I wrote a Neural Networks prediction model in Python.
My data has a few inputs and two outputs. In order to make it work, I have to normalise every column on data for good prediction results.
However, I had an issue. I run several times the prediction model with the same inputs to get the average and standard deviation of it. But obviously, those are also normalised to 0-1. On the test data I knew the min and max, so I could denormalised them. On the prediction values, I cannot the min and max real value.
How do you solve this kind of problem and if you cannot, are there any other decent prediction techniques without the need of normalisation?