Zero-Boilerplate ML Data Validation That Actually Feels Good to Use

I love data validation, but I hate writing 500 lines of YAML just to check a CSV. Tools like Great Expectations are powerful, but they feel way too heavy for quick checks, internal scripts, or fast-moving projects.

So I built data-fitcheck — a lightweight Python library that does three things without any config:

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

30-Second Quickstart

bash
pip install data-fitcheck && fitcheck demo
GitHub: https://github.com/neoline361-art/fitcheck
PyPI: https://pypi.org/project/data-fitcheck/