Market Basket Analysis & Recommendation System

Developed a data mining and recommendation system for analyzing grocery transaction data using association rule mining techniques. The project includes data cleaning, transaction preprocessing, basket matrix construction, exploratory data analysis, and comparison between Apriori and FP-Growth algorithms. It also generates association rules using support, confidence, lift, leverage, and conviction, and provides a rule-based product recommender. The system includes an interactive Streamlit dashboard with Rule Explorer, Product Recommender, Network Graph, and configurable mining thresholds.