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Good fundamentals. Normalizing company domains (strip www, trailing slashes, tracking params) before deduping catches more duplicates than matching on names ever will.
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This looks so minimalistic.
OCR extracts business names, formation dates, and addresses from uploaded documents automatically. But users still need to trust what the system found before anything gets submitted.
We designed the confirmation flow around editable fields that surface extracted data clearly....
📊 Excel Data Cleaning & Quality Control | Portfolio Sample
A practical demonstration of data cleaning, spreadsheet organization, and quality control using synthetic business records.
Project Results:
• 110 records processed
• 14 duplicate records removed
• 96 unique records organized
• 88 records passed quality checks
• 8 records flagged for manual review
Key Deliverables:
✓ Clean and organized Excel dataset
✓ Duplicate detection and removal
✓ Data formatting and standardization
✓ Quality control and exception tracking
✓ Documented cleaning process
This independent sample project demonstrates my approach to delivering structured, accurate, and reliable data.
All records are fictional and created for demonstration purposes.
#DataCleaning #MicrosoftExcel #DataEntry #DataManagement #VirtualAssistant
I cleaned and formatted an Excel dataset by correcting inconsistent text and removing unnecessary spaces. I used functions such as TRIM and PROPER to improve data consistency and readability
