Market Basket Analysis
Market basket analysis applies association rule mining to retail transactions. Each basket is the set of items a customer bought together, and the goal is to learn which products co occur and what to do about it.
From baskets to rules
The pipeline reuses the unsupervised tools from itemset mining.
- Treat each receipt as a transaction of items.
- Use apriori or FP growth to find frequent itemsets.
- Convert them into rules scored by support, confidence, and lift.
A classic example is discovering that bread and butter appear together far more than chance, giving a lift well above one.
Turning patterns into action
The value is in the decisions the rules suggest.
- Cross selling: recommend Y to customers who bought X.
- Store layout: place associated items near each other, or far apart to widen the path.
- Promotions: discount one item to lift sales of its partner.
Pitfalls to avoid
- A rule may reflect a popular item rather than a real link, so always check lift, not just confidence.
- Correlation in baskets is not causation, so test promotions rather than assuming the rule will hold.
- Very low support rules can be noise and should be treated with caution.
Key idea
Market basket analysis mines co occurring products from baskets and turns high lift rules into cross selling, layout, and promotion decisions you should test.