Data preprocessing is the essential step of preparing raw data for analysis or model training. It involves tasks such as eliminating duplicates, handling missing values, and correcting any inaccuracies in the data. Additionally, the data is transformed into a format that is suitable for the AI model, which may include normalizing numerical values, encoding categorical variables, or converting text into numerical vectors. Effective data preprocessing ensures that the model receives clean, structured, and properly formatted input, which ultimately leads to more accurate and reliable results during training and prediction.
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