Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Author: search.filters.author.Sayar, AlperenSubject: search.filters.subject.Machine Learning

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  • Conference Object
    Predicting Credit Repayment Capacity With Machine Learning Models
    (Ieee, 2024) Filiz, Gozde; Çakar, Tuna; Yaslidag, Nihal; Sayar, Alperen; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    This study examines the transformation in the financial services sector, particularly in banking, driven by the rapid development of technology and the widespread use of big data, and its impact on credit prediction processes. The developed credit prediction model aims to more accurately predict customers' credit repayment capacities. In pursuit of this goal, demographic and financial data along with credit histories of customers have been utilized to employ data preprocessing techniques and test various classification algorithms. Findings indicate that models developed with XGBoost and CATBoost algorithms exhibit the highest performance, while the effective use of feature engineering techniques is revealed to enhance the model's accuracy and reliability. The research highlights the potential for financial institutions to gain a competitive advantage in risk management and customer relationship management by leveraging machine learning models.
  • Conference Object
    Citation - Scopus: 1
    Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making
    (Ieee, 2024) Filiz, Gozde; Çakar, Tuna; Son Turan, Semen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna; 02.02. Department of Computer Engineering; 04.03. Department of Business Administration; 02. Faculty of Engineering; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF University
    Functional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research.
  • Conference Object
    Customer Segmentation and Churn Prediction via Customer Metrics
    (IEEE, 2022) Bozkan, Tunahan; Çakar, Tuna; Sayar, Alperen; Ertugrul, Seyit; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data-driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) was carried out. It was estimated by the XGBoost model with an F1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customers who will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.