Missing data refers to the absence of values in a dataset, which can occur for various reasons such as errors in data collection, non-responses in surveys, or data corruption. This absence can significantly impact data analysis and machine learning models, as they rely on complete datasets to produce accurate insights and predictions. Addressing missing data is crucial in data preprocessing and feature engineering to ensure the integrity and usability of the data.
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