Missing values refer to the absence of data in a dataset where information is expected. This can occur due to various reasons such as errors in data collection, data corruption, or the non-responsiveness of participants in surveys. The presence of missing values can significantly affect data quality and analytical results, making it crucial to identify and handle them appropriately during preprocessing.
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