Big difference between val-acc and prediction accuracy in Keras Neural Network

Question

I have a dataset that I used for making NN model in Keras, i took 2000 rows from that dataset to have them as validation data, those 2000 rows should be added in .predict<

Answer 1

The training data you posted gives high validation accuracy, so I'm a bit confused as to where you get that 65% from, but in general when your model performs much better on training data than on unseen data, that means you're over fitting. This is a big and recurring problem in machine learning, and there is no method guaranteed to prevent this, but there are a couple of things you can try:

• regularizing the weights of your network, e.g. using l2 regularization
• using stochastic regularization techniques such as drop-out during training
• early stopping
• reducing model complexity (but you say you've already tried this)