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Self-directed CSV Evidence Normalizer: cleans messy tabular inputs into schema-checked, traceable outputs with validation errors, idempotent processing, and explicit provenance. Built for repeatable data QA rather than one-off manual cleanup.
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BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
Concept Project — Data Cleanup & QA
A practical example of turning inconsistent spreadsheet data into a clean, structured, analysis-ready dataset using validation rules, duplicate checks, standardized formats, and a final QA pass.
Cleaned and formatted a raw dataset in Excel by removing duplicates, fixing formatting issues, handling missing values, and organizing the data into a structured format for analysis.
