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`).

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.

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

Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Predict Prices Using Two Features

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Predict Prices Using Two Features

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