Evaluation of the Model for Bitcoin Prediction Using Machine Learning Algorithms and Blockchain Technology

Blockchain technology can be used to analyze and process data through the effective integration of financial resources.Likewise, machine learning is one of the most notable technologies in recent years.Both technologies are data-driven, and therefore there is a rapidly growing interest in integrating them for more secure and efficient data sharing and analysis.This paper shows how these two technologies, blockchain technology and machine learning, can be combined to predict bitcoin volatility.To analyze and predict the volatility of bitcoin, real-time series bitcoin data was used, and the random forest algorithm was utilized.To evaluate the model, the following statistical errors were analyzed: mean absolute error, root mean square error, mean absolute percentage error, median absolute percentage error and symmetric mean absolute percentage error in cases using the different split ratios of the training and test sets.The obtained results have shown that the prediction model is well-designed.