bkee06/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",
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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": [
      "--2021-05-28 04:50:58--  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 0.001s  \n",
      "\n",
      "2021-05-28 04:50:58 (60.3 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": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\n",
    "\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
    "\n",
    "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
    "\n",
    "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using **np.random.rand()** function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 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": [
    "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": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.40835751]]\n",
      "Intercept:  [124.38877643]\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": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 23.61\n",
      "Residual sum of squares (MSE): 1013.15\n",
      "R2-score: 0.73\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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