Handling Missing Values
I n real world data, there are some instances where a particular element is absent because of various reasons, such as, corrupt data, failure to load the information, or incomplete extraction. Handling the missing values is one of the greatest challenges faced by analysts, because making the right decision on how to handle it generates robust data models. There are some frameworks for understanding missing data. Missing At Random: Missing data are missing at random (MAR) when the probability of missing data on a variable is related to some other measured variable in the model, but not to the value of the variable with missing values itself. The probability of missing values, at random, in a variable depends only on the available information in other predictors. For example, when men and women respond to the question “have you ever taken parental leave?”, men would tend to ignore the question at ...