Bitcoin Price Prediction Using Deep Learning

Registration ID: IJNRD_186777

Published ID: IJNRD2302004

: http://doi.one/10.1729/Journal.32915

Siddhartha Vaddempudi, Dammavalam Srinivasa Rao

Bitcoin Price prediction machine learning lstm

Cryptocurrency, especially Bitcoin, is one of the most volatile markets today and has gained a lot of attention from investors across the globe. Cryptocurrency, being a novel technique for transaction systems, has led to a lot of confusion among investors and any rumors or news on social media has been claimed to significantly affect the prices of cryptocurrencies. Implemented linear regression and Long Short-Term Memory ( LSTM) models for bitcoin price prediction. The goal of this proposed work is to predict prices for Bitcoin using Machine learning techniques for the next day and prepare a strategy to maximize gains for investors. We also aim to find out if there is a correlation between fluctuating Bitcoin prices and related news. The proposed method compared to other Machine Learning models from relevant studies, it was revealed that the linear regression model's accuracy rate is quite high; it was shown to be 99.87 percent correct. On the other hand, the LSTM model has a minor error rate of 0.08 percent. The neural network model is thus shown to be more optimal than the machine learning model as a result of this. Ensemble learning and other eminent deep learning techniques may improve the bitcoin price prediction accuracy.

Published Paper   E-Certificate

Siddhartha Vaddempudi & Dammavalam Srinivasa Rao (February-2023). Bitcoin Price Prediction Using Deep Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(2), a22-a27. http://doi.one/10.1729/Journal.32915

Volume 8 Issue 2, February-2023

Pages : a22-a27

Paper Reg. ID: IJNRD_186777

Published Paper Id: IJNRD2302004

Downloads: 000122047

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: HYDERABAD, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2302004.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2302004

Crossref DOI: http://doi.one/10.1729/Journal.32915

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