manishkumarmailbox/ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb

## ML0101EN-Reg-Simple-Linear-Regression-Co2.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "text": [
      "--2021-02-11 02:34:27--  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 0.08s   \n",
      "\n",
      "2021-02-11 02:34:27 (923 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"
   ]
  },
  {
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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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   "cell_type": "markdown",
   "metadata": {
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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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       "  <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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       "  <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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       "      <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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\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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\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",
    "\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Cylinder\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "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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qaZEdKUWrVMgzoQ7NA8sNaRfepFTVUH4K6MWL48tPPTV5ILhZO91k0jvUKEhbur02cRl1SLpQ9/W1/wu37AvulVfGl//2bycPBDdTRlJpRd1H0pYq1iYuug5p3R7tdIvk7ZrJ+xlFdP3k7eJKo+6jmafj7qMwkug/mdn2cMLZrWZ2a7HVlJmkDgnMOq1D2i/mFSvi328uj3ZhnXpq/DFFTfQq4k4kbd5BXkmT25LKpd6ydh99EXgtcB/w5cgmPaoOCcyy1KHVmEPaxLF9+6Y3AM2DzM1dWC+/HHxWVKv1ipN+ZSeVF9X102reQZw6jB9JlyQlRYpuwINZ9uv2poR41alDArO0OmSp48aNk0np+vuD13lkSVjX6u+SdxnKpGR1eeudR9rfMS2pn9QPLRLiZW0U/iuwNsu+3dzUKFSryHV+y6hD0gV7eLiY87tnXzc56TPbqWOnDVmW75WnjnkbNqleEY3CS8BJ4JXw+UvAj7McW+amRkFaSbpgm2U7PsudRpY7hVafWcUdV97PTPs79vXFv9/XV953kM60ahQyjSm4+2nu3ufup4TPT3P31xTelyVSoKT0E2lpKRriMqBGF5uHIH3F4GD6ufr64vvjR0eDZTGjg77r15ebITTL94pKG7vJOi6icYmZIfM8BTO7wsz+PNzeUWalpDcVfdH4yU/ylTeLC3eNK/cMYZfRpS+jcynGx+GWW6ZmOL3lltbf/eyzJ5PjmQWv88gbtRWX3ymazylLRNT4OFxzzdQ5Jddco4ahlpJuIaIbcDOwG7gu3O4Fbs5ybJmbuo9mjzK6UTrt685yfFL3UX9/0L2SNAjb6I9fsCD+/QUL4uu0eHH8/osXZ/+7tDOO0WoMIsvfaXAw/v3Bwez1luLQovso0+Q1M3sYWOVBumvCJHffcfc3ltRWZaLJa7NHGZPhOp1UleX4pNnPZkFW06InyBUxUWx8HK67LliXuWFwEG69tb1uq/7+4Ls26+ubvFvQBLd6KSr30emR56/tqEYiTeowGa4daf3tdU0p0Xwh7uTCfMMN+cql3rI2Cv8N+I6ZfcbMdgB7gT8pr1rSa+owGa4dRfS3l6HV+MyWLXDs2NT9jx1LHmhOU/QMaalYUr9S80awPsIVwJXAv8p6XJmbxhRmj5073QcGpvc5N8IdG/H4eWL0s/TXt+orX7Ei/vgVK6bXvd25EnnHPbKMKezcOb0Pf3Bwsl6dhuq2Y82a+M9cs6a8z5RktDtPAfiF8PHNcVurY7uxqVGYPeIuZFm3pIYhrVHIMrjd3DA0NwhZvlerz8jbKLhPbxiaB5nTvnfewe2iNDcMahCq06pRaDnQbGbb3X2DmX01/ibDLy7uniU/DTRXa3x86opjW7e2H1+fNNCcRbsZSLuV6bXV36mMAdi0c551FvzgB9PfW7AAnn++vc+UmaXVQHNpqbPN7BTg68BcgmU/P+fuHzazM4G/BZYRLMd5tbv/MDxmM3A9cAL4PXe/p9VnqFGoTiMRXHQS1NBQsEZBOw1DJ2sYQPyxaWmm0yKHuqGKRqEO31uqVUTq7HeZ2Wnh8/9sZl8wszelHPYqcLG7/zKwCrjczN4CfAjY7e7LCeY+fCg87wpgHbASuBwYC0NfpYbyzopN08mAclIkT9og70wd3E7TnKW1uXy2fm8pRtboo//i7i+Z2a8BlwE7gE+2OiDsuno5fDkQbk4wUL0jLN8BXBU+vxK4w91fdffHgQPAhVm/iHRX1tm+WWVNFxEnKQX2/Pmty9Mih4pS9EzttPMl/dpvlK9dG/9+Urn0lqyNQuM319uBbe7+RSD1n7CZ9ZvZg8BzwL3u/m3g9e5+GCB8fF24+9nAU5HDD4VlUkNlxN9n6S6ZPz976GNamovR0aC7a3g46DoZHm6v+2vTpqCryix43LRp8r3xcbj22qnpHa69tv2GoYhlUO+6K1+59JikEejoBnwJ+BTwLwST2OYCD2U5Njz+dOCrwBuAF5ve+2H4+D+A90TKPw38u5hzbQD2AHuWLl1azFC85JYlaqaI9MxJ4ZxZztlOZE9eaesbpEX65K1jlhQVrc7ZKtV3JyGpdUijLtlRQOrsIeA3geXh60XA27IcGznHh4EPAI8BiyLneSx8vhnYHNn/HuCtrc6pkNTqpF2c8uYyytogNG9FLmATJy0kNW2BmbQ65A0PzfKd2v1btpuHqA4LLkk+rRqFrN1Hi4Avu/v3zewi4F3A/a0OCNd1Pj18fipwCfA94E5gfbjbeoKlPgnL15