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Data cleaning is the meticulous process of identifying and rectifying errors, inconsistencies, and redundancies in a dataset. It involves several key steps: firstly, assessing the quality of the data to identify missing values, outliers, and duplicates. Secondly, correcting these issues through methods such as imputation for missing data, statistical techniques for outliers, and merging or removing duplicates. Thirdly, standardizing formats and units across variables to ensure consistency. Lastly, validating the cleaned data to verify that it meets the requirements for analysis or further processing. Effective data cleaning ensures that datasets are accurate, reliable, and ready for meaningful analysis, minimizing potential biases and errors in subsequent data-driven decisions.