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@article{198834,
        author = {M. Deen Mohamed and M Mohamed Rafi},
        title = {Bitcoin Price Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12220-12225},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198834},
        abstract = {The cryptocurrency market, particularly Bitcoin (BTC), is characterized by extreme volatility and complex non-linear price dynamics, posing significant challenges for accurate forecasting. This paper presents a comprehensive comparative study of two machine learning models — Linear Regression and Random Forest — applied to the problem of Bitcoin price prediction. Daily historical BTC/USD data spanning January 2018 to December 2023 is used for training and evaluation. A rich feature set including technical indicators such as 7-day and 30-day Moving Averages, Daily Return, and Price Volatility is engineered from raw OHLCV (Open, High, Low, Close, Volume) data. Both models are rigorously evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R²). Experimental results indicate that the Random Forest model significantly outperforms Linear Regression, achieving an R² of 0.9534 and reducing prediction error by over 50%. The study demonstrates the effectiveness of ensemble learning for cryptocurrency forecasting and provides a reproducible framework for further research.},
        keywords = {Bitcoin; Cryptocurrency Forecasting; Machine Learning; Linear Regression; Random Forest; OHLCV Features; Time-Series Prediction.},
        month = {April},
        }