I am doing some predictive churn modelling. I use around 250 independent variables (not all at same time). Those variables are transactional, demographics, external data etc. The database is fairly large, 100' plus. I am using backward elimination with 0.01 as P-value lever to enter model.
After I have run the logistic regression and eliminated variables for correlation between variables, I have approx 35 significant variables.
My impression is that this is too many predictors and I would prefer too have less. Anyone that have an opinion of how many significant variables you can put in a model without overfitting and making it to complex?