Web Reference: Feb 7, 2026 · Real-world data is often incomplete, noisy, and inconsistent, which can lead to incorrect results if used directly. Data preprocessing in data mining is the process of cleaning and preparing raw data so it can be used effectively for analysis and model building. It involves extracting irrelevant or duplicate data, handling missing values, and correcting errors or inconsistencies. This ensures that the data is accurate, comprehensive, and ready for analysis. Data cleaning and preprocessing typically involve the following steps: When a table has messy data, you can use different data preprocessing techniques to clean the table by filling in or removing missing values and rearranging table rows and variables in a different order. Data preprocessing is useful for applications involving images, including AI.
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Last Updated: April 5, 2026

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