A research paper detailing the model building process of principal component regression using mathematical notation and a demonstration using the superconductivity dataset from the UCI machine learning repository. The goal was to build a model using principal component regression to make predictions about the critical temperature of a superconductor.
The full report can be found/read in the pdf file uploaded above (still working on it). I have also uploaded the code used in the analysis in a Jupyter Notebook file, R file, as well as presentation slides associated with the project.