@adityashrm21-Treating of missing value is a very important for improving the performance of the classifier.
There are four common methods for interpretation of missing value.
Case Deletion (CD) - Also is known as complete case analysis. It is available in all statistical packages and is the default method in many programs. This method consists of discarding all instances (cases) with missing values for at least one feature.A variation of this method consists of determining the extent of missing data on each instance and attribute and delete the instances and/or attributes with high
levels of missing data. Before deleting any attribute, it is necessary to evaluate its relevance to the analysis.
Mean Imputation (MI) - This is one of the most frequently used methods.It consists of replacing the missing data for a given feature (attribute) by the mean of all known values of that attribute in the class where the instance with missing attribute belongs.
Median Imputation (MDI) - Since the mean is affected by the presence of outliers it seems natural to use the median instead just to assure robustness. In this case, the missing data for a given feature is replaced by the median of all known values of that attribute in the class where the instance with the missing feature belongs. This method is also a recommended choice when the distribution of the values of a given feature is skewed.
KNN Imputation (KNNI) - This method the missing values of an instance are imputed considering a given number of instances that are most similar to the instance of interest. The similarity of two instances is determined using a distance function.
Hope this helps!