We have arrived at the concluding chapter of the book. In respect of the previous chapters, the present one is very practical in its essence, since it mostly contains lots of code and no math or other theoretical explanation. It comprises four practical examples of real-world data science problems solved using linear models. The ultimate goal is to demonstrate how to approach such problems and how to develop the reasoning behind their resolution, so that they can be used as blueprints for similar challenges you'll encounter.
For each problem, we will describe the question to be answered, provide a short description of the dataset, and decide the metric we strive to maximize (or the error we want to minimize). Then, throughout the code, we will provide ideas and intuitions that are key to successfully completing each one. In addition, when run, the code will produce verbose output from the modeling, in order to provide the reader with...