It is one of the methods to treat missing values other than direct deletion, imputation using a mean/median/mode value, etc. In kNN imputation, the missing values of an attribute are imputed using the given number of attributes that are most similar to the attribute whose values are missing. The similarity of two attributes is determined using a distance function. Pros and Cons are described below. Pros Cons It can predict both qualitative & quantitative attributes. Creation of predictive model for each attribute with missing data is not required. Attributes with multiple missing values can be easily treated. Correlation structure of the data is taken into consideration. It is very time-consuming in analysing large database. It searches through all the dataset looking for the most similar instances. Hence complex and takes time. Choice of k-value is very critical. Higher value of k would include attributes which are significantly different from what we need whereas lower value of k implies missing out of significant attributes