Answer / Jaydeep Bajpai

Box-Cox transformation is used to normalize data and stabilize variances prior to fitting a regression model. It helps in handling skewed and heavy-tailed distributions by transforming the data according to the power parameter, which can be estimated from the data itself or set manually. The transformed data then follows a more normal distribution, facilitating better model performance.

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