deb-barman166/data-masterpiece ? reverse-engineered prompt

Build me a Python data preprocessing framework that can take messy real world datasets and turn them into clean machine learning ready data.

I want it to handle the usual stuff like missing values, outliers, noise, encoding, feature engineering, and splitting data into train, validation, and test sets. It should feel modular so I can plug in new steps easily, and it should support both an automated mode and a manual mode where I can choose exactly what happens. If possible, include a simple multi agent style workflow for organizing the processing steps.

Make it scalable and easy to extend, with a clean structure that would work for data science and machine learning projects. Include a basic demo or example showing how someone would run a pipeline end to end on a sample dataset. If you need current best practices for Python libraries or preprocessing patterns, look up the latest docs online.