Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)

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Bitcoin Price Prediction Using Machine Learning Algorithms

Authors
P. Nagamani1, *, G. Jaya Anand1, S. Ganga Prasanna1, B. Sai Raju1, M. H. S. V. Siva Satish1
1Computer Science and Engineering, Godavari Institute of Engineering and Technology (Autonomous), Rajamahendravaram, India
*Corresponding author. Email: nagamani@giet.ac.in Email: nagamanipedapati@gmail.com
Corresponding Author
P. Nagamani
Available Online 9 November 2023.
DOI
10.2991/978-94-6463-252-1_43How to use a DOI?
Keywords
Bitcoi; machinelearning; KNN; SVM; dataset; classification; regression; prediction; accuracy
Abstract

The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence. Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market. For a specific time, we keep the daily data. We achieve excellent results by utilising tactics supported by cutting-edge algorithms. The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms. The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017. Today’s market involves more than 1500 crypto currencies. For usage in online transactions, the crypto currency can be created. A crypto money technology is bitcoin. Bitcoin's value changes constantly, second by second. As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case. We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate. Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices. The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
Series
Advances in Engineering Research
Publication Date
9 November 2023
ISBN
10.2991/978-94-6463-252-1_43
ISSN
2352-5401
DOI
10.2991/978-94-6463-252-1_43How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Nagamani
AU  - G. Jaya Anand
AU  - S. Ganga Prasanna
AU  - B. Sai Raju
AU  - M. H. S. V. Siva Satish
PY  - 2023
DA  - 2023/11/09
TI  - Bitcoin Price Prediction Using Machine Learning Algorithms
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 389
EP  - 396
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_43
DO  - 10.2991/978-94-6463-252-1_43
ID  - Nagamani2023
ER  -