  We demonstrate that machine learning enables the capability to infer an
individual's propensity to vote from their past actions and attributes. This is
useful for microtargeting voter outreach, voter education and get-out-the-vote
(GOVT) campaigns. Political scientists developed increasingly sophisticated
techniques for estimating election outcomes since the late 1940s. Two prior
studies similarly used machine learning to predict individual future voting
behavior. We built a machine learning environment using TensorFlow, obtained
voting data from 2004 to 2018, and then ran three experiments. We show positive
results with a Matthews correlation coefficient of 0.39.
