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

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…