In this chapter, the goal was to provide an introduction to how to use R in order to build and test association rule mining (market basket analysis) and recommendation engines. Market basket analysis is trying to understand what items are purchased together. With recommendation engines, the goal is to provide a customer with other items that they will enjoy based on how they have rated previously viewed or purchased items. It is important to understand the R package that we used (recommenderlab) for recommendation is not designed for implementation, but to develop and test algorithms. The other thing examined here was longitudinal data and mining it to learn valuable insights, in our case, the order in which customers purchased our products. Such an analysis has numerous applications, from marketing campaigns to healthcare.

We are now going to shift gears back to supervised learning. In the next chapter, we are going to cover some of the most exciting and important methods in practical machine learning, that is multi-class classification and creating ensemble models, something that is very easy to do in R with recent package releases.