video1.0<iframe src="https://www.loom.com/embed/301819edf8bd44e29cd43ea4c58ee331" frameborder="0" width="1730" height="1297" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12971730Loomhttps://www.loom.com12971730https://cdn.loom.com/sessions/thumbnails/301819edf8bd44e29cd43ea4c58ee331-e37f5536d4aaf03a.gif86.763In this video, I introduce Voter Turnout, a tool that utilizes neighborhood polling methods to enhance the accuracy of election predictions by minimizing bias and social fears. Using a sample from San Francisco, I demonstrate how to upload polling data and send it to users for verification. After receiving responses, I show how the data influences our overall turnout estimations through a Bayesian Statistical Modeling System, similar to what Harvard employs in traditional polling. I observed that my neighbors indicated a lower likelihood of voting, which adjusted our current estimates downwards. I encourage you to explore this tool and consider how it can improve our understanding of voter behavior.