- 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.
- Set execution_count to null for specific code cells in 2025_TP_1_M2_ISF.ipynb to reset execution state.
- Replace output display of DataFrames with print statements in 2025_TP_1_M2_ISF.ipynb for better visibility during execution.
- Clean up import statements in 2025_TP_2_M2_ISF.ipynb by adding noqa comments for better linting and readability.
- Created a Docker Compose file to set up a MySQL container named M2_SQL_COURSE with an empty password and a database named TP.
- Added a Makefile with a target to execute a SQL script (TP1.sql) inside the MySQL container and log the output.
- Implemented the TP1.sql script to create tables for Magasin and Localite, insert initial data, and perform several queries.
