Binary Classification I wanted to start off by saying this is not an exact duplicate of the other question. I checked it and it didn't have what I urgently need.
So here is the problem. I have a dataset with 30 features and 100000 rows of train data. I want to make a Binary classification model which determines whether the person is eligible or not for membership at our club. I am a rookie data scientist, and binary classification is a first for me. So please help me and tell me which model would be the most accurate for this purpose. Also, the time taken by the model to train doesn't matter... Thank you so much

update:
Ok, I have used logistic regression and instead of giving some members as accepted(1) it is showing me al members do not get the membership...So I thought there might be a mistake in choosing the Model, so I am looking for other models...
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.