The automobile and transport industries have always tried to reduce
down-time by the way of preventive maintenance. In the past decade, electromechanical
sensors have become more accurate along with novel innovations in
IoT and Machine Learning, and automobiles have leveraged this. In this paper,
an end to end open source predictive maintenance solution is presented to predict
the severity of faults in a car using onboard historical and real-time sensor
data using IoT and Machine Learning. Sensor data is collected from a Suzuki
Swift VXi Model, and classifiers like Logistic Regression, Random Forest , and
Gradient Boosting trees are used to train the data with imputed faults. F1 score
and AUC are used as evaluation metrics. An end-to-end On-Board Diagnostics
(OBD) data to the user dashboard pipeline is proposed with final predicted
faults visible on a real-time dashboard.