I have telecom line item (invoice) data. which looks like this:
The data has a monthly granularity and in total there is 6 months of data. By summing up all the line items for a subscriber for every month we can get their monthly revenue, cost, profit etc. But ofcourse we lose the service class information if we aggregate. My question is can I train a model on 5 months of data (profit column) and try to predict the 6th month (profit) as a binary choice e.g subscriber which will show 10 percent loss in profitability and also identify patterns of services that might have lead to it? if yes how may I approach this problem and which algorithms should I look at. Also Im sure there are quite a lot of subscribers who dont have the full 6 months data i.e no line items for some months.