Course
Market Basket Analysis in Python
IntermediateSkill Level
Updated 01/2026PythonMachine Learning4 hr15 videos52 Exercises4,350 XP14,523Statement of Accomplishment
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Prerequisites
Data Manipulation with pandas1
Introduction to Market Basket Analysis
In this chapter, you’ll learn the basics of Market Basket Analysis: association rules, metrics, and pruning. You’ll then apply these concepts to help a small grocery store improve its promotional and product placement efforts.
2
Association Rules
Association rules tell us that two or more items are related. Metrics allow us to quantify the usefulness of those relationships. In this chapter, you’ll apply six metrics to evaluate association rules: supply, confidence, lift, conviction, leverage, and Zhang's metric. You’ll then use association rules and metrics to assist a library and an e-book seller.
3
Aggregation and Pruning
The fundamental problem of Market Basket Analysis is determining how to translate vast amounts of customer decisions into a small number of useful rules. This process typically starts with the application of the Apriori algorithm and involves the use of additional strategies, such as pruning and aggregation. In this chapter, you’ll learn how to use these methods and will ultimately apply them in exercises where you assist a retailer in selecting a physical store layout and performing product cross-promotions.
4
Visualizing Rules
In this final chapter, you’ll learn how visualizations are used to guide the pruning process and summarize final results, which will typically take the form of itemsets or rules. You’ll master the three most useful visualizations -- heatmaps, scatterplots, and parallel coordinates plots – and will apply them to assist a movie streaming service.
Market Basket Analysis in Python
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FAQs
What is the Apriori algorithm and will I learn to apply it?
The Apriori algorithm identifies frequently co-purchased items in transaction data. You learn to apply it along with pruning and aggregation strategies to generate actionable product rules.
What kinds of recommendation scenarios are covered in the exercises?
You build recommendations for a grocery store, a library, an e-book seller, a novelty gift retailer, and a movie streaming service, giving you practice across diverse retail contexts.
Which metrics does the course use to evaluate association rules?
You apply six metrics: support, confidence, lift, conviction, leverage, and Zhang's metric. Each helps you quantify how meaningful a relationship between purchased items really is.
What visualization techniques are taught for presenting results?
Chapter 4 covers heatmaps, scatterplots, and parallel coordinates plots for summarizing rules and guiding the pruning process when narrowing down useful item associations.
What Python skills do I need before starting?
You need Intermediate Python and Data Manipulation with pandas. The course uses these skills to process transaction data and implement market basket analysis techniques.
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