Mulitple-Linear-Regression-Co2.ipynb

{
 "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",
    "# Multiple 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 Multiple Linear Regression\n",
    "* Create a model, train it, test it and use the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#understanding-data\">Understanding the Data</a></li>\n",
    "        <li><a href=\"#reading_data\">Reading the Data in</a></li>\n",
    "        <li><a href=\"#multiple_regression_model\">Multiple Regression Model</a></li>\n",
    "        <li><a href=\"#prediction\">Prediction</a></li>\n",
    "        <li><a href=\"#practice\">Practice</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Needed packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n",
    "!pip install matplotlib\n",
    "!pip install pandas \n",
    "!pip install numpy \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-11-10 17:50:29--  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 0.002s  \n",
      "\n",
      "2024-11-10 17:50:30 (44.5 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": [
    "\n",
    "<h2 id=\"understanding_data\">Understanding the Data</h2>\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",
    "- **FUELTYPE** e.g. z\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": [
    "<h2 id=\"reading_data\">Reading the data in</h2>\n"
   ]
  },
  {
   "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>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",
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       "    <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": 4,
     "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": {},
   "source": [
    "Let's select some features that we want to use for regression.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "        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_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</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>9.9</td>\n",
       "      <td>6.7</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>11.2</td>\n",
       "      <td>7.7</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>6.0</td>\n",
       "      <td>5.8</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>12.7</td>\n",
       "      <td>9.1</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>12.1</td>\n",
       "      <td>8.7</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>11.9</td>\n",
       "      <td>7.7</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>11.8</td>\n",
       "      <td>8.1</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>12.8</td>\n",
       "      <td>9.0</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>13.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0         2.0          4                   9.9                  6.7   \n",
       "1         2.4          4                  11.2                  7.7   \n",
       "2         1.5          4                   6.0                  5.8   \n",
       "3         3.5          6                  12.7                  9.1   \n",
       "4         3.5          6                  12.1                  8.7   \n",
       "5         3.5          6                  11.9                  7.7   \n",
       "6         3.5          6                  11.8                  8.1   \n",
       "7         3.7          6                  12.8                  9.0   \n",
       "8         3.7          6                  13.4                  9.5   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  CO2EMISSIONS  \n",
       "0                   8.5           196  \n",
       "1                   9.6           221  \n",
       "2                   5.9           136  \n",
       "3                  11.1           255  \n",
       "4                  10.6           244  \n",
       "5                  10.0           230  \n",
       "6                  10.1           232  \n",
       "7                  11.1           255  \n",
       "8                  11.6           267  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot Emission values with respect to Engine size:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": [
    "#### Creating train and test dataset\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 model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
    "\n",
    "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. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the  __np.random.rand()__ function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train data distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": [
    "<h2 id=\"multiple_regression_model\">Multiple Regression Model</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In reality, there are multiple variables that impact the co2emission. When more than one independent variable is present, the process is called multiple linear regression. An example of multiple linear regression is predicting co2emission using the features FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that multiple linear regression model is the extension of the simple linear regression model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[11.2951328   7.57275429  9.48389051]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit (x, y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, __Coefficient__ and __Intercept__  are the parameters of the fitted line. \n",
    "Given that it is a multiple linear regression model with 3 parameters and that the parameters are the intercept and coefficients of the hyperplane, sklearn can estimate them from our data. Scikit-learn uses plain Ordinary Least Squares method to solve this problem.\n",
    "\n",
    "#### Ordinary Least Squares (OLS)\n",
    "OLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ($\\hat{y}$) over all samples in the dataset.\n",
    "\n",
    "OLS can find the best parameters using of the following methods:\n",
    "* Solving the model parameters analytically using closed-form equations\n",
    "* Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"prediction\">Prediction</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE) : 575.52\n",
      "Variance score: 0.84\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/sklearn/base.py:486: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "print(\"Mean Squared Error (MSE) : %.2f\"\n",
    "      % np.mean((y_hat - y) ** 2))\n",
    "\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % regr.score(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Explained variance regression score:__  \n",
    "Let $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n",
    "\n",
    "$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var\\{ y - \\hat{y}\\}}{Var\\{y\\}}$  \n",
    "The best possible score is 1.0, the lower values are worse.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"practice\">Practice</h2>\n",
    "Try to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your code here\n",
    "regr = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit (x, y)\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
    "print('Variance score: %.2f' % regr.score(x, y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "regr = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit (x, y)\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
    "print('Variance score: %.2f' % regr.score(x, y))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\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",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "    \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  | Made changes in URL |\n",
    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
    "|   |   |   |   |\n",
    "|   |   |   |   |\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "--!>\n"
   ]
  }
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