How Low-rating Restaurant Crack Business
- 10.2991/icem-17.2018.13How to use a DOI?
- Multinomial logistic regression; Random forest; Rating prediction; Restaurant; EDA
This work is focusing on "Yelp" businesses. The main idea is to analyze the data from the Yelp web site. These two methods are used for grade prediction and feature selection: multinomial logistic regression and random forest. In conclusion, the results of the two methods are mostly the same with acceptable difference. Although reviews are too sophisticated to generate, some trials are included in the very last section for interest because it is a hot topic these days and it is indeed very effective for rating prediction.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Bo Gao AU - Xinjian Qiang AU - Shuyu Chen PY - 2018/01 DA - 2018/01 TI - How Low-rating Restaurant Crack Business BT - Proceedings of the 2017 7th International Conference on Education and Management (ICEM 2017) PB - Atlantis Press SP - 52 EP - 56 SN - 2352-5428 UR - https://doi.org/10.2991/icem-17.2018.13 DO - 10.2991/icem-17.2018.13 ID - Gao2018/01 ER -