The dataset includes various vehicle engine health characteristics and measurements, such as engine speed, temperature, pressure and other sensor data.
The objective of the project using this dataset is to build a predictive maintenance model for automotive engines. Investigating patterns and trends in the data, a machine learning model is trained and built to predict when an engine is likely to require maintenance or repair. This could help vehicle owners and mechanics proactively address potential problems before they become more severe, resulting in better vehicle performance and longer engine life.
Once trained, the model could be integrated into a larger system for monitoring the health of automotive engines. For example, sensors could be installed in vehicles to collect real-time data on engine performance, which could then be sent to a central server for analysis. The predictive maintenance model could then generate alerts or recommendations for maintenance or repair.