Missing values refer to the absence of data in a dataset where information is expected. This can occur due to various reasons such as data entry errors, equipment malfunctions, or simply because the information was not applicable. Understanding and addressing missing values is essential in data cleaning and preprocessing techniques, as they can lead to biased analysis, incorrect conclusions, or ineffective decision-making if not handled properly.
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