Missing Data 편집하기 (부분)

== Choosing a Method for Handling Missing Data ==
Selecting the right approach for handling missing data depends on the data type, amount of missingness, and analysis requirements:
*'''For Small Amounts of Missing Data:''' Simple methods like mean or median imputation may suffice if only a small percentage of data is missing and is MCAR.
*'''For MAR or MNAR Data:''' Advanced methods like MICE, KNN imputation, or EM are often preferred, as they account for patterns or dependencies in the data.
*'''For Time Series or Sequential Data:''' Methods like forward and backward filling may be effective, where missing values are filled based on previous or subsequent observations.
* '''For Informative Missingness:''' Indication methods may be used to allow the model to capture patterns in the missing data itself.