Through this, we seek to identify models that accelerate the prediction of chemical shifts while minimizing the loss of accuracy in addition to elucidating the optimal radii when encoding neighboring atoms as model features. Out of the thirteen machine learning models tested, we found that Gradient Boosting Regressor was most suitable for this specific task. Furthermore, we found 3 Å to be the optimal radius when extracting local atomic information of fluorine atoms, as all thirteen models generally performed their best when trained with this dataset.