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Big Data Applications in Guangzhou Restaurants Analysis.

Victor Chang1, Ziyang Ji2, Qianwen Ariel Xu1,2

  • 1Artificial Intelligence and Information Systems Research Group, School of Computing and Digital Technologies, Teesside University, Middlesbrough, United Kingdom.

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|September 28, 2021
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Summary
This summary is machine-generated.

Big data analytics can uncover valuable business insights from raw data. This study demonstrates how flavor, environment, and service predict restaurant levels, with J48 model achieving 88.89% accuracy.

Keywords:
big data analyticscatering industryinternet of thingsmachine learning

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Area of Science:

  • Business Analytics
  • Data Science
  • Machine Learning

Background:

  • Modern information and communication technologies generate vast amounts of data.
  • Many businesses struggle to process this data for critical business value.
  • The Dianping website provides a rich dataset for analysis.

Purpose of the Study:

  • To demonstrate the application of big data analytics techniques on raw business data.
  • To identify key factors influencing restaurant levels.
  • To compare the performance of different machine learning models for prediction.

Main Methods:

  • Descriptive analysis using kernel density estimation.
  • Predictive modeling using multilinear regression, Naive Bayes, and J48 algorithms.
  • Utilizing Dianping website data for restaurant level prediction.

Main Results:

  • Flavor, environment, and service scores are identified as essential factors determining restaurant level.
  • The J48 machine learning model achieved the highest prediction accuracy.
  • The J48 model demonstrated an accuracy of 88.89% in predicting restaurant levels.

Conclusions:

  • Big data analytics is effective in extracting valuable information from raw business data.
  • Restaurant attributes like flavor, environment, and service significantly impact perceived level.
  • The J48 algorithm offers a robust and accurate method for restaurant level prediction.