  Geometric information such as the space groups and crystal systems plays an
important role in the properties of crystal materials. Prediction of crystal
system and space group thus has wide applications in crystal material property
estimation and structure prediction. Previous works on experimental X-ray
diffraction (XRD) and density functional theory (DFT) based structure
determination methods achieved outstanding performance, but they are not
applicable for large-scale screening of materials compositions. There are also
machine learning models using Magpie descriptors for composition based material
space group determination, but their prediction accuracy only ranges between
0.638 and 0.907 in different kinds of crystals. Herein, we report an improved
machine learning model for predicting the crystal system and space group of
materials using only the formula information. Benchmark study on a dataset
downloaded from Materials Project Database shows that our random forest models
based on our new descriptor set, achieve significant performance improvements
compared with previous work with accuracy scores ranging between 0.712 and
0.961 in terms of space group classification. Our model also shows large
performance improvement for crystal system prediction. Trained models and
source code are freely available at \url{https://github.com/Yuxinya/SG_predict}
