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Bitcoin Price Prediction Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence / Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1712740
Abstract
Bitcoin is one of the most volatile financial assets in the digital market. Accurate prediction of Bitcoin price movements is highly valuable for investors, traders, and financial institutions. This paper presents a machine learning-based approach for predicting Bitcoin prices using historical market data. Supervised learning algorithms including Linear Regression, Long Short-Term Memory (LSTM), and Random Forest are trained and evaluated. Experimental results show that LSTM outperforms traditional models in terms of prediction accuracy. The proposed system demonstrates the feasibility of applying deep learning for cryptocurrency price forecasting.
Keywords
Bitcoin, Cryptocurrency, Machine Learning, LSTM, Time Series Forecasting.
How to cite this paper
@article{1712740,
author = {Vikas N, Lokesh S, Suprith D, TharunGowda M N, Adbul Rehaman},
title = {Bitcoin Price Prediction Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {684-686},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712740.pdf},
abstract = {Bitcoin is one of the most volatile financial assets in the digital market. Accurate prediction of Bitcoin price movements is highly valuable for investors, traders, and financial institutions. This paper presents a machine learning-based approach for predicting Bitcoin prices using historical market data. Supervised learning algorithms including Linear Regression, Long Short-Term Memory (LSTM), and Random Forest are trained and evaluated. Experimental results show that LSTM outperforms traditional models in terms of prediction accuracy. The proposed system demonstrates the feasibility of applying deep learning for cryptocurrency price forecasting.},
keywords = {Bitcoin, Cryptocurrency, Machine Learning, LSTM, Time Series Forecasting.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712740}
}