Predictive Modeling of Bitcoin Prices using Machine Learning Techniques

Authors

  • Gagandeep Kaur Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Poorva Agrawal Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Latika Pinjarkar Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Rutuja Rajendra Patil Vishwakarma Institute of Information Technology, Pune, India
  • Rupali Gangarde Symbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Pune, India
  • Priya Parkhi Shri Ramdeobaba College of Engineering and Management, Nagpur
  • Bhagyashree Hambarde Shri Ramdeobaba College of Engineering and Management, Nagpur

Keywords:

Bitcoin Price Prediction, Machine Learning, Linear Regression, Logistic Regression, SARIMA, KNN

Abstract

This research paper aims to comprehensively examine diverse algorithms employed in forecasting the price dynamics of bitcoin. The study's outcomes have undergone careful analysis, shedding light on emergent trends poised to exert influence on the cryptocurrency market in the proximate horizon. Notable among the algorithms scrutinized are the K-Nearest Neighbors (KNN), Logistic Regression, Linear Regression, and Seasonal Autoregressive Integrated Moving Average (SARIMA). A brief comparison of these algorithms has been done, with the intent of identifying the ideal machine learning-based algorithm for predicting Bitcoin's price. The preeminent criterion for model selection is predicated upon achieving optimal accuracy, culminating in the recognition of Linear Regression as the most adept algorithm for precise Bitcoin price predictions.

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Published

23.02.2024

How to Cite

Kaur, G. ., Agrawal, P. ., Pinjarkar, L. ., Patil, R. R. ., Gangarde, R. ., Parkhi, P. ., & Hambarde, B. . (2024). Predictive Modeling of Bitcoin Prices using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 578–586. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4923

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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