This project focuses on conducting market basket analysis, a data mining technique used to uncover relationships between items purchased together in transactions. By analyzing customer transaction data, we aim to identify frequent itemsets and association rules, providing insights that can inform business decisions such as product placement, marketing strategies, and customer experience enhancement.
The analysis generated association rules highlighting significant item associations, support, confidence, and lift values. These insights can be used to optimize product placement, design targeted marketing campaigns, and improve customer experience in retail settings.