Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper explores the application of machine learning techniques for predicting the value of Bitcoin, one of the most prominent cryptocurrencies. As Bitcoin continues to gain traction as a digital asset, understanding its price dynamics is crucial for investors, traders, and policymakers. We employed various machine learning algorithms, including linear regression, decision trees, and recurrent neural networks, to analyse historical price data alongside relevant macroeconomic indicators and social media sentiment. The study utilizes a comprehensive dataset spanning several years, incorporating technical analysis features, trading volume, and external market factors. Our results demonstrate that machine learning models can effectively capture the complex patterns in Bitcoin price movements, with certain models achieving a significant predictive accuracy. We also discuss the implications of our findings for investment strategies and the potential of machine learning in financial forecasting. This work contributes to the growing body of literature on cryptocurrency valuation and underscores the importance of innovative analytical approaches in navigating the volatility of digital currencies.
Bitcoin, Cryptocurrency, Random Forest Regression, Linear Support Vector Machine
Keywords:
Bitcoin, Cryptocurrency, Random Forest Regression, Linear Support Vector Machine
Cite Article:
"Bitcoin Value Prediction Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a512-a520, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506057.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator