Missing data imputation involves imputation of missing data from the available data. Improper imputation produces bias result. Therefore proper attention is needed to impute the missing values. Imputation techniques help to impute the missing value. The accuracy can be measured in terms of percentage. Pre-processing has to be done before imputing the values using imputation techniques. kNN classifier helps to classify the datasets into several groups by using the given training dataset. The imputation techniques are separately imputed in each dataset and checked for accuracy. The results are then compared.