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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Paper Title
ANALYSIS ON APPLYING MACHINE LEARNING AND CLASSIFICATION APPROACHES TO PREDICT THE FRAUDULENT REVIEWS ON THE YELP DATASET
Authors
Dr.S.Vijaya Ragavan,, M.Sangeetha, D.Shalini, Shaik Ishaq
Keywords
Classification, regression, svm, navie bayes, logestic regression, supervised learning
Abstract
The goal of our study, which is summarized in this article, is to create a machine learning model that can determine if reviews in Yelp's dataset are authentic or not. To determine which machine learning categorization approach would produce the best results, we specifically applied and contrasted them. To make it easier to understand why some approaches are preferable to others in specific situations, brief explanations are provided for each of the categorization strategies. The SVM classification algorithm produced the best result, with an F-1 score of 0.91 in the forecast
IJCRT's Publication Details
Unique Identification Number - IJCRTV020035
Paper ID - 231084
Page Number(s) - 202-207
Pubished in - Volume 6 | Issue 4 | November 2018
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Dr.S.Vijaya Ragavan,, M.Sangeetha, D.Shalini, Shaik Ishaq, "ANALYSIS ON APPLYING MACHINE LEARNING AND CLASSIFICATION APPROACHES TO PREDICT THE FRAUDULENT REVIEWS ON THE YELP DATASET", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 4, pp.202-207, November 2018, Available at :http://www.ijcrt.org/papers/IJCRTV020035.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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