A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms

Authors

  • M. Akhil Sai  UG students, Computer Science and Engineering, VVIT, Nambur, Andhra Pradesh, India
  • K. Sarath Chandra Sai  UG students, Computer Science and Engineering, VVIT, Nambur, Andhra Pradesh, India
  • M. Manu Koushik  UG students, Computer Science and Engineering, VVIT, Nambur, Andhra Pradesh, India
  • K. Gowri Raghavendra Narayan  Assistant Professor Computer Science and Engineering, VVIT, Nambur, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT206237

Keywords:

LSTM, Decision tree, RNN, Bitcoin

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.

References

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  8. Avinash Nalvani, Decision tree classification https://www.datacamp.com/community/tutorials/decision-tree-classification-python
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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. Akhil Sai, K. Sarath Chandra Sai, M. Manu Koushik, K. Gowri Raghavendra Narayan, " A Comparative Study of Bitcoin Price Prediction Using Machine Learning Algorithms , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.160-163, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT206237