Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age and square_feet).


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import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv')
print(df.head())

Your task is to build a Multiple Linear Regression model using the OLS class. Also, you will print the summary table to look at the p-values of each feature.

Task

Swipe to start coding

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

Solution

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 5

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