nZXDM7F1ie9hlSnXnzWpcXPRCdpOhzRruCzGD//qnv798PK1dOvu50xvIrr+QrL1M0F1Ie3fpvLd2RNSHeg8AIQRjpPQQX8PPdPXFoyszeSDCQ3E8wdrHL3f/YzBYAu4ClwJPAu9z9hfCYLQRZWI8D73f3v29VL4WkVqfosMdW58si6bPy7L9pE2zblu/z0sJe0+YElJEQr5O/ZYbLwTQKcZ15ikiId9LdjxN0IX3c3X+f4O4hkbs/7O5vcvc3uvsb3P2Pw/IfuPsad18ePr4QOWaru5/n7uenNQhSb90Me0wKwcw7GL59e/7PTop8apR/4hPTo6oGB4Py2UIhrrNL1kbhmJm9G3gvwaAzBCGmIrHyhnu2G44K6SGYWcvbSVKXlgxudDRISR2NcGo3RXVWw8PtHbdmTXvHdSu0V7oja6NwLfBWYKu7Px72+e8sr1rS7dj2ouUN97z++uLrkPcXbNIdR7MVK6a+Xr0aliwJvueSJcHrqNHRIG3GyZPBY/RvkDbRrNnOhH910fK4i/TgYNBl1fhv0fwd1qyB++6LP3eaokJ7pSaSRqBnwjZbo4+Kjuao46pmzfKEpGYNpdy5c/qi8n19yd973rz0z2pOPtfp3zYtpDXpe6WFfypEVFqhgyypu8LH7wIPR7bvAg+3OrYb22xtFNpZLrGb53MvvlFoFT+fZYuT94KbpQ7NF/y05TjTLsg7d04Pa+3v10VcytWqUUjLkrrI3Q+bWWwvpbu3mdSgGLM1+qjoaI4yokOKTuR22mnw8svp++X5zLTIoGZZM7VGs6hmSeTXKlFgO5lai8xOK72p7egjn0xHcTBsAH4IvBTZpARFR3PMhOiQpJQUncg7hyCuLz5OdInQLH/DVjH7eZchLSLNRRm6PWYlJUq6hYhuwA3AswSprh8Pt/+b5dgyt9nafZS2clY756v7mEInXUfRLbpwS9ps4zjRvvik46PdbnF/2zx/l7xde2V0BXZKM5pnHgqY0fwBYKW7L3P3c8Pt50pooyTU3CXRTpdMQ5bokG780uvGZ+zeDZdcEjxPm0OQ5vTTYaAp8Lo51LL5b5skaW5E3nDOvHcW3aAZzbNMUmsR3YC7gaEs+3Zzm613Ct3+NdjOL728v4g7WZayna0hz5rOcdFKZkEeoqxRPO3cQRWROLDKO4Uq1nyWztDuQHNDuKDObcC3CRbPaTQov1dOU5WNBpqL0c5gZ39/fF36+ib77KMDotHyuM/oNM1Fs3burObPjx/bmDcv+yB4Eedo5ZJLgruhZp3MM+hUt5Y1leIUkebiU8BXgH8C9kY2KUG3B4bb6ZJImy3cPCCaNLjb7qI8ZUhbfyGL5m6UtPK8vva1fOXdoBnNs8ucjPsdd/c/KLUmMmHrVrjuuqlZKwcHy/tHtnRp/MW5k0Yorp85TieL8iRpN11DEZLuUDoZE4rqNCtrGRpjUwqTnR2y3il81cw2mNkiMzuzsZVasx7XHEcfF1dflDJ+6WUd+Cz6YtZJN0relBNFyTMAX8aKd0VolcpDZpikwYboxmQYanRTSGpJ8i68Eqd58HLjxtaDmXnTIqQNqGZNW5FlBbJ2wz3zfq92Uk40S0qVMW9ecv3yDPIXUUcROl15ra7bbG0U2rn4RWWJnR8YKHeeQtLFq6xGIe6i305UVZ5opTh58y21E03UaR3LoFxLM0vbjQLwwcjzdzW99yetju3GpkYhXt5f6WXUMeluJ2n/ThuFxhad5FdV+GaeC+RsCOfU5LWZp1WjkJb76AF3f3Pz87jXVZitIalpq3WlyRPe2eI/f9uf4Z6vDmbt1yNO4+80E1YEmw3hnLPhO/SaTkJSLeF53OvmDz3HzL5qZo+a2T4zuyks/4iZPW1mD4bb2sgxm83