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.

翻译：我们证明,机器学习能够从个人过去的行为和属性中推断出其投票倾向。 这对微小定位选民外联、选民教育和退出选票(GOVT)运动有用。政治科学家开发了自1940年代后期以来日益先进的估计选举结果的技术。前两项研究同样利用机器学习来预测个人未来的投票行为。我们利用TensorFlow建立了机器学习环境,从2004年至2018年获得了投票数据,然后进行了三次实验。我们用0.39的马修斯相关系数展示了积极的结果。