(untitled) (Al-Farooq Journal of Sciences, 2026)
In today’s financial landscape, understanding and predicting cryptocurrency trends are of utmost importance due to the significant growth of digital currencies. This paper delves into a comprehensive study of predictive modeling for six major cryptocurrencies: Bitcoin, Dogecoin, Ethereum, USDCoin, BinanceCoin, and Cardanocoin. Using a wide range of machine learning and deep learning algorithms, we carefully assess how well each model can capture the complex nature of cryptocurrency markets. We employed a rigorous methodology, including models like Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGBoost), among others. We evaluated each model’s performance using standard metrics like RMSE and R². The results show that different models perform differently, with some algorithms excelling in predicting specific cryptocurrencies.