Apprendre Challenge: Evaluating the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


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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/houses_poly.csv')
print(df.head())

Next, we'll create a scatterplot for this data:


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import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)
plt.show()

A straight line is a poor fit here: prices rise for both very new and very old houses. A parabola models this trend better — that’s what you will build in this challenge.

But before you start, recall the PolynomialFeatures class.

fit_transform(X) needs a 2-D array or DataFrame. Use df[['col']] or, for a 1-D array, apply .reshape(-1, 1) to convert it into 2-D.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Tâche

Swipe to start coding

Assign the X variable to a DataFrame containing column 'age'.
Create an X_tilde matrix using the PolynomialFeatures class.
Build and train a Polynomial Regression model.
Reshape X_new to be a 2-D array.
Preprocess X_new the same way as X.
Print the model's parameters.

Solution

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Section 1. Chapitre 15

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