Akshay__Singh__/Day_7/Simple_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple_Linear_Regression (SLR) on FuelConsumption "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": [
    "#Read Data\n",
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets select some feature to explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now lets look at features vs emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": 10,
   "metadata": {},
   "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": 11,
   "metadata": {},
   "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(\"Cylinder\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets split data set into training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using sk_learn to model thw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[39.46431248]]\n",
      "Intercept:  [125.31534524]\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": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 19,
     "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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evalutaion matrix to evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 26.40\n",
      "Residual sum of squares (MSE): 1172.27\n",
      "R2-score: 0.64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "test_y_hat = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_hat - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_hat - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y_hat , test_y) )"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}