I have a large dataset with 6 predictors, with a goal to predict bank loan interest, based on year income, time at work, loan amount, credit balance, credit utilization rate, etc. I use python with scikit-learn to do this prediction, specifically ElasticNet. When I train the model and check final model coefficients, I see something like
[0.09342575, 0.06866633, 0.01102091, 0.07865676, -0. , 0.16359722]
which means that some predictors are more important than others, so predictor #5 got coefficient = 0, and probably does not influence the final result much (it might change if I change ElasticNet parameters, though).
Does it mean that I should reconsider my selection of predictors, remake training data set (it might be useful in terms of memory utilization, and calculation time for example), exclude this "unimportant" predictor #5 and re-run the training? Or should I leave them all in the model and dataset?
BTW, is there a way to use ElasticNet with some categorical data (like home ownership: rent, own, mortgage, etc), somehow encoded? For neural net it would be one-of-N, like "rent" is "0,0,1", own is "0,1,0", etc, but how about ElasticNet?