Polynomial features in Data Analysis Python - Mini Project: Build & Apply

Polynomial Features

📖 Scenario: Imagine you are analyzing how the size of a house affects its price. Sometimes, the relationship is not just straight but curved. Polynomial features help us capture this curved relationship by adding powers of the original size.

🎯 Goal: You will create a small dataset of house sizes, set a degree for polynomial features, generate these features, and then display the new data with polynomial terms.

📋 What You'll Learn

Create a list of house sizes called house_sizes with values 1000, 1500, 2000, 2500, 3000

Create a variable called degree and set it to 3

Use PolynomialFeatures from sklearn.preprocessing to transform house_sizes into polynomial features of degree 3

Store the transformed data in a variable called poly_features

Print the poly_features array

💡 Why This Matters

🌍 Real World

Polynomial features help in real estate price prediction where the effect of size on price is not just straight but curved.

💼 Career

Data scientists use polynomial features to improve models that predict outcomes based on nonlinear relationships.

1

Create the house sizes list

Create a list called house_sizes with these exact values: 1000, 1500, 2000, 2500, 3000.

Data Analysis Python

# Create the list house_sizes with the given values
# Your code here

Hint

Use square brackets to create a list and separate values with commas.

2

Set the polynomial degree

Create a variable called degree and set it to 3.

Data Analysis Python

house_sizes = [1000, 1500, 2000, 2500, 3000]
# Create the variable degree and set it to 3
# Your code here

Hint

Just assign the number 3 to the variable named degree.

3

Generate polynomial features

Import PolynomialFeatures from sklearn.preprocessing. Then create a PolynomialFeatures object called poly with degree set to degree. Next, reshape house_sizes into a 2D array called sizes_reshaped with one column. Finally, use poly.fit_transform(sizes_reshaped) to create polynomial features and store them in poly_features.

Data Analysis Python

house_sizes = [1000, 1500, 2000, 2500, 3000]
degree = 3
# Import PolynomialFeatures
# Create poly with degree
# Reshape house_sizes to sizes_reshaped
# Generate poly_features using poly.fit_transform
# Your code here

Hint

Use a list comprehension to reshape the list into a list of lists, each with one element.

4

Print the polynomial features

Print the variable poly_features to display the polynomial features array.

Data Analysis Python

from sklearn.preprocessing import PolynomialFeatures

house_sizes = [1000, 1500, 2000, 2500, 3000]
degree = 3

poly = PolynomialFeatures(degree=degree)
sizes_reshaped = [[size] for size in house_sizes]
poly_features = poly.fit_transform(sizes_reshaped)
# Print poly_features
# Your code here

Hint

Use the print() function to show the array.