Devising an Algorithm for Election Prediction Using Survey of Voters Opinions

Abstract

The purpose of this research study is to analyze how we use voter polls to predict elections and to design an algorithm to predict elections. We propose a method of prediction based on learning algorithm to determine the political profile of a voter group by obtaining a linear hierarchy on the attributes that weights the number of instances that are more relevant. Our process starts with opinion survey collected directly from the target group of voters. Having a linear attribute hierarchy that expresses the political preferences of voters allows the application of a holistic approach to distribute the potential number of votes among the parties involved. We applied our electoral outlook model in the Kosovo election case study in from February 2021. The devised algorithmic model may also be applied to other situations. Data analysis not only provides new analysis opportunities, but also faces many challenges. In our case, we listed the limitations of the research. The research attempts to promote the implementation of the algorithm by extending the processing of the information generated by the learning algorithm to improve the prediction of elections and winning parties. Discussed of all data analysis challenges, and present, discuss, and argue insights.