- Bump Python version from 3.11 to 3.13 in .python-version file.
- Reset execution counts to null in Jupyter Notebook for reproducibility.
- Improve code readability by adjusting comments and formatting in the notebook.
- Change the policy definition to use numpy.ndarray for better clarity.
- Modify pyproject.toml to enable E501 rule for line length management.
- Added xgboost version 3.1.2 to pyproject.toml dependencies.
- Updated uv.lock to include xgboost package with its dependencies and wheel URLs.
- Added nvidia-nccl-cu12 package to uv.lock for compatibility with xgboost on specific platforms.
- 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.
- 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.
- Created a new Python script for portfolio analysis using historical stock data.
- Implemented functions for normality testing of prices and returns.
- Added histogram plots for prices and returns.
- Included logic for random portfolio allocation and efficient frontier calculation.
- Updated `pyproject.toml` to include `pandas-stubs` for type hinting support.
- Modified `uv.lock` to reflect the addition of `pandas-stubs` and its dependencies.
- Updated import order in Point_Fixe.ipynb for consistency.
- Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb.
- Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability.
- Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.
- Standardized spacing around operators and function arguments in TP7_Kmeans.ipynb and neural_network.ipynb.
- Enhanced the formatting of model building and training code in neural_network.ipynb for better clarity.
- Updated the pyproject.toml to remove a specific TensorFlow version and added linting configuration for Ruff.
- Improved comments and organization in the code to facilitate easier understanding and maintenance.
