In this chapter, we have carried on introducing linear regression, extending our example from a simple to a multiple one. We have revisited the previous outputs from the Statsmodels linear functions (the classical statistical approach) and gradient descent (the data science engine).

We started experimenting with models by removing selected predictors and evaluating the impact of such a move from the point of view of the R-squared measure. Meanwhile we also discovered reciprocal correlations between predictors and how to render more linear relations between each predictor and the target variable by intercepting the interactions and by means of polynomial expansion of the features.

In the next chapter, we will progress again and extend the regression model to make it viable for classification tasks, turning it into a probabilistic predictor. The conceptual jump into the world of probability will allow us to complete the range of possible problems where linear models can be successfully...