dataset-health-audit

You can audit data quality by running a 12-dimension checker on your CSV file, which analyzes missing values, duplicates, and type consistency. It outputs a structured JSON report containing an overall quality score, per-dimension details, and actionable remediation suggestions.

A 12-dimension data quality audit evaluates tabular data for missing values, duplicates, type consistency, value ranges, format compliance, uniqueness, whitespace, constant columns, distribution skew, column naming, cardinality, and cross-column checks to produce a comprehensive quality grade.

Yes, the data quality checker supports CSV, Excel, TSV, and JSON formats. It handles large datasets by applying data sampling, ensuring efficient computation of weighted dimension scores and an overall quality grade without processing the entire file at once.

You can identify distribution skew and format compliance issues by running a tabular data audit. The checker evaluates these dimensions along with 10 others, producing a structured report that highlights specific issues and provides targeted remediation suggestions to fix them.

Yes, leveraging pandas and numpy, the automated data quality checker calculates a weighted overall score across 12 dimensions. It processes tabular data and outputs a structured JSON report complete with an overall grade and targeted remediation suggestions.