edisoncastro01/ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb

## 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"
   ]
  },
  {
   "cell_type": "markdown",
   "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"
   ]
  },
  {
   "cell_type": "markdown",
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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": [
      "--2020-11-21 15:40:51--  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  --.-KB/s    in 0.04s   \n",
      "\n",
      "2020-11-21 15:40:51 (1.83 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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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       "\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,
   "metadata": {
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets select some features to explore more.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
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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": 9,
   "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": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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": 12,
   "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": 13,
   "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(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": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.20805169]]\n",
      "Intercept:  [124.73461127]\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": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 23.95\n",
      "Residual sum of squares (MSE): 947.34\n",
      "R2-score: 0.79\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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