBCFKWucpzXmdkTZnZfpO9UM7vNzB4MX0+JHNtkZvvN7AEzu7Qqucom776FLCGtSkshhKiLKn0K1wNvaOr7MLDb3c8BdoefMbNRYC2wMhyz1cxmRbb/vBO49ikIIbqZKms0/yPQXCvscmBH+H4H8OZI/w3u/ry7PwTsBy6sSrYyyTuBa5+CEKKb6XSai9Pc/TCAux82s5eH/WcA/xw571DY1/U0Juo8+xaypJxQWgohRB10S+6juKKRsR5wM1sPrAdY0SVGdk3gQoheodP7FB43s2UA4esTYf8h4MzIecuBx+Iu4O7b3X3M3ceWLl1aqbBCCDHX6LRSuAlYF75fB3wx0r/WzBaY2VnAOcAdHZZNCCHmPJWZj8zsc8BFwBIzOwR8BLgauNHM3gs8DLwVwN33mtmNwD7gGHCluydUHhBCCFEVlSkFd397wqHVCedvARR0KYQQNaLcR0IIISaZ1WkuzOwIEJNkIjNLgKMliVMVkrEcJGM5SMZyqFvGYXePjdSZ1UqhXcxsT1L+j25BMpaDZCwHyVgO3SyjzEdCCCEmkVIQQggxyVxXCtvrFiADkrEcJGM5SMZy6FoZ57RPQQghxHTm+kpBCCFEhDmnFOKK/3QbZnammX3VzO43s71mdlXdMjVjZieZ2R1mdk8o48fqlikJM+s3s2+Z2c11y5KEmR0ws2+b2d1mVm85wQTM7GQz+xsz+074f/M1dcsUxczOC3+/Rvuhmb2/brmaMbMPhH8z95nZ58zspLplijLnzEdm9svAM8Bn3P0VdcsTR5gscJm732VmLwHuBN7s7vtqFm0SMzNgkbs/Y2YDwNeBq9z9n1OGdhwz+x1gDHipu7+pbnniMLMDwJi7d218vZntAP63u3/KzOYDg+7+/ZrFiiUs0vUo8PPu3s5eplIxszMI/lZG3f25ML3PLe5+fb2STTHnVgoJxX+6Cnc/7O53he+fBu6ny+pLeMAz4ceBsHXdE4aZLQfeCHyqbllmM2b2UuCXgWsB3P2FblUIIauB73aTQogwD1hoZvOAQRIyQtfFnFMKsw0zGwFeBXyzZlFmEJpl7iZIgX6bu3edjMAngA8BJ2qWIw0Hvmxmd4Y1Q7qNnwCOAJ8OTXGfMrNFdQvVgrXA5+oWohl3fxT4OEFC0MPAD9z9y/VKNR0phS7GzBYDnwfe7+4/rFueZtz9uLufT1D/4kIz6ypznJm9CXjC3e+sW5YMrHL3VwO/ClwZmjm7iXnAq4Ft7v4q4EeENda7jdC0dRnwP+uWpRkzO4Wg/PBZwOnAIjN7R71STUdKoUsJ7fSfBybc/Qt1y9OK0IzwNeAN9Uoyg1XAZaG9/gbgdWa2s16R4nH3x8LXJ4C/pftqlB8CDkVWg39DoCS6kV8F7nL3x+sWJIaLgYfc/Yi7vwh8AfjXNcs0DSmFLiR04l4L3O/uf1q3PHGY2VIzOzl8v5DgP/t3ahWqCXff5O7L3X2EwJzwFXfvqqcyADNbFAYUEJpkXg90VXScu/8/4BEzOy/sWk1Q/6QbeTtdaDoKeRj4BTMbDP/OVxP4DLuGOacUwuI/twPnmdmhsOBPt7EKeCfBk20jvG5N3UI1sQz4qpndC/wLgU+ha0M+u5zTgK+b2T0EFQf/zt1vrVmmOH4LmAj/zc8H/rBecWZiZoPAJQRP4F1HuNL6G+Au4NsEc3BX7W6ecyGpQgghkplzKwUhhBDJSCkIIYSYREpBCCHEJFIKQgghJpFSEEIIMYmUgpgzmNnxpiyahXfkmtk/lSlb07XHzOzPq7q+EK1QSKqYM5jZM+6+uG45hOhmtFIQc56wlsHHzOyusKbBT4X9S83strD/L83soJktCY89E75eZGZfi9QZmAh3qmJmF5jZP4RJ7r4UpkRvvvdbw7z695jZP0aueXP4/pbIyuYHZrYuTET4x2b2L2Z2r5ld0anfSvQ+UgpiLrGwyXz0byPHjoYJ6bYBHwz7PkKQGuPVBPmIViRc91XA+4FRgmyiq8LcVf8N+HV3vwC4DtgSM/b3gEvd/WcJkrhNw93XhEkH3wscBP5X+P4H7v5zwM8Bv2FmZ2X8DYRoyby6BRCigzwXTrBxNNIi3An8Wvj+F4G3ALj7rWb2vYSxd7j7IYAwlfgI8H3gFcBt4cKhnyBVcjPfAK4Pi63EpmYIVyefBd7m7j8ws9cDrzSzXw9PeRlwDvBQgnxCZEZKQYiA58PX40z9XVjOsdHxBux195YlK939fWb28wSFgO42s/Ojx8MKYjcAv+/ujSR5BvyWu38po3xCZEbmIyGS+TrwNoDw6fyUHGMfAJZaWMfYzAbMbGXzSWZ2trt/091/DzgKnNl0ytXAve5+Q6TvS8CG0ESFmZ3b5QVvxCxCKwUxl1gYmnca3OrurcJSPwZ8LvQ9/AOB+efpLDdy9xdC886fm9nLCP7WPgHsbTr1j83sHIKn/93APcCvRI5/ENgbkfv3CEqLjgB3hU7tI8Cbs8glRBoKSRUiATNbABx392PhE/+2Fj4JIXoCrRSESGYFcKOZ9QEvAL9RszxCVI5WCkIIISaRo1kIIcQkUgpCCCEmkVIQQggxiZSCEEKISaQUhBBCTCKlIIQQYpL/D/ZoRF15citXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "\n",
    "Plot **CYLINDER** vs the Emission, to see how linear is their relation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": [
    "# write your code here\n",
    "\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"CYLINDERS\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\n",
    "\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
    "\n",
    "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
    "\n",
    "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using **np.random.rand()** function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Simple Regression Model\n",
    "\n",
    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffHE8kV0z27ePP8+yqlmRK7EtWxZffsYZ7S/HqYykAgoK0sPi2tpfew3OPDPfL9x201zk8a53xZdffnn25TiTqLlHQEFBelgZ+XbKTtewZUu+8rpo4Fq3Ln6f4eG2qibTRKagEI4k+j0z2xJOOLvRzG4su3IiZUq7gCfN6m0sjya0O3IEZs6c+H6RE72KWIxm06YgMNTvDGq14HUrncyQHGSSyqW7Zb1TuA14A3A38M3IJtLVmmUgTcvcGZfuoXH0UWO/RH2YZ72ZplYLJo4ljT5K6r9IKi9qMZpNm4JOZffgsdWAALBy5eTPr9WCcpmCkiYwRDfgwSz7dXrT5DVpJkuStXYXnM+SsK5ZYre8CfS6cYH7tKR+0n1oMnkta1D478DqLPt2clNQqFa7F9SyFXGxSvsb0zKjpn1mK3Vct258ZnOtVm1AcE9fVU26TxFB4QXgNeDl8PkLwE+yHFvmpqBQnW5Jddzsot3uxSrL35h1ucykz+yW77EdSnMx9bQdFLp1U1CoTjc0GaRdUNu9