10.1. Introduction

In this chapter, we will learn how retailers such as Amazon, eBay, and Instacart predict consumer shopping behavior and make suggestions accordingly. We will take a close look at the various methods used to find common patterns in consumer behavior and recommend items that the consumer is more likely to purchase. We will also explore tools such as recommender systems, which are a form of filtering systems that predict consumer’s preferences. We will learn how to use Python libraries to find common purchasing combinations and consumer’s purchasing preferences in a large data set.

10.1.1. Learning Goals

  • Look for common patterns in a large data set

  • Analyze data and determine if it is sparse

  • Visualize associations between different items in a large data set

10.1.2. Learning Objectives

  • Find relationships between a particular group of items in an extensive data set using the Market basket analysis technique

  • Construct, analyze, and retrieve information from an item-item matrix

  • Create a square adjacency matrix to build a chord diagram to visualize the data

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