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APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
This project aims to analyze diabetes data using data management, captivating visualizations, and cutting-edge machine learning techniques to predict the presence of diabetes in individuals. Our robust dataset includes comprehensive health exam results and family history.