I am trying to create a logistic regression model to predict whether a customer given a loan will be a bad or a good customer: bad meaning missing a certain amount of payments and good meaning frequent enough and in time with payments. For the purpose of the model I have coded Bad as 1 and Good as 0 and tried different combinations with the variables.
One of the models I have built has an AIC of 5383.7 and Gini coefficient of 0.416733. This is the result after I play around with the threshold:
FALSE TRUE 0 3327 638 1 165 95
So the model guessed that 165 customers would be good, but they are bad, but also put 638 good customers into the bad customers group.
The second model I built has an AIC of 5734.6 (350.9 higher), but its Gini is 0.4190394 and is slightly better at predicting the bad customers:
FALSE TRUE 0 3537 673 1 177 105
[UPDATE] Okay. After checking a few things - It turns out that one of the variables has missing values and the model excludes the observations that have them by default. Hence the difference in observations in my models. I know about multiple imputation, but I don't really feel alright with it. My question is should I impute the missing data or should I exclude it from the data set so I can compare models with different number of variables?