Prediction of the Variation in Price of Bitcoin Using Machine Learning

Abstract

This paper presents an approach for predicting the price of Bitcoin using machine learning techniques. We used historical data of Bitcoin prices and extracted relevant features such as trading volume, social media sentiment, and market capitalization to train and test the models. Numerous machine learning algorithms, such as random forest, gradient boosting, and neural networks, were tested by us and evaluated their performance using metrics such as mean squared error and accuracy. Our results show that machine learning models can effectively predict the price of Bitcoin with a reasonable degree of accuracy. We also discuss the limitations of our approach and suggest future research directions to improve the performance of Bitcoin price prediction models. Our findings suggest that machine learning can be a useful tool for investors and traders in making informed decisions about Bitcoin investments.