An Intelligent Machine Learning Framework for Predictive Analysis of Large-Scale Data Systems

This paper presents an intelligent machine learning framework designed for predictive analysis in large-scale data systems. The proposed framework integrates data preprocessing, feature engineering, scalable model training, and real-time prediction mechanisms to efficiently handle high-volume, high-velocity, and high-variety datasets. Advanced machine learning algorithms such as ensemble learning and deep neural networks are incorporated to enhance prediction accuracy and system adaptability. The framework supports distributed computing environments to ensure computational efficiency and reduced processing time. Experimental evaluation demonstrates improved predictive performance, scalability, and robustness compared to traditional analytical models. The system is suitable for applications in smart industries, financial analytics, healthcare monitoring, and IoT-based infrastructures, providing reliable decision support for complex data-driven environments.