There are various procedures involved in handling missing data in a dataset. To choose the best course of action, first evaluate the degree and pattern of missingness. Typical approaches include deleting rows or columns that have an excessive number of missing values, employing algorithms that support missing values, and imputing missing values using the mean, median, mode, or more sophisticated methods like regression models or K-nearest neighbors. The type of data and the objectives of the investigation will determine which approach is best. For the dataset to remain intact and provide reliable findings, missing data must be handled carefully. Consider signing up for a data science certification course if you want to learn more.
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