Hi Sahil Bajaj,
This generally happens when your model is learning the data instead of learning the pattern. This scenario is called 'Overfitting'.
You can try the following few things:
- Use of regularization technique
- Make sure each set (train, validation and test) has sufficient samples like 60%, 20%, 20% or 70%, 15%, 15% split for training, validation and test sets respectively.
- Perform k-fold cross validation
- Randomly shuffle the data before doing the spit, this will make sure that data distribution is nearly the same.If your data is in datastore you can use 'shuffle' function else you can use "randperm" function.
