Predicting cryptocurrency investment suitability using machine learning techniques

Abstract

The study aims to predict the close prices of four different cryptocurrencies (Bitcoin, Ethere-um, Dogecoin, and Cardano) using machine learning techniques and determine which of these cryptocurrencies is suitable for investment. To achieve this goal, we used two popular gradi-ent boosting algorithms: Extreme Gradient Boosting (XGBoost) and Light Gradient-Boosting Machine (LightGBM). Prediction accuracy of the trained model is evaluated by Mean Abso-lute Error (MAE) generated by the methodology of Cross-Validation. Our results show that both XGBoost and LightGBM can effectively predict the close prices of the four cryptocur-rencies, with LightGBM achieving slightly better performance in terms of prediction accura-cy. Based on our analysis, we were able to identify which cryptocurrencies were suitable for investing and provide recommendations for potential investors. Overall, our study highlights the potential of machine learning techniques in predicting cryptocurrency close prices and identifying suitable investment opportunities.