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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Simple Linear Regression</center></h1>\n",
    "\n",
    "\n",
    "<h4>About this Notebook</h4>\n",
    "In this notebook, we learn how to use scikit-learn to implement simple linear regression. We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, evaluate your model using test set, and finally use model to predict unknown value.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#understanding_data\">Understanding the Data</a></li>\n",
    "        <li><a href=\"#reading_data\">Reading the data in</a></li>\n",
    "        <li><a href=\"#data_exploration\">Data Exploration</a></li>\n",
    "        <li><a href=\"#simple_regression\">Simple Regression Model</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Importing Needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "deletable": true,
    "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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Downloading Data\n",
    "To download the data, we will use !wget to download it from IBM Object Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-04-02 13:24:03--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/FuelConsumptionCo2.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|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",
      "2020-04-02 13:24:03 (1.08 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "\n",
    "<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
    "\n",
    "### `FuelConsumption.csv`:\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)\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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"reading_data\">Reading the data in</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "deletable": true,
    "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",
    "# take a look at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"data_exploration\">Data Exploration</h2>\n",
    "Lets first have a descriptive exploration on our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "deletable": true,
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "deletable": true,
    "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 features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "deletable": true,
    "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "deletable": true,
    "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,
    "deletable": true,
    "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",
    "plot __CYLINDER__ vs the Emission, to see how linear is their relation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "deletable": true,
    "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(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Creating train and test dataset\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: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "deletable": true,
    "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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"simple_regression\">Simple Regression Model</h2>\n",
    "Linear Regression fits a linear model with coefficients $\\theta = (\\theta_1, ..., \\theta_n)$ to minimize the 'residual sum of squares' between the independent x in the dataset, and the dependent y by the linear approximation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "deletable": true,
    "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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.94740507]]\n",
      "Intercept:  [122.83920019]\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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can plot the fit line over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 13,
     "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,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\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",
    "<ul>\n",
    "    <li> 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.</li>\n",
    "    <li> 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.</li>\n",
    "    <li> Root Mean Squared Error (RMSE): This is the square root of the Mean Square Error. </li>\n",
    "    <li> 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).</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 24.19\n",
      "Residual sum of squares (MSE): 958.02\n",
      "R2-score: 0.68\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_hat = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_hat - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_hat - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y_hat , test_y) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "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=\"http://cocl.us/ML0101EN-SPSSModeler\">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://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
   ]
  }
 ],
 "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": "3.6.7"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}