Exploring the Impact of Big Data on Improving Machine Learning Model Accuracy and Efficiency

Abstract

Big data has revolutionized the field of machine learning by providing vast amounts of diverse and high-dimensional data, enabling the development of more accurate and efficient predictive models. This paper examines the impact of big data on machine learning, highlighting its role in improving model generalization, reducing bias, and enhancing scalability. Key challenges such as data quality, storage, and computational requirements are addressed, along with strategies for overcoming these hurdles, including distributed computing frameworks and advanced data preprocessing techniques. Through case studies in healthcare, finance, and autonomous systems, the paper illustrates how big data accelerates innovation in machine learning applications. The analysis also explores emerging trends, such as the integration of big data with federated learning and real-time analytics. The findings underscore that leveraging big data not only boosts model accuracy but also facilitates the creation of robust, adaptable machine learning systems capable of solving complex real-world problems.