Introduction
Analytic Solver Data Science’s Missing Data Handling utility allows users to detect missing values in the dataset and handle them in a way you specify. Analytic Solver Data Science considers an observation to be missing if the cell is empty or contains an invalid formula. Analytic Solver Data Science also allows you to indicate specific data that you want designated as "missing" or "corrupt".
Analytic Solver Data Science offers several different methods for dealing with missing values. Each variable can be assigned a different “treatment”. For example, if there is a missing value, then the entire record could be deleted or the missing value could be replaced by an estimated mean/median/mode of the bin/column or even with a value that you specify. The available options depend on the variable type.
Click the forward arrow to read through several examples that discuss how to use this utility in practice. In the following examples, we will explore the various ways in which Analytic Solver Data Science can treat missing or invalid values in a dataset.