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This project applies a range of tree-based machine learning models, including Decision Trees, Random Forest, Bagged Trees, and Boosted Trees, to predict loan approval outcomes. By leveraging hyperparameter tuning and cross-validation techniques, this analysis optimizes model performance on a loans dataset to identify patterns and improve decision-making accuracy.