I have a dataset with 330 samples and 27 features for each sample, with a binary class problem for Logistic Regression.
According to the "rule if ten" I need at least 10 events for each feature to be included. Though, I have an imbalanced dataset, with 20% o positive class and 80% of negative class.
That gives me only 70 events, allowing approximately only 7/8 features to be included in the Logistic model.
I'd like to evaluate all the features as predictors, I don't want to hand pick any features.
So what would you suggest? Should I make all possible 7 features combinations? Should I evaluate each feature alone with an association model and then pick only the best ones for a final model?
I'm also curious about the handling of categorical and continuous features, can I mix them? If I have a categorical [0-1] and a continuous [0-100], should I normalize?
I'm currently working with Python.
Thanks a lot for your help!