The correct answer is B. Data Imputation.
Data imputation is a technique used to fill in missing values in a dataset. This can be done by using a variety of methods, such as mean imputation, median imputation, or multiple imputation.
Data augmentation is a technique used to increase the size of a dataset by creating new data points that are similar to the existing data points. This can be done by using a variety of methods, such as data generation, data synthesis, or data augmentation with noise.
Data transformation is a technique used to change the form of the data in a dataset. This can be done by using a variety of methods, such as normalization, standardization, or feature scaling.
Data normalization is a technique used to make the data in a dataset have a mean of 0 and a standard deviation of 1. This can be done by using a variety of methods, such as z-score normalization or min-max normalization.