# Core dependencies
numpy>=1.20.0
pandas>=1.3.0
scikit-learn>=1.0.0
dask>=2022.1.0  # For large dataset handling
dask-ml>=2022.1.0  # For distributed machine learning
vaex>=4.16.0  # For out-of-memory data handling
pyarrow>=12.0.0  # For efficient data serialization
fastparquet>=2023.2.0  # For efficient parquet file handling
psutil>=5.9.0  # For system resource monitoring

# Advanced ML and Optimization
scikit-optimize>=0.9.0  # For Bayesian optimization
shap>=0.40.0  # For model interpretability
lime>=0.2.0  # For local interpretability
xgboost>=1.5.0  # For advanced boosting
lightgbm>=3.3.0  # For advanced boosting
catboost>=1.0.0  # For advanced boosting
optuna>=2.10.0  # For hyperparameter optimization
statsmodels>=0.13.0  # For advanced statistical models
scipy>=1.7.0  # For scientific computing

# Visualization
matplotlib>=3.4.0
seaborn>=0.11.0
plotly>=5.3.0  # For interactive visualizations
dash>=2.0.0  # For dashboards

# Deep Learning (optional)
torch>=1.10.0  # For deep learning models
tensorflow>=2.8.0  # For deep learning models

# Model Deployment
fastapi>=0.68.0  # For API serving
uvicorn>=0.15.0  # ASGI server
onnx>=1.12.0  # For model export
pmml2json>=0.1.0  # For PMML support

# Monitoring and Logging
prometheus-client>=0.12.0  # For metrics
python-json-logger>=2.0.0  # For structured logging
mlflow>=1.20.0  # For experiment tracking

# Report generation
jinja2>=3.0.0
pdfkit>=1.0.0  # Optional, for PDF report generation

# Development dependencies
pytest>=6.2.0
pytest-cov>=3.0.0  # For test coverage reporting
black>=21.5b2
flake8>=3.9.0
mypy>=0.910
isort>=5.9.0

# Type checking
typing-extensions>=4.0.0

# Documentation
sphinx>=4.0.0
sphinx-rtd-theme>=0.5.0

# Added from the code block
tqdm>=4.62.0
joblib>=1.1.0 