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This study presents a predictive model for college admissions using machine learning and statistical techniques. While models such as Random Forest, XG Boost, and Multi-layer Perceptron (MLP) faced challenges due to data imbalance, a statistical model leveraging historical admission trends achieved 88% accuracy. This paper highlights the advantages of statistical methods in imbalanced datasets over conventional ML approaches.