This question evaluates your ability to preprocess datasets with missing values, which is crucial for building robust machine learning models. Recruiters assess your knowledge of imputation methods, handling missingness mechanisms, and trade-offs between these methods. Common pitfalls include failing to consider the type of missingness (e.g., MCAR, MAR, MNAR) or ignoring the impact on model performance. A strong answer includes justifying the chosen imputation method and considering its impact on the dataset.
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