I have a system that manages equipments. When these equipments are faulty, they will be serviced. Imagine my dataset looks like this:
ID Type # of times serviced
|ID| Type | #serviced | |1 | iphone | 1 | |2 | iphone | 0 | |3 | android | 1 | |4 | android | 0 | |5 | blackberry | 0 |
What I want to do is I want to predict "of all the equipments that have not been serviced, which ones are likely to be serviced" ? (ie) identify "at risk" equipments.
The problem is my training data will be #serviced > 0. Any #serviced=0 will not be frozen and dont seem to be valid candidates to include in training. (ie) When it fails, it will be serviced hence the count will go up.
Is this a supervised or unsupervised problem ? (supervised because I have serviced and not-serviced labels, unsupervised because I want to cluster not-serviced with serviced and there by identify at-risk equipments)
What data should I include in training ?
The example is obviously simplified. In reality I have more features that describe the equipment.