Praktikum_Machine_Learning/Regression/Syabrina Bening Putri_F5A2_202310715235_ML0101EN-Reg-Polynomial-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",
    "# Polynomial 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 Polynomial 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=\"#download_data\">Downloading Data</a></li>\n",
    "        <li><a href=\"#polynomial_regression\">Polynomial regression</a></li>\n",
    "        <li><a href=\"#evaluation\">Evaluation</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": 13,
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"download_data\">Downloading Data</h2>\n",
    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-10-20 07:46:03--  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 0.001s  \n",
      "\n",
      "2025-10-20 07:46:03 (49.8 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": [
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://www.ibm.com/us-en/cloud/object-storage?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\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": {},
   "source": [
    "## Reading the data in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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": 15,
     "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": 16,
   "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_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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n",
    "\n",
    "In essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n",
    "\n",
    "\n",
    "$$y = b + \\theta_1  x + \\theta_2 x^2$$\n",
    "\n",
    "\n",
    "\n",
    "Now, the question is: how we can fit our data on this equation while we have only x values, such as __Engine Size__? \n",
    "Well, we can create a few additional features: 1, $x$, and $x^2$.\n",
    "\n",
    "\n",
    "\n",
    "__PolynomialFeatures()__ function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, _ENGINESIZE_. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  2.  ,  4.  ],\n",
       "       [ 1.  ,  2.4 ,  5.76],\n",
       "       [ 1.  ,  3.5 , 12.25],\n",
       "       ...,\n",
       "       [ 1.  ,  3.2 , 10.24],\n",
       "       [ 1.  ,  3.  ,  9.  ],\n",
       "       [ 1.  ,  3.2 , 10.24]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "\n",
    "\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "train_x_poly = poly.fit_transform(train_x)\n",
    "train_x_poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**fit_transform** takes our x values, and output a list of our data raised from power of 0 to power of 2 (since we set the degree of our polynomial to 2).   \n",
    "\n",
    "The equation and the sample example is displayed below.   \n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "    v_1\\\\\\\\\\\\\n",
    "    v_2\\\\\\\\\n",
    "    \\vdots\\\\\\\\\n",
    "    v_n\n",
    "\\end{bmatrix}\\longrightarrow \\begin{bmatrix}\n",
    "    [ 1 & v_1 & v_1^2]\\\\\\\\\n",
    "    [ 1 & v_2 & v_2^2]\\\\\\\\\n",
    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
    "    [ 1 & v_n & v_n^2]\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "    2.\\\\\\\\\n",
    "    2.4\\\\\\\\\n",
    "    1.5\\\\\\\\\n",
    "    \\vdots\n",
    "\\end{bmatrix} \\longrightarrow \\begin{bmatrix}\n",
    "    [ 1 & 2. & 4.]\\\\\\\\\n",
    "    [ 1 & 2.4 & 5.76]\\\\\\\\\n",
    "    [ 1 & 1.5 & 2.25]\\\\\\\\\n",
    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like feature sets for multiple linear regression analysis, right? Yes. It Does. \n",
    "Indeed, Polynomial regression is a special case of linear regression, with the main idea of how do you select your features. Just consider replacing the  $x$ with $x_1$, $x_1^2$ with $x_2$, and so on. Then the 2nd degree equation would be turn into:\n",
    "\n",
    "$$y = b + \\theta_1  x_1 + \\theta_2 x_2$$\n",
    "\n",
    "Now, we can deal with it as a 'linear regression' problem. Therefore, this polynomial regression is considered to be a special case of traditional multiple linear regression. So, you can use the same mechanism as linear regression to solve such problems. \n",
    "\n",
    "\n",
    "\n",
    "so we can use __LinearRegression()__ function to solve it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         50.57406358 -1.44659762]]\n",
      "Intercept:  [106.21698993]\n"
     ]
    }
   ],
   "source": [
    "clf = linear_model.LinearRegression()\n",
    "train_y_ = clf.fit(train_x_poly, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', clf.coef_)\n",
    "print ('Intercept: ',clf.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, __Coefficient__ and __Intercept__ , are the parameters of the fit curvy line. \n",
    "Given that it is a typical multiple linear regression, with 3 parameters, and knowing that the parameters are the intercept and coefficients of hyperplane, sklearn has estimated them from our new set of feature sets. Lets plot it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "XX = np.arange(0.0, 10.0, 0.1)\n",
    "yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)\n",
    "plt.plot(XX, yy, '-r' )\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"evaluation\">Evaluation</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.32\n",
      "Residual sum of squares (MSE): 843.55\n",
      "R2-score: 0.76\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x_poly = poly.transform(test_x)\n",
    "test_y_ = clf.predict(test_x_poly)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"practice\">Practice</h2>\n",
    "Try to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         29.80281416  4.22812695 -0.46674758]]\n",
      "Intercept:  [128.48038184]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.33\n",
      "Residual sum of squares (MSE): 840.89\n",
      "R2-score: 0.76\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "poly3 = PolynomialFeatures(degree=3)\n",
    "train_x_poly3 = poly3.fit_transform(train_x)\n",
    "\n",
    "clf3 = linear_model.LinearRegression()\n",
    "clf3.fit(train_x_poly3, train_y)\n",
    "\n",
    "print('Coefficients: ', clf3.coef_)\n",
    "print('Intercept: ', clf3.intercept_)\n",
    "\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
    "XX = np.arange(min(train.ENGINESIZE), max(train.ENGINESIZE), 0.1)\n",
    "yy = clf3.intercept_[0] + clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX,2) + clf3.coef_[0][3]*np.power(XX,3)\n",
    "plt.plot(XX, yy, '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"CO2 Emission\")\n",
    "plt.show()\n",
    "\n",
    "test_x_poly3 = poly3.transform(test_x)\n",
    "test_y3_ = clf3.predict(test_x_poly3)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y, test_y3_))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python    \n",
    "poly3 = PolynomialFeatures(degree=3)\n",
    "train_x_poly3 = poly3.fit_transform(train_x)\n",
    "clf3 = linear_model.LinearRegression()\n",
    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
    "\n",
    "# The coefficients\n",
    "print ('Coefficients: ', clf3.coef_)\n",
    "print ('Intercept: ',clf3.intercept_)\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "XX = np.arange(0.0, 10.0, 0.1)\n",
    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
    "plt.plot(XX, yy, '-r' )\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "test_x_poly3 = poly3.transform(test_x)\n",
    "test_y3_ = clf3.predict(test_x_poly3)\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
    "\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",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "\n",
    "<!--## Change Log\n",
    "\n",
    "\n",
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "prev_pub_hash": "4dc110debac287dfd374a575573c16e62a80a935b3bbe2b2f6d5a0598e6e33f6"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}