D glossary test

Correcting or deleting erroneous, corrupted, improperly formatted, duplicate, or missing data from a dataset is known as a data cleaning process.

There are many possibilities for data duplication or mislabeling by integrating different data sources. And if the data is accurate, the results and algorithms are inconsistent. Since data cleaning methods differ from dataset to dataset, there is no one-size-fits-all approach to prescribing the exact steps throughout the method. However, creating a blueprint for your data cleaning procedure will ensure that you do it correctly every time.