Praktikum-Machine-Learning/Machine Learning with Python - Regression/ML0101EN-Reg-Polynomial-Regression-Co2-Adhwa.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": 1,
   "metadata": {
    "tags": []
   },
   "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": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-10-20 13:50:13--  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",
      "2025-10-20 13:50:13 (37.9 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": 4,
   "metadata": {
    "tags": []
   },
   "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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
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       "    <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RLX</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28</td>\n",
       "      <td>230</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>TL AWD</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "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",
       "5       2014  ACURA         RLX     MID-SIZE         3.5          6   \n",
       "6       2014  ACURA          TL     MID-SIZE         3.5          6   \n",
       "7       2014  ACURA      TL AWD     MID-SIZE         3.7          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",
       "5          AS6        Z                  11.9                  7.7   \n",
       "6          AS6        Z                  11.8                  8.1   \n",
       "7          AS6        Z                  12.8                  9.0   \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  \n",
       "5                  10.0                        28           230  \n",
       "6                  10.1                        28           232  \n",
       "7                  11.1                        25           255  "
      ]
     },
     "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(8)"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
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       "      <td>196</td>\n",
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       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
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       "    <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",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot Emission values with respect to Engine size:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "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": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  2.  ,  4.  ],\n",
       "       [ 1.  ,  2.4 ,  5.76],\n",
       "       [ 1.  ,  1.5 ,  2.25],\n",
       "       ...,\n",
       "       [ 1.  ,  3.  ,  9.  ],\n",
       "       [ 1.  ,  3.2 , 10.24],\n",
       "       [ 1.  ,  3.2 , 10.24]])"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         52.03884901 -1.6971995 ]]\n",
      "Intercept:  [104.27341021]\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": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 23.62\n",
      "Residual sum of squares (MSE): 918.98\n",
      "R2-score: 0.77\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": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         38.49813327  2.09672931 -0.32050742]]\n",
      "Intercept:  [118.48379347]\n",
      "Mean absolute error: 23.41\n",
      "Residual sum of squares (MSE): 907.86\n",
      "R2-score: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\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_ ) )"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "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"
   ]
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    "### 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"
   ]
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