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Project Description: The goal of this project is to use publicly-available election returns and US Census Bureau data to build a model that predicts turnout in midterm elections in the state of Georgia. The machine learning model's predictiveness will then be measured against the traditional method of projecting voter turnout in an election.

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projecting-voter-turnout-in-midterm-elections's Introduction

Predicting voter turnout in midterm elections using machine learning

PROJECT COMPLETED: June 2022

Tools Used:Python: NumPy, pandas, seaborn, scikit-learnGoogle Big Query - used to extract and aggregate census dataJupyter Notebooks - used to publish the project’s technical documentationAdobe InDesign - used to create the project’s slide presentation

Project By:

David White GitHub Profile: @davidwhitemsm

Email: [email protected]

Date: June 2022

Accompanying Presentation Slides: https://tinyurl.com/david-white-ga-project-slides

About 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.

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