relational space then $GRG(X_a,X_b)$ is generally not equal to $GRG(X_b,X_a)$ for any $b$.

4.  Approachability: $GRG(X_a,X_b)$ decreases as the difference between $X_{aj}$ and $X_{bj}$ increases, other values in and remaining constant.

GRA is generally preferred over metrics such as Heterogeneous Euclidean Overlap Metric (HEOM) for grey systems with missing data [@huang2004grey]. It gives us a normalized measuring function for both missing/available and categorical/continuous data due to its normality. It also gives whole relational orders due to its wholeness over the entire relational space. So instead of $d(X_a,X_b)$ in , if we use $GRG(X_a,X_b)$ to select the $k$-nearest neighbors and then proceed with the KNN Imputation technique without using weights, then it becomes Grey KNN (GKNN) Imputation [@zhang2012nearest].

Transformation of the Data {#trans}
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Before we apply our version of the algorithm, we make some transformation of the continuous features contained in the training dataset, since we deal with a wide variety of features whose ranges vary vastly. For example, the range of marks in a 10 point exam would be less than the range of marks for a 100 point exam, and both these marks may be in the same training dataset. The distance metric and subsequently the $k$-nearest neighbor 