In this chapter, we covered a lot of ground! We started with a discussion about how trends are detected and predicted in the retail vertical. Then we dived into what market basket analysis really means and the core concepts, mathematical formulae underlying the algorithms, and the critical metrics which are used to evaluate the results obtained from the algorithms, notably, support, confidence, and lift. We also discussed the most popular techniques used for analysis, including contingency matrix evaluation, frequent itemset generation, and association rule mining. Next, we talked about how to make data driven decisions using market basket analysis. Finally, we implemented our own algorithms and also used some of the popular libraries in R, such as arules, to apply these techniques to some real world transactional data for detecting, predicting, and visualizing trends. Do note that these machine learning techniques only talk about product based recommendations purely based on purchase and transactional logs. The human element is missing here since we don't take into account the likes and dislikes based on user purchase or ratings.

In the next chapter, we will be tackling some of these very problems and building robust recommendation engines for recommending products taking into account products as well as user interests.