Machine Learning-Based Customer Segmentation for Retail Insights

The dynamic retail environment demands data-driven approaches to decipher customer preferences,enhance marketing precision, and boost profitability. This project leverages advanced machine learningtechniques to segment mall customers by analyzing demographic and transactional data. Through thoroughdata cleaning, visualization, and feature analysis, critical patterns in spending behavior, income distribution,and customer demographics were uncovered. Four clustering algorithms — K-Means++, Gaussian MixtureModel (GMM), Hierarchical Clustering, and DBSCAN — were applied to categorize customers, withperformance assessed using Silhouette Score and Davies-Bouldin Index metrics. K-Means++ emerged asthe top performer, yielding the highest Silhouette Score and lowest Davies-Bouldin Index, indicatingsuperior cluster quality. Visualizations such as scatter plots, dendrograms, and correlation matrices enrichedthe analysis by revealing behavioral insights and inter-attribute relationships. This system surpassesconventional segmentation by providing automated, scalable, and accurate customer grouping, enablingretailers to craft targeted promotions, optimize resource allocation, and elevate customer satisfaction.Future improvements could include real-time data integration, deep learning methods like autoencoders,and an interactive platform for actionable insights.