The article analyzes different methods for grouping credit card customers based on their data using machine learning algorithms. The goal is to see which method works best for clustering techniques like K-means, algometrive, and gaussian distribution. By dividing similar customers into groups, businesses can better tailor their marketing strategies. Clustering helps find patterns in data without needing prior labels. The study tested various clustering models, such as K-means, DBSCAN, Agglomerative Hierarchical, and gaussian mixture model, to analyze high-dimensional data and find useful insights.