Deciding input variables for an ANN I am trying to create an ANN with as many as 50 independent variables. Based on my intuition and business logic, I have tried to create many combination of input variables to feed the ANN, retrain it and try to predict. But the results are not as expected and although the model make very good test set predictions it fails when new data is fed. Can someone plz guide where I am doing wrong? My immediate thought is I need to select the input variables based in a better way. Statistically is there a way to decide that?

What information can I provide about the model or results which would help to solve this?
I feel that is something more fundamentally wrong if your test scores are good but your predictions on new data are bad. I think it sounds like your new prediction data is not coming from the same distribution as your train/test data. That means it is different in some way which will cause you model to perform more poorly on the new data.