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

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())

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)

Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.

But before you start, recall the `PolynomialFeatures` class.

The `fit_transform(X)` method requires `X` to be a 2-D array (or a DataFrame).  
Using `X = df[['column_name']]` will get your `X` suited for `fit_transform()`.  
And if you have a 1-D array, use `.reshape(-1, 1)` to make a 2-D array with the same contents.

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

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.

Linear Regression with Python

Challenge

Task