WGX5G7dudZ81Kz0oTKVf/nnNnh3/N8+ePXG/br+z7CXNgkLmIalmdpmZfTbc3ltkv4ZMPWUM52zWKRwnbp5BdKLYCy/EH5dU3ijr3+je/DzNRh+NjgYpraOZWUdGmv/tp5wyniHVLHhdpWPH0svTVpiTLpIULaIbcD2wHfiNcLsLuD7LsWVuulOoTtF3Cq00o6Q1D+XtxG100knxx550Uvr3UKuN/yJety75F3Le73HevPj9583L9jeVIcv33A13ljKOAvoUHgb6Iq9rwMNZji1zU1CoTtFt4Vkuro3nTrvQtBsUshyfFpjSvqe8dWz3b6orsimnry++Pn194/uoM7q7FBUUTo28PlVBQYq8sGQZxRM3nLTIC26jIn4BFx24iggKRQf0LMNkdafQXYoICh8E9gM3AyPA48CaLMeWuSkoTB9ZR/E0XkSaBaZOBIW0C2zRTVxZ92/WeV3GBTqts3w6jLKaTtoOCsE5WAhcBlwO/Kusx5W5KShMH1u3JjdDxF28stydZGmyaBZUVq2KP37Vqsl1b7XPIEvTS1SWPoW0X+5VNeVo9FH3aDkoAD8dPr4tbmt2bCc2BYXpI+lClrY1+7U5d278MXPnBu/HDSedNat5YGgMCGnSfiHPmRNfxzlzks/ZGBgaO5nTAk3a9yLTX7Og0HSRHTPb4u7DZnZP/MAlvzDHQKfCaZGdakUXqFmyJBh22eoKYzNmtJ7Pf2AgyADaKG3xl9NOC3IVNZo/H555prW6wOTvZfVq2LYt/nsqY4GatHP29cWf2yw59bdML80W2Slt5TUzOwn4NjCbYNnPr7r7p8zsVOAvgUGC5Tg/4O7PhcdsAK4ETgC/7e53NvsMBYXqxK041myFsTRZVxRLOjbuYpYUaOorjJVxQc77vVQRFLRSmrS98pqZvd/MXhc+/89m9nUzOzflsGPAhe7+c8A5wKVm9g7gWmC7uy8lmPtwbXjeZcAaYDlwKbApXN9ZulDaxLG82lngJSkFdhFppvMq+ntpxZw5zcuLyrQq01PWGc3/xd1fMLNfAC4hGIH0Z80OCJuujoQvZ4abE3RUj4TlI8AV4fPLgVvc/Zi7Pw7sBc7P+odIZyWt+ZtlLeA4WRZ4mTUr31oFc+c2L58/P/79pPIskmZB79+ffaZ2mvXrg7sgs+Bx/fqJ77/jHfHH1csvuCD+/aRy6S1Zg0L9t9V7gM3ufhswK+0gM6uZ2YPA08Bd7n4fcLq7HwIIH98Y7r4IeDJy+IGwTLpQ0b82V66MX0OgXlarwZVXwk03TVyLeO3a4Fd43AX3xRfjP6te/oUvTA4yM2cG5XlEL9LNml/cg+Dw67/eemBYvx42b56YFmPz5omB4d5744+tl+/dG/9+Urn0mKQe6OgGfAO4AfhnYB5BP8FDWY4Nj58H3AO8BXi+4b3nwsf/BXwoUv4l4N/GnGsY2AHsWLJkSTFd8ZJblvHyeYYgZpmnYDYxZcT8+ZNHD+WdvNbuMMlWR03Vk/JlqWNUfS5A41arZf9vo9nFQgGT1/qBXwWWhq8XAu/OcmzkHJ8CPg7sARZGzrMnfL4B2BDZ/07gnc3OqSGp1UnLQJp3slIrF9akrag0F+7uZ5wx8bgzzpj4ftJFun6RTatD3kyuWf6mtMDRyjDYNJqDMLU0CwpZm48WAt9098fM7ALg/cD9zQ4I13WeFz4/GbgI+AFwO7A23G0twVKfhOVrzGy2mZ0FLE37DKnO8883L6+yw7WdTK3RpiAzOHhw4vsHD8KiSKNms07rLMM7X345X3kWSf0z9fKXXop/P6k8jTKgTjNJ0SK6AQ8SDCt9M0ET0h8D21KOeSvwPYK8SY8AnwzL5xOMOnosfIzmVLouPP8e4FfS6qU7heoU3UTRDXcKeZqC6tJ+laedI28ds+7fLO1EEXdQUcprNPXQ6uS1OjN7wN3fZmafAF5y9z81s++5e9qw1FJpnkJ10sa6Dw7Gj0RqZaJZHtE5AXnH4+eZQFc/vt7x22jdOti0qfg5A0XMMUibv5GXJsNNPW3PUwBeNbMPAh8h6HSGYIipSKyNG4MLdFSz4aPt6OsbH43U6uQ5aG3+wqZNQQCoj7qq1cYDApQz7LVdac1LeSXNE0kql+6WNSj8OvBOYKO7Px62+W8tr1qSdxWyTp8v6RdrvXxoKLhAR4ePtnPBbsY9+EW6b1855290xhkTX69cCYsXB3/n4sXB67qihr3WrVuXr7wu+t9/2zZYtSo5kOXVyR8A0gFJ7UpTYZuufQpFpxkuI21x0e3SzUbxpG2zZsWfM+/InixrOjQmn8vy3RaRiTUq75rOnUhbrdFHUwttZEm9NXz8PkGHcX37PlpkpzRFd9yV0RFYdFBoNSA0+8y8F9wsn9N4MW33u82yQE271BEsjZoFhbQsqQvd/ZCZDSTcZbSY1KAY07WjueiOuzI6AotOqpaUsTSruM/M26GataM52lne7nfbSqfv+vVBU9yJE8F+w8PNm37UESyNWu5o9vF0FPvDAPAc8EJkkxIU3XHXqx2BeRPiZe1ojc6DSPoO+/qy9d/krWOWNBeNevW/v7Qma5bUq8zsKYKmo53hNv1+oneJojvuspyv6I7ovNq5S4DxyWbLl4+X5c3P1DiSKEn0Yhr33UJwsXZPn8iVt45btuQrT6qjOoIlUVK7UnQjmGh2WpZ9O7lN1z4F9+I77pqdr5WOyFb6FJrVIetSnFm2ZcuCc7bSXh+t4/z57jNnpn8v0WOSOsyT2u/z1rHVvhx1BEsUBeQ+ugPoz7JvJ7fpHBQ6qZWOyLwXp7TAU1RAaKxDnpE6cetE14ND1otpK8nm8tQxS0I8kTTNgkLWGc3nAjcB9xEsnlO/y/jt4u9dspuuHc2QvzOxHa10RGbpaI4uS9nXF99OXu+0LWpGc2Md8pg7Nz7d9pw5cOTI5PKyztHM8uWwe/fk8mXLYNeu9s8vvaFZR/OMjOe4AfgWwVBUjVcoWWPqhHpnIpQTGJYsiU9J0U5HZOOylEkdp60uylOGtPUXsmhMAphWnteePfnKRfLKOqP5uLv/rrvf5O4j9a3UmvWwVjoT21FGR2RcltQ4ZSwBuWxZ8efMKukOpdmdS55O/iqWGJXekjUo3GNmw2a20MxOrW+l1qyHdfoffhkpKbLeART9N7XTjBK38luz8iKMjgYrsUXTTjdbmU3rK0vZsv7v/u8JFsH5ezQkdUpo/PW5fn3zX6NDQ0HbflE5hLJepIpKDFfvcm2nXf2qq/KVx5mVsEhtUvk118Crr04se/XVoDxO0cnsRCZJ6oGeCtt0HX3U6rDDuriRPo3bzJnl5j7KOkooy7KUeUYbtStvXqFGZaykVnQdRWgjzcUn3P0Pwufvd/e/irz3++7+e+WHrWTTdfRRuykkktYyaDR/PjzzTOZqTZBWx1otewoF9/ZHH5kFHeMbN3YmU2qSvCO5ik4XIpJFO+sprIk839Dw3qVt1UoStZuDP2t7fruziJvJk1OniDb7LO3xnZA3pUQ3rrcgvS3tn6MlPI97PfFNszPN7B4ze9TMdpnZNWH5p83sh2b2YLitjhyzwcz2mtkeM7sk118yjXzhC5PboGfNaj0Hf7cr8hdxs/b4Tsg7kqvX/lvLFJDUrhQ2Kz0Q9zzudcyxC4G3hc9fB/xfYBnwaeDjMfsvAx4CZgNnEazVXGv2GdO1T8G9vbQEedvzW/m8tLbwLGsTlLlVKe93qRQU0mm00adwAniR4K7gZKA+8tyAk9w985KcZnYb8D+BlcARd/9sw/sbwiD1P8LXdwKfdvd/SDrndO1TaFeW9vlZs+DGG4P298aJZjBxreO8n1FEH0G71B4vkqyd1Nk1d3+9u7/O3WeEz+uv8wSEQeBcgjQZAB81s4fN