Course Content

# Linear Regression with Python

4. Choosing The Best Model

Linear Regression with Python

## Challenge

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

Let's build a scatterplot of this data.

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`

.

# Task

- 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.
- Print the model's parameters.
- Reshape
`X_new`

to be a 2-D array. - Preprocess
`X_new`

the same way as`X`

.

Everything was clear?