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All Journal International Journal of Electrical and Computer Engineering
Baitemirov, Madiyar
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Computer Science & IT Electrical & Electronics Engineering

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Forecasting creditworthiness in credit scoring using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Amirov, Azamat; Tassuov, Bolat; Makhatova, Valentina; Kaipova, Assemgul; Makhazhanova, Ulzhan; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5534-5542

Abstract

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Development of a decision-making module in the field of real estate rental using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Ignatovich, Artyom; Bayegizova, Aigulim; Tanirbergenov, Adilbek; Tynykulova, Assemgul; Bapiyev, Ideyat; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5430-5442

Abstract

The research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype is an innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.
Co-Authors Amirov, Azamat Bapiyev, Ideyat Bayegizova, Aigulim Ignatovich, Artyom Kaipova, Assemgul Makhatova, Valentina Makhazhanova, Ulzhan Mukhamedrakhimova, Galiya Mukhanova, Ayagoz Ospanova, Tleugaisha Tanirbergenov, Adilbek Tassuov, Bolat Tynykulova, Assemgul