Market Basket Analysis in R
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Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
Course Description
Last time you were at the supermarket, what was in your shopping basket? Was there a connection between the products you purchased, like spaghetti and tomatoes or ham and pineapple? Whether online or offline, retailers use information from millions of customer’s baskets to analyze associations between items and extract insights using association rules.
What You’ll Learn
Introduction to Market Basket Analysis
What’s in your basket? In this first chapter, you’ll learn how market basket analysis (MBA) can be used to look into baskets and dig into itemsets to better understand customers and predict their needs. Using tidyverse and dplyr you’ll discover how many baskets can be created from a given set of items, and learn the power of using market basket analysis for online shopping, offline shopping, and use cases beyond retail.
Visualization in Market Basket Analysis
Let’s get visual. In this chapter, you’ll visually inspect the set of rules you have previously extracted. Visualizations in market basket analysis are vital given that often you are dealing with large sets of extracted rules. You’ll use the arulesViz package to create barplots, scatterplots, and graphs to visualize your sets of inferred rules. You’ll then turn sets of plots into interactive plots, making it is easier to drill into the mined association rules.
Metrics & Techniques in Market Basket Analysis
In this chapter, you’ll convert transactional datasets to a basket format, ready for analysis using the Apriori algorithm. You’ll then be introduced to the three main metrics for market basket analysis: support, confidence, and lift, before getting hands-on with the Apriori algorithm to extract rules from a transactional dataset. Lastly, You explore how the arules package is used for market basket analysis to retrieve basket rules and to help you find the most informative and relevant rules.
Case Study: Market basket with Movies
We’re going to the movies. In this final chapter, you’ll apply everything you’ve learned as you work with a movie dataset. Using market basket analysis you’ll turn this dataset into a movie recommendation system, using information from movie transactions to understand and predict what your audience might want to watch next.
