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Box-Cox transformation is used to stabilize variance and make the data more normally distributed, which is essential for many statistical methods that assume normality, such as linear regression. By transforming the data, it can help improve model performance and validity of results. Additionally, it can reduce skewness and improve homoscedasticity, making it a valuable tool in data preprocessing.

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