I have a dataset with quotes from an insurance company. I am trying to create a model to predict how much should the company charge the customer according to the different variables. Two of the variables are related to a second driver. One of them is
driver2_licence_typeand the other one is
I am interested in known how to deal with missing values on
driver2_licence_years in order to perform either a multilinear regression or a decision tree/random forest regression. There are two main cases.
driver2_licence_type is not
NaN I thought it was safe to fill
driver2_licence_years with average number of years, because we know there is a second driver but we just don't know how much experience the driver has. However, the price is not likely to follow a linear relation with experience, since very old drivers may be charged more due to loss of abilities. However, I don't know the precise effect. Should I instead do a previous analysis on how years of driving experience explains insurance fees and choose an age that gives the average price? Is it better to try to find what sort of functional relation can be drawn between years and price and then transform the variables accordingly?
When both variables are
NaN we're assuming that there is no second driver. I originally thought of filling
driver2_licence_years with zeros, but I am not sure if the effect of having a non-experience driver should be the same as not having a second driver (one could say it is more dangerous having someone with little experience that having no one). Here I am not sure what to do. From the point of view of a decision tree, it maybe be sensible to add another variable that specifies whether there is a second driver or not, and use this to decide whether to look at years or not. Or maybe I should simply have two different models depending on whether there is a second driver or not. What would you suggest in this case? This answer provides some ideas and I have also read about giving it the value -1, but I am still unsure.