🛒 Market Basket Analysis using the Apriori property 📊
In this video, we build further on the Market Basket Analysis. Using a simple yet powerful example from a restaurant 🍽️, we explore how the Apriori algorithm helps in this analysis.
Useful links:
Definitions - https://rasbt.github.io/mlxtend/user_...
Dataset - https://archive.ics.uci.edu/dataset/3...
Imagine a restaurant where customers can order four different items. The possibilities of ordering these items range from none to all four. We illustrate how these combinations relate to the binomial distribution, where the total possibilities of such orders can be represented as 2^m, showcasing the vast potential for understanding customer preferences.
The Apriori algorithm's magic lies in its ability to leverage the Apriori property. This property dictates that if a subset of items is infrequent, then the supersets containing those items cannot be frequent either. This crucial rule helps us cut down significantly on the scope of analysis, focusing only on relevant itemsets.
Moreover, we explain how the Apriori algorithm helps us set a minimum confidence threshold for selecting association rules. By ensuring that these rules are statistically significant, we can make informed decisions about product placements, promotions, and more, ultimately enhancing customer satisfaction and boosting sales.
Join us on this enlightening journey as we demystify Market Basket Analysis with the Apriori algorithm, empowering businesses to make data-driven decisions and stay ahead in the competitive market landscape. 💡