The correct answer is A. Data Imputation.
Data imputation is the process of filling in missing values in a dataset. This can be done using a variety of methods, such as mean imputation, median imputation, or multiple imputation.
Mean imputation is the simplest method of imputation. It involves replacing each missing value with the mean of the non-missing values for that variable.
Median imputation is similar to mean imputation, but it replaces each missing value with the median of the non-missing values for that variable.
Multiple imputation is a more sophisticated method of imputation. It involves creating multiple datasets, each with a different set of missing values. The missing values are then imputed using one of the methods described above. The final dataset is then created by combining the multiple datasets.
Data normalization is the process of rescaling data so that it has a mean of 0 and a standard deviation of 1. This can be done by subtracting the mean from each value and then dividing by the standard deviation.
Data transformation is the process of changing the form of data so that it is easier to analyze. This can be done by taking logarithms, square roots, or other transformations.
Data encoding is the process of converting data into a form that can be used by a computer. This can be done by converting text into numbers, or by converting images into vectors.