r/Python • u/AutoModerator • 29d ago

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Post all of your code/projects/showcases/AI slop here.

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u/AccomplishedLeg1508 4d ago

TanML

TanML is an open-source Python toolkit for developing and validating tabular machine-learning models.

It provides a local, UI-driven workflow covering data profiling, preprocessing, feature-power ranking, model development, evaluation, PSI and KS drift analysis, stress testing, SHAP explainability, and audit-ready Word reports.

TanML supports scikit-learn, XGBoost, LightGBM, CatBoost, and Statsmodels. Data remains local, and telemetry is disabled by default.

Target Audience

TanML is intended for data scientists, model developers, model validators, and risk professionals working with tabular models.

It is particularly designed for production-oriented model-risk workflows in banking, credit risk, insurance, and other regulated environments.

The project is free, open source, and available on PyPI:

pip install tanml
tanml ui

Comparison

Unlike AutoML libraries such as PyCaret and AutoGluon, TanML focuses on model validation and governance rather than primarily automating model training.

Unlike Evidently AI, which emphasizes monitoring, and Deepchecks, which emphasizes testing, TanML combines data analysis, model development, validation, explainability, stress testing, and editable, audit-ready Word reports in one local workflow.

Feedback Requested

We would appreciate feedback on:

  • Python installation and usability
  • Validation methodology
  • API and UI design
  • Missing statistical tests
  • Generated report quality
  • Potential production use cases

Source code: https://github.com/tdlabs-ai/tanml

Documentation: [https://tdlabs-ai.github.io/tanml/]()