Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi.
The expansion of branch networks of large retail chains, the growth of their customer base, and the increasing diversity of customer profiles are exacerbating the complexity of sales forecasting processes. Managing this diversity and its implications presents a significant challenge for retailers in...
| Publicado en: | Journal of Selçuk University Social Sciences Vocational School Vol. 27; no. 1; pp. 65 - 91 |
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| Formato: | Artículo |
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Journal of Selcuk University Social Sciences Vocational School
nis2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177441120&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177441120 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13024191 MG37 jtl: Journal of Selçuk University Social Sciences Vocational School issn: 13024191 maglogo: N pubinfo: dt: nis2024 vid: 27 iid: 1 pid: 75553 pub: Journal of Selcuk University Social Sciences Vocational School artinfo: ui: 177441120 ppf: 65 ppct: 26 formats: tig: atl: Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi. aug: au: SİNAP, Vahid affil: Dr. Öğretim Üyesi, Ufuk Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümü su: Retail industry Technological innovations Market segmentation Sales forecasting K-nearest neighbor classification Regression analysis Machine learning Random forest algorithms sug: subj: Retail industry Technological innovations Market segmentation All Other Miscellaneous Store Retailers (except Tobacco Stores) All other miscellaneous general merchandise stores All other miscellaneous store retailers (except beer and wine-making supplies stores) Sales forecasting K-nearest neighbor classification Regression analysis Machine learning Random forest algorithms keyword: Machine Learning Regression Algorithms Retail Industry Sales Forecasting Black Friday Machine Learning Makine Öğrenmesi Perakende Sektörü Regression Algorithms Regresyon Algoritmaları Retail Industry Sales Forecasting Satış Tahminlemesi Machine Learning Regression Algorithms Retail Industry Sales Forecasting Black Friday Machine Learning Makine Öğrenmesi Perakende Sektörü Regression Algorithms Regresyon Algoritmaları Retail Industry Sales Forecasting Satış Tahminlemesi ab: The expansion of branch networks of large retail chains, the growth of their customer base, and the increasing diversity of customer profiles are exacerbating the complexity of sales forecasting processes. Managing this diversity and its implications presents a significant challenge for retailers in terms of both strategic planning and operational implementation. At this point, developing customer segmentation and personalized marketing strategies, determining unique approaches for each customer group, and effectively managing this diversity are becoming increasingly crucial. The emerging technologies, particularly machine learning methods, present the potential to cope with these challenges. In light of this, the main objective of the research is to perform sales forecasting on a retail company's Black Friday sales data using machine learning algorithms named Linear Regression, Random Forest Regression, K-Nearest Neighbors Regression, XGBoost Regression, Decision Tree Regression and LightGBM Regression and determine the best performing algorithm by comparing their performances. It is also aimed to tune the hyperparameters using GridSearchCV and examine the effect of these adjustments on the performance of the models. Additionally, Exploratory Data Analysis will be conducted on the dataset to create a sample example for businesses in the retail sector on how they can extract useful information from their available data and effectively evaluate it. According to the results obtainedfrom the research, the most successful algorithm in predicting sales was the XGBoost Regression with hyperparameters tuned using GridSearchCV. It has been determined that the majority of the company's customers consist of individuals aged 26-35, with male customers making significantly higher purchases compared to females and single customers spending more than married ones. Furthermore, when examining the average amount of purchases made by each age group, it was identified that those within the range of 51-55 years had the highest average spending rate. pubtype: Academic Journal doctype: Article src: R language: Turkish refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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