Repo contains the final project for APSTA 2011 Supervised and Unsupervised Machine Learning. The project is an unsupervised approach to discover underlying patterns or groupings between NBA compensation vs. overall team skillsets. It uses K-means, hierarchical, and model-based clustering along with other techniques and tools such as principal component analysis, standardizing, scaling, and web-scraping.
To run the analyses, first ensure seasons_stats_clean.csv is within/Data. If it isn't, run Data/Clean_all_data.R which cleans the original data and outputs the .csv.
/Analyses contains the clustering analyses
/Data contains the cleaned data, cleaning scripts, and scraping scripts
/Inputs contains the raw data. Original data can be found on kaggle.com
See blog post: marlo.works/posts/nba/