sgJk9ZmaXpdRt1up0ta6yB0aLVmSDAJMN6kzI+TQbwjnrOMta2pd2+fCE53Gvmx0H/tDdfxF4C3CjmTWW9vhLd18VbncBhO+tA1YClwNjZlZxTEU1Ol2tK+uv4AULJp8XnYIieu6qzIQL7mxYoGYmNL6SQ1K/UtitdAL4MUFG1OPh88brY62OjTnXF4FLgY8AH4h5fzOwOfL6HuCtrc45W8cUOpW3772MNBdx6wR0c2vQAGj5NKYw89BuQjx373f317j7ae4+J3zeeJ15jWYzWwa8iSBNBsD7zOxhM7vVzM4Iy84Gnoocdigsk5ySfqX39cXfeZSV0KwO3Vi9Gj/fzVTWs+FuRyaV/s/WzOYDnwfe7+4/BrYB5wGrgMPAxxq7xhw+rYsqnES3x8z2HDlypJxKz3Cf+ESwwEzUnDlw++3xF8cy+oS3bIFjx9o/XtpXxZoLvdr4zkalNgpmNkDQIIy7+xcA3P1Zdz/h7ieBvwYuDHc/BJwTOXwJ8EzzOd19u7uPuPvIwoULy6x+pTZtCi7kZsHjpk35jm+O/mkVDVRGn7AGGaujVNbSidIaBTMz4NPAo+7+F5HyRZHd3gk8Ej6/E1hnZnPN7FxgOXB/WfWrs02bYNu2yRQQJ04Er7M2DHG/0o8dS74olDEgq0HG6igaSDpR5p3CauAa4OKm8NM/NbPvmtnDwK8Dvw/g7vuAXcB+gvUbbnT3CtN8VWf79nzlzZL+8R88GH/nUUafcFxDMzAQjHcUnSZbplI0kHSitEbB3b/h7ubub/RI+Km7X+PuvxSWX+HhOtDhMVvd/Tx3P9/d/76sutVdp5kw0/7x573ziHPKKa3L4xqa224LJpV1OnGscc6kxqUO4bBVmgmhuFJfNYgPkWZJF7usv7DXrk3fBybvPMbH4b3vnTow+d73th6Y/LmExViTystw5pnTs4MODGSf5DdbKRpIOpFp5bW6mq1pLtJSSKQ59VR45ZVsn+Xe3mphaXVsRMBEBzyHhiYvTkV1IQ0MwGteAy+8oDz+IlkVsfKa1ESWuPOsDUJDESuONetWBMyxY0GjplBIkWKoUZhhiow7L3PAt5sRMIqqESmOGoUZqohf3Y2uqE7HMOJ0MwJGUTUixVGjMIMV9Qu5OVIlrTyLtAiYopaPVFSNSLHUKJSgW3lnivqF3Nz3n1aeRVoEzEUXtX9uRdWIlGdO+i6Sx/g4XHvt5IzigweD11DsxavIX8hLl8YvktJpozM6mvydDxxo75yDg1q4RaRMulMo2E03xaeYuOmmYs5fxi/kKiY7tdv1df31xdZDRKbSnULB4pbRbFWeVxnLSEZTaD/5ZHfi/ZPuTtLcdVfxdRGRSbpTqEA3c91n1e3Ux3F3J1ko/FSkXGoUCpYW3llFrvsyzJ+fr7xZ80B0Vgo/FSmXGoU2tPqln5SGolFe11z3eddvuOaafOVxoncnixen76/wU5HyqVHIqdNf+nXMdd/O+g27duUrTzOQsLhrf7/CT0W6SQnxclq2LH6AdHg4+NV72mnxSeTmz4eXXko/HjpPiJenO8Y9uDOIS8vd3w/Hj+f/jHb+lyr6fCKSTAnxCpQUMdMoT8oq2ijfunX6r+KBgWq6RU49NXjsdP2GIiTNcC5q5rOIZKNGIaciLl551k8u089+Fjy2852SFrJpd4GbOjRMIlLuGs3nmNlXzexRM9tnZjeF5Wea2b1m9v3w8YzIMZvN7ICZPWZml5VVt050evHasgWOHp1advRotQPNGzbkK4dgIZvBwallg4PtL3AzPJyvXETKUeadwnHgD939F4G3ADea2QrgQ8Bud18O7A5fE763DlgJXA6MmVntOg86vXildT8V4fTT8+0/NgYbN07eGfT3B6/HxpKPGR2FW2+dmofo1lvbHwhOWi0u6ypyIlKMMtdoPuzuD4TPXwIeBc4GrgR2hLvtAK4Kn18J3OHur7r748AB4MKy6teumXDxevHF/MeMjQWDyu7BY6sGoaHICW9JM5U1g1mku7oypmBmy4A3Ad8GXu/uhyFoOIDXhbudDTwVOexQWFYrSRep7duDeQszSd47ijLVMVRXpBeVfhkzs/nA54H3u/uPW+0aUzYtGNHMNpjZHjPbc+TIkaKqmVlSN8+JE90JnSwyNUY7dxRl6eaiPCKSrNRGwcwGCBqEcXf/Qlj8rJktCt9fBDwXlh8CzokcvgR4pvmc7r7d3UfcfWThwoXlVT5B1SGSMzk1RitVZGoVkenKjD4y4NPAo+7+F5G37gTWh8/XA1+MlK8zs7lmdi6wHLi/rPq1qy4hknVIjVFkYr/RUVi/fupg9/r1msEs0m1l3imsBq4BLjazB8NtLXAzcKmZfR+4NHyNu+8DdgH7gbuBG929Jpfg4hS5HnJSf/u8edmO7+Sup+jEfuPj8MlPTk218clPzq67IZGZQGkucupkopl7tnQOWT8jmhojqpHLKM2CBfD889k+q1mWdB15nHIKvPrq9PK5c+GVV/KfT0SSKc3FLNSqvz1rUrpOFv4pOloorkFoVS4i5VCjMMNkyRia9WLfSQitooVEZictxznDFLkcZyfn2ro1GEOIrg2haCGRmU93CrNQu0np8mheOa3T9Q7WrMlXLiLlUKMwC8Ulq4vTaeNRZJqL++6b3gCsWROUi0j3qPtoFmpcnLdsCQZ+h4bgJz+Zvt/VV3e3XmnUAIhUT3cKs1T0V3yS22/vWnVEZIZQo9AD4u4SWpVXpcgZ0iLSHnUfSS00Zkg3opkaM6RBqS5Eukl3Cl2WZRnLjRvj90kqT1Nkao2ybNkyNbwV6pHfSaTXqFHIqd2IncWLg8ekwd1o+djY9LUOTj8928I3cZImqdVp/QetpyBSDzW6LMwM7ebhGRgIHpNSUETLV66cvtbBiy8G5e3odF3pbtAMaZF6UKOQU7uDs41fvEkpKKLl+/fH75NUPhtoPQWRelCjEGPTJpgzJ+hznzMneN2pKn/xzoTuo6JnSItIe2p0WaiHRtrpaF7/bds6axiiv3irGPS94YZ85VUpcoa0iLRHjUKT7dtbl+cZaI77xZu0fEWZy1qMjQWRS9FVzTZubH/gWkRmL81TaJI2KHv11dkWsIH42cQLFsSPK5SdxG5sTI2AiKQrc43mW83sOTN7JFL2ETN7uml5zsZ7m83sgJk9ZmaXlVWvNElLVDbK77or+3nanZmbtJxm1mU2RUTaVWb30WeAy2PK/9LdV4XbXQBmtgJYB6wMjxkzsw5WEG5fYxZtUnnWuPkTJ+LXLn7hhfj9o+Wf+tT0QeC+vqBcRKRMpTUK7v51IOESOM2VwB3u/qq7Pw4cAC4sq26tpPW/J0UR9fcHYwhxdxrRmblZ4vFHR4NkddFInNtv18CriJSvioHm95nZw2H30hlh2dnAU5F9DoVlpUgLOV29GpYsCd5fsiR43ZAUT79jRzCGkJSVtHGHkTUeX5E4IlKFbjcK24DzgFXAYeBjYXlcQGZsPI6ZbTCzPWa258iRI7krkBZy2kjMdvBgfPdPXDz9+vXBnUBfX3Lsf+NOIGs8fqcZQ5VxVETa4u6lbcAy4JG094DNwObIe/cAb007/wUXXOB59fe7B5f7qVt/f/D+8HDy+2bB+zt3Tp5v5073oaH4Yxrb0NDUY9LEnTPPOTo9XkRmN2CPJ1xXu3qnYGaLIi/fCTQik+4E1pnZXDM7F1gO3F9GHdJCTpMGkpMGjuOye8LkGEM7M3M7zRiqjKMi0i7zkmZNmdlngYuAs4BngQ+Hr1cRdA09Adzg7ofD/bcA1wHHgfe7+9+nfcbIyIjv2bMnV73mzIlvGPr74fjxoKvl4MH08wwPB339fX3xE8/MWq961kqn50w6vqG/P2jYNG9BpDeZ2V53H4l7r8zoo3e7+yJ3H3D3Je7+aXe/xt1/yd3f6O5XNBqEcP+t7n6eu5+fpUFoV1rI6dq18e83a9xRlJHd88wz85Xn/ewiUneIyOzUc2ku0kJOs05Oa1x465jdM65OcZJSeohI7+q5RgGCBuD48aCL5fjxqd0oWSanRS/6ZWT3zDLBrZXmOiWp03oKIlIPpY0pdEM7YwppksYU+vuD/vylS4MGocx5A0l1aIxj5JU2jiIivaWSMYWZKm1yWjcmkhXdJXXRRfnKRaR3qVFoUofFXoquw4ED+cpFpHepUYhRdoqJbs82ThonyZrcT0R6hxqFNnRyUU9Lo5F1nzzKCJsVkdlJjUJOnV6ws8w2LnpGch3DZkWkntQo5NTpBTtLV07R3T11GCcRkZlBy3Hm1OkFe+nS+HDTaFdOln3yGh1VIyAi6XSnkFOn/fNZunLU3SMiVVGjkFOnF+wsXTnq7hGRqmhGcxvGx4MxhCef7M4MZxGRImlGc8E6nceQJaRVK6eJSBXUKBQgzwW8inkKIiJZqfuoQ40LeDRMdWgoeQwgS7K7ohPiiYhEteo+UqPQobwX8CyrqpWxmpuISEMlYwpmdquZPWdmj0TKzjSze83s++HjGZH3NpvZATN7zMwuK6teRcs7byFLSKvSUohIVcocU/gMcHlT2YeA3e6+HNgdvsbMVgDrgJXhMWNm1l9i3QqT9wKueQoiUmdlrtH8daB5rbArgR3h8x3AVZHyO9z9VXd/HDgAXFhW3YqU9wKueQoiUmfdTnPxenc/DODuh83sdWH52cA/RfY7FJbVXuNCnWfeQpaUE0pLISJVqEvuo7iVhGNHwM1sA7ABYGlNOtl1AReR2aLb8xSeNbNFAOHjc2H5IeCcyH5LgGfiTuDu2919xN1HFi5cWGplRUR6TbcbhTuB9eHz9cAXI+XrzGyumZ0LLAfu73LdRER6XmndR2b2WeAi4CwzOwR8GLgZ2GVm1wNPAu8CcPd9ZrYL2A8cB2509xNl1U1EROKV1ii4+7sT3lqTsP9WQEGXIiIVUu4jERGZMKPTXJjZESAmyURmZwHPF1SdsqiOxVAdi6E6FqPqOg67e2ykzoxuFDplZnuS8n/UhepYDNWxGKpjMepcR3UfiYjIBDUKIiIyodcbhe1VVyAD1bEYqmMxVMdi1LaOPT2mICIiU/X6nYKIiET0XKMQt/hP3ZjZOWb2VTN71Mz2mdlNVdepmZmdYmb3m9lDYR0/WnWdkphZv5l9x8y+VHVdkpjZE2b2XTN70MyqXU4wgZmdbmafM7Pvhf9vvrXqOkWZ2fnh36+x/djM3l91vZqZ2e+H/2YeMbPPmtkpVdcpque6j8zs3wAvA7e7+xuqrk+cMFngInd/wMxOA/YCV7n7/oqrNsHMDJjn7i+b2QDwDeAmd/+nlEO7zsz+ABgBXuPu76i6PnHM7AlgxN1rG19vZjuA/+3ut5jZIDDk7i9WXK1Y4SJdTwO/6u6dzGUqlJmdTfBvZYW7/yxM73OXu3+m2ppN6rk7hYTFf2rF3Q+7+wPh85eAR6nZ+hIeeDl8ORButfuFYWZLgLcDt1Rdl5nMzF4D/Bvg0wDufrSuDUJoDfAvdWoQIuYAp5rZHGCIhIzQVem5RmGmMbNlwJuAb1dclWnCbpkHCVKg3+vutasj8HHgg8DJiuuRxoF/MLO94ZohdfNzwBHgtrAr7hYzm1d1pVpYB3y26ko0c/engT8nSAh6GPiRu/9DtbWaSo1CjZnZfODzwPvd/cdV16eZu59w91UE619