Answer Posted / Ambuj Kumar Verma

There are several ways to handle missing data. One approach is to use imputation methods, where we fill in the missing values using statistical methods like mean, median, or mode. Another approach is to use machine learning algorithms that can handle missing values, like regression trees and k-nearest neighbors. For corrupted data, we can check for outliers and remove them using techniques like box plots and Grubbs' test. We can also perform data preprocessing steps like normalization or standardization to ensure the data is in a consistent format.

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