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Library/ Machine Learning (Supervised + Unsupervised)/ Unsupervised Learning by Building (one technique, two mini projects)/ U05 · Build: Association Rules — A: Market Basket Analysis (Online Retail II) · B: optional German Credit patterns

subtopic

Frequent itemsets and rules with Apriori / FP-Growth

10 min applied10 min research2 curated resources

Mine frequent itemsets and rules; filter by lift and confidence; read the top 10. Concepts: Apriori principle; FP-Growth; rule filtering Project A: Top rules like 'tea cups → saucers' with lift and confidence. Project B: Optional: frequent attribute combinations, then compare their bad rate (EDA-style insight, not a model).

Notes, key code, what to skip and 2 curated resources are inside the platform.Join the waitlist and we'll build you a roadmap that includes this topic only if your goal needs it.

Frequent itemsets and rules with Apriori / FP-Growth · 1x1labs.ai