Bitcoin Price Prediction Using Machine Learning Techniques A Comparative Study
Abstract
Ritesh Pandey
The rising popularity of cryptocurrencies has led to increased interest in predicting their prices, particularly in the case of Bitcoin due to its volatility and complexity. While past research has used machine learning to improve Bitcoin price prediction accuracy, there has been limited focus on exploring diverse modeling techniques for datasets with varying structures and dimensions. This study investigates the use of machine learning models for predicting Bitcoin prices. A comprehensive comparison of various algorithms, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Long Short-Term Memory (LSTM) networks, is conducted on a historical dataset spanning from July 2010 to May 2023. The models are evaluated based on accuracy, Mean Squared Error (MSE), and other performance metrics. Random Forest achieved the highest predictive accuracy, while LSTM provided promising results for sequential data. Additionally, a web-based application was developed for real-time prediction. The results demonstrate the potential of machine learning in the forecasting of cryptocurrency prices and highlight areas for future improvement in model performance.
