Bitcoin Price Prediction using Machine Learning

This podcast explores Bitcoin price prediction using machine learning techniques. It focuses on the use of the XGBoost classifier, demonstrating its high accuracy (99.08%) compared to other algorithms such as logistic regression, SVM, and Random Forest. The study analyzes a historical Bitcoin price dataset, applying data preprocessing and evaluating model performance through metrics such as accuracy, recall, and F1 score. The results show the effectiveness of XGBoost in predicting Bitcoin price changes, offering valuable insights for investors. Future research is discussed to improve the model's accuracy by integrating additional data and ensemble learning techniques.