Complete data are very rare because some data are usually missing. There are typically four strategies to handle missing data.

-Ignore the variables with missing data.

-Delete the records that have some missing values.

-Impute the missing values (i.e., simply fill in the missing values with some other value, such as the mean of similar records with data).

-Use a data mining technique that can handle missing values, such as CART, which is one of a few techniques that handle missing data pretty well.

Suppose you are working on a project to model a hotel company's customer base. The goal is to determine which customer attributes are correlated with a high number of hotel stays. You have access to data from a customer survey, but one field (which is optional) is yearly income and has some missing values equaling roughly 5% of the records. Which of the strategies should be used? Explain why. Suppose you chose the third strategy. Explain how you would impute the missing incomes.