In this project, we evaluate Bayesian versus Frequentist methods for predicting election results and perform casual inference for those results. This project was originally designed as a final project for Data 102: Data Inference and Decisions at UC Berkeley, taught by Professor Jacob Steinhardt and Professor Ramesh Sridharan.
Set up the environment by running conda env create -f environment.yml in the command line. The main source code for the project is included in Project.ipynb.