Comparative analysis and implementation of credit risk prediction through distinct machine learning models

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Opis bibliograficzny
Główni autorzy:	Turjo, Aquib Abtahi, Karim, S.M. Mynul, Biswas, Tausif Hossain, Rahman, Yeaminur, Dewan, Ifroim
Kolejni autorzy:	Hossain, Muhammad Iqbal
Format:	Praca dyplomowa
Język:	English
Wydane:	Brac University 2021
Hasła przedmiotowe:	Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest
Dostęp online:	http://hdl.handle.net/10361/15189

id	10361-15189
record_format	dspace
spelling	10361-151892022-01-26T10:08:25Z Comparative analysis and implementation of credit risk prediction through distinct machine learning models Turjo, Aquib Abtahi Karim, S.M. Mynul Biswas, Tausif Hossain Rahman, Yeaminur Dewan, Ifroim Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest Machine Learning Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 43-45). Predicting the risk while lending money has always been a challenge for financial institutions. To make such decisions many banks or financial organizations follow different techniques to analyze a set of data. Manual prediction and analysis of credit risk can not only be very hectic but also quite time-consuming. To solve this issue, what is needed is a system that ensures high predictive accuracy and optimality. Machine Learning algorithms such as various Regression models, Gradient Boosting, Deep Learning, Neural Networks, Support Vector, Random Forest and others can be used to anticipate whether a consumer is eligible for taking a loan with high accuracy. In this thesis, an attempt has been made to find a good ML algorithm that shall help various banks and/or financial institutions to reliably predict the credit risk on an individual by analyzing appropriate datasets. Following that, a highly accurate result for said institutions can be ensured, which they can use to determine whether a consumer requesting credit should be allotted credit or not. Aquib Abtahi Turjo S.M. Mynul Karim Tausif Hossain Biswas Yeaminur Rahman Ifroim Dewan B. Computer Science 2021-10-10T05:36:50Z 2021-10-10T05:36:50Z 2021 2021-06 Thesis ID 17101073 ID 17101162 ID 17101374 ID 17101406 ID 17126016 http://hdl.handle.net/10361/15189 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest Machine Learning Deep Learning
spellingShingle	Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest Machine Learning Deep Learning Turjo, Aquib Abtahi Karim, S.M. Mynul Biswas, Tausif Hossain Rahman, Yeaminur Dewan, Ifroim Comparative analysis and implementation of credit risk prediction through distinct machine learning models
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Turjo, Aquib Abtahi Karim, S.M. Mynul Biswas, Tausif Hossain Rahman, Yeaminur Dewan, Ifroim
format	Thesis
author	Turjo, Aquib Abtahi Karim, S.M. Mynul Biswas, Tausif Hossain Rahman, Yeaminur Dewan, Ifroim
author_sort	Turjo, Aquib Abtahi
title	Comparative analysis and implementation of credit risk prediction through distinct machine learning models
title_short	Comparative analysis and implementation of credit risk prediction through distinct machine learning models
title_full	Comparative analysis and implementation of credit risk prediction through distinct machine learning models
title_fullStr	Comparative analysis and implementation of credit risk prediction through distinct machine learning models
title_full_unstemmed	Comparative analysis and implementation of credit risk prediction through distinct machine learning models
title_sort	comparative analysis and implementation of credit risk prediction through distinct machine learning models
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15189
work_keys_str_mv	AT turjoaquibabtahi comparativeanalysisandimplementationofcreditriskpredictionthroughdistinctmachinelearningmodels AT karimsmmynul comparativeanalysisandimplementationofcreditriskpredictionthroughdistinctmachinelearningmodels AT biswastausifhossain comparativeanalysisandimplementationofcreditriskpredictionthroughdistinctmachinelearningmodels AT rahmanyeaminur comparativeanalysisandimplementationofcreditriskpredictionthroughdistinctmachinelearningmodels AT dewanifroim comparativeanalysisandimplementationofcreditriskpredictionthroughdistinctmachinelearningmodels
_version_	1814307560625274880

Comparative analysis and implementation of credit risk prediction through distinct machine learning models

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