Optimized Random Forest Classifier for Predicting Ideal ‎Candidate for The General Election

  • Authors

    • K Raju Department of Artificial Intelligence & Machine Learning, Rajalakshmi Engineering college, Chennai-602105
    • R. Lavanya Department of Computer Science and Engineering, School of Computing, SASTRA Deemed University ، Thanjavur-613401
    • R. Lalitha Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, (Deemed to be University), ‎Chennai - 600 119‎
    • Siva Subramanian R Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Trichy -‎‎621105
    https://doi.org/10.14419/zxxek146

    Received date: July 22, 2025

    Accepted date: September 10, 2025

    Published date: September 17, 2025

  • Bayesian Optimization Technique; Decision Trees; Election Candidate; Election Party; Random Forest Classifiers

  • Abstract

    Electoral systems and candidate selections are the two important pillars of modern democratic elections. The integrity and ‎competence of candidates are significant contributing factors to government enactment. Hence, the selection of competent candidates ‎is an indispensable process in the electoral system. This research proposes a new model, named RANWIN (Random forest ‎classifier-based model for selecting Winning candidates), for predicting the winning probability of the candidate and the party. Our ‎model integrates Random Forest Classifier (RFC) and Bayesian Optimization Technique (BOT) to achieve outstanding ‎performance. It considers several factors for predicting the winning probability of candidates, including the popularity of the candidate, ‎past election history, party change or personalization, vote bank, etc. For predicting the winning probability of the party, this method ‎considers the manifestoes, vote bank, leaders, electoral history, intraparty struggles, major rallies hosted in the constituency, and ‎the strength of alliance parties etc. After calculating the impact of all these parameters, RANWIN uses RFC to predict the winning ‎possibility of the candidates as well as parties. To enhance the efficiency of the intended framework, we apply BOT in the prevailing ‎RFC to find out the optimal hyperparameters of the classification process. To prove the better performance of our framework, its ‎enactment is related to other existing approaches regarding accuracy, precision, the Area Under the receiver operating Characteristic ‎‎(AUC) curve, recall, F-measure, and Root Mean Square Error (RMSE). The empirical results demonstrate that RANWIN can be ‎considered as a more effective method with higher predictive accuracy, precision, AUC, recall, F-measure, and lower RMSE of ‎‎94.32%, 95.5%, 90.6%, 97.6%, 98.0%, and 16.23%, correspondingly. As a result, we can prove that RANWIN is an improved ‎framework for candidate selection and has a positive impact on the current political landscape as related to approaches based on ‎other machine learning techniques. The key goal of this research is to help political parties select the right candidates to win public ‎office‎.

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  • How to Cite

    Raju , K., Lavanya, R. ., Lalitha , R. ., & R, S. S. (2025). Optimized Random Forest Classifier for Predicting Ideal ‎Candidate for The General Election. International Journal of Basic and Applied Sciences, 14(5), 649-658. https://doi.org/10.14419/zxxek146