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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   "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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     "text": [
      "--2020-11-24 17:50:53--  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)... 67.228.254.196\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|67.228.254.196|: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   207KB/s    in 0.3s    \n",
      "\n",
      "2020-11-24 17:50:54 (207 KB/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"
   ]
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   "cell_type": "markdown",
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   "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"
   ]
  },
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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>"
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      "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,
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     "output_type": "execute_result"
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   ],
   "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"
   ]
  },
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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": 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,
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       "  <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",
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       "    <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": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KupNsXAHg0oj4YWG1MmuxZgaUwWMIMzU8XD8wSLB2rYNBN6vbfSTpxen5VLJNc3amx/xUZtbVDjvMAaGd6nUHLVkCN9zggNDtGrUU3gWsAT5U5VwAZ7a8RmYt0kwwcMqK1ih94ZdmHw0PZ4HCgaB3FLbzmqTnAV8GDicLPp+JiPdLeiHwKeA44C7grRHxrKTDgeuBXweeAN4SEdvr/QyvU7Bamhk/cAoFGzSz3nlN0pskHZ2O/0jS5yS9tMFtzwBnRsRLgBXAayS9HPhz4IqIOAl4EnhHuv4dwJOp/Ip0ndmM5A0I8+c7IJiVyzsl9X9GxE8lnQ6sAq4Brqp3Q2T2pJdz06PU5fSZVH4dcE46Pju9Jp1fKTXbG2yDbvny5rqNdu0qri5mvShvUCjNJ3gdMBERXwIOa3STpGFJW4DHgFuA7wE/jojSwvadwIJ0vAB4CCCdf4qsi8ksF8mb4pjNVt6gsEvS1cBbgJtT/3/DeyNiKiJWAAuB04AXz7SiJZLWSNosafPu3btn+3ZWkHamvpaaax1EOCBUKs3SKj0Oa/gnn/WrvEHhzcD/BV4dET8GjgXek/eHpHvuAH4TmCepNOtpIVBqwO8CFgGk8y8gG3CufK+JiBiLiLHR0dG8VbA2amfqa083nb3DDoPnnju47LnnHBgGVd6gcCLwpYj4rqQzgDcB36h3g6RRSfPS8RHAq4AHyILDG9NlF5Jt9QlwU3pNOn97FDU1ygrVjtTXzbYOAObObd3P73XlLbnKgFBSq9z6W96g8FlgStJJwATZX/SfaHDPicAdku4BvgncktJtXwq8S9I2sjGDa9L11wDHpfJ34TQaPavW1op5tlzMYybTD+bOhWefbc3P72Xj41kgOP/8Ay05s3J5cx/tj4h9kv4T8NcR8deSvlXvhoi4Bzhk2mpEfJ9sfKGy/OdkLRDrQZOTWUvgwQdrb2TfitTXCxY0vqaSv/gypQymZvXkDQrPSToPuAD4nVTmxrgBB8YQSl1G1QJCK1JfV+v7bsQBAVatyr/3dDl3tw2mvN1HbycbJN4QET9Iq5JvKK5a1kuqjSFAluKgVamvpeYCgmcYZWYTENzdNpgKS3PRDk5z0VmNdthq1faZnmE0c3k/u5ER74k8SGac5kLSjen5Xkn3lD3uTQPINqDy7LDVijGEZnbmcusgMz4Oc+bkDwhHHeWAYAc0GlO4JD2/vuiKWO+YnGw8YNmqMYQ8LY35852uYibdRM5gatXUDQoR8Uh63gEg6fmN7rH+Nj4OV9XNepWNIWzYMPsxhDzmznVAmElA8K5yVkuuL3hJFwF/DPycLKkd6fmXCqqXdaHJySwg1OuiGR6G7dtn/jOGh5sbh/BgqAOCtVbev/rfDZwSEY8XWRnrbuvXN+6zn81G7B5Qzq/RIH+l4WHYt6/xdWZ5g8L3AO9LNeAarUie6WY1IyPw9NP5r6+1OK7fLV/eXBbYcrMJ1jZY8gaFy4F/lfR1ss1zAIiI3yukVtYVylcpL14Mxx4LTxySojD7C/+GG2Y2hjCT/EWD2GXUzE5ylVau9GCy5Zc3KFwN3A7cC7Rg5rl1u8pVyjt2ZF/Ihx128JeyBGvXticgwGAGhMnJmQWEVgz42+DJGxTmRsS7Cq2JdZVqq5Sfew6OOy6b115qPcz0S2d8vLnrB7HLqNnuIo8bWCvkDQr/KGkN8A8c3H30o0JqZR1Xa/zgRz+Cx2c53cADyo3NZPzA4wbWCnmDwnnp+fKyMk9J7WOLF2ddRtXKZ8MBobqZ5iiCA114HjewVsiVEC8iXljl4YDQxzZsyGYFlZvNKmVvmVnbbALCunXZug4HBGuVRrmP3lt2/KaKc39aVKWs81avzubBL1ky+0ynbh1UV9r9rNmAsGQJbNqUfU4OBtZqdbOkSro7Ik6tPK72uhOcJbU3OCgcaqatg2XLYOvW1tfHBsuMs6QCqnFc7XXlD10k6Q5J90vaKumSVP4BSbskbUmPs8ruuVzSNkn/JunVDepmXW4m+ygPQkAYH3dAsO7VaKA5ahxXe11pH/CHEXG3pKOBuyTdks5dERF/WX6xpGXAucByYD5wq6RfjogBm4jYHxwMDuakddYrGgWFl0j6CVmr4Ih0THr9vHo3pgyrpSyrP5X0AFBvh92zgU9FxDPADyRtI9vL+auN/xnWLbxl5qEcEKyX1O0+iojhiHh+RBwdEXPScel17h1cJS0FXgp8PRW9M23Wc62kY1LZAuChstt2Uj+I2AyUBjeHhrLnycnWvbe3zKyumYBQGkB2QLBOybtH84xJOgr4LPD7EfETYCPwImAFWUviQ02+3xpJmyVt3r17d6ur29dKqSt27Mi+eHbsyF63IjDMJGWFHWzlSqeksM4rNChImksWECYj4nMAEfFoRExFxH7gY2RdRAC7gEVlty9MZQeJiImIGIuIsdHR0SKr33eqpa7Yuzcrn6mZDCbDYLQQ8hoeztYbuHVg3aCwoCBJwDXAAxHx4bLyE8suewNwXzq+CThX0uGSXgicDHyjqPoNolqpKxqlxK5lpsFg0ALCypW1z61bl+Ur8noD6xZFbq35CuCtwL2StqSy9wHnSVpBNntpO3ARQERslXQjcD/ZzKWLPfOotYpKXZHXoAWDkltvrT7YPNP9J8yKVHfxWrfz4rXmVKbDhix1RbMrlT3d9OCdz4aHs8/VX/DWK2azeM36SCtSVzggZAFh48YDqbynprLXzaYDN+tGbilYbg4ImTlzqu/t4P0MrFfUaykUOaZgfaTZDKf9rNZmP4O2CZD1J3cf9ZHx8eyvWCl7blV3xqCuQRgfPzDltvRYtSprEVRTq9ysl7il0CcqZ7eU+rlh5gOgg7z+oDRuUOm222D+/Op7JnvnM+sHbin0gcnJ2qkUJiZm9p4zGT/ol4AA9T+3hx/OppOWWgalxWeefWT9wEGhx42Pw/nn1z4/k37uQR1QLs8L1ehzu/LKbFA5wovPrL+4+6iH1eriKNdMP/egdheNj8NVV/XHv8VsthwUelierqG8/dyDHBAaBdZK9dJWmPU6dx/1sEZdHCtXFtet0Q8BAZofc/E+B9bv3FLoYcPDtQPDpk35VioP6vhBSaPAumQJbN/elqqYdQW3FHpI5QY5Z5xR/bp16xwQ8qo35jIyAhs2tK8uZt3AQaFHVNsg56tfzbozmp0a2eweCHPn9mdAgNpjLkcd1XxeKLN+4O6jHlFrg5xt25rLt+PWwcFKAdQZT80yTojXI4aGqn9BS7B/f773aDYgzJ0Lzz7b3D1m1v2cOrsP1NoIJ+8GOQ4IZpaHg0KP2LAhG/gsl3cgdCZdRg4IZoOpyD2aF0m6Q9L9krZKuiSVHyvpFknfTc/HpHJJ+qikbZLukXRqUXXrRTPdIKeZgFCri8rMBkeRLYV9wB9GxDLg5cDFkpYBlwG3RcTJwG3pNcBrgZPTYw3Q5DrT/rd6dTZnfv/+7LleQGh2hlGE9wMwswKDQkQ8EhF3p+OfAg8AC4CzgevSZdcB56Tjs4HrI/M1YJ6kE4uqXz/zDCMzm6m2jClIWgq8FPg6cEJEPJJO/RA4IR0vAB4qu21nKhsIrdggp9nWATggmNnBCg8Kko4CPgv8fkT8pPxcZPNhm/pakrRG0mZJm3fv3t3CmnbG+HjWlz/bjeD7OaFd5UruyclO18isfxUaFCTNJQsIkxHxuVT8aKlbKD0/lsp3AYvKbl+Yyg4SERMRMRYRY6Ojo8VVvg2WL8++/Gt9Oc90g5w8eikgVK7kXrPGgcGsKEXOPhJwDfBARHy47NRNwIXp+ELgC2XlF6RZSC8HnirrZuo74+Nw//31r8kz8NvvXUa1VnKvX9+Z+pj1u8JWNEs6HfgKcC9QWnP7PrJxhRuBxcAO4M0R8aMURP4GeA2wF3h7RNRdrtzLK5rnzGn8pT88XD+FRT8Hg5JWrOQ2s4PVW9FcWO6jiPgXoNbX1iHblKTxhYuLqk+3WLWq9n7KlWolaxsZgaefbu7n9mJAgGzF9o4d1cvNrPW8ormNli/PHxBqZTuVmgsIEb0bEGB2K7nNrHnOktomk5ONxxAAli2DrVurn5vJDKNeV1qgt349PPhg1kLYsMEprc2K4pZCG4yPw/nn17+mtBdCrYAwE73cQijXzEpuM5sdtxQKlndj+EEfUDaz7uCWQsHyrDVYeciw+wEOCGbWTm4pFKzRtNNly+DWWw8tdzAws05wS6Fg9TaG37Sp+hiCA4KZdYqDQsFqrTVYt676gGmz6Rvmzm2+TmZmtTgoFOzKK7MAUGoxlGYZVVuDsGpV41lK5bppy0wnrTPrD4WluWiHXk5zUamXu4xKSevKcxSNjOTbGc7M2q9emgu3FDpswYLeCwiVez+sXeukdWb9wrOPOmh4uLmkbp0OBnDououpKdizp/q1Dz7YnjqZWeu4pdAhzQaEemsZ2qE0ZpBnIV6Jk9aZ9R4HhTYrdRc1GxCqrWVol/KNbvJy0jqz3uTuozZasAAefri5e7qhy6jaRjeVpKxl4KR1Zr3NQaGNmgkI3TTdNM/YwNq11afZmllvcfdRm4yP57uutNNYtwQEqD82UG/dhZn1niL3aL5W0mOS7isr+4CkXZK2pMdZZecul7RN0r9JenVR9Wq30vTNPAO0Q0P59mVut1ob3WzalGV3dUAw6x9FthQ+TrbfcqUrImJFetwMIGkZcC6wPN1zpaQ6WYN6Q2n6Zp4v+vnzuzMgQDY2MDEBS5ZkYwdLlnhhmlm/KnKP5i9LWprz8rOBT0XEM8APJG0DTgO+WlT92iFP2mzojsHkRlavdhAwGwSdGFN4p6R7UvfSMalsAfBQ2TU7U1lPWrUq+4s6z1/+69YVXx8zs7zaHRQ2Ai8CVgCPAB9q9g0krZG0WdLm3bt3t7h6s1MKBrfd1vhaD9CaWTdqa1CIiEcjYioi9gMfI+siAtgFLCq7dGEqq/YeExExFhFjo6OjxVa4CQsW5AsGkAUDD9CaWTdqa1CQdGLZyzcApZlJNwHnSjpc0guBk4FvtLNuszE+nm8NglsHZtbtChtolvRJ4AzgeEk7gfcDZ0haAQSwHbgIICK2SroRuB/YB1wcEV06F+eA5cvh/vvzX79vX3F1MTNrhSJnH51XpfiaOtdvAHomW06zKSs6ndDOzCwPr2iegcnJ5gNCJxPamZnl5dxHM5B385j582FX1eFyM7Pu5JZCE0p7CjRKIV0aUHZAMLNe45ZCDpOTcMkl8MQTja9dtgy2bi2+TmZmRXBQaKDapvS1zJ/vgGBmvc3dRw1cdFHjgLBkSZYx1N1FZtbr3FKoY9Uq+NnP6l+zZAls396W6piZFc4thRomJxunrfA+xGbWbxwUamg07fS447yngJn1H3cf1VBvX+KhIXj88fbVxcysXdxSqKHevsQXXdS+epiZtZODQg3V9iWGLGWFs5yaWb9yUKih2r7EmzY5h5GZ9beBDQrj4zBnTvaFP2dO9rrS6tXZdNP9+7NnDyqbWb8buIHmyUlYuxb27DlQNjUFGzdmx+4aMrNBNlAthVLKivKAUG5ior31MTPrNgMVFNavr5+yYqrr93ozMytWYUFB0rWSHpN0X1nZsZJukfTd9HxMKpekj0raJukeSacWUad6aw8gS3ltZjbIimwpfBx4TUXZZcBtEXEycFt6DfBa4OT0WANsLKJC9dYeQNa1ZGY2yAoLChHxZeBHFcVnA9el4+uAc8rKr4/M14B5kk5sdZ1qrT2Qsk1xPMhsZoOu3WMKJ0TEI+n4h8AJ6XgB8FDZdTtTWUvVWnuwf78DgpkZdHBKakSEpGj2PklryLqYWNyoP6iK1au93sDMrJZ2txQeLXULpefHUvkuYFHZdQtT2SEiYiIixiJibHR0tNDKmpkNmnYHhZuAC9PxhcAXysovSLOQXg48VdbNZGZmbVJY95GkTwJnAMdL2gm8H/ggcKOkdwA7gDeny28GzgK2AXuBtxdVLzMzq62woBAR59U4tbLKtQFcXFRdzMwsn4Fa0WxmZvUp+yO9N0naTdYN1SrHA95TrTZ/PvX586nPn0997fx8lkRE1Zk6PR0UWk3S5ogY63Q9upU/n/r8+dTnz6e+bvl83H1kZmbTHBTMzGyag8LBvKNCff586vPnU58/n/q64vPxmIKZmU1zS8HMzKY5KACStku6V9IWSZs7XZ9u0MwmSYOoxufzAUm70u/RFklndbKOnSRpkaQ7JN0vaaukS1K5f4eo+/l0/HfI3UdkQQEYiwjPoU4kvRLYQ7bPxSmp7C+AH0XEByVdBhwTEZd2sp6dUuPz+QCwJyL+spN16wYp4eWJEXG3pKOBu8j2T3kb/h2q9/m8mQ7/DrmlYFU1uUnSwKnx+VgSEY9ExN3p+KfAA2R7pPh3iLqfT8c5KGQC+GdJd6X9Gqy6Wpsk2QHvTPuMXzuoXSOVJC0FXgp8Hf8OHaLi84EO/w45KGROj4hTyfaKvjh1DVgdKYmh+x4PthF4EbACeAT4UEdr0wUkHQV8Fvj9iPhJ+Tn/DlX9fDr+O+SgAETErvT8GPB54LTO1qhr1dokyYCIeDQipiJiP/AxBvz3SNJcsi+8yYj4XCr271BS7fPpht+hgQ8Kko5MAz1IOhL4beC++ncNrFqbJBnTX3Ilb2CAf48kCbgGeCAiPlx2yr9D1P58uuF3aOBnH0n6JbLWAWT7S3wiIjZ0sEpdoXyTJOBRsk2S/g9wI7CYtElSRAzkYGuNz+cMsmZ/ANuBiwZ1B0FJpwNfAe4F9qfi95H1mw/871Cdz+c8Ovw7NPBBwczMDhj47iMzMzvAQcHMzKY5KJiZ2TQHBTMzm+agYGZm0xwUzMxsmoOCtZSkqbK0v1skLZX0Nkl/U3HdnZLG0nF56vItkj6ayj8u6Y1VfsYvS7o5pV++W9KNkk5I506X9A1J30mPNWX3fUDSXkm/UFa2p+x4fUpjfE+qx8vK6nd82XVnSPpiOn6bpJC0quz8OansjWX/1n+T9G1J/0/Sr0j6fPoZ2yQ9VfZv/w8Vn80LJF2frvteOn5BOrc0/Zz/Ufaz/0bS2xr8N3p3+my2SPqmpAtS+WGSPpJ+1nclfUHSwrL7QtKmstdzJO2u+Cx2p/fdKukzkkbq1cW6j4OCtdrTEbGi7LE9532/VXbP79W6SNLzgC8BGyPi5JSz6kpgVNIvAp8A1kbEi4HTgYskva7sLR4H/rDK+/4m8Hrg1Ij4NWAV8FDOut8LnFv2+jzg2xXXrI6Il5BlBv3fEfGGiFgB/DfgK2X/9n+tuO8a4PsRcVJEvAj4AfB3ZecfAy6RdFieikpaC7wKOC39/JWA0uk/BY4GfiUiTiZbrPi5tPoW4GfAKZKOSK9fBeyq+BGfTv+O5cCzwFvy1Mu6h4OC9Zr/Anw1Iv6hVBARd0bEfcDFwMfLUhI/DrwXuKzs/muBt0g6tuJ9TwQej4hnSvdGxMM56/QV4DRJc1OCs5OALTWu/XI635Ckk4BfB/5XWfGfAGOSXpRe7wZu40DqiEbeB6wrJaeLiJ9ExHXpL/q3A38QEVPp3N8DzwBnlt1/M1AKsucBn6xR9znAkcCTOetlXcJBwVrtiLKukM83vnzaHWX3/UGd604h25CkmuVVzm1O5SV7yALDJRXX/TOwSNK/S7pS0n9sou4B3Aq8mmy/gJvqXPs7ZC2LPJYBW0pf0gDpeAsH/5v+HHi3pOF6bybp+cDREfH9KqdPAh6szGTKoZ/fp4BzU4vt1ziQ7rnkLZK2kLUgjgX+AespDgrWauXdR29IZbVyqZSXl3cfXVFwHT8KXKiUCBEgIvaQ/VW+huyv70+X9c1Xq39l2afIupDOpfpfz5Ppy/IVwLtnU/lDKpJ9yX+drBVVqIi4B1hK1kq4ucoln07dUr9IFvzeU3SdrLUcFKwdngAqNws5lqx/v1lbyb68q7m/yrlfT/dMi4gfk409XFxRPpW6ot4PvBP4z+lUZf0PqXtEfAP4VeD4iPj3KnVbnQLeORGRd6zifmCFpOn/T9PxinSu3J8Cl3JgfOAQqRWwR1kSyErfAxaXB8rkkM+PrCX0l9ToOko/K8haCd6bpMc4KFg7fBN4RRoIJs2sOZz8A7nlPgH8h/LBY0mvlHQK8LfA2yStSOXHkXWt/EWV9/kwcBFZZlzSjKCTy86vIMviCXAn8NZ03TBwPnBHlfe8jKzPviUiYhvwLeCPyor/CLg7nSu/9jtkgeJ3GrztnwF/m7qSkHSUpAsi4mdkg+AfLnVDpVlJI8DtFe9xLfDHEdGoG+x0smBjPWROpytg/S8iHpV0CXBz+kt3D3Be2kik5A5Jpb7zeyLignR8taSPpOOHIuI3Jb0e+Egqfw64B7gk/ZzzgY+lv3gFfKR8ULqsTo+nMY/S+MVRwF9LmgfsA7aRdSVBNtC7UdK303v+E7CJChHxj819Mrm8I9Wr9OX61VRWzQayIFLPRrJ/6zclPUf2+ZV297qcrAXw75L2A98B3hAVqZQjYidZF1w1b1GWFnoI2Am8rUF9rMs4dbaZmU1z95GZmU1z95FZH5L0t2Qzncr9VVp7YFaTu4/MzGyau4/MzGyag4KZmU1zUDAzs2kOCmZmNs1BwczMpv1/q6yLX/d4nVIAAAAASUVORK5CYII=\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": 9,
   "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",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"No of cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n",
    "-->\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": 10,
   "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": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\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": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.97548265]]\n",
      "Intercept:  [125.51674978]\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": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 24.19\n",
      "Residual sum of squares (MSE): 982.13\n",
      "R2-score: 0.76\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,
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    "<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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