Undergraduate research on machine learning for predictive maintenance in automobile engines — from an initial decision-tree classifier to a two-stage ensemble diagnostic pipeline.
Extends the original decision-tree work into an ensemble and gradient-boosting framework tested on two datasets. A two-stage Random Forest pipeline (detect, then diagnose) reaches 100% accuracy on binary fault detection and 64.8% on fault-subtype identification on EngineFaultDB, while a validation-tuned gradient boosting classifier nearly doubles fault-class recall on the original dataset. Both trained models are packaged into a public, browser-only diagnostic dashboard — no install, no internet connection required.
Final-year dissertation developing a decision-tree predictive model to classify automobile engine condition as GOOD or BAD from six sensor parameters — engine RPM, fuel pressure, lubricant oil pressure and temperature, coolant temperature and pressure — with a root-cause feature that identifies which parameter is responsible for a bad reading.