My dataset is a log of phone calls. Each row represents a customer interaction with attributes such as customer age, customer job, and interaction outcome ('buy' vs 'no buy'). EDIT: the interaction outcome is what I would like to predict with any binary classification algo.
Here's the problem: I have no way to retrieve a customer ID and I am told the dataset definitely contains rows that are related to the same customer (i.e. someone who called twice or more). I just have no idea to find out which rows because I have no customer ID.
I do have a timestamp value (day-month-year), and a flag attribute telling me if the customer is calling for the first time or not.
So I am wondering: if I apply any binary classification learner without making any arrangements for this peculiarity, won't I have a prediction that may overrepresent certain customer types and therefore certain outcomes based on the fact that some are having more than one interaction? I would presume that is a bad thing right?
Having no way of assigning a customer ID to calls indicating that this is not the first time the caller is being contacted, am I not better off just dropping rows where the customer is not new?
EDIT: this is the full description of the dataset I am using: https://archive.ics.uci.edu/ml/datasets/bank+marketing I created the 'first time customer contact' flag myself deriving it from the 'campaign' field which shows the number of calls with the customer up to that point.
I also created the timestamp field my self by cross matching the euribor3m field (which changes on a daily basis) with a third party datasource.