These errors can include typos, misformatted numbers, or incorrect data

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incomplete. This can occur due to errors in data collection, human intervention, or system failures. Missing data can lead to biased or inaccurate analyses if not handled properly. Duplicate Data: Duplicate records can occur when data is entered multiple times, either due to human error or system malfunctions.


Duplicate entries can lead to inflated or skewed results, russian mobile list especially in data-driven processes like analysis or machine learning models. Inconsistent Data: Inconsistent data arises when data across different sources or records do not align. This can happen due to differences in formatting, units of measurement, or terminology. For example, dates might be formatted differently in various records, or product names might be spelled inconsistently across datasets.


Outliers: Outliers are extreme values that are significantly different from the rest of the data. These values can skew statistical analyses and lead to misleading conclusions. Identifying and dealing with outliers is an important part of data cleaning. Data Entry Errors: Errors made during data entry are common, especially in manual data collection processes.types, which all need to be corrected during data cleaning.