I’m sitting on a collection of raw text files that need a thorough clean-up before they can be pushed into our reporting pipeline. Your job is to load each file, inspect every field, and transform the content into a reliable, analysis-ready dataset. Here’s the scope in plain terms: • Parse the provided .txt files accurately. • Identify and correct any inconsistencies or obvious entry mistakes. • Return the cleaned data in a structured format of your choice (CSV or Excel preferred), along with a short changelog that outlines what you fixed. I already have the files organized and ready to share immediately upon kickoff, so you can dive straight into the task. If you work with Python (pandas), R, or similar tools, mention it—automation is welcome as long as accuracy stays high. Deliverables are simple: the cleaned dataset and the brief changelog. Once verified, the project is complete.