1_S2_Lab_Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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 it, test it 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": [
      "--2022-01-14 22:15:18--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
      "Résolution de 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",
      "Connexion à 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… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 200 OK\n",
      "Taille : 72629 (71K) [text/csv]\n",
      "Sauvegarde en : « FuelConsumption.csv »\n",
      "\n",
      "FuelConsumption.csv 100%[===================>]  70,93K   367KB/s    ds 0,2s    \n",
      "\n",
      "2022-01-14 22:15:19 (367 KB/s) — « FuelConsumption.csv » sauvegardé [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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01)\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",
    "Let's 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": [
    "Let's 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 features:\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, let's plot each of these features against the Emission, to see how linear their relationship is:\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 relationship is:\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": [
    "# 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": [
    "<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 that 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 model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
    "\n",
    "This means that we know the outcome of each data point in the testing dataset, making it great to test with! 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",
    "Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using **np.random.rand()** function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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] # ~ = valeur opposée"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Simple Regression Model\n",
    "\n",
    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "\n",
    "Using sklearn package to model data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Model = Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.18777543]]\n",
      "Intercept:  [125.13595605]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit(train_x, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "print ('Intercept: ',regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, **Coefficient** and **Intercept** in the simple linear regression, are the parameters of the fit line.\n",
    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data.\n",
    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot outputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the fit line over the data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\n",
    "\n",
    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
    "\n",
    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set:\n",
    "\n",
    "*   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",
    "\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",
    "\n",
    "*   Root Mean Squared Error (RMSE).\n",
    "\n",
    "*   R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 21.66\n",
      "Residual sum of squares (MSE): 773.88\n",
      "R2-score: 0.80\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": {},
   "source": [
    "## Exercise\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
    "\n",
    "Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train[['FUELCONSUMPTION_COMB']]\n",
    "\n",
    "test_x = test[['FUELCONSUMPTION_COMB']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
    "\n",
    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train a Logistic Regression Model using the `train_x` you created and the `train_y` created previously\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "regr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "regr.fit(train_x, train_y)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the predictions using the model's `predict` function and the `test_x` data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = regr.predict(test_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "predictions = regr.predict(test_x)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 20.21\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
    "#print(\"Residual sum of squares (MSE): %.2f\" % np.mean((predictions - test_y) ** 2))\n",
    "#print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the MAE is much worse than it is when we train using `ENGINESIZE`\n"
   ]
  },
  {
   "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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "Azim Hirjani\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 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}