Fit & predict for regression

Now, you will fit a linear regression and predict life expectancy using just one feature. You saw Andy do this earlier using the 'RM' feature of the Boston housing dataset. In this exercise, you will use the 'fertility' feature of the Gapminder dataset. Since the goal is to predict life expectancy, the target variable here is 'life'. The array for the target variable has been pre-loaded as y and the array for 'fertility' has been pre-loaded as X_fertility.

A scatter plot with 'fertility' on the x-axis and 'life' on the y-axis has been generated. As you can see, there is a strongly negative correlation, so a linear regression should be able to capture this trend. Your job is to fit a linear regression and then predict the life expectancy, overlaying these predicted values on the plot to generate a regression line. You will also compute and print the \(R^2\) score using sckit-learn's .score() method.

Instructions

Import LinearRegression from sklearn.linear_model.
Create a LinearRegression regressor called reg.
Set up the prediction space to range from the minimum to the maximum of X_fertility. This has been done for you.
Fit the regressor to the data (X_fertility and y) and compute its predictions using the .predict() method and the prediction_space array.
Compute and print the \(R^2\) score using the .score() method.
Overlay the plot with your linear regression line. This has been done for you, so hit 'Submit Answer' to see the result!

Take Hint (-30xp)