7EYzOyUsWwQ8GTnsQFjWk8rKWlqfh1APCBA/0ezo0aC8VWoTF5masqa5aJmZzQW+BnzM3X9iZpuB/wZ4+Pg54DeI76OY9HvPzIaBYYAl0zQhfOMv93r6ZWh/ZE1cB3B0fYAs5SIyfZU4VxPMbCZBQBh1968DuPtT7n7C3V8D/hw4P9z9AHBm5PDFwMHGc7r7Fndf4e4rFixYUGb1K9PuL/e5c/OVl7EIy49+1PqxIlKd0oKCmRnwJeBRd/+jSPnCyG7vAx4Jn98OrDGz2WZ2FrAUuL+s+nWzpCGlWYea5k3sVkbuo1OVBEVkSirzTmEl8GHgwobhp39gZt83s4eBXwZ+B8DddwG3ArsJ1m+42t2V5qsFSZ2s9Q7gGTMmLt9YRu6jJH195XdCJ90RiUi60voU3P07xPcTbGtyzEZAiwS2qVZrnmiuiFTcq1bB9u3x5ZDcfOQe9GssWgQHJzUOtm/GDPizPyv+vCK9otQ+BanGBRdk26+eint0FD7ykYmZOj/ykfZGPKX1U/zLv7R+7iizYKRT/Q7n5purTXMhMtUpKJSgrOGkWX33u9n2q99NXHXV5FFJr73WPDto3F1CtHz16vj36+V50mA04w4f+EBx2V1Fep2CQsHqw0mjv7qHhzsbGF5+Od/+Raw41mhbQiNhUnk7ylp8SKQXKSgUrIyJYGUps8O3k3MftOqYSHEUFAo2lSaClZkKooy5D0m06phIcRQUCtbJi2E3S5v7UOSFXKuOiRRHQaFgGzfCzIasUDNntjcRbCpKm/tw9tmtn7seUGo1WLeu9WG1IjJZ6bmPelHcSJ5uljSvod1f80NDyaOB9uxp7ZyzZsGxY63XSUSa051Cwa65ZvIF9sSJahd+SVPFYvCtdg7/4i8WWw8RmUh3CgVLWuKyzKUv21VvftmyJbhY12pBQCizWSZt1nWSe+8tvCoiEqE7hQqsXx+kY4jLQ1SVTZvg+PFgRNLx4+W307d6F6LhpyLlUlAoWNIi9PXy9euDvEP1i1s9D1E3BIY88qbnbrRpU9BJnLffQsNPRcqloFCwpE7lennS7NuqZ+XmvXt5+9vzlceJ3p2ccUa2YzT8VKRc6lMoWNpInqTmjyqaRQYGgsf63Uu0LmlZVJPa9ltt828cxtuoE/0cIgLmU3iF8xUrVviOHTuqrsYEaQvaz5iRHDSOH892jnbqUNfXB3/xF8GQ0Sx1yvMZrfwv1dcXf5xZ9w/pFZlqzGynu6+Ie0/NRwVLavOulyeltc6a7roor70GN90UPG/l7iXt78xLM8FFuoOCQsHSLrB798a/n1Repnqa61Yu8EXPbShjSVARya/MNZrPNLN7zOxRM9tlZteE5aea2V1m9lj4eErkmA1mttfM9pjZJWXVrUqdSJiXN/tpKxf4xtFD7aacGBqCtWsnnm/tWq2PINJpZd4pHAf+o7v/DPAO4GozWwZcC2x396XA9vA14XtrgOXApcAmM+vKAYjtLKLTiWaSn/mZfPu3eoEvcm7D6CiMjEwcqjsy0vkFikR6XWlBwd0PufsD4fMXgEeBRcDlwEi42whwRfj8cuAWdz/m7o8De4Hzy6pfq9pdROfNb85X3ordu/Mf0+nJa42m0joUItNZR/oUzGwQOBe4Dzjd3Q9BEDiAN4a7LQKejBx2ICzrKkkXr7VrkyeuRaUtY9mrptI6FCLTWelBwczmAl8DPubuP2m2a0zZpEGKZjZsZjvMbMfhw4eLqmZm+/fHl584Ue6iNdOdRh+JdIdSg4KZzSQICKPu/vWw+CkzWxi+vxB4Oiw/AJwZOXwxcLDxnO6+xd1XuPuKBQsWlFf5BEqzUA6NPhLpDmWOPjLgS8Cj7v5HkbduB9aGz9cCt0XK15jZbDM7C1gK3F9W/VqlhGzl0Ogjke5Q5p3CSuDDwIVm9mC4rQauBy42s8eAi8PXuPsu4FZgN3AHcLW7d90leCrcKcyfn22/rPmGOmF0dDx1NwSPW7Zo9JFIpynNRU555wBEuWdLD9FuComLLsrWcT0wAPv2pe/XCXPnwosvTi6fMweOHOl8fUSmM6W5KFDWX+FV+ta3su3XTSN74gJCs3IRKYeCQk4vv1x1DdJlvfk79dRy6yEiU4+CQk765VqOtMWJRKQz9E9uGsra7/GjH5VbjzyuuipfuYiUQ4vsTENZm4+6aWJYPa1GfQSSFtURqYbuFArUzsikItVXVEuzenW59RCRqUdBoUDdskJY3OzgOLfeWn5dsqovCRqdp7B5c/pa0SJSLAWFAmXpFC16xbI4Q0NBM8zAQPO7l2efLe4z27VlS75yESmHgkKBsrTlt7L0ZSuGhoKJad1y95KmU9+LiDSnoNBhSZPfouWrVsXvk1SeZu7cfOVV6MQdlIikU1DokHnzgsdjx+Lfj5YXvY7zSy/lK69C0Ws+i0hrNCS1Q97whuAxKY9PtDxpzYak8jRToWlGQ1JFuoMS4uXU6rBTs6B9vxMJ8eI+u8jzicjUpoR4Oa1fDzNmBBfTGTOKGRapPEMiMhUoKDSYjuPli+64FpHpS0GhQdp4+ayzhRtVmWfo7rsnB4BVq4JyEZEoBYUGaZ2yWWcLN6o6z9Dddwf9B/VNAUFE4pS5RvONZva0mT0SKfu0mf2wYXnO+nsbzGyvme0xs0vKqhcESzwODgYzkAcHJy75mDZevnG2cJZx9DNnji9AnyVF9KxZ8fsklYuIFKXMO4WbgUtjyv/Y3c8Jt20AZrYMWAMsD4/ZZGalTFsaHQ2GOu7fH/xi3r8/eF0PDFnGy0dnC4+MpN85REf/JM0wjpbfeOPkEUNmQbmISJlKCwru/m0ga0v65cAt7n7M3R8H9gLnl1Gv666Do0cnlh09GpRDMC5+3brxO4BaLXidNF4+y53DK6+Mnz+pTyJaPjQEX/7y+DkHBoLXQ0PZ/04RkVZU0afwUTN7OGxeOiUsWwQ8GdnnQFhWuKR1iaPlK1fC4sXBBXnx4uB1M9E7h6Q7gfr54/ok+vvHm5fizrlvnwKCiHRGp4PCZuBNwDnAIeBzYXnc9KrYaVVmNmxmO8xsx+HDh3NXIKmpp16e1rwUJ9pHkdRnUO9obryzGBgIXjde9Jv1e4iIlMbdS9uAQeCRtPeADcCGyHt3Au9MO/95553nefX1RcfgjG99fcH7AwPx79dq7mbB+1u3jp9v61b3/v74Y+pbf//EY9LEnbOVcwwMxNdZRHobsMOTrttJbxSxNQYFYGHk+e8Q9CNA0MH8EDAbOAv4f0At7fytBIVmF2/34CLabJ/GC3TeIJJF0jkHBrIdX0RQEZHpq1lQKC33kZl9BbgAOA14CvhU+PocgqahfcBV7n4o3P864DeA48DH3P1v0j6jldxHM2bEz0Wo1eD48aCpJkviuYGBoK2/ry8+f1A911Er2j1n0t9QqwXHL1kS9GGon0KkN1WS+8jdP+juC919prsvdvcvufuH3f1n3f2t7n5ZPSCE+2909ze5+9lZAkKr0oacZl23uN5xnDQprZ3Jakl5krLmT0rqTD9xIns/iYj0pp6b0Zw25HTbtmznqV/0s44m6qQsASk6DFdEpK7nggIEAeD48eBX8/HjE+cgJP3Kjope9LOOJsojKU9S1vxJWVNxZPlbRaS39GRQaCbpV3atlnzRL3pOQbtNUllTcVSdj0lEuo+CQoOk5qCRkeIu+mlzEIpokooGqqR+lKz9JyLSOxQUGpTRHBSVZXJc0XVI6ifJ2n8iIr1DQSFGmSkm0nIvlSFLag8REVBQaEk7KSiS5kBEy1tJtdFMGcNmRWR6UlDIqd0Ldtp6DVD83UQ3DpsVke6koJBTuxfstJXdoPjmnrL7SURk+phRdQWmmnYv2AMD8U1I0fUUliyJ36ed5p6hIQUBEUmnO4Wc2m2fz9KUo+YeEamKgkJO7V6wszTlqLlHRKpSWpbUTmglS2oRRkeDPoQnnlDGURGZeirJkjqdtTuPIcuQVq28JiJVUFAoQJ4LeJYhrUXPUxARyUrNR22qX8Cjw1T7+5P7AJIWwKkv2pN1HxGRVjVrPlJQaFPeC3iWVdXKWM1NRKSukj4FM7vRzJ42s0ciZaea2V1m9lj4eErkvQ1mttfM9pjZJWXVq2h55y1kGdKqtBQiUpUy+xRuBi5tKLsW2O7uS4Ht4WvMbBmwBlgeHrPJzBISQnSXvBdwzVMQkW5W5hrN3wYa1wq7HBgJn48AV0TKb3H3Y+7+OLAXOL+suhUp7wVc8xREpJt1Os3F6e5+CMDdD5nZG8PyRcA/RvY7EJZ1vfqFOs+8hSwpJ5SWQkSq0C25jyymLLYH3MyGgWGAJV3SyK4LuIhMF52ep/CUmS0ECB+fDssPAGdG9lsMHIw7gbtvcfcV7r5iwYIFpVZWRKTXdDoo3A6sDZ+vBW6LlK8xs9lmdhawFLi/w3UTEel5pTUfmdlXgAuA08zsAPAp4HrgVjO7EngCeD+Au+8ys1uB3cBx4Gp3T1h5QEREylJaUHD3Dya8tSph/42ABl2KiFRIuY9ERGTMlE5zYWaHgZgkE5mdBjxTUHXKojoWQ3UshupYjKrrOODusSN1pnRQaJeZ7UjK/9EtVMdiqI7FUB2L0c11VPORiIiMUVAQEZExvR4UtlRdgQxUx2KojsVQHYvRtXXs6T4FERGZqNfvFEREJKLngkLc4j/dxszONLN7zOxRM9tlZtdUXadGZnaSmd1vZg+FdfxM1XVKYmY1M/uemX2j6rokMbN9ZvZ9M3vQzKpdTjCBmc0zs6+a2Q/C/zffWXWdoszs7PD7q28/MbOPVV2vRmb2O+G/mUfM7CtmdlLVdYrqueYjM/sl4AjwF+7+lqrrEydMFrjQ3R8ws9cBO4Er3H13xVUbY2YGzHH3I2Y2E/gOcI27/2PKoR1nZr8LrABe7+7vrbo+ccxsH7DC3bt2fL2ZjQD/x92/aGazgH53f77iasUKF+n6IfB2d29nLlOhzGwRwb+VZe7+UpjeZ5u731xtzcb13J1CwuI/XcXdD7n7A+HzF4BH6bL1JTxwJHw5M9y67heGmS0G3gN8seq6TGVm9nrgl4AvAbj7K90aEEKrgH/upoAQMQM42cxmAP0kZISuSs8FhanGzAaBc4H7Kq7KJGGzzIMEKdDvcveuqyPweeATwGsV1yONA39rZjvDNUO6zb8GDgM3hU1xXzSzOVVXqok1wFeqrkQjd/8h8FmChKCHgB+7+99WW6uJFBS6mJnNBb4GfMzdf1J1fRq5+wl3P4dg/YvzzayrmuPM7L3A0+6+s+q6ZLDS3d8G/ApwddjM2U1mAG8DNrv7ucCLhGusd5uwaesy4K+qrksjMzuFYPnhs4AzgDlm9qFqazWRgkKXCtvpvwaMuvvXq65PM2Ezwr3ApdXWZJKVwGVhe/0twIVmtrXaKsVz94Ph49PAX9N9a5QfAA5E7ga/ShAkutGvAA+4+1NVVyTGRcDj7n7Y3V8Fvg78m4rrNIGCQhcKO3G/BDzq7n9UdX3imNkCM5sXPj+Z4H/2H1RaqQbuvsHdF7v7IEFzwrfcvat+lQGY2ZxwQAFhk8y7ga4aHefu/wI8aWZnh0WrCNY/6UYfpAubjkJPAO8ws/7w3/kqgj7DrtFzQSFc/OcfgLPN7EC44E+3WQl8mOCXbX143eqqK9VgIXCPmT0M/BNBn0LXDvnscqcD3zGzhwhWHPymu99RcZ3i/BYwGv43Pwf4/WqrM5mZ9QMXE/wC7zrhndZXgQeA7xNcg7tqdnPPDUkVEZFkPXenICIiyRQURERkjIKCiIiMUVAQEZExCgoiIjJGQUF6hpmdaMii2fKMXDP7+yLr1nDuFWb2J2WdX6QZDUmVnmFmR9x9btX1EOlmulOQnheuZfAZM3sgXNPgp8PyBWZ2V1h+g5ntN7PTwveOhI8XmNm9kXUGRsOZqpjZeWb2d2GSuzvDlOiNn/3+MK/+Q2b27cg5vxE+3xa5s/mxma0NExH+oZn9k5k9bGZXdeq7kulPQUF6yckNzUf/LvLeM2FCus3Ax8OyTxGkxngbQT6iJQnnPRf4GLCMIJvoyjB31Z8Cv+bu5wE3Ahtjjv0kcIm7/xxBErcJ3H11mHTwSmA/8L/D5z92958Hfh74TTM7K+N3INLUjKorINJBL4UX2Dj1tAg7gV8Nn/8C8D4Ad7/DzJ5LOPZ+dz8AEKYSHwSeB94C3BXeONQIUiU3+i5wc7jYSmxqhvDu5MvAB9z9x2b2buCtZvZr4S5vAJYCjyfUTyQzBQWRwLHw8QTj/y4s57HR4w3Y5e5Nl6x09/9gZm8nWAjoQTM7J/p+uILYLcB/dfd6kjwDfsvd78xYP5HM1Hwkkuw7wAcAwl/np+Q4dg+wwMJ1jM1sppktb9zJzN7k7ve5+yeBZ4AzG3a5HnjY3W+JlN0JrAubqDCzn+ryBW9kCtGdgvSSk8Pmnbo73L3ZsNTPAF8J+x7+jqD554UsH+Tur4TNO39iZm8g+Lf2eWBXw65/aGZLCX79bwceAt4Vef/jwK5IvT9JsLToIPBA2Kl9GLgiS71E0mhIqkgCM5sNnHD34+Ev/s1N+iREpgXdKYgkWwLcamZ9wCvAb1ZcH5HS6U5BRETGqKNZRETGKCiIiMgYBQURERmjoCAiImMUFEREZIyCgoiIjPn/SBcMzOdwfeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "\n",
    "Using sklearn package to model data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[40.2398282]]\n",
      "Intercept:  [121.713134]\n"
     ]
    }
   ],
   "source": [
    "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",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "print ('Intercept: ',regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, **Coefficient** and **Intercept** in the simple linear regression, are the parameters of the fit line. \n",
