Predicting Ethereum Price Using Machine Learning Models: A Comparative Analysis
Predicting Ethereum Price Using Machine Learning Models: A Comparative Analysis
Shriya Tyagi
2025 · DOI: 10.22214/ijraset.2025.66472
International Journal for Research in Applied Science and Engineering Technology · 0 Citations
TLDR
Logistic Regression outperformed the other models with the lowest MSE and highest accuracy and demonstrates the potential of combining traditional and advanced machine learning techniques to achieve robust price prediction in the cryptocurrency domain.
Abstract
The cryptocurrency market, known for its high volatility and immense data availability, provides an excellent
opportunity for predictive modeling. This paper explores the prediction of Ethereum’s price using four distinct models: RandomForest, Logistic Regression, Long Short-Term Memory Networks (LSTM), and CNN-LSTM hybrid models. The study evaluatesthe performance of these models based on metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Rsquared ( R2), and Accuracy (%). The findings highlight that Logistic Regression outperformed the other models with the lowestMSE (6741.12) and highest accuracy (98.66% ) [Table 1]. This research demonstrates the potential of combining traditional andadvanced machine learning techniques to achieve robust price prediction in the cryptocurrency domain.