DataCamp
FreeMarket Basket Analysis in Python
4.9(267)14k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Python course explores Market Basket Analysis to extract recommendation rules from customer transaction logs. Using the Apriori algorithm and association rules, you will convert retail and streaming data into actionable cross-selling strategies.
Who It's For
Data analysts and e-commerce specialists interested in building recommendation systems and association rules in Python.
Key Takeaways
- Apriori Algorithm: Implement the Apriori algorithm to discover itemsets and generate association rules from transactional data.
- Metrics & Pruning: Evaluate metrics like support, confidence, and lift, and apply pruning strategies to filter relevant rules.
- Interactive Case Studies: Build recommendations for grocery stores, libraries, e-book platforms, retailers, and movie streaming services.
About this course
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.
Price
Free
Advertisement
