Exploration of Big Data Analytics Using Machine Learning (ML): A Comprehensive Survey

This chapter delves into the synergy between machine learning (ML) and big data analytics, highlighting the transformative impact on decision-making processes across various industries. It explores the characteristics of big data, including volume, velocity, variety, veracity, and value, and discusses the significant obstacles in big data analytics such as data storage, processing, integration, security, and scalability. The chapter provides a comprehensive overview of machine learning techniques, including supervised learning for classification and regression tasks, unsupervised learning for clustering and dimensionality reduction, and advanced methods like semi-supervised and reinforcement learning. It also examines the role of big data platforms like Hadoop and Spark, machine learning frameworks such as TensorFlow and PyTorch, and cloud platforms like AWS, Google Cloud, and Azure in supporting big data and machine learning initiatives. The chapter concludes by emphasizing the importance of addressing challenges like data quality, computational complexity, and scalability to drive innovation and unlock new opportunities in diverse sectors.