This repo contains R code, data, writeup and powerpoint files for Evan Phillippi's final project in the data science practicum at Regis University.
The goal of my final project was to apply dimensionality reduction analysis methods to a dataset containg sales from a bookstor using principal component analysis. After applying PCA, I then applied kmeans clustering to the resulting principal compoenet scores to classsify customers into unique segments. In addition to segmentation, I also sought to understand if the resulting clusters indicated a prevalence to purchase a specific book, "The art history of Florence" given cluster membership. Full explanations of the analysis and results can be found in the "Final Report" pdf file in the repo.