Optimizing Machine Learning Models with Big Data for Improved Accuracy

Abstract

In recent years, the integration of big data with machine learning (ML) has revolutionized predictive analytics and model optimization across various industries. This paper explores the process of optimizing machine learning models by leveraging big data techniques to enhance accuracy. Through a comprehensive review of the latest advancements in data preprocessing, feature selection, and model training methods, we present strategies that improve ML model performance, especially when working with large datasets. We examine case studies where big data analytics have led to significant improvements in fields such as healthcare, finance, and e-commerce. Finally, the paper discusses the challenges and future directions for the development of more accurate and scalable ML models.