Which algorithm can be used in value imputation in both categorical and continuous categories of data?

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KNN is the only algorithm that can be used for imputation of both categorical and continuous variables.

 

One commonly used algorithm for value imputation in both categorical and continuous categories of data is the k-Nearest Neighbors (k-NN) algorithm.

For categorical data, the algorithm considers the majority class of the k-nearest neighbors, and for continuous data, it takes the average (or weighted average) of the values of the k-nearest neighbors. This makes k-NN a versatile choice for imputing missing values in datasets with a mix of categorical and continuous features.