Binary Classification
Looks like to me this is a classic imbalance binary classification problem (see comments above). What loss are you using ? It looks like your model is predicting the non-membership class because it’s minimising it’s averaging loss. Here are some techniques you might wanna try to solve this issue:


use regularization
over sampling the membership class
under sampling the non-membership class
select variables, linked to regularization above with l1 and l2 penalties
feature engineering: you specified you have 30 features but are all of them useful ? How do you preprocess them to feed the model ? Are they numerical or categorical ? 


I hope this gives you some ideas about how tackling the problem. Changing the model won’t miraculously solve your issue.