- Created a new Jupyter notebook: 2025_M2_ISF_TP_4.ipynb for supervised machine learning exercises, including data preparation, model building, and performance analysis.
- Added 'imblearn' as a dependency in pyproject.toml to support handling imbalanced datasets.
- Updated uv.lock to include the 'imbalanced-learn' package and its dependencies.
- Updated execution counts for multiple code cells to maintain consistency.
- Removed redundant imports and organized import statements.
- Improved formatting for better readability in train-test split section.
- Added markdown explanations for model performance metrics (MAE, RMSE).
- Enhanced cross-validation training loop with detailed output for each fold's metrics.
