Prediction of Home Loan Approval with Machine Learning

Yıl 2024, Cilt: 4 Sayı: 2, 87 - 95, 30.12.2024

Gamze Güder , Utku Köse

https://doi.org/10.54569/aair.1585994

Öz

With the introduction of computers into our lives, the size and complexity of data have increased. The growing amount of data made manual processing more difficult, and machine learning methods were adopted to minimize human errors. In the banking sector, the increasing volume of data necessitated the use of machine learning techniques. Numerous studies have been conducted in the literature on the banking sector. In this study, machine learning methods, including k-nearest neighbors, random forest algorithm, support vector machines, and logistic regression, were used to predict whether a bank would approve a housing loan or not. Two different datasets were used for the analysis. The results were compared and presented using performance metrics. This study aims to minimize human errors, make the credit approval processes in banks safer, and provide faster results for loan applications.

Anahtar Kelimeler

KNN algorithm, Random forest algorithm, Support vector machines, Logistic regression

Kaynakça

• Kösedağ, E. (2023). Türkiye’de Konut Hakkı ve Bu Hakkın Kullanılmasında Ortaya Çıkan Sorunlara Yönelik Değerlendirme. Kent Akademisi, 16(1), 595-611. https://doi.org/10.35674/kent.1220084
• Mevzuat Bilgi Sistemi, 1982. Türkiye Cumhuriyeti Anayasası Erişim Tarihi: 08.01.2024. https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=2709&MevzuatTur=1&MevzuatTertip=5
• Koyuncu, C., & Berrin, S. (2011). Takipteki Kredilerin Özel Sektöre Verilen Krediler Ve Yatırımlar Üzerindeki Etkisi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (31).
• Arun, K., Ishan, G., & Sanmeet, K. (2016). Loan approval prediction based on machine learning approach. IOSR J. Comput. Eng, 18(3), 18-21.
• Gautam, K., Singh, A. P., Tyagi, K., & Kumar, M. S. (2020). Loan Prediction using Decision Tree and Random Forest. International Research Journal of Engineering and Technology (IRJET), 7(08), 853-856.
• Aphale, A. S., & Shinde, S. R. (2020). Predict loan approval in banking system machine learning approach for cooperative banks loan approval. International Journal of Engineering Trends and Applications (IJETA), 9(8).
• Gupta, A., Pant, V., Kumar, S., & Bansal, P. K. (2020, December). Bank loan prediction system using machine learning. In 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 423-426). IEEE.
• Ndayisenga, T. (2021). Bank loan approval prediction using machine learning techniques (Doctoral dissertation).
• Fati, S. M. (2021). Machine learning-based prediction model for loan status approval. Journal of Hunan University Natural Sciences, 48(10).
• Kadam, A. S., Nikam, S. R., Aher, A. A., Shelke, G. V., & Chandgude, A. S. (2021).Prediction for loan approval using machine learning algorithm. International Research Journal of Engineering and Technology (IRJET), 8(04).
• Khan, A., Bhadola, E., Kumar, A., & Singh, N. (2021). Loan approval prediction model a comparative analysis. Advances and Applications in Mathematical Sciences, 20(3), 427-435.
• Udhbav, M., Kumar, R., Kumar, N., Kumar, R., Vijarania, D., & Gupta, S. (2022). Prediction of Home Loan Status Eligibility using Machine Learning. Swati, Prediction of Home Loan Status Eligibility using Machine Learning (May 27, 2022).
• Tütüncü, T. E. (2022). Makine öğrenmesi algoritmaları ile kredi temerrüt riskini tahmin etme (Master's thesis, Bursa Uludağ Üniversitesi).
• Oral, M., Okatan, E., & Kırbaş, İ. (2021). Makine öğrenme yöntemleri kullanarak konut fiyat tahmini üzerine bir çalışma: Madrid örneği. Uluslararası Genç Araştırmacılar Öğrenci Kongresi, Burdur, Turkey.
• Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1-13.
• Tumuluru, P., Burra, L. R., Loukya, M., Bhavana, S., CSaiBaba, H. M. H., & Sunanda, N. (2022, February). Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 349-353). IEEE.
• Viswanatha, V., Ramachandra, A. C., Vishwas, K. N., & Adithya, G. (2023). Prediction of Loan Approval in Banks Using Machine Learning Approach. International Journal of Engineering and Management Research, 13(4), 7-19.
• Uddin, N., Ahamed, M. K. U., Uddin, M. A., Islam, M. M., Talukder, M. A., & Aryal, S. (2023). An ensemble machine learning based bank loan approval predictions system with a smart application. International Journal of Cognitive Computing in Engineering, 4, 327-339.
• Çelik, E., & Gür, Ö. Banka kredisi tahmini için makine öğrenmesi algoritmalarının performans analizi: Topluluk öğrenmesi algoritmalarının üstünlüğü. Artıbilim: Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi Fen Bilimleri Dergisi, 7(1), 1-20.
• Prasad, P V V S V, Nageswara Rao, P.V (2024). Loan Approval Prediction System Using Machina Learning. International Journal of Innovative Science and Research Technology, 9(4), 278-281
• Sendel, E. (2023). Skin lesion classification with machine learning, Makine öğrenmesi ile cilt lezyonu sınıflandırması.
• Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
• Peker, Özkaraca, O., & Kesimal, B. (2017). Enerji tasarruflu bina tasarımı için isıtma ve soğutma yüklerini regresyon tabanlı makine öğrenmesi algoritmaları ilemodelleme. Bilişim Teknolojileri Dergisi, 10(4), 443-449.
• Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random forest algorithm for the classification of neuroimaging data in Alzheimer's disease: a systematic review. Frontiers in aging neuroscience, 9, 329.
• Ekelik, H., & ALTAŞ, D. (2019). Dijital Reklam Verilerinden Yararlanarak Potansiyel Konut Alıcılarının Rastgele Orman Yöntemiyle Sınıflandırılması. Journal Of Research İn Economics, 3(1), 28-45.
• Kazan, S., & Karakoca, H. (2019). Makine öğrenmesi ile ürün kategorisi sınıflandırma. Sakarya University Journal of Computer and Information Sciences, 2(1), 18-27.
• Kleinbaum, D. G., Klein, M. (2010). Logistic regression: a self-learning text (Third Edition). New York: springer.
• Zheng, A. (2015). Evaluating Machine Learning Models, Farnham, U.K.:O’Reilly Media, Inc.
• Santra, A. K., & Christy, C. J. (2012). Genetic algorithm and confusion matrix for document clustering. International Journal of Computer Science Issues (IJCSI), 9(1), 322.