A model for predicting Ethereum future price rates using machine learning

Abstract

Considered by many to be the forerunner of smart contract technology, Ethereum has become a widely used blockchain platform. Beyond being a cryptocurrency, Ethereum's main purpose is to offer a decentralized platform for smart contract execution. There are several benefits to using machine learning to predict Ethereum prices in the ever-changing cryptocurrency markets. Machine learning algorithms are able to identify complex patterns and trends that may be difficult for humans to analyze by examining large amounts of historical data. A thorough analysis of Ethereum's price fluctuations is made possible by this data-driven strategy, which considers market sentiment, technical indications, and outside events. In this study, I examine historical Ethereum data that I gathered from the Yahoo Stock Market between 2017 to the present. I used the regression model and the neural network model as my two sorts of algorithms. I utilize the Huber Regressor, Least Angle Regression, Linear Regression, Orthogonal Matching Pursuit, and Lasso Least Angle Regression for my regression models, and I use LSTM for my neural network model. According to my research, the Huber Regressor performs best when taking R2 score into account, having the greatest R2 score of 0.9906. LSTM, however, handles errors less frequently. 35.9866 is the Mean Absolute Error (MAE), which is less than that of any other model. High accuracy is correlated with low error. The Accuracy of LSTM is 98.3582% which is higher when taking MAE into account. According to this research, the best model for high-volume timestamp data is an LSTM.