    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot outputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the fit line over the data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PFPopJbDWUqgtJgqGUSGl3qirTSFRiqizkattOfjJF4QdegVfzy989NGuL8EjSoVeqkVVKELZrb29st/NKI6JgmEkSLXDPYtRTcio1BbmU1YM/h/n5hX4I5/UobzbSxyLPSNLqZZCGkN7BzLFRMEmrxlGlfhXHQtKEwHFs6YWy0Aa15KZQaxdG/zMC7gYpYHz+BEASziQ4WzmMB7gPYb2mqAXJXNnqQlwaWSbNUIIU4tqN2Aobo3lJ4DlwMWe/SLgZWCZtx3nKzMbWAWsBI4p9QxrKRj1RnNz8Btvc3Pw9aX6LcoZbVRtOOnxaT/KMz7NZN2Jt0q2NKKGfqJOgLOWQvKQRvgIEKDZ2x8EPAoc4onCOQHXT/YEZAgwEXgByBR7homCUQ9EHR0URKVhFf82eXJ1ndFf54o8wytDJ+oIXi9ZLtsfEMew3Fpnmx3oFBOFxMJH3rM3e4eDvE2LFDkRuEFVt6nqalyL4aCk/DOMOCjMfVQupcImYedFctXn8uUu11E291GxcI4/ed3XmI8i/IyzAFjPh2DjRsa++1cObB9Z0vdsaGvTpuDzYfYgOjryv0Nh/iajdiTapyAiGRFZBrwK3KWqj3qnzhSRJ0VkgYhk//eNA17yFV/n2QyjZpS7rGTQcpzlUCoDabmZS8HloBs8ON/W1AQLF7o+j2+Ovg5FmM/pALzJznyI9Rzast5lMwXuvjsnOosWFe8PiAt/38yaNSYIaZGoKKhqt6ruB4wHDhKRjwLzgD2A/YD1wGXe5UEJeHu9e4nIDBFZIiJLNm7cmIjfxsCkkmUl166t7pn+VSiD7GEZTrOrWIrAuHH5rZVNm9zn6NG5t+7p0+F/vrkYRPjJpq8AsJ1BjOclRvIm7zR9KLSSL/UW39wcXC7MbtQ5YXGluDfgQgr6EoBW4GlvfzYw23fuDuDQYve0PgUjTiqZQBVWJmp8Pcozowx5LdYvcf+3ftfr5J6Nq3X06Hgm2KWR/8moDtLoUxCRMSIywtsfBhwJPCsiY32XfQ542tu/BZgmIkNEZCIwCTd6yTBqQraFENVe6lyWwYPhiisqf6Y/rBLleVn2Xns7iHDYT07qsU3iOQRlVVdrT4ti3Tp46KHo9y3k9dfLsxv1TWOC9x4LLBSRDC5MtVhVbxWR60RkP1xoaA24wKaqLheRxcAKoAs4Q1XL+BMwjOrIZMIr3WwHbXu7i7dnaWkpHkLKZOCrX61tfPxw7uM+jsizTWY5zzA58PpsmAxg7tzyn5fJQFdXsN3oe9gazcaAxr++clMTbNlSuoxfGLKjj4p1Njc1hY+kibS+ccTr/xcP8RCfyDcuW0briftG6vsIq9xLUe53MNLH1mg2jAAKh5Nu2eJm+DaU+Ku4557cfmEnbNDb8datTnjiYHLAy34bf0aRPEH4w8WPuS+1776Rl+MsJzRl9F9MFIwBS9Bw0h07YPfdy3vDrTbNRTkcdlhufx+eQBH+7J/O8+CDoMqxF3w8zz+/cIVh4R4DTBSMAUwS+XYqmVdQDvPnw96sQBGeYL8e+5EN9zolmzo1sJxfuGbODL73jBnx+Gj0bSKJgjeS6P+IyHxvwtkCEVmQtHOGkSSlKvD29uDzhXZ/QrvNm2HQoPzzsU30ev55urqFFUzpMX2GPyAo9+z4dOTbzJ3rhCHbMshk3HElncwQLjJhdqO+idpSuBnYGbgb+L1vM4y6plgG0lKZO+++u7cAFI4+KuyXyA7zzIZpMhk3cSxs9FFY/0WeffVqd8OPfKTHdAI3Iyh38Jme55TD3LmuU1nVfVYqCOAaJ4XPz2RCGy1GvRM2gcG/AcuiXFfrzSavGcWIkmSt4mUpPaIkrCuW2K1oAr2XXlJtbMwz/uKo3wRem+YC95bhtO9BtVlSgR/gS3FdL5uJQrpUW6EmTRyVVanvGHURnLBnBvm4K+v1bdkp3/irX/WUmTlTi66HXGtqsU61ES9xiMI7wA7gPW//HeDtKGWT3EwU0qNeUh0Xq7SrrayqWaM56jP9zxjNRv0bH8wvOH9+hb9M7bA0F32PqkWhXjcThfSoh5BBqUq72soqyndctEh18ODSolDsdzn7tNd1NQUPu+KKyn6UFBgyJPg7DxmSf129tywHEsVEIfKQVBE5QUT+zduOj7Nfw+h7JDGcs1incBBB8wz8E8XeeSe4XJi9kKjfUbX4fUJHH739Nm+N25vLFoyiFTfl+Dv8iOFNSufob4Teb+TIXIZUEXecJtu2lbYXdsgXLulp1BFhauHfgEuAe4DTvO0u4JIoZZPcrKWQHnG3FCoJR5UKDxV7c4/C0KHBZYcOLf07ZDK5N+KZM/PfkK//5WbVAw7IK3ABF0X6HUeMCH7eiBHRvlMSRPmd66FlaeQghj6FJ4EG33EGeDJK2SQ3E4X0iLtPIUrlWnjvUhVNtaIQpXwpYfL/TkPZqn9iat6FP+Q8hR2Rfaz2O2WJM5TT0BDsT0ND7hrrjK4v4hKFUb7jUSYKRpwVS5RRPEHDSYsJUy1EIcoay4PYpndyZN4FV+90luqOYDFIWhTiFvSZM4P98Y+KspZCfRGHKJwCrAWuBRYCq4FpUcomuZko9B+ijuIprESKCVMtRKFoBbt9u97M3+ed/DkzVOiuOMQV9fpiw1aTqKBLDZOtl9FqhqNqUXD3YCxwAnAi8KGo5ZLcTBT6D4sWhYchgiqvKK2TKCGLYqLS3h5cvr29t+/+e3T+qkv15JPzCv2KL2kDXb0q4CihFz9R+hRKvbmnFcqx0Uf1Q8WiAPyd93lA0FasbC02E4X+Q1hFVmor9rbZ3BxcprnZnQ8aTjp4cHFhKBSEPLq7VadPzyuw9uOf152GvR/q8/DhwT4OHx7+mEJhKOxkLiU0pX4Xo/9TjSjM9z7vC9juLVa2FpuJQrrE+eZXztrDUcMepUItsU262rGjl6rdM+w4HcT2wNFHcYa4Kvne1ulrxBI+KncDhuLWWH4CWA5c7NlHeUNan/c+R/rKzAZWASuBY0o9w0QhPeKOEVcqCMUqszChyWRKPzMSO3aonn12XsH1kz+tI4a9F/l3SUMUknim0bcoJgpRU2d/UUR28va/KyK/FZH9SxTbBhyhqvsC+wGfEZFDgPOAe1R1Em7uw3nefScD04ApwGeAud76zkYdUmriWLlUs8BLWArssJXEql5hTBW++103y+7yy53tkENgyxYO2XIvb747JO/yOFdei8Lw4cXtYb+1LbJjQPTU2d9T1XdE5BPAMbgRSD8vVsATpM3e4SBvU1xH9ULPvhA4yds/EbhBVbep6mpci8G3pJRRT4St+RtlLeAgoizwMnhweWsVNDcXt48eHXw+zA64hzU05B66775uivTDD0NTU+gs6LVro8/ULsWsWdDY6GYzNza6Yz+HHBJcLms//PDg82F2Y2ARVRSy71afBeap6s3A4FKFRCQjIsuAV4G7VPVRYFdVXQ/gfX7Qu3wc8JKv+DrPZtQhcb9tTp0avLZA1pbJwFe/Ctdck1tWsqXFrVVw/vnBFe6WLcHPytqvuKK3yAwa5Oy9uPxy99DvftcdT5oEb74Jy5Yx6zvNPZW0i4IGo+rE4dRTKxeGWbNg3rxca6e72x37heH++4PLZu2rVgWfD7MbA4ywuJJ/A24FrgJeAEYAQ4AnopT1yo/AdU5/FHiz4Nwb3ueVwJd89quBzwfcawawBFgyYcKEuENtRkSixKXL6YiOMk9BJL/TdvTo3qOHyp28VtLHK6/MLzh+vOqmTT2nKx01le3MLje+X6qfJMo9raPZIIbJa03APwCTvOOxwNFRyvrucSFwDq4TeazvPiu9/dnAbN/1dwCHFrundTSnR6mRO+V2RFdSsYZtsaS5WLAgr8BGRusYNuhuu+VfVmzUVKlZ2lF+x3J+p1I+ZYWjkmGwpbA5CH2LOERhD2CIt3848A1gRIkyY7LXAMOAPwHHAz8GzvPs5wGXevtTcCOVhgATgb8CmWLPMFFIj1IVT7mzZuMUhWoS4l3d3pl34WaadCwv55X1C0OpZ5Q6X24FHeU7lZq8Vu6EuVLYbOW+RxyisAxoBPb0Qkg/AW4rUWYf4C+4vElPAxd49tG4UUfPe5/+nErne/dfCRxbyi8ThfQoVTmVG6KIUxQqaincdFPeBd2I7s7akuWrHfZarnBFvb5Y2olKxLIYlteo7xGHKDzufX4H+Lq3/5coZZPcTBTSo1TFklZLoeyEeLfe2uvkh1lV8jlZSr2VpyUKxYjSL1EO1kfR9ygmClFHH