header

An efficient prediction approach for default customers of personal loans using machine learning / (Record no. 81053)

MARC details
000 -LEADER
fixed length control field 02660cam a2200337 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GiCUC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250223032741.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210527s2021 ua d f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GiCUC
Language of cataloging eng
Transcribing agency EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library Deposite
097 ## - Thesis Degree
Thesis Level M.Sc
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number Cai01.18.07.M.Sc.2021.Mo.E
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mohammed Hussein Mahmoud Mostafa
245 13 - TITLE STATEMENT
Title An efficient prediction approach for default customers of personal loans using machine learning /
Statement of responsibility, etc. Mohammed Hussein Mahmoud Mostafa ; Supervised Ammar Mohammed , Nesrine Ali Abdelazim
246 15 - VARYING FORM OF TITLE
Title proper/short title اسلوب كفء للتنبؤ بالعملاء المتعثرين للقروض الشخصية باستخدام تقنية تعلم الالة
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cairo :
Name of publisher, distributor, etc. Mohammed Hussein Mahmoud Mostafa ,
Date of publication, distribution, etc. 2021
300 ## - PHYSICAL DESCRIPTION
Extent 119 Leaves :
Other physical details charts ;
Dimensions 30cm
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies For Statistical Research - Department of Information Systems and Technology
520 ## - SUMMARY, ETC.
Summary, etc. In Egyptian banking sector credit approval decision for personal loans is made using a pure judgment by credit officers due to the fact that machine learning is not widely used in practice. In this thesis, we have taken upon the challenge of delivering prediction approach for default customers of personal loans using machine learning. Our main objective is to predict default customers and analyze the trustworthiness of customers for getting a loan through available personal data and historical credit data. We used ABE dataset for training and testing, also we used 10 features from the application form and i-score report class that could be a helpful tool to credit staffs to take the right decision to decrease credit risk in order to avoid random techniques for customer selection. The data obtained were analyzed with different machine learning classification algorithms based on certain features in order to achieve higher accuracy. We compared between several methods before and after feature selection. We have observed that the most important features are (activity {u2013} income {u2013} loan amount) that can lead to high accuracy and decision tree has the best performance than any other machine learning algorithms with significant prediction accuracy of almost 94.85%
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Default customers
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Machine learning
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Personal loans
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Ammar Mohammed ,
Relator term
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Nesrine Ali Abdelazim ,
Relator term
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://172.23.153.220/th.pdf">http://172.23.153.220/th.pdf</a>
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Amira
Reviser Cataloger
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Nazla
Reviser Revisor
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Holdings
Source of classification or shelving scheme Not for loan Home library Current library Date acquired Full call number Barcode Date last seen Koha item type Copy number
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 11.02.2024 Cai01.18.07.M.Sc.2021.Mo.E 01010110083518000 22.09.2023 Thesis  
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة مخـــزن الرســائل الجـــامعية - البدروم 11.02.2024 Cai01.18.07.M.Sc.2021.Mo.E 01020110083518000 22.09.2023 CD - Rom 83518.CD