Modeling for probability of event and amount of event I have built a logistic regression model to predict the probability that the event for my dataset occurs.
I also want to know the predicted amount (a regression problem) associated with that event (a customer buying my product).
Incumbent with the logistic model is including event and non-event outcomes. Is the same true for a regression (not classification) problem? Should I include in my linear model customers who didn't buy another product (and thus spent 0 dollars)? Or would that be sort of "double-dipping" if I went to multiply the likelihood * amount for an expected value of a given customer?
 A: There's nothing wrong with using a sequence of prediction models to 1: predict whether or not a product is purchased (binary outcome) then 2: if consumer buys product, predict quantity/volume purchased. The second model is referred to as a conditional model, since you are conditioning on the fact that some quantity/volume of product is purchased. It makes sense to train the second model using only the subset of the training dataset that purchased products. You should inspect a number of measures to assess both models 1 and 2 as well as their overall performance to decide whether the predictive accuracy is acceptable.
A: Since you want to predict the amount spent rather than just whether any amount is spent at all, logistic regression won't cut it. You want a model in which the amount itself is a dependent variable, such as linear regression. If lots of subjects don't buy anything, a zero-inflated model could be helpful. You can keep using logistic regression if you use a two-stage compound model in which you predict whether subjects buy anything, then, among those for whom you predicted a purchase, predict the actual amount spent with another model.
