For example, validating phone numbers to ensure that they match a proper format

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nusaiba127
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For example, validating phone numbers to ensure that they match a proper format

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Correcting Inconsistencies: Data inconsistencies can arise when different data sources or systems use different terminology or formats. For instance, a customer’s name may appear in multiple ways across different records (e.g., "John Smith" vs. "Smith, John"). These inconsistencies need to be corrected during the data cleaning process to ensure uniformity. Dealing with Outliers: Outliers should be carefully examined to determine whether they are errors or legitimate data points.


If outliers are determined to be errors, they should be corrected russian mobile list or removed. However, if they are legitimate, they may need to be treated differently in analysis or modeling. Data Validation: Data validation involves checking the data against certain rules or constraints to ensure that it is correct. or ensuring that numerical values fall within an acceptable range.


Transformation: Data transformation involves modifying the data into a format suitable for analysis. This could involve normalizing data, aggregating values, or encoding categorical variables into numerical representations. Once data cleaning is complete, the dataset should be ready for analysis or use in machine learning models. However, data cleaning is not a one-time process. As data is constantly being updated, it is important to periodically review and clean data to ensure its quality remains high.
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