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, and 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 uses the median instead of the mean to replace missing values.
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 replaced with estimates based on the non-missing values in each dataset.
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 functions of the data.
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.