While PCA is a powerful tool, it has some limitations. It is sensitive to the scaling of the data, meaning that standardization is crucial. PCA also assumes that the principal components are linear combinations of the original variables, which may not always capture complex, non-linear relationships. Finally, interpreting the principal components can be challenging, as they are combinations of many variables.
