PRAKTIKUMmachinelearning/Regression/ML0101EN-Reg-Polynomial-Regression-Co2 Muhammad Bintang Mudzaffar.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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-10-19 06:14:14--  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",
      "2025-10-19 06:14:14 (36.4 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": 23,
   "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": 23,
     "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": 24,
   "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": 24,
     "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": 25,
   "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": 26,
   "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": 27,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  2.  ,  4.  ],\n",
       "       [ 1.  ,  3.5 , 12.25],\n",
       "       [ 1.  ,  3.5 , 12.25],\n",
       "       ...,\n",
       "       [ 1.  ,  3.  ,  9.  ],\n",
       "       [ 1.  ,  3.2 , 10.24],\n",
       "       [ 1.  ,  3.2 , 10.24]])"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         49.59507462 -1.43096779]]\n",
      "Intercept:  [109.19803969]\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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.64\n",
      "Residual sum of squares (MSE): 853.69\n",
      "R2-score: 0.79\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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         28.60682627  4.34859112 -0.47824097]]\n",
      "Intercept:  [131.43552557]\n",
      "Mean absolute error: 22.56\n",
      "Residual sum of squares (MSE): 851.53\n",
      "R2-score: 0.79\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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"
   ]
  },
  {
   "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"
   ]
  }
 ],
 "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
}