This project presents a robust machine learning pipeline for forecasting next-day cryptocurrency volatility using an XGBoost model. The solution includes a reproducible data workflow, model training with time-series validation, and an interactive Streamlit application for real-time prediction and risk regime classification.
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Modifying the code to avoid error warnings
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