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# 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) |