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What is Machine Learning for Banking Segmentation about?

The study analyzes the effectiveness of machine learning algorithms—K-Means Clustering, Hierarchical Clustering, and Gaussian Mixture Models (GMM)—for customer segmentation in the banking sector. K-Means Clustering was found to be the most efficient for creating distinct customer segments, while Hierarchical Clustering provides deeper insights and GMM captures complex behaviors but requires more resources. The research emphasizes the importance of selecting the right algorithm based on specific segmentation

Author

batimly777

Language

EN