# 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)