random forest on the missing feature, using the other features as covariates and then predicts the missing values. This procedure is iterative and can handle mixed data, complex interactions, and high dimensions.

Machine Learning techniques such as Fuzzy $c$-Means [@sefidian2019missing], Multilayer Perceptrons (MLP) [@garcia2013classifying] and $k$-Nearest Neighbors (KNN) [@batista2002study] are useful non-parametric approaches to imputation of missing data. Various machine learning algorithms such as $k$-Nearest Neighbors (KNN), Support Vector Machines (SVM) and decision trees have been used in imputation by framing the imputation problem as an optimization problem and solving it [@bertsimas2017predictive]. The Nearest Neighbor Imputation (NNI) approach is simple since there is no need to build a predictive model for the data. The basic KNN Imputation (KNNI) algorithm was first used for estimating DNA microarrays with the contribution of the $k$-Nearest Neighbors weighted by Euclidean distance [@troyanskaya2001missing]. The sequential KNN method was proposed using cluster-based imputation [@kim2004missing], followed by an iterative variant of the KNN imputation (IKNN) [@bras2007improving], both of which improves on KNNI. The Shelly Neighbors (SN) method improves the KNN rule by selecting only neighbors forming a shell around the missing datum, among the k closest neighbor [@zhang2011shell]. The first significant work in improving KNN imputation for 