Model building

Goal: Produce some predictive models that solve the problem.

Here is where you build many predictive models that you will then evaluate to pick the best one. You must choose the type of model that will be trained or estimated. The term model training is associated with machine learning and the term estimation is associated with statistics. The approach, type of model, and training/estimation process you will use must be absolutely determined by the problem you are trying to solve and the solution you are looking for.

How to build models with Python and its data science ecosystem is the subject of the majority of this book. We will take a look at different approaches: machine learning, deep learning, Bayesian statistics. After trying different approaches, types of models, and fine-tuning techniques, at the end of this phase you may end up with some models considered to be finalists, and from the most promising ones of which the candidate winner will emerge: the one that will produce the best solution.