One Vendor, Four Spellings: How Deterministic Stages Beat Similarity Scores
How-To How to actually use this
What changed: A data science article demonstrates using deterministic string-matching stages before similarity scoring to clean a messy supplier list.
How to use it:
- Start with a raw list of supplier names containing variations of the same entity.
- Apply deterministic rules first (e.g., exact substring matches, known aliases, case/whitespace normalization).
- Only then run a similarity score on the remaining ambiguous pairs.
- Treat a score of 91 as a starting point for human review, not an automatic match.
Good for: data engineers cleaning messy entity lists before analysis.
Deduplicating a 10,000-row supplier list in Python, where the hard part is deciding what a similarity score of 91 means The post One Vendor, Four Spellings: How Deterministic Stages Beat Similarity Scores appeared first on Towards Data Science .
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