blakebullwinkel / 2020-Election-Prediction

Predicting the Outcome of the 2020 Election: k-NN, logistic regression, LASSO/ridge regularization, model fairness evaluation

2020-Election-Prediction

Predicting the Outcome of the 2020 Election: k-NN, logistic regression, LASSO/ridge regularization, model fairness evaluation

Description of notebooks:

• ac209a_election_prediction_analysis.ipynb: Contains the majority of our project work, including data processing, EDA, modeling, and forecasting for 2020 Presidential and House of Representative races.
• ac209a_election_prediction_covid.ipynb: Contains our analysis of unemployment data, which we used as a proxy for COVID-19 effects, and includes a number of helpful visualizations included in our report.

Description of data:

• acs_2013_variables.csv: state-level demographics data
• acs_pop_density_2010.csv: state-level population density
• elec_college_votes.csv: 2020 state-level electoral college voting outcomes
• house_results_76_18.csv: district-level congressional election outcomes from 1976-2018
• potus_results_76_16.csv: state-level presidential election outcomes from 1976-2018
• president_polls.csv: state-level polling data from 1972-2016
• state_name_crosswalk.csv: state names, shortened names, and 2-letter abbreviations, used for matching across data sources
• urbanicity_index.csv: state-level population and urbanicity data