Summary
This was a very practical and hands-on chapter where we developed some standard code to train and evaluate multiple models. Then, we reviewed a few key machine learning models and how they work behind the hood. To complete and reinforce what we learned, we applied the machine learning models we learned about to the dataset and saw how well they did.
In the next chapter, we will start combining different forecasts into a single forecast and explore concepts such as combinatorial optimization and stacking to achieve state-of-the-art results.
