archd3sai/Predictive-Maintenance-of-Aircraft-Engine ? reverse-engineered prompt

Build me a Jupyter notebook project for predictive maintenance of aircraft engines using the NASA CMAPS data. I want to explore the full workflow from data cleaning and feature engineering to model training and comparison, so I can predict both when an engine might fail and how much useful life it has left.

Please include clear notebooks for data wrangling, feature importance, and the main experiments for classification and regression. For the modeling part, compare a few approaches like a degradation based RUL estimate, a similarity based method, and deep learning models such as LSTM, RNN, and a 1D CNN. I also want binary and multiclass failure prediction, not just RUL regression.

Make it easy to run and understand, with helpful plots, explanation of the results, and a simple way to see which features matter most. If you need to, look up current docs online for any libraries or best practices.