Hybrid Machine Learning Models for Predictive Analytics in Large-Scale Data Science Applications

Abstract

The increasing availability of large-scale data has created significant challenges for accurate prediction and efficient data analysis. Traditional machine learning methods often struggle with high-dimensional and complex datasets. This paper proposes a hybrid machine learning framework for predictive analytics in large-scale data science applications. The proposed approach integrates multiple learning models to improve prediction accuracy and robustness. The framework incorporates data preprocessing, feature selection, and ensemble-based learning to extract meaningful patterns from large datasets. Experimental results demonstrate that the hybrid model achieves better predictive performance compared with individual machine learning techniques. The proposed approach provides an effective solution for scalable and reliable predictive analytics in modern data-driven environments.