This project uses association rule mining to analyze retail transaction data and uncover patterns in customer purchasing behavior. By applying the Apriori algorithm, we identified relationships between frequently bought items, providing actionable insights for cross-selling, store layout optimization, and targeted marketing strategies.

This analysis applies K-means clustering and Principal Component Analysis (PCA) to retail transaction data to uncover purchasing patterns. After data cleaning and transformation, clusters of frequently purchased items were identified to optimize store layout, marketing strategies, and inventory management. The analysis highlights the journey from initial clustering attempts using the Elbow Method to refining results with the Silhouette Method, ultimately finding that two clusters provided the best segmentation. Visualizations and insights derived from the clustering process offer actionable strategies for retail optimization.