PCR & PLS Case Study

I analyzed the College dataset using Principal Components Regression (PCR) and Partial Least

Squares (PLS) regression to predict college applications (Apps). Using cross-validation, I

determined the optimal number of components for each model and evaluated their performance.

The PCR model with 17 components achieved a slightly lower Mean Squared Error (MSE) than

the PLS model with 11 components, highlighting the effectiveness of dimensionality reduction

techniques in predictive modeling.