# Analysis
shap>=0.38.0
interpret>=0.2.7
umap-learn>=0.5.2
ipywidgets>=7.6.5
pandas-profiling>=3.1.0
explainerdashboard>=0.3.8  # For dashboard method
autoviz>=0.1.36  # For EDA method
fairlearn>=0.7.0  # For check_fairness method

# Models
xgboost>=1.1.0
catboost>=0.23.2
kmodes>=0.11.1
mlxtend>=0.19.0

# Tuners
tune-sklearn>=0.2.1
ray[tune]>=1.0.0
hyperopt>=0.2.7
optuna>=2.2.0
scikit-optimize>=0.9.0

# MLOps
mlflow>=1.24.0
gradio>=2.8.10
fastapi>=0.75.0  # For web api
uvicorn>=0.17.6  # For web api
m2cgen>=0.9.0  # For model conversion
evidently>=0.1.45.dev0  # for drift reporting

# NLP
nltk>=3.7
pyLDAvis>=3.3.1
gensim>=4.1.2
spacy>=3.2.3
wordcloud>=1.8.1
textblob>=0.17.1

# Others
psutil>=5.9.0
fugue>=0.6.5
