Election Voting Trend Prediction System
I. C. Emeto, D.C. Elenwo, U.W. Anthony, J.C Shawulu, C.O Ajayi, A.A Galadima
Asian Journal of Research in Computer Science · pp. 37–44 · Published 5 Oct 2024
10.9734/ajrcos/2024/v17i10508Abstract
This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.
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