In this term project for UNC INLS 625 Predictive Analytics, I combined data from the ProPublica Congress API, govtrack.us, and DW_NOMINATE to predict what happened to bills from the 112th to 115th Congresses. I used Python, R, and KNIME to scrape, organize, and clean the data, then apply k-Means cluster analysis, RandomForest decision tree modeling, Naïve Bayesian modeling, and logit regression.
Index.html (location / live) contains the final report of my findings.