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Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis

Author

Listed:
  • Sanjeev Kumar Punia

    (JIMS Engineering Management Technical Campus, India)

  • Manoj Kumar

    (School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, India)

  • Thompson Stephan

    (Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore,Noida, India)

  • Ganesh Gopal Deverajan

    (Galgotias University, India)

  • Rizwan Patan

    (Velagapudi Ramakrishna Siddhartha Engineering College, India)

Abstract

In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the collected research data set. The comparison of different machine learning classification algorithms is concluded.

Suggested Citation

  • Sanjeev Kumar Punia & Manoj Kumar & Thompson Stephan & Ganesh Gopal Deverajan & Rizwan Patan, 2021. "Performance Analysis of Machine Learning Algorithms for Big Data Classification: ML and AI-Based Algorithms for Big Data Analysis," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(4), pages 60-75, July.
    • Handle: RePEc:igg:jehmc0:v:12:y:2021:i:4:p:60-75
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    Cited by:

    1. Pooja Chaturvedi & Kruti Lavingia & Gaurang Raval, 2023. "Detection of traffic rule violation in University campus using deep learning model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(6), pages 2527-2545, December.
    2. Gul Shaira Banu Jahangeer & T. Dhiliphan Rajkumar, 2024. "Cloud storage based diagnosis of breast cancer using novel transfer learning with multi-layer perceptron," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 60-72, January.
    3. Mehrdad Maghsoudi & Sajjad Shokouhyar & Nafiseh Sanaee & Sina Shokoohyar, 2025. "Enhancing sustainability reporting practices in the notebook manufacturing industry: a multifaceted analysis integrating traditional reports and social media data," Annals of Operations Research, Springer, vol. 345(1), pages 317-349, February.

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