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The rapid evolution of data in various domains has necessitated the development of advanced analytics techniques to extract meaningful insights from vast datasets. In this study, we look at how Big Data analytics and machine learning work together, and how this synergy is changing data analysis. It provides an overview of key machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning, and their applications in handling Big Data. The paper also discusses the essential tools and technologies, such as Hadoop, Spark, TensorFlow, PyTorch, and cloud platforms, that facilitate efficient data processing and analysis. By examining case studies and addressing future trends, the paper illustrates how machine learning can enhance decision-making, optimize processes, and drive innovation. The challenges associated with scalability, data quality, and computational complexity are also considered. This comprehensive review aims to provide a deeper understanding of how machine learning techniques and Big Data technologies can be leveraged to unlock valuable insights and address complex analytical problems.

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

  • 会议论文

  • 发表日期：

    2026年

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  • E. JohnAlex,
  • P. Venkatapathi,
  • Chintala Cury,
  • Yasmeen,
  • S. Samatha,
  • B. V. Krishnaveni

摘要

The rapid evolution of data in various domains has necessitated the development of advanced analytics techniques to extract meaningful insights from vast datasets. In this study, we look at how Big Data analytics and machine learning work together, and how this synergy is changing data analysis. It provides an overview of key machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning, and their applications in handling Big Data. The paper also discusses the essential tools and technologies, such as Hadoop, Spark, TensorFlow, PyTorch, and cloud platforms, that facilitate efficient data processing and analysis. By examining case studies and addressing future trends, the paper illustrates how machine learning can enhance decision-making, optimize processes, and drive innovation. The challenges associated with scalability, data quality, and computational complexity are also considered. This comprehensive review aims to provide a deeper understanding of how machine learning techniques and Big Data technologies can be leveraged to unlock valuable insights and address complex analytical problems.