The correct answer is A. Data Encoding.
Data encoding is the process of converting data from one form to another. In the context of data preprocessing, data encoding is used to convert categorical data into binary format. This is done by assigning a unique number to each category. For example, if a dataset contains the categories “male” and “female”, they could be encoded as 0 and 1, respectively.
Data normalization is the process of rescaling data so that it has a mean of 0 and a standard deviation of 1. This is done to make the data more comparable across different datasets.
Data imputation is the process of filling in missing values in a dataset. This is done by using a variety of methods, such as the mean, median, or mode of the remaining values.
Data aggregation is the process of combining data from multiple sources into a single dataset. This is done to create a more comprehensive view of the data.
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