Proceedings of the 2017 7th International Conference on Education and Management (ICEM 2017)

How Low-rating Restaurant Crack Business

Authors
Bo Gao, Xinjian Qiang, Shuyu Chen
Corresponding Author
Bo Gao
Available Online January 2018.
DOI
https://doi.org/10.2991/icem-17.2018.13How to use a DOI?
Keywords
Multinomial logistic regression; Random forest; Rating prediction; Restaurant; EDA
Abstract
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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Economics, Business and Management Research
Publication Date
January 2018
ISBN
978-94-6252-463-7
ISSN
2352-5428
DOI
https://doi.org/10.2991/icem-17.2018.13How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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
PB  - Atlantis Press
SP  - 52
EP  - 56
SN  - 2352-5428
UR  - https://doi.org/10.2991/icem-17.2018.13
DO  - https://doi.org/10.2991/icem-17.2018.13
ID  - Gao2018/01
ER  -