Predictive analytics leverages historical data and statistical algorithms to forecast future outcomes across diverse application domains, including healthcare diagnostics, financial risk assessment, and retail demand forecasting. Although numerous machine learning algorithms have been proposed for classification and regression tasks, a systematic domain-specific comparison that simultaneously evaluates accuracy, computational scalability, interpretability, and robustness remains absent in current literature. This paper presents a rigorous comparative analysis of seven widely adopted machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Gradient Boosting, and Multilayer Perceptron Neural Networks—applied to three real-world predictive analytics datasets. Experiments are conducted under unified preprocessing and evaluation protocols to ensure fair comparison. Results indicate that ensemble methods, particularly Gradient Boosting, achieve the highest accuracy and AUC-ROC, while linear models offer superior interpretability and training efficiency. The findings provide evidence-based guidance for algorithm selection in practical predictive analytics deployments.

Keywords — Machine Learning, Predictive Analytics, Comparative Study, Random Forest, Gradient Boosting, Classification, Ensemble Methods, SVM, Neural Networks