requirements.txt

# Core Libraries for Machine Learning
scikit-learn==1.6.1          # Essential library for machine learning models (Random Forest, Decision Trees, etc.)
numpy==1.26.4                # Numerical operations (required for model input/output processing)
pandas==2.2.3                # Data manipulation and preprocessing

# Plotting and Visualization Tools
matplotlib==3.10.1            # Visualization library (used for plotting confusion matrices)
seaborn==0.13.2              # Advanced data visualization, helpful for heatmaps (confusion matrix)

# Saving and Loading Models
joblib==1.4.2                # For saving and loading machine learning models (used for Random Forest, Decision Trees, etc.)

# Hugging Face Hub Integration
huggingface_hub==0.30.2      # 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==7.2.2              # For running Jupyter Notebooks in your project

# Optional - TensorBoard for Visualizing Training Process (if applicable to larger models)
tensorboard==2.19.0          # For tracking and visualizing model training

# Extras for performance and speedups
xgboost==3.0.0               # Gradient boosting library (optional, if you want to use advanced tree-based models)
lightgbm==4.6.0              # LightGBM for fast gradient boosting (optional, for high performance)