Which of the following is a common method for handling missing data in feature e

Which of the following is a common method for handling missing data in feature engineering?

Which of the following is a common method for handling missing data in feature engineering?

Step 1: Understand that missing data can occur in datasets, which can affect analysis and model performance.
Step 2: Learn about imputation, which is a method used to fill in missing values.
Step 3: Know that one common way to impute missing values is to use the mean (average) of the available data for that feature.
Step 4: Alternatively, you can use the median (the middle value) of the available data for that feature to fill in missing values.
Step 5: By imputing missing values with the mean or median, you keep more data points, which can improve the quality of your analysis.

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