An online store uses linear regression to predict future book sales. One numeric feature is 'duration' (days the book has been listed). Exploratory analysis shows the relationship between sales and duration is skewed and non-linear. Which transformation is most appropriate to improve the model?

Quantile binning is the most appropriate transformation because it addresses both the skewed and non-linear relationship between duration and sales. By dividing the duration into bins based on quantiles, it creates new categorical features that can capture non-linear patterns. This allows the linear regression model to learn different relationships within each bin, effectively modeling the non-linearity. One-hot encoding is for categorical features, not a continuous numeric feature like duration. A Cartesian product transformation is used for feature crosses, which might be helpful later but doesn't directly address the skewed and non-linear nature of a single feature. Normalizing values scales the data but doesn't inherently resolve skewness or non-linearity in the relationship with the target variable.