Comparative Bitcoin Price Prediction Using Multiple Machine Learning Techniques

DOI

10.2991/978-94-6463-471-6_120How to use a DOI?

Keywords

Bitcoin; Price Prediction; Machine Learning; Support Vector Machines; Logistic Regression; Random Forests; Decision Trees; Cryptocurrency Markets

Abstract

The cryptocurrency market is known for its inherent volatility, making accurate predictions a challenging endeavor. In this research study, investigate the efficacy of logistic regression, support vector machines (SVM), decision trees and random forests for the task of Bitcoin price prediction. To address this, conduct a thorough analysis and comparison of these machine learning models using historical Bitcoin price data. By rigorously assessing their performance and predictive capabilities, this study aims to provide valuable insights for both cryptocurrency traders and researchers operating in the dynamic digital asset landscape. These results illuminate the strengths and weaknesses of each model, shedding light on their respective abilities to forecast Bitcoin price movements. Through this research, contribute to the growing body of knowledge surrounding cryptocurrency market analysis and prediction techniques. This analysis can inform traders’ decision-making processes and assist researchers in developing more robust models in the exciting and rapidly evolving realm of cryptocurrency investment and analysis.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Gowri Sree Lakshmi
AU  - V. Ajaykumar
AU  - B. Ashish
AU  - K. Hemalatha
AU  - T. Manohar
AU  - V. Sivashankerreddy
PY  - 2024
DA  - 2024/07/30
TI  - Comparative Bitcoin Price Prediction Using Multiple Machine Learning Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1248
EP  - 1258
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_120
DO  - 10.2991/978-94-6463-471-6_120
ID  - Lakshmi2024
ER  -