Machine learning algorithms for big data analytics

Kumar Rahul, Rohitash Kumar Banyal, Puneet Goswami, Vijay Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


A machine learning algorithm (MLA) is an approach or tool to help in big data analytics (BDA) of applications. This tool is suitable to analyze a large amount of amount generated by an application for effective and efficient utilization of the data. Machine learning algorithms considered to find out meaningful data and information for industrial applications. It is one of the services under big data analytics (BDA). Big data analytics (BDA) is suitable for identifying risk management, cause of failure, identifying the customer based on their procurement detail records, detection of fraud, etc. So, this paper deals with the work done in this field to analyze the importance of machine learning tools and techniques, identify the field where it is suitable to use including industries such as marketing, human resource, healthcare, insurance, banking, automobile, etc. This paper identifies different challenges of machine learning tools and technologies including the current status of adoption in industries.

Original languageEnglish
Title of host publicationComputational Methods and Data Engineering - Proceedings of ICMDE 2020
EditorsVijendra Singh, Vijayan K. Asari, Sanjay Kumar, R.B. Patel
Number of pages9
ISBN (Print)9789811568756
StatePublished - 2021
Externally publishedYes
EventInternational Conference on Computational Methods and Data Engineering, ICMDE 2020 - Sonipat, India
Duration: 30 Jan 202031 Jan 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Computational Methods and Data Engineering, ICMDE 2020

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2021.


  • Big data
  • Filtering
  • Healthcare
  • K-Means
  • Machine learning


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