LocalBiz Scout: Building Safer Business Data Cleaning with Python and Local AI
This is my submission for the Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle . What I Built I built LocalBiz Scout , a Python tool for analyzing business CSV data, reviewing potential data-quality issues, and applying explicitly approved corrections. Business data cleaning is not just about finding inconsistencies. An incorrect phone-number correction or a false duplicate match can…
LocalBiz Scout is a Python tool designed to analyze business CSV data, identify potential data-quality issues, and apply approved corrections. The tool is built with three main stages: Analyze, Review, and Remediate. In the Analyze stage, the tool generates structured findings, a Markdown report, and duplicate candidates. In the Review stage, correction proposals and a decision template are created, with proposals defaulting to deferred status.
In the Remediate stage, only accepted, allowlisted formatting corrections are applied, and an audit trail is produced. The tool is intended for developers and data teams working with business directories, lead lists, and other CSV datasets. LocalBiz Scout uses Python, RapidFuzz for fuzzy matching, Pytest for testing, and a small local web interface. AI is optional and integrated as local inference, rather than a prerequisite for the core workflow.
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