Data cleaning involves tasks like removing duplicates and fixing incorrect data formats to correct errors and ensure accuracy of the data.
Preprocessing uses techniques like feature scaling to improve data quality. Through these two processes, more reliable results can be produced
in modeling tasks used in data science.
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