Impute the missing values in Feature A with the mode of the feature, assuming that the most common category is the best replacement for missing data.

Replace the missing values in Feature A with values from the feature that has the highest Pearson correlation with Feature A, under the assumption that correlated features can provide reasonable substitutes.

Remove Feature A from the dataset if more than 15% of its values are missing, to avoid the complexity of handling missing data, despite its predictive power.

Introduce a new category within Feature A to represent missing values and create an additional binary feature indicating the presence or absence of a value in Feature A, to explicitly model the missingness.