An Ensemble Prediction Algorithm for Elections

Abstract

Elections are very interesting and most important for every country. It is most essential to elect the any of the person who is participating in Elections and the candidate belongs to one political party. Candidates participating in the elections will have to follow the rule and regulations of the Election commission. Prediction of election results is most widely tedious task to expect the results before. Many machine learning (ML) algorithms are present to predict the Election results based on people data. In this paper, An Ensemble Prediction Algorithm which is integrated with Random Forest and Ada-boost for analysis and prediction of the elections and determine, compare and predict the results using synthetic training data. We apply these algorithms to mesa-level data from United State of America’s 2016 national elections. It would be entirely affirming to see what more machine learning, analytics, and other modern day technologies can do in politics and how they can change the course of political campaigning.