Ethereum Price Prediction Using Extra Trees and Random Forest Regressors

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Distributed Computing and Technology
Received Date: 05/01/2024
Acceptance Date: 05/26/2024
Published On: 2024-11-13
First Page: 9
Last Page: 25

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By: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli

Abstract

The Ethereum market is famous for its unpredictability and the possibility of significant profits, presenting both opportunities and obstacles for investors and stakeholders. Given the dynamic nature of this ecosystem, accurate prediction of Ethereum’s price movements is essential for informed decision-making. In this project, we propose an innovative approach that leverages ensemble learning techniques, specifically extra trees and random forest regressors, for Ethereum price prediction. By harnessing a diverse dataset encompassing historical Ethereum price data and a comprehensive array of market indicators such as trading volume, sentiment analysis, and technical metrics, we develop and evaluate our models through a rigorous methodology. Our study highlights the effectiveness of our approach in forecasting Ethereum prices with notable accuracy. Through extensive experimentation, we highlight the superior performance of ensemble learning techniques in capturing the intricate patterns inherent in Ethereum’s price dynamics. Additionally, we conduct feature importance analysis to uncover the underlying factors driving Ethereum price movements, thereby providing valuable insights for investors and analysts seeking to navigate this complex market. The findings of our research make significant contributions to the expanding field of cryptocurrency price prediction, particularly within the context of Ethereum. Moreover, our project establishes a sturdy groundwork for continued investigation and enhancement of machine learning techniques in predicting cryptocurrency trends. This work aims to empower stakeholders with actionable intelligence, enabling them to make well-informed decisions amidst the nuances of the Ethereum market and the broader cryptocurrency landscape.

Keywords: Ethereum price prediction, cryptocurrency price prediction, learning techniques, machine learning algorithms

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Citation:

How to cite this article: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli, Ethereum Price Prediction Using Extra Trees and Random Forest Regressors. International Journal of Distributed Computing and Technology. 2024; 10(02): 9-25p.

How to cite this URL: Sushiladevi B. Vantamuri, Shreyas Salimath, Megha Mantur, Rutuja Bhosale, and Misba Sindoli, Ethereum Price Prediction Using Extra Trees and Random Forest Regressors. International Journal of Distributed Computing and Technology. 2024; 10(02): 9-25p. Available from:https://journalspub.com/publication/ijdct/article=11888

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