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In data preprocessing, it's a common practice to perform tasks like spell correction and punctuation cleanup before one-hot encoding features. These initial data cleaning steps are essential because they ensure the quality and consistency of the data, making it suitable for subsequent analytical processes such as one-hot encoding, which is crucial for transforming categorical variables into a numerical format that machine learning algorithms can work with effectively.
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