CSY-023. Projecting voter turnout in midterm elections using machine learning

PROJECT COMPLETED: June 2022

Tools Used:
Python: NumPy, pandas, seaborn, scikit-learn
Google Big Query - used to extract and aggregate census data
Jupyter Notebooks - used to publish the project’s technical documentation
Adobe InDesign - used to create the project’s slide presentation

THE STORY OF THIS PROJECT

I used data from the US Census Bureau and the Georgia Secretary of State’s office to build a machine learning model that predicts voter turnout in midterm elections. My goal was to develop a method of projecting turnout that is more predictive than simply averaging the turnout totals of the last three similar elections.

Final Results:

Projecting Voter Turnout in Midterm Analysis | Stakeholder Slide Presentation by David White

Projecting Voter Turnout in Midterm Analysis | Machine Learning Model by David White

 
case studies, machine learningDavid WhiteSQL, Google BigQuery, Python, Adobe InDesign, pandas, scikit-learn, seaborn

3. Work Samples - Content | Work samples of data visualizations, data dashboards, websites, infographics, publication-quality reports, slide decks and visual presentations I've created (detailed)