Imputation methods for incomplete data

Although sometimes encounter data sets that contain one or more missing feature values (incomplete data). Many existing industrial and research data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors and incorrect measurements. The important factor for selection of approach to missing values is missing data mechanism. There are various strategies for dealing with missing values. Some analytical methods have their own approach to handle missing values. Finally missing values problem can be handled by missing values imputation. This paper presents simple methods for missing values imputation like EMSI and MMSI.