Okwy009/Automotive_Vehicles_Engine ? reverse-engineered prompt

Build me a clean Jupyter Notebook project for predictive maintenance on automotive engine health.

I want it to load an engine sensor dataset, clean the data, handle missing values and outliers, and explore the main signals like RPM, temperature, pressure, and other readings. Please include helpful charts so I can see trends, distributions, correlations, and any unusual readings.

Then create a few simple engineered features from timestamps and sensor values, train a machine learning model to predict engine condition or likely failure, and show how well it performs with a clear evaluation summary. A Random Forest model would be great if that fits.

Also make the notebook easy to follow, with short explanations before each step, and save the important plots so they can be used in a report. If you need to look up current best practices for sklearn or plotting, feel free to do that online.