Bitcoin Price Predictive Dynamics Using Machine Learning Models

Abstract

Cryptocurrency markets, exemplified by the notable volatility in Bitcoin prices, have become pivotal arenas for financial exploration and investment. In response to this, our research undertakes a comprehensive comparison of diverse machine-learning models to predict Bitcoin prices. The models scrutinized include Linear Regression, Ridge Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Neural Networks. This study evaluates and contrasts these models based on performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R -squared. This work encompasses the collection and preprocessing of historical Bitcoin price data, the engineering of pertinent features, and the division of the dataset into training and test sets. Each machine learning model is meticulously trained on the training set, allowing for the tuning of hyperparameters to optimize predictive capabilities. Subsequently, the models’ performance is systematically assessed on the test set, providing valuable insights into their accuracy and reliability. The scope of this research is delimited by a specific timeframe, focusing on historical Bitcoin price data up to 15/11/2023. By addressing these objectives, this research aspires to guide investors, researchers, and analysts in navigating the intricate landscape of cryptocurrency investment decisions. The findings contribute to an enhanced understanding of the nuanced strengths and limitations inherent in different machine learning models when applied to the volatile context of Bitcoin price prediction. Ultimately, this research aims to facilitate more informed and strategic decision-making processes in the cryptocurrency market.