kpeters/exploring-nasas-turbofan-dataset ? reverse-engineered prompt

Build me a notebook based project that explores NASA’s turbofan degradation dataset and shows a few different ways to predict remaining useful life for the engines.

I want a clean set of notebooks that walk through the data from basic exploration and a simple baseline, then try a few model approaches like regression, survival style thinking, random forest, a time series view, and at least one neural network option. Please make it easy to follow, with clear explanations and plots so I can understand what’s happening even if I’m not a specialist.

Use the official CMAPSS data structure, assume the data is downloaded locally, and make the notebooks runnable from a normal Python environment with a requirements file. If anything depends on newer best practices or docs, look them up online first. Also include a short reproducible results guide so someone can rerun the experiments and compare outcomes across the different FD001 to FD004 cases.