Introduction

In this chapter, we are going to change direction entirely. The previous chapter, which explored topic models, focused on natural language processing, text data, and applying relatively recently developed algorithms. Most data science practitioners would agree that natural language processing, including topic models, is toward the cutting edge of data science and is an active research area. We now understand that topic models can, and should, be leveraged wherever text data could potentially drive insights or growth, including in social media analysis, recommendation engines, and news filtering.

This chapter takes us into the retail space to explore a foundational and reliable algorithm for analyzing transaction data. While this algorithm might not be on the cutting edge or in the catalog of the most popular machine learning algorithms, it is ubiquitous and undeniably impactful in the retail space. The insights it drives are easily interpretable, immediately actionable, and instructive...