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Linear Regression with Python


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.


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

Everything was clear?

Section 4. Chapter 0
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