I built a zero-boilerplate alternative to Great Expectations for ML data validation

#datascience #machinelearning #python

I love data validation, but I hate writing 500 lines of YAML just to check a CSV. So I built data-fitcheck — a lightweight Python library that does three things without any config:

Validates CSVs (missing data, outliers, type mismatches)
Detects data drift between training and production sets
Evaluates sklearn models and generates self-contained HTML reports

Install it:
bash

pip install data-fitcheck

Run the demo:
bash

fitcheck demo

It instantly generates three HTML reports in your folder. No setup, no YAML, no database.

GitHub: https://github.com/neoline361-art/fitcheck
PyPI: https://pypi.org/project/data-fitcheck/

I'd love feedback on the API design or feature requests!