Polynomial Regression: Topic-Based Exploration¶

This notebook is organized by key machine learning topics, progressing from basic to advanced, and is designed for hands-on experimentation and project work. You are encouraged to explore, modify, and extend each section.

Objectives¶

  • Use scikit-learn to implement polynomial regression
  • Create, train, test, and use a regression model
  • Share my personal insights and workflow

Table of contents

  1. Downloading Data
  2. Polynomial regression
  3. Evaluation
  4. Practice

Importing Needed packages¶

In [2]:
# import piplite
# await piplite.install(['pandas'])
# await piplite.install(['matplotlib'])
# await piplite.install(['numpy'])
# await piplite.install(['scikit-learn'])
In [3]:
# #This function will download the dataset into your browser 

# from pyodide.http import pyfetch

# async def download(url, filename):
#     response = await pyfetch(url)
#     if response.status == 200:
#         with open(filename, "wb") as f:
#             f.write(await response.bytes())
In [4]:
import matplotlib.pyplot as plt
import pandas as pd
import pylab as pl
import numpy as np
%matplotlib inline

Downloading Data

To download the data, we will use !wget to download it from IBM Object Storage.
In [5]:
path= "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"

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

Understanding the Data¶

FuelConsumption.csv:¶

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

  • MODELYEAR e.g. 2014
  • MAKE e.g. Acura
  • MODEL e.g. ILX
  • VEHICLE CLASS e.g. SUV
  • ENGINE SIZE e.g. 4.7
  • CYLINDERS e.g 6
  • TRANSMISSION e.g. A6
  • FUEL CONSUMPTION in CITY(L/100 km) e.g. 9.9
  • FUEL CONSUMPTION in HWY (L/100 km) e.g. 8.9
  • FUEL CONSUMPTION COMB (L/100 km) e.g. 9.2
  • CO2 EMISSIONS (g/km) e.g. 182 --> low --> 0

Reading the data in¶

In [6]:
# await download(path, "FuelConsumption.csv")
In [7]:
df = pd.read_csv(path)
# take a look at the dataset
df.head()
Out[7]:
MODELYEAR MAKE MODEL VEHICLECLASS ENGINESIZE CYLINDERS TRANSMISSION FUELTYPE FUELCONSUMPTION_CITY FUELCONSUMPTION_HWY FUELCONSUMPTION_COMB FUELCONSUMPTION_COMB_MPG CO2EMISSIONS
0 2014 ACURA ILX COMPACT 2.0 4 AS5 Z 9.9 6.7 8.5 33 196
1 2014 ACURA ILX COMPACT 2.4 4 M6 Z 11.2 7.7 9.6 29 221
2 2014 ACURA ILX HYBRID COMPACT 1.5 4 AV7 Z 6.0 5.8 5.9 48 136
3 2014 ACURA MDX 4WD SUV - SMALL 3.5 6 AS6 Z 12.7 9.1 11.1 25 255
4 2014 ACURA RDX AWD SUV - SMALL 3.5 6 AS6 Z 12.1 8.7 10.6 27 244

Let's select some features that we want to use for regression.

In [8]:
cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]
cdf.head(9)
Out[8]:
ENGINESIZE CYLINDERS FUELCONSUMPTION_COMB CO2EMISSIONS
0 2.0 4 8.5 196
1 2.4 4 9.6 221
2 1.5 4 5.9 136
3 3.5 6 11.1 255
4 3.5 6 10.6 244
5 3.5 6 10.0 230
6 3.5 6 10.1 232
7 3.7 6 11.1 255
8 3.7 6 11.6 267

Let's plot Emission values with respect to Engine size:

In [9]:
plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')
plt.xlabel("Engine size")
plt.ylabel("Emission")
plt.show()
No description has been provided for this image

Creating train and test dataset¶

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.

In [10]:
msk = np.random.rand(len(df)) < 0.8
train = cdf[msk]
test = cdf[~msk]

Polynomial regression

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.

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):

$$y = b + \theta\_1 x + \theta\_2 x^2$$

Now, the question is: how we can fit our data on this equation while we have only x values, such as Engine Size? Well, we can create a few additional features: 1, $x$, and $x^2$.

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:

In [11]:
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
train_x = np.asanyarray(train[['ENGINESIZE']])
train_y = np.asanyarray(train[['CO2EMISSIONS']])

test_x = np.asanyarray(test[['ENGINESIZE']])
test_y = np.asanyarray(test[['CO2EMISSIONS']])


poly = PolynomialFeatures(degree=2)
train_x_poly = poly.fit_transform(train_x)
train_x_poly
Out[11]:
array([[ 1.  ,  2.4 ,  5.76],
       [ 1.  ,  1.5 ,  2.25],
       [ 1.  ,  3.5 , 12.25],
       ...,
       [ 1.  ,  3.  ,  9.  ],
       [ 1.  ,  3.2 , 10.24],
       [ 1.  ,  3.2 , 10.24]])

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).

