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Praktikum-Machine-Learning/Faadhilah Zahraan - ML0101EN-Reg-Polynomial-Regression-Co2.ipynb

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{
"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 08:51:44-- 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 0.005s \n",
"\n",
"2025-10-20 08:51:44 (14.1 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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
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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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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ENGINESIZE</th>\n",
" <th>CYLINDERS</th>\n",
" <th>FUELCONSUMPTION_COMB</th>\n",
" <th>CO2EMISSIONS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>8.5</td>\n",
" <td>196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.4</td>\n",
" <td>4</td>\n",
" <td>9.6</td>\n",
" <td>221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.5</td>\n",
" <td>4</td>\n",
" <td>5.9</td>\n",
" <td>136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.5</td>\n",
" <td>6</td>\n",
" <td>11.1</td>\n",
" <td>255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.5</td>\n",
" <td>6</td>\n",
" <td>10.6</td>\n",
" <td>244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.5</td>\n",
" <td>6</td>\n",
" <td>10.0</td>\n",
" <td>230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.5</td>\n",
" <td>6</td>\n",
" <td>10.1</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.7</td>\n",
" <td>6</td>\n",
" <td>11.1</td>\n",
" <td>255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.7</td>\n",
" <td>6</td>\n",
" <td>11.6</td>\n",
" <td>267</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ENGINESIZE CYLINDERS FUELCONSUMPTION_COMB CO2EMISSIONS\n",
"0 2.0 4 8.5 196\n",
"1 2.4 4 9.6 221\n",
"2 1.5 4 5.9 136\n",
"3 3.5 6 11.1 255\n",
"4 3.5 6 10.6 244\n",
"5 3.5 6 10.0 230\n",
"6 3.5 6 10.1 232\n",
"7 3.7 6 11.1 255\n",
"8 3.7 6 11.6 267"
]
},
"execution_count": 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. , 3.5 , 12.25],\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. 51.97357754 -1.76656756]]\n",
"Intercept: [106.2464918]\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": {
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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: 23.89\n",
"Residual sum of squares (MSE): 956.70\n",
"R2-score: 0.78\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": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coefficients: [[ 0. 27.08436317 5.04696617 -0.56155495]]\n",
"Intercept: [132.8497039]\n",
"Mean absolute error: 24.06\n",
"Residual sum of squares (MSE): 959.95\n",
"R2-score: 0.78\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"poly3 = PolynomialFeatures(degree=3)\n",
"train_x_poly3 = poly3.fit_transform(train_x)\n",
"clf3 = linear_model.LinearRegression()\n",
"train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
"\n",
"# The coefficients\n",
"print ('Coefficients: ', clf3.coef_)\n",
"print ('Intercept: ',clf3.intercept_)\n",
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
"XX = np.arange(0.0, 10.0, 0.1)\n",
"yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
"plt.plot(XX, yy, '-r' )\n",
"plt.xlabel(\"Engine size\")\n",
"plt.ylabel(\"Emission\")\n",
"test_x_poly3 = poly3.transform(test_x)\n",
"test_y3_ = clf3.predict(test_x_poly3)\n",
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
"print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
"print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python \n",
"poly3 = PolynomialFeatures(degree=3)\n",
"train_x_poly3 = poly3.fit_transform(train_x)\n",
"clf3 = linear_model.LinearRegression()\n",
"train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
"\n",
"# The coefficients\n",
"print ('Coefficients: ', clf3.coef_)\n",
"print ('Intercept: ',clf3.intercept_)\n",
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
"XX = np.arange(0.0, 10.0, 0.1)\n",
"yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
"plt.plot(XX, yy, '-r' )\n",
"plt.xlabel(\"Engine size\")\n",
"plt.ylabel(\"Emission\")\n",
"test_x_poly3 = poly3.transform(test_x)\n",
"test_y3_ = clf3.predict(test_x_poly3)\n",
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
"print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
"print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
"\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2>Want to learn more?</h2>\n",
"\n",
"IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
"\n",
"Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"\n",
"## Author\n",
"\n",
"Saeed Aghabozorgi\n",
"\n",
"\n",
"### Other Contributors\n",
"\n",
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
"\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
"\n",
"\n",
"<!--## Change Log\n",
"\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"|---|---|---|---|\n",
"| 2021-01-11 | 2.3 | Lakshmi | Changed R2-score calculation in polynomial regression |\n",
"| 2020-11-04 | 2.2 | Lakshmi | Made changes in markdown of equations |\n",
"| 2020-11-03 | 2.1 | Lakshmi | Made changes in URL |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"| | | | |\n",
"| | | | | --!>\n",
"\n",
"\n"
]
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