{
 "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 10:27:37--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 72629 (71K) [text/csv]\n",
      "Saving to: ‘FuelConsumption.csv’\n",
      "\n",
      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.003s  \n",
      "\n",
      "2025-10-17 10:27:37 (24.8 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://www.ibm.com/us-en/cloud/object-storage?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Understanding the Data\n",
    "\n",
    "### `FuelConsumption.csv`:\n",
    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
    "\n",
    "- **MODELYEAR** e.g. 2014\n",
    "- **MAKE** e.g. Acura\n",
    "- **MODEL** e.g. ILX\n",
    "- **VEHICLE CLASS** e.g. SUV\n",
    "- **ENGINE SIZE** e.g. 4.7\n",
    "- **CYLINDERS** e.g 6\n",
    "- **TRANSMISSION** e.g. A6\n",
    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the data in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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.  ,  3.2 , 10.24],\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.         52.15079093 -1.72242874]]\n",
      "Intercept:  [105.04586083]\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: 22.90\n",
      "Residual sum of squares (MSE): 855.38\n",
      "R2-score: 0.75\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": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[ 0.         35.84225925  2.73074001 -0.36603531]]\n",
      "Intercept:  [122.48512973]\n",
      "Mean absolute error: 22.84\n",
      "Residual sum of square (MSE): 846.79\n",
      "R2-score: 0.75\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",
    "# Coeffiecient\n",
    "\n",
    "print('Coefficients: ', clf3.coef_)\n",
    "print('Intercept: ', clf3.intercept_)\n",
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='green')\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 square (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": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "\n",
    "<!--## Change Log\n",
    "\n",
    "\n",
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\n",
    "| 2020-11-03  | 2.1  | Lakshmi  |  Made changes in URL |\n",
    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
    "|   |   |   |   |\n",
    "|   |   |   |   | --!>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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