Mitigating dataset corruption

Dataset corruption is different from dataset modification because it usually infers some type of accidental modification that could be relatively easy to spot, such as values out of range or missing altogether. The results of the corruption could appear random or erratic. In many cases, assuming the corruption isn’t widespread, it’s possible to fix the dataset and restore it to use. However, some datasets are fragile (especially those developed from multiple incompatible sources), so you might have to recreate them from scratch. No matter the source or extent of the data corruption, a dataset that suffers from corruption does have these issues:

• The data is inherently less reliable because you can’t ensure absolute parity with the original data.
• Any model you create from the data may not precisely match the model created with the original data.
• Hackers or disgruntled employees may purposely corrupt a dataset to keep...