| # Core Libraries for Machine Learning | |
| scikit-learn==1.5.1 # Essential library for machine learning models (Random Forest, Decision Trees, etc.) | |
| numpy==1.23.0 # Numerical operations (required for model input/output processing) | |
| pandas==1.5.2 # Data manipulation and preprocessing | |
| # Plotting and Visualization Tools | |
| matplotlib==3.7.0 # Visualization library (used for plotting confusion matrices) | |
| seaborn==0.12.1 # Advanced data visualization, helpful for heatmaps (confusion matrix) | |
| # Saving and Loading Models | |
| joblib==1.2.0 # For saving and loading machine learning models (used for Random Forest, Decision Trees, etc.) | |
| # Reporting and Metrics | |
| scikit-learn==1.5.1 # For generating classification reports, confusion matrices, and model evaluation metrics | |
| # Hugging Face Hub Integration | |
| huggingface_hub==0.29.0rc7 # Integration with Hugging Face Hub (for model uploading, downloading, sharing) | |
| transformers==4.26.1 # Hugging Face Transformers library (for model usage on the Hub) | |
| # Optional - Jupyter Notebooks for Model Development and Experimentation | |
| notebook==7.0.0 # For running Jupyter Notebooks in your project | |
| # Optional - TensorBoard for Visualizing Training Process (if applicable to larger models) | |
| tensorboard==2.10.1 # For tracking and visualizing model training | |
| # Extras for performance and speedups | |
| xgboost==1.6.2 # Gradient boosting library (optional, if you want to use advanced tree-based models) | |
| lightgbm==3.3.5 # LightGBM for fast gradient boosting (optional, for high performance) | |