{
 "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": [
      "--2025-10-17 01:55:05--  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",
      "2025-10-17 01:55:05 (28.3 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": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": 3,
     "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": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<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": 4,
     "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": 5,
   "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": 6,
   "metadata": {
    "tags": []
   },
   "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": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, positive=False):\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  precompute=False, eps=np.finfo(np.float).eps,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=np.finfo(np.float).eps, random_state=None,\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
     ]
    },
    {
     "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": 7,
     "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": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         51.34464277 -1.64112566]]\n",
      "Intercept:  [105.83262962]\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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 22.99\n",
      "Residual sum of squares (MSE): 939.81\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": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         27.21448026  4.98041569 -0.54670275]]\n",
      "Intercept:  [105.83262962]\n",
      "Mean absolute error: 22.97\n",
      "Residual sum of squares (MSE): 942.88\n",
      "R2-score: 0.76\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: ' , clf.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"
   ]
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   "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"
   ]
  },
  {
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