Crypto Currency Price Prediction Using Machine Learning Techniques

Authors(6) :-Gopikrishnan. A, Jone Solomon. D, Kaviyarasan. N, Vignesh. T, Harshavardhini. S, Dr. S. Jothi Lakshmi

Crypto-currency such as Bitcoin is more popular these days among investors. In the proposed work, it is studied to forecast the Bitcoin price precisely considering different parameters that influence the Bitcoin price. This study first handles, it is identified the price trend on day by day changes in the Bitcoin price while it gives knowledge about Bitcoin price trends. The dataset till current date is taken with open, high, low and close price details of Bitcoin value. Exploiting the dataset machine learning module is introduced for prediction of price values. The aim of this work is to derive the accuracy of Bitcoin prediction using different machine learning algorithm and compare their accuracy. Experiment results are compared for Random Forest and regression model.

Authors and Affiliations

Gopikrishnan. A
UG Scholar, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Jone Solomon. D
UG Scholar, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Kaviyarasan. N
UG Scholar, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Vignesh. T
UG Scholar, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Harshavardhini. S
UG Scholar, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India
Dr. S. Jothi Lakshmi
Associate Professor, Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Machine Learning, Bitcoin, Prediction, Crypto Currency.

  1. Chan, S., Chu, J., Nadarajah, S. and Osterrieder, J., 2017. A statistical analysis o fcryptocurrencies. Journal o f Risk and Financial Management, 10(2), p. 12.
  2. Sovetov, Y., 2018. Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litecoin, and monero. Journal o f Economics and Financial Analysis, 2(2), pp.1-27.
  3. Miglani, A. and Kumar, N., 2019. Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications, 20, p.100184.
  4. Makkar, A. and Kumar, N., 2020. An efficient deep learning-based scheme for web spam detection in the IoT environment. Future Generation Computer Systems, 108, pp.467-487.
  5. Teker, D., Teker, S. and Ozyesil, M., 2019. Determinants of Cryptocurrency Price Movements.
  6. Peng, Y., Albuquerque, P.H.M., de Sa, J.M.C., Padula, A.J.A. and Montenegro, M.R., 2018. The best o f two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, pp.177-192.
  7. Song, H., Sui, S., Han, Q., Zhang, H. and Yang, Z., 2020. Autoregressive integrated moving average model-based secure data aggregation for wireless sensor networks. International Journal o f Distributed Sensor Networks, 16(3), p.1550147720912958.
  8. Garg, S., 2018, December. Autoregressive integrated moving average model based prediction o f bitcoin close price. In 2018 International Conference on Smart Systems and Inventive Technology (ICCSIT) (pp. 473-478). IEEE.
  9. Roy, S., Nanjiba, S. and Chakrabarty, A., 2018, December. Bitcoin price forecasting using time series analysis. In 2018 21st International Conference o f Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.

Publication Details

Published in : Volume 9 | Issue 3 | May-June 2022
Date of Publication : 2022-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 508-512
Manuscript Number : IJSRST1229379
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Gopikrishnan. A, Jone Solomon. D, Kaviyarasan. N, Vignesh. T, Harshavardhini. S, Dr. S. Jothi Lakshmi, " Crypto Currency Price Prediction Using Machine Learning Techniques", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 9, Issue 3, pp.508-512, May-June-2022.
Journal URL : https://ijsrst.com/IJSRST1229379
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