{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "\n",
    "# Simple Linear Regression\n",
    "\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "    \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": {},
   "source": [
    "### Importing Needed packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": {},
   "source": [
    "### Downloading Data\n",
    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-10-20 06:58:57--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 72629 (71K) [text/csv]\n",
      "Saving to: ‘FuelConsumption.csv’\n",
      "\n",
      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2025-10-20 06:58:57 (51.0 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": [
    "In case you're working **locally** uncomment the below line. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Understanding the Data\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": {},
   "source": [
    "## Reading the data in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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": 24,
     "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": {},
   "source": [
    "### Data Exploration\n",
    "Let's first have a descriptive exploration on our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<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": 25,
     "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": 26,
   "metadata": {},
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 26,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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 relationship is:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "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": {},
   "source": [
    "#### Creating train and test dataset\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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Regression Model\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": {},
   "source": [
    "#### Train data distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {},
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.04591701]]\n",
      "Intercept:  [125.73189084]\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": {},
   "source": [
    "#### Plot outputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the fit line over the data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": {},
   "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",
    "* 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.36\n",
      "Residual sum of squares (MSE): 835.25\n",
      "R2-score: 0.76\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "test_y_ = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[15.96373638]]\n",
      "Intercept:  [70.91468093]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]\n",
    "\n",
    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show() \n",
    "\n",
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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_)\n",
    "\n",
    "plt.scatter(train.FUELCONSUMPTION_COMB, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.ylabel(\"Emission\")\n",
    "\n",
    "train_x = train[[\"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": [
    "<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": [
    "<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": [
    "<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 Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[15.96373638]]\n",
      "Intercept:  [70.91468093]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[['FUELCONSUMPTION_COMB']])\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_)\n"
   ]
  },
  {
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[109.22764825]\n",
      " [145.94424193]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [153.92611013]\n",
      " [118.80589008]\n",
      " [118.80589008]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [134.76962647]\n",
      " [166.69709923]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [118.80589008]\n",
      " [118.80589008]\n",
      " [118.80589008]\n",
      " [118.80589008]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [166.69709923]\n",
      " [118.80589008]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [118.80589008]\n",
      " [141.15512102]\n",
      " [141.15512102]\n",
      " [118.80589008]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [109.22764825]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [169.88984651]\n",
      " [169.88984651]\n",
      " [169.88984651]\n",
      " [169.88984651]\n",
      " [109.22764825]\n",
      " [128.38413191]\n",
      " [155.52248376]\n",
      " [155.52248376]\n",
      " [128.38413191]\n",
      " [109.22764825]\n",
      " [109.22764825]\n",
      " [ 93.26391187]\n",
      " [ 93.26391187]\n",
      " [155.52248376]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [109.22764825]\n",
      " [161.90797832]\n",
      " [128.38413191]\n",
      " [128.38413191]\n",
      " [109.22764825]\n",
      " [109.22764825]\n",
      " [ 93.26391187]\n",
      " [ 93.26391187]\n",
      " [ 93.26391187]\n",
      " [144.3478683 ]\n",
      " [144.3478683 ]\n",
      " [102.8421537 ]\n",
      " [157.1188574 ]\n",
      " [126.78775827]\n",
      " [169.88984651]\n",
      " [126.78775827]\n",
      " [150.73336285]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [126.78775827]\n",
      " [110.82402189]\n",
      " [155.52248376]\n",
      " [155.52248376]\n",
      " [155.52248376]\n",
      " [139.55874738]\n",
      " [139.55874738]\n",
      " [155.52248376]\n",
      " [109.22764825]\n",
      " [128.38413191]\n",
      " [155.52248376]\n",
      " [102.8421537 ]\n",
      " [ 99.64940642]\n",
      " [109.22764825]\n",
      " [109.22764825]\n",
      " [ 94.86028551]\n",
      " [102.8421537 ]\n",
      " [123.595011  ]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [129.98050555]\n",
      " [126.78775827]\n",
      " [129.98050555]\n",
      " [150.73336285]\n",
      " [121.99863736]\n",
      " [109.22764825]\n",
      " [121.99863736]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [ 99.64940642]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [109.22764825]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [153.92611013]\n",
      " [118.80589008]\n",
      " [102.8421537 ]\n",
      " [118.80589008]\n",
      " [150.73336285]\n",
      " [150.73336285]\n",
      " [150.73336285]\n",
      " [110.82402189]\n",
      " [144.3478683 ]\n",
      " [150.73336285]\n",
      " [126.78775827]\n",
      " [129.98050555]\n",
      " [118.80589008]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [129.98050555]\n",
      " [ 94.86028551]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [110.82402189]\n",
      " [102.8421537 ]\n",
      " [110.82402189]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [ 99.64940642]\n",
      " [126.78775827]\n",
      " [126.78775827]\n",
      " [145.94424193]\n",
      " [158.71523104]\n",
      " [145.94424193]\n",
      " [158.71523104]\n",
      " [126.78775827]\n",
      " [145.94424193]\n",
      " [158.71523104]\n",
      " [158.71523104]\n",
      " [126.78775827]\n",
      " [158.71523104]\n",
      " [169.88984651]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [102.8421537 ]\n",
      " [109.22764825]\n",
      " [118.80589008]\n",
      " [102.8421537 ]\n",
      " [110.82402189]\n",
      " [134.76962647]\n",
      " [ 96.45665914]\n",
      " [ 96.45665914]\n",
      " [125.19138464]\n",
      " [131.57687919]\n",
      " [131.57687919]\n",
      " [131.57687919]\n",
      " [114.01676917]\n",
      " [118.80589008]\n",
      " [147.54061557]\n",
      " [118.80589008]\n",
      " [118.80589008]\n",
      " [110.82402189]\n",
      " [110.82402189]\n",
      " [102.8421537 ]\n",
      " [128.38413191]\n",
      " [110.82402189]\n",
      " [134.76962647]\n",
      " [ 99.64940642]\n",
      " [134.76962647]\n",
      " [114.01676917]\n",
      " [126.78775827]\n",
      " [134.76962647]\n",
      " [161.90797832]\n",
      " [126.78775827]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [102.8421537 ]\n",
      " [128.38413191]\n",
      " [110.82402189]\n",
      " [121.99863736]]\n"
     ]
    }
   ],
   "source": [
    "predictions = regr.predict(test_x)\n",
    "print(predictions)"
   ]
  },
  {
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 111.31\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))"
   ]
  },
  {
   "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 when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "Azim Hirjani\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
    "\n",
    "<!--\n",
    "## Change Log\n",
    "\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",
    "--!>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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