Identifying and dealing with missing values
In most real-life cases, we do not work with clean, complete data. One of the potential problems we are bound to encounter is that of missing values. We can categorize missing values by the reason they occur:
- Missing completely at random (MCAR)—The reason for the missing data is unrelated to the rest of the data. An example could be a respondent accidentally missing a question in a survey.
- Missing at random (MAR)—The missingness of the data can be inferred from data in another column(-s). For example, a missing response to a certain survey question can be to some extent determined conditionally by other factors such as gender, age, lifestyle, etc.
- Missing not at random (MNAR)—When there is some underlying reason for the missing values. For example, people with very high incomes tend to be hesitant about revealing it.
- Structurally missing data—Often a subset of MNAR, the data is missing because of a logical reason...
