How to handle unseen class feature in test dataset feature   Definition    Key
survival    Survival    0 = No, 1 = Yes
pclass   Ticket class   1 = 1st, 2 = 2nd, 3 = 3rd
sex       Sex           M/F
Age     Age in years    


feature notes given below

pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5


Suppose I have dataset same as mentioned above.

How to handle a case where I can have a unseen value for a class. For example : In our dataset we have 3 different value for pclass. But how to handle a case where pclass value may have 4rth class say "elite" which did not appear in training dataset but appeared in the test dataset
Depends on the model.  I would say most of the time the new level is dropped or mapped to a NA level.  If possible, you should try to do a stratified sample for your training set to ensure that you get every level you care about.