Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations. What do Amazon product recommendations and Netflix movie suggestions have in common? They both rely on Market Basket Analysis, which is a powerful tool for translating vast amounts of customer transaction and viewing data into simple rules for product promotion and recommendation. In this course, you’ll learn how to perform Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, aggregation and pruning, and visualization. You’ll then reinforce your new skills through interactive exercises, building recommendations for a small grocery store, a library, an e-book seller, a novelty gift retailer, and a movie streaming service. In the process, you’ll uncover hidden insights to improve recommendations for customers.
In this project, Olist.com data is used to Identify patterns in consumer decision-making, use metrics to evaluate the properties of patterns, Construct association rules that provide concrete recommendations for businesses, and perform pruning to identify useful rules.