Based on our confidence in DFT's accuracy of predicting Al NMR parameters, we built a simple machine learning model to predict Al C Q based on a large VASP calculated NMR dataset of 1681 aluminum-containing solid materials. The structural and elemental features that we built were proven to be effective in capturing the variation of local environments to which NMR parameters are very sensitive. It is surprising for us to find that among all the features, the pure geometrical variations such as variations in bond length and bond angles are the dominating features for C Q prediction which shows the possibility of building simple but effective features for the prediction of materials' properties instead of using larger universal features. Also, we can get a better understanding of the relationship between local geometry and SSNMR spectra that, specifically, SSNMR spectra for quadrupolar nuclei are determined primarily by local geometry distortions.