I am not sure how to formulate this problem clearly into a machine learning task yet. So hope you guys can chime in and give me some help.
Problem : To predict whether someone will pick up their phone during office hours in week n+2 by looking at customer's behaviour in week n.
Data : I have calling records for about 3 months, which is aggregated on customer level. The various attributes include, num of calls, duration of calls, time of calls, amount of data traffic. But of course, these main attributes are further split into at about 20 attributes.
Current Approach (Very Manual) : I look at data at week n+2 and get the group of guys who picked up the phone during office hours (duration of calls > 5s and time of call). This is the target group, T.
I look at data at week n and manually try all possible combinations of the attributes to get as close to T as possible. But trying manually seems tiring after some time. The baseline is of course using the same conditions as at week n+2. But the whole idea will be to increase this number.
Question : Is there any way I can transform this dataset so that I would be able to do accomplish it as a machine learning task ?