Application of Machine Learning Algorithms for Assessing and Predicting Tenant Reliability in Rental Property Systems: Case Legislation of the Republic of Kazakhstan
Rakhimova D. Shormakova A. Mukhitova A. Akhmetova D. Ocheretin M. Sergey K. Abuev J.
2025Institute of Electrical and Electronics Engineers Inc.
International Conference on Computer Science and Engineering, UBMK
2025Issue 2025767 - 772 pp.
This article addresses the problem of classifying tenant reliability using machine learning methods. Leveraging a rich dataset with diverse features such as average debt, total number of registered complaints, rental duration in months, number of successfully resolved complaints, and other parameters, a classification task was formulated. A comparative analysis of classification models - BERT, Random Forest, and K-Nearest Neighbors - was conducted to assess tenant reliability. The transformer-based model (BERT), after fine-tuning, demonstrated the highest performance across metrics: ROC-AUC (0.95), Precision (0.94), Recall (0.96), and F1-score (0.95). The results indicate the models high applicability for rental risk assessment tasks. The proposed approach highlights the potential of advanced transformer-based models in addressing real-world social and economic challenges.
BERT , binary classification , K-Nearest Neighbors , machine learning , Random Forest , tenants
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Faculty of Information Technology al Farabi Kazakh National University, Almaty, Kazakhstan
Faculty of Information Technology al Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026