I have to train a classification model to predict if a customer will buy a product or not. I have multiple (eg. 3 or 4) data sources. The variable distributions among the different data sources is quite different (eg. in the first one I have a vast majority of young people, while in the second one there are many adults). When the model will be used in production, the test dataset will be made of records from only one data sources (I don't know in advance which one).
My question is: which is the best way to combine these different sources? The data are not so much, so I cannot train an independent model for each data source. Can I just concatenate the data frame or do I have to perform some other kind of step?