We will start with the NULL Flags.str stream.

To use an @NULL multiple Derive node to explore missing data, perform the following steps:

There is no substitute for lots of hard work during Data Understanding. Some of the patterns here could be capitalized upon, and others could indicate the need for data cleaning. The Using the Feature Selection node creatively to remove or decapitate perfect predictors recipe in Chapter 2, Data Preparation – Select, shows how circular logic can creep into our analysis.

Note the large number of data and amount-related variables in the Generated model. These variables indicate that the potential donor did not give in those time periods. Failing to give in one time period is predicted with failing to give in another; it makes sense. Is this the best way to get at this? Perhaps a simple count would do the trick, or perhaps the number of recent donations versus total donations.

Also note the TIMELAG_null variable. It is the distance between the first and second donation. What would be a common reason that it would be NULL? Obviously the lack of a second donation could cause that problem. Perhaps analyzing new donors and established donors separately could be a good way of tackling this. The Using a full data model/partial data model approach to address missing data recipe in Chapter 3, Data Preparation – Clean, is built around this very idea. Note that neither imputing with the mean, nor filling with zero would be a good idea at all. We have no reason to think that one time and two time donors are similar. We also know for a fact that the time distance is never zero.

Note the Wealth2_null variable. What might cause this variable to be missing, and for the missing status alone to be predictive? Perhaps we need a new donor to be on the mailing list for a substantial time before our list vendor can provide us that information. This too might be tackled with a new donor/established donor approach.