When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.
Is your model underfitting or overfitting?
You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.
You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.
Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.
If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.