The correct answer is A. Dropping missing values.
Missing data is a common problem in machine learning. It can occur for a variety of reasons, such as data entry errors, equipment malfunctions, or intentional deletion. Missing data can make it difficult to train machine learning models, as they may not have enough data to learn from.
There are a number of ways to deal with missing data. One common method is to drop the rows or columns that contain missing values. This can be effective if the amount of missing data is small. However, it can also lead to a loss of information.
Another common method is to impute missing values. This involves filling in the missing values with estimates. There are a number of different imputation methods, such as mean imputation, median imputation, and multiple imputation.
Creating new features can also be used to deal with missing data. This involves creating new features that are based on the existing features. For example, if a feature is missing, a new feature could be created that is the average of the other features.
Normalizing the data is not a common method for dealing with missing data. It is a process of rescaling the data so that it has a mean of 0 and a standard deviation of 1. This can be useful for some machine learning algorithms, but it does not address the issue of missing data.
In conclusion, the correct answer is A. Dropping missing values. This is a common method for dealing with missing data that can be effective if the amount of missing data is small. However, it can also lead to a loss of information.