This project aims to analyze customer transaction data using market basket analysis techniques. The objective is to discover associations between products, identify frequently purchased item combinations, and support retail decision-making through data-driven product placement and promotion strategies.
Collect transactional retail data containing invoice numbers, product IDs, and quantities.
Perform data cleaning to remove duplicate transactions and irrelevant records.
Transform transactional data into a suitable format for association rule mining.
Conduct exploratory data analysis to understand purchase frequency and item popularity.
Implement association rule mining using Apriori or FP-Growth algorithms.
Generate association rules based on support, confidence, and lift measures.
Analyze strong and weak product associations.
Visualize frequent itemsets and association rules using graphs or network diagrams.
Interpret results to understand cross-selling and bundling opportunities.
Document business insights derived from discovered patterns.
Prepare a detailed project report explaining algorithms, results, and retail use cases.
