Mapping the Political Landscape: A Comparative Study of Electoral Prediction Algorithms

Abstract

This study examines different algorithms used to predict election results and evaluates their effectiveness. This analysis contrasts conventional statistical models with advanced machine learning (ML) methods to assess their advantages, disadvantages, and performance across various electoral situations. The algorithms under investigation include logistic regression, random forest, support vector machines (SVM), decision trees, naive Bayes, and k-nearest neighbors (KNN). The evaluation of their efficacy is conducted using metrics such as accuracy, scalability, interpretability, and computational efficiency. By analyzing historical election data and social media sentiment, the study demonstrates how data preprocessing, feature engineering, and model selection impact accuracy. SVM achieved the highest classification accuracy, at 77.55%, particularly in distinguishing between political groups. The study also highlights the importance of neutral and positive sentiments in shaping voter perceptions. Ethical issues, such as algorithmic bias and data transparency, are discussed to promote responsible predictive modeling in democratic processes. The research concludes by recommending hybrid models and interdisciplinary approaches to improve election predictions.