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dc.contributor.advisorKerestecioglu, Fezaen_US
dc.contributor.advisorÇevik, Mesuten_US
dc.contributor.authorERDEM, GÖRKEM
dc.description.abstractRetail companies monitor inventory stock levels regularly and manage stock levels based on forecasted sales to sustain their market position. The accuracy of inventory stocks is critical for retail companies to create a correct strategy. Many retail com- panies try to detect and prevent inventory record inaccuracy caused by employee or customer theft, damage or spoilage and wrong shipments. This study is aimed to detect inaccurate stocks using machine learning methods. It uses the real inven- tory stock data of Migros Ticaret A.S¸. of Turkey’s largest supermarket chains. A multiple of machine learning algorithms such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM) were used to detect abnormal stock values. On the other hand, generally, researchers use public data to develop methods, and it is challenging to apply machine learning algorithms to real-life data, especially in unsupervised learning. This thesis shows how to handle real-life data noises, missing values etc. The experimental findings show the perfor- mances of machine learning methods in detecting anomalies in low and high level inventory stock.en_US
dc.publisherKadir Has Üniversitesien_US
dc.subjectMachine Learningen_US
dc.subjectAnomaly Detectionen_US
dc.subjectInventory Stocken_US
dc.titleAnomaly detection via machine learningen_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Ana Bilim Dalıen_US

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