Getting Started with Predictive Maintenance Models
This was originally posted on the Silicon Valley Data Science blog. In a previous post, we introduced an example of an IoT predictive maintenance problem. We framed the problem as one of estimating the remaining useful life (RUL) of in-service equipment, given some past operational history and historical run-to-failure data. Reading that... Read more
Are Your Predictive Models like Broken Clocks?
A wise philosopher (or comedian) once said, “Even a broken clock is right twice a day.” That same statement might also apply to some predictive models. Since prediction is about the future (usually), then random chance (like broken clockwork) may allow our model to be right occasionally (just by accident). The... Read more
Predicting Resignation in the Military
In the 2015 hackathon organized by Singapore’s Ministry of Defense, one of the tasks was to predict resignation rates in the military, using anonymized data on 23,000 personnel which included their age, military rank, years in service, as well as performance indicators such as salary increments and promotions. Our team... Read more