Decision Tree Model for Predicting Ethereum Price Movements Based on Trends

Abstract

This research investigates the application of a Decision Tree model for predicting Ethereum price movements using historical trend data. The dataset includes key attributes such as open, high, low, close prices, and trading volume, offering insights into market dynamics. The research emphasizes preprocessing and feature engineering techniques, including normalization and the introduction of derived metrics like moving averages and Relative Strength Index (RSI). Despite the model's simplicity and interpretability, it achieved an accuracy of 49.10%, indicating limited effectiveness in capturing non-linear relationships in volatile cryptocurrency markets. Analysis reveals challenges in distinguishing price trends and handling data imbalances, leading to suboptimal performance. These findings highlight the complexities of financial prediction and underscore the need for advanced machine learning methods. Future work should explore ensemble models, richer datasets incorporating sentiment analysis, and resampling techniques to improve robustness and predictive accuracy. This research contributes to the growing literature on machine learning applications in cryptocurrency analytics.