Bitcoin Price Prediction Pipeline: An Ensemble Approach to Forecasting Cryptocurrency Markets
This article presents a Python-based price prediction pipeline that combines machine learning techniques and deep learning algorithms to forecast Bitcoin's closing price. The pipeline fetches historical price data, preprocesses it, and trains an ensemble of models, including Linear Regression, Bayesian Ridge, Support Vector Regression, and Random Forest Regressor. A neural network with two hidden layers is also employed, and hyperparameter tuning is performed using grid search and cross-validation. The ensemble approach aims to improve prediction accuracy by leveraging the strengths of different models.
