Level3-Machine-Learning-Python/code/2020 Jan 24 (Multi Linear Regression on Fuel Consumption Dataset).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression Model\n",
    "A linear regression model that contains more than one predictor variable is called a multiple linear regression model. The following model is a multiple linear regression model with two predictor variables, ***$X_1$*** and ***$X_2$***.\n",
    "then\n",
    "\n",
    "**$Y = \\beta_0 + \\beta_1X_1 + \\beta_2X_2 + \\epsilon$**\n",
    "\n",
    "#### For more description [Click Here](http://reliawiki.org/index.php/Multiple_Linear_Regression_Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Datasets Link](tiny.cc/apssdc-datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": [
    "fuel = pd.read_csv('FuelConsumptionCo2.csv')\n",
    "fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1067, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MODELYEAR', 'MAKE', 'MODEL', 'VEHICLECLASS', 'ENGINESIZE', 'CYLINDERS',\n",
       "       'TRANSMISSION', 'FUELTYPE', 'FUELCONSUMPTION_CITY',\n",
       "       'FUELCONSUMPTION_HWY', 'FUELCONSUMPTION_COMB',\n",
       "       'FUELCONSUMPTION_COMB_MPG', 'CO2EMISSIONS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
    "\n",
    "### `FuelConsumption.csv`:\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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fuel['MAKE'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FORD             90\n",
       "CHEVROLET        86\n",
       "BMW              64\n",
       "MERCEDES-BENZ    59\n",
       "GMC              49\n",
       "TOYOTA           49\n",
       "AUDI             49\n",
       "PORSCHE          44\n",
       "VOLKSWAGEN       42\n",
       "DODGE            39\n",
       "MINI             36\n",
       "NISSAN           33\n",
       "KIA              33\n",
       "CADILLAC         32\n",
       "JEEP             31\n",
       "MAZDA            27\n",
       "HYUNDAI          24\n",
       "SUBARU           23\n",
       "LEXUS            22\n",
       "JAGUAR           22\n",
       "HONDA            21\n",
       "INFINITI         21\n",
       "LAND ROVER       19\n",
       "CHRYSLER         19\n",
       "MITSUBISHI       16\n",
       "BUICK            16\n",
       "RAM              13\n",
       "ACURA            12\n",
       "VOLVO            11\n",
       "LINCOLN          11\n",
       "FIAT             10\n",
       "SCION             9\n",
       "BENTLEY           8\n",
       "ROLLS-ROYCE       7\n",
       "ASTON MARTIN      7\n",
       "MASERATI          6\n",
       "LAMBORGHINI       3\n",
       "SMART             2\n",
       "SRT               2\n",
       "Name: MAKE, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel['MAKE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
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       "    <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",
       "  </tbody>\n",
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      "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = fuel[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.pairplot(data = features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = features[['ENGINESIZE','FUELCONSUMPTION_COMB']]\n",
    "Y = features[['CO2EMISSIONS']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression() # Regression Object\n",
    "pred = reg.fit(X,Y)\n",
    "Y_pred = reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8582144778981454"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[200.00402631]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2.0,8.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x18d42f396a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(X)\n",
    "df['CO2EMISSION'] = pd.DataFrame(Y_pred)\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19.49628353,  9.72995852]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([78.30681186])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.3, random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8721283450220715"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1 = LinearRegression()\n",
    "\n",
    "reg1.fit(X_train,Y_train)\n",
    "\n",
    "Y_pred = reg1.predict(X_test)\n",
    "\n",
    "reg1.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = reg1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8721283450220715"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.score(X_test,Y_test)"
   ]
  }
 ],
 "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
}