This served as our final project for our Statistical Machine Learning class. We "predicted" the 2016 presidential election using data wrangling and cleaning, visualization, and modelling with methods learned in class such as classification trees, principal component analysis, logistic regression, LASSO regression, clustering, and boosting.
We found a lot of interesting variables that played a big part in determining the candidate, so we hope you enjoy this project as much as we enjoyed creating it.