<ul><li>Data preprocessing refers to the set of techniques used to prepare raw data for analysis or model training.</li><li>Preprocessing involves cleaning, transforming, and organizing the data to improve its quality and ensure it meets the needs of analytical methods or machine learning models.</li><li>Data preprocessing is essential, not optional. It improves data quality, reduces noise and inconsistencies, handles missing values, and ensures consistency across the entire data pipeline.</li><li>Skipping data preprocessing can lead to problems such as difficulty in detecting patterns, biased results, increased training time, runtime errors, and decreased trust in the model.</li></ul>

Understanding Data Preprocessing: A Critical Step in Data Science

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