data-auditor-cleaner

Data auditing and cleaning for contest data involves inventoring datasets, applying reversible rules for missing values and anomalies, and generating a reproducible cleaning log. This ensures downstream modeling relies on traceable, trustworthy inputs.

A read-only raw data policy ensures original contest datasets remain unmodified during data cleaning. The auditor reads from raw inputs, applies explicit cleaning rules, and outputs a separate cleaned dataset to maintain strict traceability.

Generating a data audit report and cleaning log requires running an auditor on raw data to inventory fields, identify quality issues, and document cleaning decisions. This produces transparent artifacts for auditability.

This data cleaning approach applies across data fields, quality checks, missing-value handling, and anomaly rules for contest data. It enforces a read-only raw data policy while producing a cleaned dataset and audit report.

Explicit anomaly rules for missing value handling ensure data cleaning steps are reversible and traceable. By applying documented rules rather than ad-hoc fixes, the data audit maintains reproducibility for downstream modeling and paper sections.

The best way to inventory datasets and identify quality issues is running a data auditor that checks fields, units, and metadata. This produces a comprehensive data report highlighting inconsistencies before downstream modeling.