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Procurement Data Quality · Fuzzy Matching
Supplier Normalization Workbench
A supplier data-quality workbench for standardizing messy vendor names, detecting duplicate supplier records, and creating cleaner supplier-family mappings for spend analytics.
PythonStreamlitProcurementData QualityRapidFuzz
Core Features
Supplier name cleaning and standardization
Known alias matching
RapidFuzz fuzzy duplicate detection
Match confidence scoring
False-positive risk flagging
Human review queue
Golden supplier record recommendations
Before-and-after supplier count impact
Exportable normalized supplier data
What It Demonstrates
This project demonstrates procurement data-quality thinking, fuzzy matching logic, human-in-the-loop review design, and the foundational role of supplier normalization in spend analytics and sourcing diagnostics.