DEVELOPMENT OF A MARKET BASKET ANALYSIS USING APRIORI ALGORITHM.

IX. ABSTRACT The rapid growth of e-commerce and the increasing availability of consumer purchase data have intensified the need for intelligent systems that can uncover hidden patterns in transaction datasets. This project focuses on the development of a market basket analysis system using the Apriori algorithm, implemented in Python, to identify frequent itemsets and generate association rules that reveal customer buying habits. The system is designed to process transactional data, uncover correlations between items, and support strategic decision-making in marketing and product placement. Python’s data analysis libraries, including Pandas and mlxtend, are utilized for efficient data handling, preprocessing, and implementation of the Apriori algorithm. The system allows users to input transaction datasets, configure minimum support and confidence thresholds, and receive interpretable output of item associations. The backend leverages modular code design, data validation, and visual analytics for enhanced insight. By applying data mining techniques, this system empowers retailers, marketers, and analysts to make data-driven decisions, optimize inventory, and increase cross-selling opportunities. The final outcome is a scalable, efficient, and interpretable market basket analysis tool that aligns with modern data-driven business strategies.