MACHINE LEARNING FOR PREDICTIVE ANALYTICS: MODELS, METHODS, AND USE CASES

Abstract

Machine Learning (ML) has emerged as a transformative force in the field of predictive analytics, offering robust tools and frameworks for uncovering patterns and forecasting future trends across various industries. This paper provides a comprehensive overview of the core models and methods employed in ML-driven predictive analytics, including supervised learning algorithms such as decision trees, support vector machines, and ensemble methods, as well as unsupervised techniques and neural networks. We explore how these models are trained on historical data to make informed predictions and decisions, highlighting the roles of feature engineering, model selection, evaluation metrics, and overfitting mitigation strategies. Furthermore, the study examines a range of practical use cases—such as fraud detection in finance, customer churn prediction in telecommunications, and demand forecasting in supply chain management—to demonstrate the real-world impact of ML in predictive contexts. The discussion also addresses current challenges such as data quality, model interpretability, and ethical considerations, underscoring the need for responsible AI practices. By synthesizing theoretical foundations with applied case studies, the paper aims to provide a valuable reference for researchers, practitioners, and decision-makers leveraging ML for predictive analytics.