INTEGRATING ARTIFICIAL INTELLIGENCE WITH DATA SCIENCE FOR PREDICTIVE ANALYTICS IN COMPLEX SYSTEMS

Abstract

The convergence of Artificial Intelligence (AI) and Data Science offers transformative potential in analyzing, modeling, and predicting behaviors in complex systems. This paper explores a hybrid framework that integrates AI methodologies—such as machine learning, deep learning, and reinforcement learning—into the data science lifecycle, focusing on real-world predictive analytics applications. By leveraging structured and unstructured data from dynamic environments, the proposed model enhances accuracy, scalability, and decision-making. Case studies in healthcare diagnostics and energy grid forecasting demonstrate the system's effectiveness, achieving up to 90% prediction accuracy. The study underscores the need for explainable AI models, data quality assurance, and ethical considerations in scalable deployments.