• Utilized a dataset of customer transactions, focusing on key features such as purchase behavior, demographics, and transaction frequency to group customers into meaningful segments for targeted marketing strategies and further behavioral analysis.

• Conducted data cleansing, preprocessing, exploratory data analysis, and feature engineering, followed by applying K-Means and Hierarchical Clustering algorithms to identify the most optimal customer groups, resulting in 3 distinct clusters for targeted marketing.

• K-Means clustering achieved a higher silhouette score (0.714) and lower Davies-Bouldin Index (0.706), indicating more distinct and well-separated clusters compared to Hierarchical Clustering, thus demonstrating superior clustering performance.