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A governmental dataset contains the following columns: “ID”, “Gender”, “Age”, “Income”, “debt”. The dataset contains 100,000 entry each corresponding to information about a person in a specific city. 10,000 income’s data are found missing which resemble 10% of the total income data. Income’s data are in USD and range from $2,000 to $5,000,000. The data was collected a few months ago and the government has no intention to spend time or budget on requesting the missing data from citizens. The machine learning engineer should search for a solution to complete those missing data without introducing bias. What are the suitable techniques for imputing missing data in this case? (Select TWO.)