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.

This report provides an in-depth analysis of various treatment methods for kidney stones, focusing on patient outcomes and success rates.