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Leveraging Big Data Analytics to Enhance Machine Learning Algorithms

EasyChair Preprint no. 12014

19 pagesDate: February 10, 2024


In today's data-driven world, the exponential growth of big data presents both challenges and opportunities for advancing machine learning algorithms. This paper explores the utilization of big data analytics to enhance the performance and capabilities of machine learning algorithms. By harnessing large volumes of diverse and complex data, researchers and practitioners can uncover valuable insights, patterns, and correlations that traditional approaches may overlook. This abstract outline key methodologies and techniques for leveraging big data analytics in machine learning, including data preprocessing, feature engineering, model selection, and optimization. Moreover, it discusses the significance of scalability, parallel processing, and distributed computing frameworks such as Apache Hadoop and Spark in handling massive datasets efficiently. Additionally, the abstract highlights the importance of domain expertise and interdisciplinary collaboration in developing robust machine learning solutions tailored to specific industry domains. Furthermore, it examines the ethical considerations and privacy concerns associated with big data analytics and underscores the need for responsible data usage and regulatory compliance. Overall, this paper underscores the transformative potential of leveraging big data analytics to enhance machine learning algorithms, paving the way for innovative applications across various domains.

Keyphrases: Big Data Analytics, data preprocessing, Engineering, feature, Machine Learning Algorithms, model selection, Optimization, parallel processing, Scalability

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Haney Zaki},
  title = {Leveraging Big Data Analytics to Enhance Machine Learning Algorithms},
  howpublished = {EasyChair Preprint no. 12014},

  year = {EasyChair, 2024}}
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