Understanding Voting Outcomes through Data Science

After the surprising results of the 2016 presidential election, I wanted to better understand the socio-economic and cultural factors that played a role in voting behavior. With the election results in the books, I thought it would be fun to reverse-engineer a predictive model of voting behavior based on some of the widely available county-level data sets. For example, if you want to answer the question "how could the election have been different if the percentage of people with at least a bachelor's degree had been 2% higher nationwide?" you can simply toggle that parameter up to 1.02 and click "Submit" to find out. The predictions are driven by a random forest classification model that has been tuned and trained on 71 distinct county-level attributes. Using real data, the model has a predictive accuracy of 94.6% and an ROC AUC score of 96%.