01_ML0102EN-Reg-Mulitple-Linear-Regression-Co2__M.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": [
      "--2024-11-28 05:10:44--  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 0.003s  \n",
      "\n",
      "2024-11-28 05:10:44 (26.2 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",
       "    </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": 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:  [[10.50103333  7.56612295  9.71953935]]\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) : 509.12\n",
      "Variance score: 0.88\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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[10.5330457   7.2988167   6.06867055  3.42026482]]\n",
      "Residual sum of squares: 507.97\n",
      "Variance score: 0.88\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": [
    "# 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"
   ]
  },
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   "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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