# Preprocessing of non-linear and censored variables

I now ran into two variables that give me problems. I know that I want to try to include them in the final model, but I'm unsure how the values should be treated in order to reflect the true nature of the phenomena they are reflecting.

Case 1


The variable depicts the age/duration of a certain contract type. If the customer subscribes to this specific type, the variable shows the age of this contract since it start. However, if the customer doesn't have this type of contract, the value is zero. If I use this variable straight out of the box as a integer, you see the problem: the value "0" is quite different from any number of days (although it is in a way a correct answer). How should/could this variable be preprocessed? Durations go from days to several years.

Case 2


In my modeling I use a dataset that goes back some 3 years. I then based on this dataset calculate if and how long a go, certain changes happened. If there has been a change, I can include the change and the time elapsed since this change. But what is the best way to include these variables? Missing values indicate no change (although a cange might well have occured, but outside the collected data), while a integer indicates n days since a change. Both cases carry valuable information, but the data is presented in slightly different form.