Year: 2025
Type: article
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.
Authors: Vikas N, Lokesh S, Suprith D, TharunGowda M N, Adbul Rehaman
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7114804527
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