70vIqcAX8F1OoMbYmoYgcyZ44aL+ik2fLQaGhpyo5Hmz4eOjgiF7rrLFTo+t17UXjyLoPyVPSI/e+5cmDkzN+oqk3HHc+e644qGvSZM2PDfKMOCgwibJxJmN+qbqKJwKnAoMEdVV4vIRGBRcm4Z5a5CVuv7iRS3d3S4Cto/fDRyhV0mqrBjB6xZU/r+n+QBFIGjj84Zn3wSVHmOvSI9b7fd8o+nToXx4933HD/eHWcpa9hrBGbOLM+exf/vf9tt0N4eLmTlUssXAKMGhDUh+sLWX8NHcXfcJdERGHdcuprcR4MHB9/TP7LnYB7uXXDp0rzro6zpUJh8LspvG0cmVj+l0lQXUouOYBt91LegioR4i73Pp3AdxtntKWyRncSIu+MuiY7AuEWhUkEo9sz2dtX9Wdrr4jPbHq7Yh8LKtNrfNsoCNdViHcFGIcVEQdz5YERkrKquF5GWkFZGhUkN4qGtrU2XLFmSpguJ0NDg/mwLEXFhkrTvly0bRpH/UqHssgts2lSZL4HPfOop2GefPNOn+CN/4lNkMtDV1fsejY3R8iK1tLhQFVT/24Y9M8xHcLOX58935TIZ1xdQLPSTxL+/0bcRkaWq2hZ0rmifgubSUaz1BOAN4B3fZiRA3B13A6ojcOVKV9v5BOEo7kRQ/sSngPCKP2pHqz+/Udhv2NAQrf+m3KR9UdJcFDKg/v2N6glrQvg34HRgA7AGtxTnauCvUcomufXX8FEafQrlxoTrLXz0YVb1Mv59w62B1xYbeumP14dt/rBL0G9bKuTkp9zhoZUMJ7XJZUYhxDBP4XlglyjX1nLrr6KgGn/HXbH7VVJpVCIKxXyIuhRn4bY7a3sZvzH+JlWtLF7v93H0aNVBg0r/Lv4yYZV2WPy+XB8rFWPrCDb8xCEKtwNNUa6t5dafRaGWVNIRWW7lVEp4yhWDsbysWxiWZzyFzl4+lDNSJ2id6Kw4RK1MK5nIVY6PcU88MwYmxUShaEdzFm9BnWuAR3GL52RDT9+ILY5VAf21oxnK70yshko6IqN0NHd2urTWL77onhEUJ8922ha7n58xvMoz7M1oXu+xncoCruXUQB/Kobk5ON328OGweXNve1L3KMaUKbBiRW/75MmwfHn19zcGBsU6mhsj3uMq4F7cUFQbr5Aw2c7ELNnOREhGGCZMCF4cp5qOyM5OJ2TZ1dnCOk6jLsozktd5kn0Yz8s9tllcyTyK9LCWSan1F6JQuBpdKXu5rFxZnt0wyiXqjOYuVT1bVa9R1YXZLVHPBjDz55dnr5YkZqQGLdcZRKlFeT7AWzzHJF5ndI8gnM1lCBoqCJMnl+ttfIS1UIq1XMqZbZ7YEqOG4RFVFO4TkRkiMlZERmW3RD0bwNT6Dz+JlBRRWwBh32k4m1nGvrzFCCbhlgQ7nx8gKD/h7ND7VRNGCVr5rZg9Djo73Upsa9c64Si1Mputr2wkTlhng38jNwzVv9mQ1ISodISJn8LRJv4Vy+IYfVLKx6hpKwpXIBvGFn2Yg/Mu+j7fVdgRy+9SjDhmFxeuBJfdoqTiCPpdkvDRMKh29FG9biYKwUQZOz9oULK5j6KOIspWfoN5T+/l8LyTP+bbJcUgTlFQLT+vUCFJrKQWt4+GUUwUSqW5+I6qXurtf1FV/9N37oeq+n+Sa8OUpr+OPqo2hURra7TwzejR8Nprkd3Ko5SPmUzENA+8z/vHngh/+EOP7UpmcSb/AUQckuT5M2GC6wdJIhNrVModyRV3uhDDiELFaS6Aab792QXnPlOVV0Yo1ebgjxrPrybXUClKCUKGLm7k87zP4B5BuIZ/ooFuzuRKyhEEiBaPrwXlppSox/UWjIFNKVGQkP2g4/yTIruLyH0i8oyILBeRszz7RSLysogs87bjfGVmi8gqEVkpIseU9U36EVdcAYMH59sGD648B3890UA31/EluhjE5/ktADfwj2To4jSuIfq6T8G8/z6cdVYcnlZGuSO5+vO/tdFHCYsreWGlx4P2g44Dyo4FDvD2dwKeAyYDFwHnBFw/GXgCGAJMxK3VnCn2jP7ap6BaXVqCcuP5lTyvVCy8cGav0K1X8bU84+84QRvZHtnfcrY0Kfe3tBQURq2hij6FbmALrlUwDMiOPBdgqKpGXpJTRG4G/gOYCmxW1X8rOD/bE6n/5x3fAVykqg+H3bO/9ilUS5TZwYMHw4IFLv5eONEM3NttsWGppWLhufPKAk7jVK7tOX8nR3E8t7rQUUJYPN4wwqkmdXZGVT+gqjupaqO3nz0uRxBagf1xaTIAzhSRJ0VkgYiM9GzjgJd8xdZ5tgFJ3MtnZsnOQ8gKAgRPNNu61dkrZfQoZQkHojT0CMKf+ATD2Mox3JmoIBiGUTlR01xUjIg0AzcB31TVt0VkHvB/AfU+LwNOI7iPotf7nojMAGYATOinCeEL39zXrs3l+q92ZE1QB7B/fYAo9pIccQSvvX5fnqmZd9hCc4U3NAyjViQ4VxNEZBBOEDpV9bcAqrpBVbtVdQfwC+Ag7/J1wO6+4uOBVwrvqarzVbVNVdvGjBmTpPupUe2be3NI3Rtmj2sRlps5AUXgvpwgjOFVBDVBMIw+QmKiICICXA08o6qX++xjfZd9Dnja278FmCYiQ0RkIjAJeCwp/+qZsCGlUYealpvYrdrcR4voQBFO4L97bFNGvoKgvEb/FG7D6K8k2VKYCnwZOKJg+OmlIvKUiDwJfBr4FoCqLgcWAytw6zecoaqW5qsCwjpZsx3AjY35yzdWmvvo55yOInTw6x7bBFwSnw0NYwPLNDRET5NdKWEtIsMwSpNYn4KqPkhwP8FtRcrMAarIzWmAm01cLHle1am4zzkH5bI80548zwvsSXu7O3799YByOGHasQPGjYNXegUHq6exEX7+8/jvaxgDhUT7FIx0OPzwaNdlU3F3dsJXvpKfqfMrXwkY8XThhe41/7KcIExmOYLyAnvmXVqqn+Jvf4vmYylE3OzfbAvn2mvTTXNhGH0dE4UESGo4aVQeeijaddnWxOmn9x6VtGOHswNw6aWu1v3+93vO78/jCMoz5C9ecM897vO44wgka4+SFykKqnDyye5+a9aYIBhGtZgoxEx2OKn/rXvGjNoKw3vvlXd9WAf0P235DycG556bMz78MKiyjP2L3vO2kCBhmL0aklp8yDAGIiYKMZPERLCkCOvwPZUFKMJ/8PWc8f77ncodckike8c+96EItuqYYcSHiULM1LIyrJbCUUr/yA0owgK+mjPefru78LDDyrp3XHMfomCrjhlGfJgoxEwtK8O4OIGbUYQbOKXHdhL/haBwTGXJakvNfYizIs/O9jYMo3pMFGJmzhwYVJAVatCg6BPBaslR3Aki3MxJPbZT+DWC5tkqodTch732qvzeWUHJZGDmzAqH1RqGEUjiuY8GIkEjeeqJT/FH/sjhebbTuJprOC3PVu3bfEdH+GiglSsru+fgwbBtW+U+GYZRHGspxMxZZ/Xu+OzuTnfhlywH8SiK5AvCv/87s2ZqL0GAZMMylXYOf/KT8fphGEY+JgoxE7bEZZJLX5ZiX5ahCI+SGzl0Lpe4PoMzz2TuXBeGqWVYptJWyP33x+qGYRgFmCikwKxZLh1DUB6iONmbFSiSN6fg+3wPQbmUc/OunTsXurrcQKOuruTj9JW2Qmz4qWEki/UpxExDQ3AfQoMnv7Nm5fIOQQx5iALYlb+xkr3Ymbd7bJdxNucU5CuqhuZm2Lw52B6F7HedP7+8it6GnxpGslhLIWbCOpWz9rDZt7HMyn3tNfjQh/gbY3sE4SpmIGhJQSi39XLwweXZg/C3TnbbLVoZG35qGMliohAzYW+yWXvYW3FVYZE33oCJE2HMGNiwAYBvcAWC8i9cFVqspcV9ZlsvWR+yrZdiwhAW26805l84jLcQG35qGLVBtA+vcN7W1qZLlixJ2408Si1o39gYLACZjHtrjnKPHt5+26WdeOaZnO2SS5Dzzu1VrpCGBvjVr9yQ0Sg+FRLZx4g0NASXE6m/Ib2G0dcRkaWq2hZ0zloKMVOqpRCW1jpqumvAZbBra4Odd84JwoUXulr13NKCAK6iveYat19J66XU9yyXvjgT3DD6IyYKMVOqgl21Kvh8mN3PUN6FT3zC9eYuXeqM557raviLLirb12ya60oq+LDYfqUx/2qXBDUMIx6SXKN5dxG5T0SeEZHlInKWZx8lIneJyPPe50hfmdkiskpEVopIZUl36pxKEuYNYjt3cDTv0pRbLOEb33BicMklvWI55S53WUkFH/fcho4OmD49/37Tp9v6CIZRc1Q1kQ0YCxzg7e8EPAdMBi4FzvPs5wE/8vYnA08AQ4CJwAtAptgzDjzwQE2DRYtUW1pURdznokW5cy6GE7ypuuuDzrW09L5HI9v1d5yQf+E//7Nqd3dR/yZPLu5HoU+qqjNnqmYyzpbJuONasmiRalNTvm9NTfm/rWEY8QAs0bC6O+xE3BtwM3AUsBIYqznhWOntzwZm+66/Azi02D3TEIVSlVepCri9Pfhce3vuGQ106fX8Y94F19GhDXRF8jGqIPhFIW2iiKVhGPFQTBRq0qcgIq3A/sCjwK6quh7A+/ygd9k44CVfsXWera4IW0Rn+vTcBLViZOP4gfYdO+DUU+mmkWn8BoAb+TyNvM+XWcQO+u/Mrb60DoVh9GcSn9EsIs3ATcA3VfVtCQ94B53oNUhRRGYAMwAmpDA0Ze3aYHt16ReUKzkDMrmpzr/nOE7id3RRYgB/P2HChODf1kYfGUZtSbSlICKDcILQqaq/9cwbRGSsd34s8KpnXwfs7is+Hnil8J6qOl9V21S1bcyYMck5H0K8aRaUH3MOSgOz8ATh059mKO9yPL8fMIIANvrIMOqFJEcfCXA18IyqXu47dQsw3dufjutryNqnicgQEZkITAIeS8q/SokrIdv3+R5KQ0/6iUc42M0/uPdetjE0nof0IWz0kWHUB0m2FKYCXwaOEJFl3nYccAlwlIg8j+t4vgRAVZcDi4EVwO3AGapadzkxq20pzOaHKML3+AEAy9iXZt7hUB7p/apcIaNHR7suar6hWtDZmZ8cr7vbHXd2puuXYQw0LM1FmZQ7ByDLt7icy/l2z/FzTOLj/Jm32bnHlv2nqDaFxJFHhndo+2lpgTVrSl9XC5qbXUOpkOHDg7OxGoZROZbmIkaivoVn+RfmoUiPILzEeEaxib14Lk8Q4uTee6NdV08je4IEoZjdMIxkMFEok/fei3bdP3ENijAPl2r0NUbDhg1M4CXeYFSCHkZPSDcqWTcMw+iD2CI7ZVLqzfUUfs2vyfWObmUYe7KK9eyGfrBIwQFOqcWJDMOoDfYnFxOf47co0iMIOxAmsJbhbGU9te3Rjdrv8frryfpRDqefXp7dMIxksJZClRzH7/k9x+fZ9mAVf2WPlDyKHj6qp4lhhctzZjIuIZ8tqmMYtcVaChXSzt0okicIe/MMgqYqCJBbUa0Uxx2XrB+GYfQ9TBTK5JM8gCLczVE9to/xJILyjP5dip7lCJodHMTixcn7EpVKlgQ1DCN+TBSi8sgjIMIDHNZjOoClCMrTfAyI1ika94plQXR0uDBMS0vx/oVNm+J7ZrXMn1+e3TCMZDBRKMXjj7ua9dBDe0yH8DCC8hcOyLs0Siy/kqUvK6Gjw01M6yvrG9fqdzEMozgmCmE8/bQTgwMPzNnuvx9BeZRDKr5t2OQ3v729PfiaMHspmpvLs6dBLVpQhmGUxkShkJUrnRh87GM52513umbAYYeFlyvBiBHuc9u24PN+ezXrOAfx7rvl2dMg7jWfDcOoDBuSmuWFF2DPPfNt//3fcPzxwdeXyc5eRouwPD5+e9iaDWH2UvSF0IwNSTWM+sAS4r34IrS25ncI3HgjfP7zgZdXmhBPxMX3oyS7qzYhXtCz47yfYRh9G0uIF8Qrr7hxmy0tuZqxsxNUmXXP52lsdJVpY2M8wyItz5BhGH2BgSkKb7wB48blgupXX+2E4X//7345Xj7ujmvDMPovAzN81NUFM2fC/vv3qu0bG4Nj7ZmMK9baWllsP83wEfReY6G9He6+u7J7GYbRtykWPhqYHc2NjfCLXwSeKtUpO2eO6wDdurW8R6adZ8gEwDCMKCS5RvMCEXlVRJ722S4SkZcLlufMnpstIqtEZKWIHJOUX+C6Dlpb3Qzk1tb8JR9LjZcvnC0cZRz9oEG5BejDZj377YMHB18TZjcMw4iLJPsUrgU+E2D/iaru5223AYjIZGAaMMUrM1dEEpm21Nnp3vTXrnWhmLVr3XFWGKKMl/fPFl64sHSeIX84KGyGsd++YEHvEJKIsxuGYSRJYqKgqg8AUTP2nwjcoKrbVHU1sAo4KAm/zj+/d+hn61ZnBzcufubMXAsgk3HHYePlo7Qctm/P3T8sg6nf3tEB112Xu2dLizvu6AguaxiGERdpjD46U0Se9MJLIz3bOOAl3zXrPFvshK1L7LdPnQrjx7sKefx4d1wMf8shrCWQvX9QBtOmplx4Keiea9aYIBiGURtqLQrzgD2A/YD1wGWePWi8TeA4GxGZISJLRGTJxo0by3YgLNSTtZcKLwXh76MI6zPIdjQXtixaWtxxYaVfrN/DMAwjMVQ1sQ1oBZ4udQ6YDcz2nbsDOLTU/Q888EAtl4YGVVfd528NDe58S0vw+UxGVcSdX7Qod79Fi1SbmoLLZLempvwypQi6ZyX3aGkJ9tkwjIENsETD6u2wE3FshaIAjPXtfwvXjwCug/kJYAgwEfgrkCl1/0pEoVjlreoq0WLXFFbQ5YpIFMLu2dISrXwcomIYRv+lmCgkNnlNRK4HDgd2ATYAF3rH++FCQ2uA01V1vXf9+cBpQBfwTVX9Q6lnVDJ5La7JaS0tLtbf0BA8oSw7Wa0Sqr1n2HfIZFz5CRNcH4b1UxjGwCSV3EeqeoqqjlXVQao6XlWvVtUvq+rHVHUfVT0hKwje9XNUdQ9V3SuKIFRKqSGnUdctznYch01Kq2ayWliepKj5k8I607u7o/eTGIYxMBlwuY9KDTm97bZo98lW+lFHE9WSKILkH4ZrGIaRZcCJAjgB6Opyb81dXflzEMLesv34K/2oo4nK4fWQ2R1h9kKChCqIKN/VMIyBxYAUhWKEvWVnMuGVftxzCqoNSUVNxZF2PibDMOoPE4UCwsJBCxfGV+mXmoMQR0jKL1Rh/ShR+08Mwxg4mCgUkEQ4yE+UyXFx+xDWTxK1/8QwjIGDiUIASaaYKJV7KQmipPYwDMMAE4WKqCYFRdgcCL+9klQbxUhi2KxhGP0TE4UyqbbCLrVeA8TfmqjHYbOGYdQnJgplUm2FXWplN4g/3JN0P4lhGP2HgbkcZxVUW2G3tASHkPzrKUyYEHxNNeGejg4TAcMwSmMthTKpNj4fJZRj4R7DMNLCRKFMqq2wo4RyLNxjGEZaJJYltRZUkiU1Djo7XR/Ciy9axlHDMPoeqWRJ7c9UO48hypBWW3nNMIw0MFGIgXIq8ChDWuOep2AYhhEVCx9VSbYC9w9TbWoK7wMIWwAnu2hP1GsMwzAqpVj4yEShSsqtwKOsqpbEam6GYRhZUulTEJEFIvKqiDzts40SkbtE5Hnvc6Tv3GwRWSUiK0XkmKT8ipty5y1EGdJqaSkMw0iLJPsUrgU+U2A7D7hHVScB93jHiMhkYBowxSszV0RCEkLUF+VW4DZPwTCMeibJNZofAArXCjsRWOjtLwRO8tlvUNVtqroaWAUclJRvcVJuBW7zFAzDqGdqneZiV1VdD6Cq60Xkg559HPCI77p1nq3uyVbU5cxbiJJywtJSGIaRBvWS+0gCbIE94CIyA5gBMKFOguxWgRuG0V+o9TyFDSIyFsD7fNWzrwN29103Hngl6AaqOl9V21S1bcyYMYk6axiGMdCotSjcAkz39qcDN/vs00RkiIhMBCYBj9XYN8MwjAFPYuEjEbkeOBzYRUTWARcClwCLReSrwIvAFwFUdbmILAZWAF3AGaoasvKAYRiGkRSJiYKqnhJyqj3k+jmADbo0DMNIEct9ZBiGYfTQp9NciMhGICDJRGR2AV6LyZ2kMB/jwXyMB/MxHtL2sUVVA0fq9GlRqBYRWRKW/6NeMB/jwXyMB/MxHurZRwsfGYZhGD2YKBiGYRg9DHRRmJ+2AxEwH+PBfIwH8zEe6tbHAd2nYBiGYeQz0FsKhmEYho8BJwpBi//UGyKyu4jcJyLPiMhyETkrbZ8KEZGhIvKYiDzh+Xhx2j6FISIZEfmLiNyati9hiMgaEXlKRJaJSLrLCYYgIiNE5EYRedb7v3lo2j75EZG9vN8vu70tIt9M269CRORb3t/M0yJyvYgMTdsnPwMufCQinwI2A79S1Y+m7U8QXrLAsar6uIjsBCwFTlLVFSm71oOICDBcVTeLyCDgQeAsVX2kRNGaIyJnA23AB1T1+LT9CUJE1gBtqlq34+tFZCHwJ1X9pYgMBppU9c2U3QrEW6TrZeBgVa1mLlOsiMg43N/KZFV910vvc5uqXpuuZzkGXEshZPGfukJV16vq497+O8Az1Nn6EurY7B0O8ra6e8MQkfHAZ4Ffpu1LX0ZEPgB8CrgaQFW316sgeLQDL9STIPhoBIaJSCPQREhG6LQYcKLQ1xCRVmB/4NGUXemFF5ZZhkuBfpeq1p2PwE+B7wA7UvajFArcKSJLvTVD6o0PAxuBa7xQ3C9FZHjaThVhGnB92k4UoqovA/+GSwi6HnhLVe9M16t8TBTqGBFpBm4Cvqmqb6ftTyGq2q2q++HWvzhIROoqHCcixwOvqurStH2JwFRVPQA4FjjDC3PWE43AAcA8Vd0f2IK3xnq94YW2TgD+M21fChGRkbjlhycCuwHDReRL6XqVj4lCneLF6W8COlX1t2n7UwwvjHA/8Jl0PenFVOAEL15/A3CEiCxK16VgVPUV7/NV4L+ovzXK1wHrfK3BG3EiUY8cCzyuqhvSdiSAI4HVqrpRVd8Hfgv8r5R9ysNEoQ7xOnGvBp5R1cvT9icIERkjIiO8/WG4/+zPpupUAao6W1XHq2orLpxwr6rW1VsZgIgM9wYU4IVkjgbqanScqv4NeElE9vJM7bj1T+qRU6jD0JHHi8AhItLk/Z234/oM64YBJwre4j8PA3uJyDpvwZ96YyrwZdybbXZ43XFpO1XAWOA+EXkS+DOuT6Fuh3zWObsCD4rIE7gVB3+vqren7FMQXwc6vX/z/YAfputOb0SkCTgK9wZed3gtrRuBx4GncHVwXc1uHnBDUg3DMIxwBlxLwTAMwwjHRMEwDMPowUTBMAzD6MFEwTAMw+jBRMEwDMPowUTBGDCISHdBFs2KZ+SKyP/E6VvBvdtE5GdJ3d8wimFDUo0Bg4hsVtXmtP0wjHrGWgrGgMdby+BiEXncW9Pg7zz7GBG5y7NfJSJrRWQX79xm7/NwEbnft85ApzdTFRE5UET+6CW5u8NLiV747C96efWfEJEHfPe81du/zdeyeUtEpnuJCH8sIn8WkSdF5PRa/VZG/8dEwRhIDCsIH/2j79xrXkK6ecA5nu1CXGqMA3D5iCaE3Hd/4JvAZFw20ale7qp/B76gqgcCC4A5AWUvAI5R1X1xSdzyUNXjvKSDXwXWAr/z9t9S1Y8DHwe+JiITI/4GhlGUxrQdMIwa8q5XwQaRTYuwFPgHb/8TwOcAVPV2EXkjpOxjqroOwEsl3gq8CXwUuMtrOGRwqZILeQi41ltsJTA1g9c6uQ44WVXfEpGjgX1E5AveJTsDk4DVIf4ZRmRMFAzDsc377Cb3dyFllvWXF2C5qhZdslJV/0VEDsYtBLRMRPbzn/dWELsB+L6qZpPkCfB1Vb0jon+GERkLHxlGOA8CJwN4b+cjyyi7Ehgj3jrGIjJIRKYUXiQie6jqo6p6AfAasHvBJZcAT6rqDT7bHcBML0SFiHykzhe8MfoQ1lIwBhLDvPBOlttVtdiw1IuB672+hz/iwj/vRHmQqm73wjs/E5GdcX9rPwWWF1z6YxGZhHv7vwd4AjjMd/4cYLnP7wtwS4u2Ao97ndobgZOi+GUYpbAhqYYRgogMAbpVtct7459XpE/CMPoF1lIwjHAmAItFpAHYDnwtZX8MI3GspWAYhmH0YB3NhmEYRg8mCoZhGEYPJgqGYRhGDyYKhmEYRg8mCoZhGEYPJgqGYRhGD/8fjUJCLVnIBIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\n",
    "\n",
    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
    "\n",
    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
    "\n",
    "```\n",
    "- Mean absolute error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
    "- Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s 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).\n",
    "- 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).\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 24.40\n",
      "Residual sum of squares (MSE): 1064.69\n",
      "R2-score: 0.72\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "test_y_ = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio\">Watson Studio</a>\n"
   ]
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    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n",
    "| ----------------- | ------- | ------------- | ---------------------------------- |\n",
    "| 2020-11-03        | 2.1     | Lakshmi Holla | Changed URL of the csv             |\n",
    "| 2020-08-27        | 2.0     | Lavanya       | Moved lab to course repo in GitLab |\n",
    "|                   |         |               |                                    |\n",
    "|                   |         |               |                                    |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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