The equation and the sample example is displayed below.

$$ \begin{bmatrix} v\_1\\\\ v\_2\\ \vdots\\ v_n \end{bmatrix}\longrightarrow \begin{bmatrix} \[ 1 & v\_1 & v\_1^2]\\ \[ 1 & v\_2 & v\_2^2]\\ \vdots & \vdots & \vdots\\ \[ 1 & v_n & v_n^2] \end{bmatrix} $$

$$ \begin{bmatrix} 2.\\ 2.4\\ 1.5\\ \vdots \end{bmatrix} \longrightarrow \begin{bmatrix} \[ 1 & 2. & 4.]\\ \[ 1 & 2.4 & 5.76]\\ \[ 1 & 1.5 & 2.25]\\ \vdots & \vdots & \vdots\\ \end{bmatrix} $$

It looks like feature sets for multiple linear regression analysis, right? Yes. It Does. 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:

$$y = b + \theta\_1 x\_1 + \theta\_2 x\_2$$

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.

so we can use LinearRegression() function to solve it:

In [12]:
clf = linear_model.LinearRegression()
train_y_ = clf.fit(train_x_poly, train_y)
# The coefficients
print ('Coefficients: ', clf.coef_)
print ('Intercept: ',clf.intercept_)

Coefficients:  [[ 0.         50.43347999 -1.55804599]]
Intercept:  [107.86764233]

As mentioned before, Coefficient and Intercept , are the parameters of the fit curvy line. 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:

In [13]:
plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
XX = np.arange(0.0, 10.0, 0.1)
yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)
plt.plot(XX, yy, '-r' )
plt.xlabel("Engine size")
plt.ylabel("Emission")
Out[13]:
Text(0, 0.5, 'Emission')
No description has been provided for this image

Evaluation

In [14]:
from sklearn.metrics import r2_score

test_x_poly = poly.transform(test_x)
test_y_ = clf.predict(test_x_poly)

print("Mean absolute error: %.2f" % np.mean(np.absolute(test_y_ - test_y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((test_y_ - test_y) ** 2))
print("R2-score: %.2f" % r2_score(test_y,test_y_ ) )

Mean absolute error: 22.17
Residual sum of squares (MSE): 850.12
R2-score: 0.80

Practice

Try to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?
In [15]:
# write your code here
poly3 = PolynomialFeatures(degree=3)
train_x_poly3 = poly3.fit_transform(train_x)
clf3 = linear_model.LinearRegression()
train_y3_ = clf3.fit(train_x_poly3, train_y)

# The coefficients
print ('Coefficients: ', clf3.coef_)
print ('Intercept: ',clf3.intercept_)
plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
XX = np.arange(0.0, 10.0, 0.1)
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)
plt.plot(XX, yy, '-r' )
plt.xlabel("Engine size")
plt.ylabel("Emission")
test_x_poly3 = poly3.transform(test_x)
test_y3_ = clf3.predict(test_x_poly3)
print("Mean absolute error: %.2f" % np.mean(np.absolute(test_y3_ - test_y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((test_y3_ - test_y) ** 2))
print("R2-score: %.2f" % r2_score(test_y,test_y3_ ) )

Coefficients:  [[ 0.         26.76090172  4.93165382 -0.53512571]]
Intercept:  [133.11913097]
Mean absolute error: 22.19
Residual sum of squares (MSE): 851.54
R2-score: 0.80
No description has been provided for this image
Click here for the solution 
poly3 = PolynomialFeatures(degree=3)
train_x_poly3 = poly3.fit_transform(train_x)
clf3 = linear_model.LinearRegression()
train_y3_ = clf3.fit(train_x_poly3, train_y)

# The coefficients
print ('Coefficients: ', clf3.coef_)
print ('Intercept: ',clf3.intercept_)
plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
XX = np.arange(0.0, 10.0, 0.1)
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)
plt.plot(XX, yy, '-r' )
plt.xlabel("Engine size")
plt.ylabel("Emission")
test_x_poly3 = poly3.transform(test_x)
test_y3_ = clf3.predict(test_x_poly3)
print("Mean absolute error: %.2f" % np.mean(np.absolute(test_y3_ - test_y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((test_y3_ - test_y) ** 2))
print("R2-score: %.2f" % r2_score(test_y,test_y3_ ) )

Personal Experimentation Space¶

Use this section to try new ideas, tune hyperparameters, or test additional algorithms. Document your experiments and insights here.

Project-Oriented Challenge¶

Design and implement your own end-to-end polynomial regression project. Define your problem statement, preprocess data, engineer features, select and tune models, and present your results. Use the space below to outline and execute your project.