# Core data science libraries
pandas>=1.3.0
numpy>=1.21.0

# Machine learning
scikit-learn>=1.0.0
joblib>=1.1.0

# Data visualization
matplotlib>=3.5.0
seaborn>=0.11.0

# Jupyter ecosystem
jupyterlab>=3.0.0
ipython>=7.0.0

# Data acquisition
kaggle>=1.5.0

# Model persistence and deployment
pickle-mixin>=1.0.2

# Performance monitoring
memory-profiler>=0.60.0

# Optional: Advanced ML libraries
imbalanced-learn>=0.8.0  # Handle class imbalance
xgboost>=1.5.0  # Alternative ensemble method
lightgbm>=3.3.0  # Fast gradient boosting

# Optional: Deep learning (if extending to neural networks)
tensorflow>=2.8.0
keras>=2.8.0

# Development dependencies
pytest>=6.0.0  # Testing framework
black>=22.0.0  # Code formatting
flake8>=4.0.0  # Linting

# Security and validation
adversarial-robustness-toolbox>=1.10.0  # Adversarial attack testing