jukomol/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://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/images/IDSNlogo.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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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",
   "metadata": {
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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": [
      "--2021-04-18 14:04:47--  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 0.04s   \n",
      "\n",
      "2021-04-18 14:04:47 (1.69 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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       "</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"
   ]
  },
  {
   "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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       "  <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": 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": 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": [
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Cylinders\")\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": 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:  [[38.78407965]]\n",
      "Intercept:  [125.97455952]\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: 23.00\n",
      "Residual sum of squares (MSE): 896.06\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,
    "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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