Preprocessing

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Preprocessing is the work done to clean and organize raw data before it’s used to train an AI model. Real-world data often contains problems like missing values, duplicates, inconsistent formats, or unusual outliers. Preprocessing fixes these issues by standardizing the data. It selects the fields that matter and converts everything into a format the model can understand.

Even advanced models rely heavily on having clean and well-structured input. Because of this, preprocessing is often the most time-consuming part of the machine learning pipeline. Still, it’s also one of the most important, since no model can perform well if the data feeding it is messy or inconsistent.

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