caGa16o4zs3cAz7n73qrrksFqd38z8BvAjWE3Z53MAd4MbHP3NwE/IVxjvW7Crq0rgP9ZdV2amdkZBMsPnwssBuaZ2XuqrdVUahRqKuyn/zww7u5fqLo+rYTdCF8DLq+2JtOsBq4I++vvAC42s53VVimeuz8TPj4H/B31W6P8EHAocjf4OYJGoo5+A3jA3Z+tuiIxLgEed/cj7n4M+ALwryuu0xRqFGooHMT9NPCou/9F1fWJY2YLzez08PmpBP+zf6/SSjVx983uvsTdlxF0J3zF3Wv1qwzAzOaFAQWEXTJvA2oVHefu/w94yszOD4vWEKx/UkfvpoZdR6EngbeY2VD473wNwZhhbfRcoxAu/vMt4HwzOxQu+FM3q4FrCH7ZNsLr1lZdqSaLgK+a2cPAPxOMKdQ25LPmXg98w8weIlhx8MvufnfFdYrzu8B4+N98FfAn1VZnOjMbAi4l+AVeO+Gd1ueAB4DvElyDazW7uedCUkVEJFnP3SmIiEgyNQoiIjJBjYKIiExQoyAiIhPUKIiIyAQ1CtIzzOxEUxbNtmfkmtn/KbJuTeceMbO/Kuv8Iq0oJFV6hpm97O7zq66HSJ3pTkF6XriWwUfN7IFwTYNfCMsXmtm9YfmnzOygmZ0Vvvdy+HiRmX0tss7AeDhTFTO7wMz+MUxyd0+YEr35s98V5tV/yMy+Hjnnl8Lnd0XubH5kZuvDRIR/Zmb/bGYPm9kN3fpbyeynRkF6yalN3Uf/PvLe82FCum3AB8KyDxOkxngzQT6ipQnnfRPwfmAFQTbR1WHuqv8O/Ja7XwDcCmyNOfaPgMvc/ZcJkrhN4e5rw6SD1wMHgf8VPv+Ru/8K8CvA75jZuRn/BiItzam6AiJd9LPwAhunkRZhL/Cb4fNfA94J4O53m9kPE469390PAYSpxJcBLwJvAO4Nbxz6CVIlN/sm8JlwsZXY1Azh3cnfAFe7+4/M7G3AG83st8JdXgssBx5PqJ9IZmoURAKvho8nmPx3YTmPjR5vwD53b7lkpbv/BzP7VYKFgB40s1XR98MVxO4A/tjdG0nyDPhdd78nY/1EMlP3kUiybwBXA4S/zs/IcexjwEIL1zE2swEzW9m8k5md5+7fdvc/Ap4Hzmna5WbgYXe/I1J2D7Ax7KLCzH6+5gveyAyiOwXpJaeG3TsNd7t7q7DUjwKfDcce/pGg++elLB/k7kfD7p2/MrPXEvxb+ziwr2nXPzOz5QS//ncDDwH/NvL+B4B9kXr/EcHSosuAB8JB7SPAVVnqJZJGIakiCcxsLnDC3Y+Hv/i3tRiTEJkVdKcgkmwpsMvM+oCjwO9UXB+R0ulOQUREJmigWUREJqhREBGRCWoURERkghoFERGZoEZBREQmqFEQEZEJ/x9V5ieykH1IegAAAABJRU5ErkJggg==\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:  [[39.64248861]]\n",
      "Intercept:  [122.94873887]\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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\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: 25.57\n",
      "Residual sum of squares (MSE): 1054.49\n",
      "R2-score: 0.71\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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