I am trying to do future 2 year value prediction at an individual customer level. Originally I tried to use a linear regression for this prediction, but was getting really poor r-squared value. (0.31) Hence, I converted to problem into a classification problem and used multinomial logistic model. (multinom from R's nnet package)
I converted the 2 year value into 5 classes, the relative proportion of the classes in the training sample is as follows:
Class 1 (not transacted)- 72%
Class 2 (< 10K )- 19%
Class 3 (10K- 25K)- 5%
Class 4 (25K-50K) - 3%
Class 5 (>=50K)- 2%
The code and results for the multinomial logistic model are pasted below:
Call: multinom(formula = REV_DEC13_NOV15_5_bands_factor ~ REV_DEC12_NOV13 + VISITS_IN_DEC12_NOV13 +QUANTITY_VISIT_DEC12_NOV13 + DISTINCT_Category_IN_DEC12_NOV13 + DISTINCT_Subcategory_IN_DEC12_NOV13 + TENURE_TILL_NOV13 , data = data_Single_Trans)
While, they are not included here, I have also got a p-values for all variables and categories.
Questions: 1. How do I know whether the model is a good one? What are the goodness of fit metrics for a multinomial logistic model?
Is multinomial logistic model a good way to address this kind of a problem? What are other techniques I could think of?
Does multinom take care of the interaction effects if any?
I scored the model to get the 5 probabilities. For doing the class-assignment, I picked the class with the highest predicted probability. Is there any other way of doing this?