Amazon User Segmentation

Abstract

In e-commerce environments, user experiences and marketing strategies are best optimized by effective user segmentation. This project classifies users according to pertinent criteria like age and buy rating by using sophisticated clustering algorithms like K-means and DBSCAN. The Elbow Method makes it easier to determine the ideal number of clusters, resulting in a more accurate and detailed segmentation. Disk plots and other visualizations provide information about different user groups and their purchase patterns. Interactive user interfaces make these segments easier to explore.

The effectiveness of K-means is compared with hierarchical clustering and DBSCAN in a thorough analysis that includes criteria such as the Silhouette Score for reliable cluster evaluation. We go into great detail about ethical issues, such as algorithmic fairness and user privacy. By strengthening our knowledge of user behavior in the context of e-commerce, this study lays the groundwork for customized marketing campaigns that cater to individual user preferences.