Big Data Applications in Guangzhou Restaurants Analysis

Victor Chang, Ziyang Ji, Qianwen Ariel Xu

Research output: Contribution to journalArticlepeer-review

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Abstract

With the development of modern information and communication technologies, such as the internet of things and big data analytics, businesses and users have become more adaptable to rapid changes. Both consumers and merchants have obtained great convenience. Meanwhile, a huge amount of data is generated. However, many businesses lack the ability to process these data, which contain critical business values. Therefore, this article uses data from the Dianping website to show how to use big data analytics techniques to exploit the valuable information from these raw data. First, descriptive analysis is conducted by using kernel density estimation. Then, multilinear regression analysis, Naive Bayes, and J48 are used to predict the level of restaurants. We found that flavor, environment, and service score are essential factors to the restaurant level. Moreover, J48 performs best among the three models with an accuracy of 88.89%.

Original languageEnglish
Pages (from-to)358-372
Number of pages15
JournalBig Data
Volume9
Issue number5
DOIs
Publication statusPublished - 1 Oct 2021

Bibliographical note

Funding Information:
This work is partly supported by VC Research (VCR 0000099).

Publisher Copyright:
© 2021 Mary Ann Liebert Inc.

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