U.S. Election Prediction

U.S. 2016 Election Result

This final report can be found here, it was granted full marks and used as sample reports for next year’s demostration.

We test the validity of predicting the US Presidential results using ensemble methods, specifically Random Forest. We train our predictor on the 2012 election results alongwith population metrics provided by the US Department of Agriculture to ’predict’ the result of the 2016 presidential election. We compare our predictor’s result to other machine learning techniques, Adaboost, k-NN and SVM to validate our prediction.

Feiyang Tang
Feiyang Tang
Ph.D. Candidate in Machine Learning

Data Enthusiast, ENFJ-T. Travelling, hiking and crime series lover. Multilingual.