AN INTELLIGENT MACHINE LEARNING–DRIVEN FRAMEWORK FOR ADVANCED PREDICTIVE DATA ANALYTICS

Abstract

Predictive data analytics has emerged as a vital tool for extracting meaningful insights from large and complex datasets. With the rapid expansion of data-driven applications, traditional analytical methods often fail to provide accurate and scalable predictions. This research presents an intelligent machine learning–driven framework designed to enhance predictive performance through automated preprocessing, feature optimization, and model selection. The proposed framework integrates advanced machine learning algorithms to handle high-dimensional and heterogeneous data effectively. It focuses on reducing prediction errors while maintaining computational efficiency. Experimental evaluations conducted on benchmark datasets demonstrate significant improvements in accuracy and reliability. The framework also adapts dynamically to changing data patterns. The results highlight the robustness and flexibility of the proposed approach. This study contributes to the advancement of intelligent predictive analytics systems. The framework is suitable for deployment across various real-world applications.