Answer / Vibhor Gupta

Data cleaning is an essential step in the Data Science pipeline because it ensures that the data used for analysis and modeling is accurate, consistent, and relevant. Cleaning a dataset helps to:n1. Remove or correct errors: Errors such as missing values, duplicates, typos, or incorrectly formatted data can lead to biased results.n2. Handle inconsistencies: Data may contain inconsistent formats, units of measurement, or abbreviations that need to be standardized for better analysis.n3. Improve data quality: By removing noise and irrelevant data, the overall quality of the dataset is improved, leading to more accurate and reliable results.n4. Prepare data for analysis: Data cleaning sets the foundation for effective data exploration, visualization, and modeling by ensuring that the data is in a format suitable for analysis.

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