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

## ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Importing Needed packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-11-27 03:43:08--  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 0.06s   \n",
      "\n",
      "2020-11-27 03:43:08 (1.25 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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Reading the data in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Data Exploration\n",
    "\n",
    "Lets first have a descriptive exploration on our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can plot each of these fearues:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets plot each of these features vs the Emission, to see how linear is their relation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "plt.scatter(cdf['CYLINDERS'] , cdf['CO2EMISSIONS'],color = 'blue')\n",
    "plt.xlabel('Cylinder')\n",
    "plt.ylabel('CO2 Emissions')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()\n",
    "\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\n",
    "\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
    "\n",
    "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
    "\n",
    "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using **np.random.rand()** function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 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": [
    "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": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.08014331]]\n",
      "Intercept:  [125.51749619]\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": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 24.21\n",
      "Residual sum of squares (MSE): 1042.72\n",
      "R2-score: 0.77\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}