Definition
Data cleansing is the process of detecting and correcting errors and inconsistencies in data, including removing duplicates, fixing typos, standardising formats, and handling missing values, so the dataset becomes accurate and reliable for downstream use.
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- Removes duplicate and conflicting records that would otherwise distort counts and metrics
- Standardises inconsistent formatting such as dates, names, and addresses into one convention
- Handles missing values deliberately rather than letting gaps silently skew analysis
- Raises overall accuracy so reports and models work from a trustworthy base
