Depending on the dataset's characteristics and missing values, data science models employ a variety of methods to deal with missing data. Imputation is a common technique that substitutes statistical measures such as the mean, median, or mode for missing information. More sophisticated techniques estimate missing values using predictive models. Alternatively, if the impact is small, deletion procedures eliminate rows or columns that contain missing data. Techniques for data encoding, such using certain indications to signify missing values, are also beneficial. Decision trees are among the algorithms that can naturally handle missing data. Consider taking a thorough Data Science course to learn these methods.
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