Big Data Analytics and Predictive Modeling for Intelligent Decision Support

Abstract

The increasing availability of large-scale digital data has created significant opportunities for organizations to improve decision-making through advanced analytics. However, extracting meaningful information from high-volume, high-velocity, and heterogeneous datasets remains a major computational challenge. This study examines a big data analytics framework that combines data preprocessing, machine learning, and predictive modeling to support intelligent decision-making. The proposed framework involves data collection, cleaning, transformation, feature extraction, predictive analysis, and visualization. Machine learning models can be applied to identify patterns and forecast future outcomes from historical datasets. The study evaluates the effectiveness of predictive analytics using measures such as accuracy, precision, recall, and computational efficiency. The research demonstrates the potential of big data technologies to transform complex datasets into actionable insights and provides a scalable foundation for intelligent decision-support systems across different application domains..