Comparative Performance Analysis of Random Forest and Logistic Regression Algorithms

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dc.authoridVarol Malkocoğlu, Ayşe Berika/0000-0003-1856-9636en_US
dc.contributor.authorMalkoçoğlu, Ayşe Berika Varol
dc.contributor.authorMalkocoğlu, Şevki Utku
dc.date.accessioned2024-07-12T21:40:37Z
dc.date.available2024-07-12T21:40:37Z
dc.date.issued2020en_US
dc.department[Belirlenecek]en_US
dc.description5th International Conference on Computer Science and Engineering (UBMK) -- SEP 09-11, 2020 -- Diyarbakir, TURKEYen_US
dc.description.abstractToday, banks are trying to meet the needs of their existing customers with the marketing activities they do in digital media. It is known to produce statistical results in order to be able to predict the behavior of customers in artificial intelligence applications by storing large-scale data obtained through marketing studies. In this study, performance comparison between random forest and logistic regression algorithms was made by using real banking marketing data that includes the characteristics of customers. In addition, these algorithms were run on WEKA, Google Colab and MATLAB platforms to compare performance on different platforms. At the end of the study, the most successful result obtained with 94.8% accuracy, 93.9% sensitivity, 94.8% recall, 94.4% fl-score and 98.7% AUC value was achieved by random forest algorithm on WEKA platform. In addition, it has been shown that the obtained performance values produce better results compared to similar studies.en_US
dc.description.sponsorshipIEEE Turkey Sect,Istanbul Teknik Univ,Gazi Univ,Atilim Univ,Dicle Univ,Turkiye Bilisim Vakfi,Kocaeli Univen_US
dc.identifier.doi10.1109/ubmk50275.2020.9219478
dc.identifier.endpage30en_US
dc.identifier.isbn978-1-7281-7565-2
dc.identifier.scopus2-s2.0-85095698261en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage25en_US
dc.identifier.urihttps://doi.org/10.1109/ubmk50275.2020.9219478
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7390
dc.identifier.wosWOS:000629055500005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2020 5th International Conference on Computer Science And Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08731
dc.subjectRandom Foresten_US
dc.subjectLogistic Regressionen_US
dc.subjectWekaen_US
dc.subjectMatlaben_US
dc.subjectBank Marketing Dataen_US
dc.titleComparative Performance Analysis of Random Forest and Logistic Regression Algorithmsen_US
dc.typeConference Object
dspace.entity.typePublication

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