Abstract
Election forecasting is highly dependent on polling data, yet there is literature surrounding the United States political environment that questions the importance of campaigns. This paper extends that theory to exclude polling data, because it will not be available at the time of research to construct election forecasting models. Relying on economic indicators, approval ratings, party occupancy in congress, and other quantifiable metrics that impact and influence a voter's decision, our forecasting will be built from Linear Regressions, Naive Bayes Classifications, and Decision Trees. We will analyze the performance of our models to identify which models are best at predicting an election with these assorted variables. By incorporating these measures in various predictive modeling techniques, we will simulate the 2020 election, and predict the outcome of the 2020 election in a variety of settings.
Advisor
Pasteur, Drew
Department
Mathematics
Recommended Citation
Yimam, Micheas, "2020 United States Election Forecasting" (2019). Senior Independent Study Theses. Paper 8548.
https://openworks.wooster.edu/independentstudy/8548
Publication Date
2019
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
© Copyright 2019 Micheas Yimam