This paper explores the application of association rule mining to analyze customer behavior in Nigeria’s automobile retail sector. Using market basket analysis, the study identifies frequent itemsets and uncovers hidden patterns in customer purchase data. These insights provide valuable information for retailers to design targeted marketing strategies, optimize product placement, and enhance cross-selling opportunities. By leveraging association rules, this research contributes to a deeper understanding of customer preferences and behavior in the Nigerian automobile retail market.

This paper explores the application of unsupervised learning methods to analyze customer behavior in Nigeria’s automobile retail sector. By employing clustering and dimension reduction techniques, I aim to uncover hidden patterns and groupings within the data, enabling a deeper understanding of customer preferences and behaviors. This analysis provides actionable insights for automotive retailers to enhance customer segmentation strategies and improve service offerings.