I built a small data-cleaning workflow around one simple rule: bad rows should never silently disappear.

Synthetic demo results:

• 20 raw order records • 16 clean rows • 2 duplicates removed • 2 invalid rows quarantined • $3,690 paid revenue reconciled

The output includes a clean CSV, a rejected-row file, an audit log, and an Excel dashboard. Dates, names, emails, regions, products, and payment statuses are standardized.

The key design choice is quarantine + reasons, not just dropping bad rows and exporting. That lets a reviewer trace exactly what changed and decide whether rejected records should be repaired.

Portfolio: https://contra.com/p/WPLXml6R-demo-automated-sales-data-cleaning-and-excel-reporting

For recurring exports, what needs the most manual review in your workflow: duplicates, inconsistent labels, or invalid amounts and dates?

Performed comprehensive web research on fitness equipment by gathering accurate information from trusted online sources. Collected details including product names, brands, equipment categories, features, specifications, prices, customer ratings, and intended use. Organized, verified, and formatted the data in Microsoft Excel to create a structured dataset suitable for market research, competitor analysis, product comparison, and business decision-making. Demonstrated attention to detail, web research, data entry, and Excel proficiency.