How to build a unbiased predictive ML model when the record of the event is less compared to the total number of records?
1) It seems that your data are unbalanced, you should look into that. Common techniques include oversampling the minority class, but you might have a bigger problem here.

2) It is unclear that you have enough information for what you are trying to achieve (type of device and location doesn't seems to be enough).

3) Based on the two preceding remarks, you have to acknoledge it is unlikely you will be able to get the date of an event. Would a human be able to guess that is a good question to ask yourself if you try to apply ML on a problem. If the answer is no, then a ML solution is unlikely to work. You can do statistical analysises to try to know if there are devices types or locations that are more susceptible of loss, but you won't be able to get exact time and location of a loss.