In this chapter, we have described real life examples of supervised and unsupervised machine learning models that have been applied to solving problems. We covered multiple regression, decision trees, and clustering. We have also shown how to choose and transform the input variables or features to be ingested by the models.
This chapter only shows the basic principles of each algorithm. In real data analysis and prediction using machine learning, models are already programmed and can be used as black boxes. It is, therefore, extremely important to understand the basics of each model and know whether we are using it correctly.
In the following chapters, we will focus on how to extract the data from different sources, transform it according to our needs, and use previously built models for analysis.
