MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03274ntm a2200325 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125111037.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250502t20232023my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010021 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .N87 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nik Nur Ain Binti Nik Jid, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Increase the efficiency of loan prediction using machine learning approach / |
| Statement of responsibility, etc. |
Nik Nur Ain Binti Nik Jid |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 84 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
The individual loan approval process is a critical and ongoing issue faced by financial institutions. As the number of loan applications increases year by year, it is crucial for bankers to take the approval process seriously to prevent loan default cases. The goal of this study is to find the best prediction model that can assist financial institutions in overcoming this problem. Loan approval prediction involves determining the likelihood of a loan application being approved or denied by a financial institution. This typically involves analysing the applicant's credit history, income, employment status, and other financial factors. The objective is to reduce risk for the lender and increase the chances of loan approval for the borrower. In this study, machine learning techniques, such as logistic regression, decision trees, support vector machines, random forest, gradient booster, and artificial neural networks, were employed to train models for loan approval prediction using a home loan dataset obtained from Kaggle. The dataset includes variables such as gender, marital status, education, number of dependents, income, loan amount, and credit history. The analysis and modelling were conducted using Python in Google Colaboratory, also used as visualization tools aiding in identifying patterns and trends in the data. The results of the project revealed that the decision trees model exhibited the highest accuracy of 94% and the lowest root mean squared error value of 0.25, making it the most effective model for loan approval prediction. To deploy the prediction model, a web application was developed using Streamlit and Visual Studio Code as the integrated development environment platform. The web application provided an interactive graphical user interface and dashboard for end-users, enabling financial institutions to easily input loan application information and receive predictions on the likelihood of loan approval. By utilizing this prediction model, financial institutions can make more informed lending decisions, ultimately reducing the risk of loan default cases. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Centre for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |