Bitcoin Price Prediction Using Machine Learning Algorithms

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 aims to forecast Bitcoin prices using machine learning techniques by categorizing them into daily prices and high-frequency prices. The project specifically employs the Random Forest Classifier algorithm to predict Bitcoin prices using historical data from July 2010 to May 2023. The dataset undergoes preprocessing and feature engineering to extract relevant features for the model. The trained algorithm is then optimized through hyperparameter tuning. Additionally, a simulation dashboard is created using Flask, a popular web framework, to allow users to evaluate different machine learning algorithms for predicting Bitcoin prices. The results of the project demonstrate that the Random Forest Classifier algorithm achieved 99% accuracy in predicting Bitcoin prices, highlighting the effectiveness of machine learning techniques in this context.