Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1911
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dc.contributor.authorSayar Alperen-
dc.contributor.authorBozkan Tunahan-
dc.contributor.authorÇakar Tuna-
dc.contributor.authorErtugrul Seyit-
dc.date.accessioned2023-03-06T06:53:17Z
dc.date.available2023-03-06T06:53:17Z
dc.date.issued2022-
dc.identifier.citationSayar, A., Bozkan, T., Cakar, T., & Ertugrul, S. (2022). Model for Estimating the Probability of a Customer to Have a Transaction. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.9919439en_US
dc.identifier.isbn9781670000000-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1911-
dc.identifier.urihttps://doi.org/10.1109/UBMK55850.2022.9919439-
dc.description.abstractIn this study, it is aimed to estimate the probability of a customer who comes to the institution for the first time to make a transaction in the next 3 months, using data-driven machine learning models, in order to provide financing to the seller company by assigning the receivables arising from the sale of goods and services in a company actively operating in the factoring sector. Accordingly, it was aimed to directly contribute to the transaction volume on a business basis by acting and taking action with more effective, efficient and correct approaches by finding high-potential and low-potential customers. In this context, provided by KKB (Credit Registration Bureau); The data set to he used in machine learning models was created with feature engineering and exploratory data analysis, using the Risk, Mersis, GIB information of the prospective customers and the historical information of the customers, check issuers, customer representatives and branches kept in the database. Since the leads coming to the institution are in two different types of organizations (Individual and Legal), two different forecasting models were applied. Multiple classification models were tried, and the highest F1-Score of 86% for private companies was obtained with the Random Forest model, and the highest F1- Score for commercial companies was obtained with the Random Forest model with 82%. © 2022 IEEE.en_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFactoringen_US
dc.subjectMachine Learningen_US
dc.subjectTransaction Forecasten_US
dc.titleModel for Estimating the Probability of a Customer to Have a Transactionen_US
dc.title.alternativeMuteri Adaymin Ilem Yapma Ihtimalinin Tahminlenmesi Modelien_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/UBMK55850.2022.9919439-
dc.identifier.scopus2-s2.0-85141883616en_US
dc.authoridÇakar, Tuna / 0000-0001-8594-7399-
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US
dc.identifier.startpage284 - 288en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisligi Bölümüen_US
dc.relation.journalProceedings - 7th International Conference on Computer Science and Engineering, Ubmk 2022en_US
dc.institutionauthorSayar, Alperen, Bozkan, Tunahan, Çakar, Tuna, Ertugrul, Seyit-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1tr-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeConference Object-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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