- Updated error messages in Gauss method and numerical methods to use variables for better readability.
- Added return type hints to function signatures in various notebooks to improve code documentation.
- Corrected minor grammatical issues in docstrings for better clarity.
- Adjusted print statements and list concatenations for improved output formatting.
- Enhanced plotting functions to ensure consistent figure handling.
- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.
- Adjusted indentation and line breaks for better clarity in function definitions and import statements.
- Standardized string quotes for consistency across the codebase.
- Enhanced readability of DataFrame creation and manipulation by breaking long lines into multiple lines.
- Cleaned up print statements and comments for improved understanding.
- Ensured consistent use of whitespace around operators and after commas.
- Added CatBoost version 1.2.8 to the project dependencies in pyproject.toml.
- Updated uv.lock to include CatBoost and its dependencies, along with the necessary wheel files.
- Included Graphviz version 0.21 in the lock file as a dependency for CatBoost.
- Updated execution counts for various code cells to maintain consistency.
- Changed the model from RandomForestClassifier to GradientBoostingClassifier.
- Modified hyperparameter grid for GridSearchCV to include learning_rate and adjusted n_estimators.
- Added stratification to train-test split for better representation of classes.
- Corrected scoring parameter in GridSearchCV to use a valid metric.
- Updated output messages to reflect changes in model evaluation metrics.
- 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.
