This repository contains datasets and supplementary materials related to cryptocurrency forecasting using transfer learning approaches. The repository includes: • Raw cryptocurrency market data collected from Yahoo Finance using the Python package yfinance. • Processed datasets used for feature engineering and model training, including lagged returns, moving averages, volatility measures, and other technical indicators. • Experimental results supporting model evaluation, including baseline and transfer learning settings for Support Vector Regression (SVR), Random Forest (RF), and XGBoost (XGB). These materials are provided to support research reproducibility and transparency in cryptocurrency forecasting.