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Data cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant parts of the data and then replacing, modifying or deleting this dirty data.
Wiki User
∙ 10y ago
Aisha Goel
The act of detecting and addressing inconsistencies in a data set or data source is referred to as data cleaning. Data cleansing can begin only once the data source has been reviewed and characterized. The main goal is to find and eliminate discrepancies while preserving the data needed to provide insights.
- Data cleansing requires rigorous and ongoing data profiling to identify data quality concerns that need to be addressed.
- All applications of purification, transformation, profiling, finding, wrangling, and so on should generally be in terms of data captured/extracted from the web.
- It's so critical and vital to eliminate these kinds of inconsistencies to improve the data set's authenticity.
Learn more about data cleaning through data science at Learnbay institute.
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