Introduction This analysis explores regression techniques for high-dimensional data across three case studies: near-infrared spectroscopy analysis, pharmaceutical compound permeability prediction, and chemical manufacturing process optimization. The assignment compares linear methods (Principal Component Regression, Partial Least Squares, Ridge Regression, Lasso, and Elastic Net) with nonlinear approaches (K-Nearest Neighbors and Support Vector Machines) to identify optimal modeling strategies for different data structures characterized by multicollinearity and high predictor-to-sample ratios. Key Questions Addressed: 1. How do dimension reduction methods (PCR, PLS) compare to regularization (Ridge, Lasso)? 2. Can predictive models reduce expensive laboratory experimentation? 3. Which process variables drive manufacturing yield?

Integrantes: Martín Estrada Agudelo Juan José López Anni Danielle Bonanno Katherin Johanna Arcila