How can I normalize my output data for neural network?

I have a dataset consisting of 5 numerical variables, like the sample in the following image:

In this dataset the first four variables are the input and the GDP is the output. I am trying to build a neural network for regression of the GDP variable. For the input variables, I have used PCA to normalize the data, while for the GDP variable I have used a MinMax normalization algorithm. However, the accuracy of the results of the neural network is very low and I guess that the problem is on the normalization of my output variable. Any suggestions on how I can improve the accuracy of my neural network?

• Why to normalise output variable. Normalisation is perfomed on dependent variable not on indipendent variable.
– g_p
Apr 23 '20 at 20:18

1 Answer

Normally, you shouldn't make normalization on your target data. If you want to increase your accuracy of model, you can try different neural network architectures or activation functions and monitor your train/val/test scores.

If you insist to normalize target data, then you may want to convert your predictions and then calculate the accuracy.

Steps may be like below:

normalize data => fit model => take predictions => denormalize predictions => calculate accuracy