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Preprocessing typically involves several key steps: data cleaning, where inconsistencies and missing values are addressed; data transformation, which includes normalization or standardization to prepare the data for analysis; and feature selection or extraction to identify the most relevant variables. Finally, the data may be split into training and testing sets to evaluate model performance effectively. These steps help ensure that the data is suitable for analysis or model building.

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