Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Author: search.filters.author.Cakar, TunaSubject: search.filters.subject.Machine Learning

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  • Conference Object
    Churn Prediction for Subscription-Based Applications Using Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2025) Özlem, Şirin; Çakar, Tuna; Kara E.; Yildiz A.; Mese Y.K.; Obali E.; Cakar T.; Gozukara, Hamza; Mese, Yasemin Kurtcu; Patel, Jay; Kara, Erkan; Yildiz, Aysenur; Cakar, Tuna; Obali, Emir; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University; 02.02. Department of Computer Engineering
    In this study, a predictive model was developed using machine learning techniques to forecast customer churn in subscription-based video streaming services. The data such as user login records, content viewing information, subscription details, and content-related features were used to interpret usage patterns and customer churn was defined based on subscription renewal status and renewal timing. Several usage-based features are extracted for users and several algorithms were used for modeling, such as Random Forest, CatBoost, XGBoost, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting. Occurring class imbalance on the target variable is handled via BorderLineSMOTE. The model's performance was evaluated using training-test accuracy plots, classification reports, and hyperparameter tuning. Even though most of the models performed similarly, the CatBoost model emerged as the top performer, achieving a macro F1-score of 0.60. However, while effective in identifying churners, the models struggled to precisely classify non-churning customers, a common challenge in imbalanced datasets even after applying oversampling techniques. The analysis of feature importance yielded a crucial insight, early and consistent user engagement is the strongest predictor of customer retention. These findings provide valuable, actionable insights for streaming platforms, emphasizing that retention strategies should focus on maximizing engagement immediately after a user subscribes. © 2025 IEEE.
  • Conference Object
    A Multimodal AI and ML Framework for Fashion Image Segmentation, Recommendation, and Similarity Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2025) Özlem, Şirin; Çakar, Tuna; Memis E.C.; Fatih Capal M.; Cakar T.; Gunay S.; Coskun H.; Gunay, Savas; Memis, Emir Cetin; Fatih Capal, Mehmet; Soyhan, Mustafa Eren; Coskun, Hasan; Cakar, Tuna; Ay, Tarik Bugra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University; 02.02. Department of Computer Engineering
    This study presents a scalable multimodal Artificial Intelligence (AI) and Machine Learning (ML) framework designed to enhance decision making in the fashion industry. The proposed system integrates garment segmentation, multimodal feature extraction, and similarity recommendation into a unified pipeline. Using Segformer for segmentation, along with the convolutional neural network (CNN)-based feature extraction models ResNet152V2 and Xception, and the transformer-based vision-language model LLaVA, the framework generates visual and semantic embeddings of garments. These representations are processed through similarity detection using OpenAI embedding models and stored in the Pinecone vector database for efficient retrieval. Real-time similarity scoring is enabled through FastAPI endpoints, offering interactive search capabilities. Preliminary results demonstrate the system's strong ability to identify conceptually and visually similar items across a large catalog, providing actionable insights for designers. This framework lays the groundwork for intelligent, interpretable, and production-ready AI systems in the fashion industry. © 2025 IEEE.
  • Conference Object
    Predicting Customer Churn in Retail Using Machine Learning on Transaction Data
    (Institute of Electrical and Electronics Engineers Inc., 2025) Çakar, Tuna; Gozukara H.; Patel J.; Kizilay A.; Sahin Z.; Tosun B.; Cakar T.; Gozukara, Hamza; Kizilay, Ayse; Patel, Jay; Bozan, Mehmet Talha; Cakar, Tuna; Tosun, Busra; Sahin, Zeynep; 01. MEF University; 02.02. Department of Computer Engineering; 02. Faculty of Engineering
    Customer churn prediction is critical for businesses to retain customers and reduce revenue loss. This paper presents a retail customer churn prediction study. We preprocess transactional data from a retail dataset comprising approximately 19.7 million transactions involving over 1 million customers. Temporal behavioral features, such as purchase frequency, monetary value, product variety, and promotional engagement metrics, are engineered using a four-month observation window. A Random Forest classifier is trained, utilizing balanced class weighting to address churn class imbalance. The churn label is defined as customers not purchasing in the subsequent six-month period. Our Random Forest model achieves approximately 84% accuracy, 86% precision, 85% recall, and an F1- score of 85%. Additionally, an XGBoost model achieves similar accuracy (≈ 84%) but higher recall (93%) and F1-score (89%), indicating improved churn prediction. The confusion matrix illustrates clear model performance. This study demonstrates that carefully engineered RFM-based features and ensemble learning approaches significantly enhance churn prediction in retail contexts. © 2025 IEEE.
  • 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.