Enhancing Predictive Accuracy: A Comparative Study of Machine Learning Algorithms

Abstract

The demand for accurate predictive models has increased significantly with the growth of data-driven decision-making in various industries. This study provides a comparative analysis of prominent machine learning (ML) algorithms, focusing on their predictive accuracy across multiple datasets and domains. By systematically evaluating algorithms such as Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, k-Nearest Neighbors, and Artificial Neural Networks, this paper explores how algorithmic architecture, data preprocessing, and hyperparameter tuning impact prediction outcomes. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are used to benchmark performance. The results highlight that no single algorithm universally outperforms others; instead, performance varies significantly with data characteristics. This study serves as a guide for practitioners to make informed choices about ML models for predictive tasks, ultimately enhancing the accuracy and effectiveness of intelligent systems.