A Study Report on Comparative Performance Evaluation of Machine Learning Algorithms for Structured Data Classification

Abstract

Artificial Intelligence (AI) has significantly transformed data analytics by enabling intelligent and data-driven decision-making through advanced computational models. This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks. The analysis is performed on a structured dataset incorporating systematic preprocessing, feature engineering, and model optimization techniques. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The experimental results demonstrate that Random Forest achieves superior generalization performance compared to the other models. The findings emphasize the critical role of selecting appropriate models based on dataset characteristics and provide valuable insights for future research in predictive analytics.