An automated tool designed to clean textual data by detecting and replacing gender-specific terms, promoting neutrality and reducing potential biases in content.

• Automated Scrubbing: Identifies and replaces gender-specific words based on a predefined list.
• Word Replacement: Not only removes but also replaces gender-specific words with neutral alternatives.
• Fuzzy Matching: Identifies and flags potential words that might be relevant but aren't clear matches.
• Feedback Loop: Allows for continuous refinement based on real-world feedback.
• Robust Error Handling: Gracefully handles unexpected situations and provides informative error messages.
• Detailed Logging: Provides a clear audit trail for operations and troubleshooting.

• Configuration: Before running the script, ensure the ScrubbingCriteria.cfg file is set up correctly. This file contains the list of words to scrub, their replacements, and the paths for input and output.
• Running the Script: Navigate to the directory containing the DataScrubber.py script and run it using the command: python DataScrubber.py.
• Feedback Integration: After manual review, any missed flags or false positives can be added to a feedback file. Use the FeedbackIntegrator.py script to integrate this feedback and refine the scrubbing criteria.

• Python version 3.11
• Required Python libraries: pandas, re, configparser

• Integration with a database for storing and retrieving scrubbing criteria.
• Machine learning-based scrubbing for improved accuracy.
• User interface for easier interaction and configuration.