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Answer: Predict the missing values using linear regression.
The correct answer is C. Predicting the missing values using linear regression is an effective approach here. Deleting rows with missing values (Option A) is not suitable since every instance is important. Applying feature crossing (Option B) will not directly handle the missing values. Replacing the missing values with zeros (Option D) could introduce bias if zeros have a specific meaning in the context of the data. Linear regression allows you to impute the missing values by utilizing existing relationships with other variables in the dataset.
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You are tasked with developing a machine learning model to predict house prices. While preparing your dataset, you realize that an important feature, 'distance from the closest school,' has a significant number of missing values and does not exhibit high variance. Considering that every instance (row) in your dataset is crucial for the model's performance, how should you address the issue of these missing values?
A
Delete the rows that have missing values.
B
Apply feature crossing with another column that does not have missing values.
C
Predict the missing values using linear regression.
D
Replace the missing values with zeros.
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