IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v6y2020i2idhcseit206237.html

A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms

Author

Listed:
  • M. Akhil Sai
  • K. Sarath Chandra Sai
  • M. Manu Koushik
  • K. Gowri Raghavendra Narayan

Abstract

ML and AI-helped exchanging have pulled in developing enthusiasm for as far back as not many years.We examine day-by-day information for different digital currencies over some stretch of time. We show that straightforward exchanging methodologies helped by innovative AI calculations outflank standard benchmarks. We have picked two Machine Learning Algorithms to play out a Comparative Study to foresee cost of a Bitcoin; we have utilized Decision tree regressor and LSTM Algorithms and watched execution of every calculation as far as anticipating the cost of Bitcoin. We saw that Decision tree regressor gives progressively effective and precise outcomes when contrasted with others.

Suggested Citation

  • M. Akhil Sai & K. Sarath Chandra Sai & M. Manu Koushik & K. Gowri Raghavendra Narayan, 2020. "A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6(2), pages 160-163, April.
    • Handle: RePEc:jbh:ijsrcs:v6:y2020:i2:id:hcseit206237
    DOI: 10.32628/CSEIT206237
    Note: Article URL: https://ijsrcseit.com/CSEIT206237
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT206237
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT206237.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT206237?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v6:y2020:i2:id:hcseit206237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.