The mechanics governing government elections in the United States, encompassing the social, economic, cultural, and political facets, are exceptionally intricate. This complexity renders the prediction of election outcomes a challenging endeavor. In recent years, political campaigns, particularly in the United States, have undergone a transformation towards a data-driven approach. They rely on vast databases housing information on millions of eligible voters, which plays a pivotal role in shaping campaign strategies and decision-making processes. While polls have traditionally been a popular choice for forecasting election results, their accuracy can be compromised by issues such as outdated polling methods and the rapidly changing information landscape. Today, the digital landscape leverages cutting-edge technologies, including artificial intelligence and machine learning, to enhance the precision and transparency of election predictions. These advancements provide researchers with more sophisticated tools than ever before for analyzing the vast quantities of election data. By developing AI systems powered by machine learning, we aim to uncover trends and phenomena that might have remained hidden in local election data. Such insights have the potential to shed light on the broader structure of the American political landscape.
