Market Basket Analysis: Mining Consumer Insights
تفاصيل العمل
Overview A professional data mining project uncovering hidden associations in retail transactions. Using the Apriori algorithm, I analyzed purchase patterns across a professional retail dataset to surface co-purchase relationships, optimize cross-selling strategies and inform product-placement decisions. The Problem Retailers sit on millions of transaction records but rarely translate them into actionable cross-sell rules. Without a systematic approach to association mining, decisions on bundling, shelf placement and promotional pairings stay intuitive — leaving measurable revenue on the table. Approach Loaded and structured the retail transaction dataset into a basket-format matrix suitable for association mining. Performed exploratory analysis on item frequency, basket size distribution and category mix. Configured the Apriori algorithm with tuned minimum support and confidence thresholds. Generated frequent itemsets and derived association rules ranked by Support, Confidence and Lift. Filtered rules with Lift > 1 to isolate genuinely positive co-purchase relationships. Translated the strongest rules into concrete cross-selling and product-placement recommendations.
مهارات العمل
