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@article{175824,
        author = {Nitin Singh and Deepak Kumar and Divyanshu Prince and Aniket Baranwal and Dr. Sreenivasa B C},
        title = {Cryptocurrency Price Prediction Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4690-4693},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175824},
        abstract = {Cryptocurrencies have emerged as a disruptive innovation in the financial sector, attracting widespread interest from investors, researchers, and regulators. However, predicting their price remains a significant challenge due to their high volatility and sensitivity to market sentiment. This paper explores the use of machine learning techniques for forecasting cryptocurrency prices, focusing primarily on Bitcoin and Ethereum. We implement and compare models such as Linear Regression, Long Short-Term Memory (LSTM), and Facebook Prophet, evaluating their performance based on historical price data. Our findings indicate that deep learning models like LSTM outperform traditional approaches in capturing temporal dependencies, offering better predictive accuracy.},
        keywords = {Cryptocurrency, Price Prediction, Machine Learning, LSTM, Time Series Forecasting, Bitcoin, Ethereum},
        month = {April},
        }