First, we provided a comparative analysis of the performance of various statistical methods in predicting chemical shift with respect to the reference DFT values, ranging from simple linear regression models based on a very few local structural descriptors (bond lengths and angles nearby Al-center) to advanced non-linear kernel ridge regression (KRR) that utilize complex SOAP descriptors of the local aluminium environment. Unsurprisingly, the KRR method was found to outperform all other tested methods for all zeolite models by more than 1 ppm. However, even a simple two-parameter regularized linear regression (LASSO), depending only on the value of the T-O-T angle and Al-O bond length, exhibited qualitatively correct description, enabling interpretation of the trends observed in chemical shifts as a function of temperature, solvation and aluminium content. We also showed that a linear correlation between the T-O-T angle and chemical shift originally proposed by Lippmaa et. al. is too simple to provide qualitatively accurate predictions, proving that the Al-O bond length is a crucial factor in determining the 27 Al chemical shifts.