Predictive Modelling Experiments

The goal of this client Proof of Concept was to determine if ML could be used to determine when certain equipment should be sent for preventative maintenance. The client experiences significant downtime with preventative maintenance to maintain a solid safety rating for their projects but wanted to reduce the overall downtime and cost burden of the activity. Device repair and utilization history data was loaded into a standard machine leaning model hosted in Azure ML Studio and using AutoML for curve fitting across multiple iterations of feature experiments. The short project was able to achieve an 84% accuracy rating for indication of when preventative maintenance should be scheduled.