A machine learning approach for predictive maintenance for mobile phones service providers

The problem of predictive maintenance is a very crucial one for every technological company. This is particularly true for mobile phones service providers, as mobile phone networks require continuous monitoring. The ability of previewing malfunctions is crucial to reduce maintenance costs and loss of customers. In this paper we describe a preliminary study in predicting failures in a mobile phones networks based on the analysis of real data. A ridge regression classifier has been adopted as machine learning engine, and interesting and promising conclusion were drawn from the experimental data.