Abstract

The volatile nature of crypto currency prices on blockchain platforms presents challenges and opportunities for investors and traders. We focus on employing machine learning algorithms to predict crypto currency prices based on historical data from the blockchain. We evaluate the performance of these models based on metrics such as mean squared error, root mean squared error, and accuracy. Additionally, we compare the performance of these models with traditional statistical methods commonly used for price prediction. The results of our study provide valuable insights into the feasibility and effectiveness of using machine learning techniques for price prediction on blockchain platforms. The findings can aid investors, traders, and financial institutions in making informed decisions and managing risks associated with crypto currency investments. Overall, this research contributes to the growing body of knowledge on the application of machine learning in the blockchain domain and provides guidance for future developments in this area.