Prisca Chimezie Opara

Reducing Model Complexity in Bank Customer Churn Prediction Using Dimensionality Reduction and Explainable Machine Learning

This study demonstrates that PLSDA-optimized machine learning achieves competitive accuracy with reduced computational complexity and enhanced interpretability in churn prediction, while meeting regulatory compliance requirements for practical banking implementations.