Missing values refer to the absence of data points in a dataset where information is expected. They can arise due to various reasons such as errors during data collection, non-responses in surveys, or data corruption. Understanding and addressing missing values is essential for accurate analysis, as they can skew results and affect model performance if not handled properly.
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