{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Simple Linear Regression\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Importing Needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# primary data structure libraries\n",
    "import numpy as np\n",
    "import pandas as pd       \n",
    "\n",
    "# primary machine learning library\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# primary plotting library\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt        \n",
    "\n",
    "# optional: for ggplot-like style of plots\n",
    "mpl.style.use(['ggplot'])        \n",
    "\n",
    "# using the inline backend\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<a id=\"understanding_data\"></a>\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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<a id=\"reading_data\"></a>\n",
    "## Reading the data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<a id=\"data_exploration\"></a>\n",
    "## Data Exploration\n",
    "\n",
    "Lets first have a descriptive exploration on our data."
   ]
  },
  {
   "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>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": 3,
     "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": 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>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": 4,
     "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": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist(figsize=(6,6))\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": 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 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": 7,
   "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": "code",
   "execution_count": 8,
   "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.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Creating train and test dataset\n",
    "\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.\n",
    "\n",
    "This means that we know the outcome of each data point in this dataset, making it great to test with! And since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
    "\n",
    "Lets split our dataset into train and test sets, 80% of the entire data for training, and the 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<a id=\"simple_regression\"></a>\n",
    "## Simple Regression Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Train data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": 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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": true
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.24588268]]\n",
      "Intercept:  [124.33351369]\n"
     ]
    }
   ],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can plot the fit line over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": true
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Evaluation\n",
    "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": 13,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": true
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 23.90\n",
      "Residual sum of squares (MSE): 1032.05\n",
      "R2-score: 0.67\n"
     ]
    }
   ],
   "source": [
    "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": {},
   "source": [
    "## Thanks for reading :)\n",
    "Created by [Saeed Aghabozorgi](https://www.linkedin.com/in/saeedaghabozorgi/) and modified by [Tarun Kamboj](https://www.linkedin.com/in/kambojtarun/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}