Machine Learning Approaches for Cryptocurrency Price Prediction in Blockchain Ecosystems

The highly volatile nature of cryptocurrency prices on blockchain platforms poses both significant challenges and potential opportunities for investors and traders. This study investigates the application of machine learning algorithms for cryptocurrency price prediction using historical blockchain data. Model performance is assessed through evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and predictive accuracy. Furthermore, the predictive capabilities of machine learning models are compared against traditional statistical approaches commonly employed in financial forecasting. The empirical findings highlight the feasibility and effectiveness of machine learning techniques in enhancing prediction accuracy within blockchain-based markets. These insights can support investors, traders, and financial institutions in making data-driven decisions and mitigating risks associated with cryptocurrency investments. Overall, this research contributes to the expanding literature on machine learning applications in the blockchain domain and provides a foundation for future advancements in algorithmic trading and financial risk management.