[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128340-en":3,"doc-seo-128340-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128340,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","INCREASE THE EFFICIENCY OF LOAN PREDICTION USING MACHINE LEARNING APPROACH - Thesis","Individual loan approval is a critical, ongoing challenge faced by financial institutions as loan applications increase year by year. Banks need to handle approval processes carefully to reduce cases of repayment failure. This study aims to find the best predictive model to support institutions in addressing the problem by estimating the likelihood that a loan will be approved or rejected. Loan approval prediction relies on analyzing applicants’ historical credit, income, employment status, and other financial factors, with the objectives of lowering lender risk and improving borrowers’ approval chances.","INCREASE THE EFFICIENCY OF LOAN PREDICTION USING MACHINE LEARNING  \nAPPROACH  \nNIK NUR AIN BINTI NIK JID  \nBACHELOR OF APPLIED SCIENCE IN DATA ANALYTICS WITH HONOURS UNIVERSITI MALAYSIA PAHANG  \nUNIVERSITI MALAYSIA PAHANG  \nDECLARATION OF THESIS AND COPYRIGHT  \nAuthor’s Full Name : NIK NUR AIN BINTI NIK JID  \nDate ofBirth : 28th April 2001  \nTitle : INCREASE THE EFFICIENCY OF LOAN PREDICTION  \nUSING MACHINE LEARNING APPROACH  \nAcademic Session : 2022/2023  \nI declare that this thesis is classified as:  \n☐ CONFIDENTIAL (Contains confidential information under the Official Secret  \nAct 1997)*  \n☐ RESTRICTED (Contains restricted information as specified by the  \norganization where research was done)*  \n ☐ OPEN ACCESS I agree that my thesis to be published as online open access  \n(Full Text)  \nI acknowledge that Universiti Malaysia Pahang reserves the following rights:  \n1. The Thesis is the Property of Universiti Malaysia Pahang  \n2. The Library of Universiti Malaysia Pahang has the right to make copies of the thesis for the purpose of research only.  \n3. The Library has the right to make copies of the thesis for academic exchange.  \nCertified by:  \n(Student’s Signature)  \nNew IC/Passport Number Date: 21st July 2023  \n(Academic Tutor’s Signature)  \nDR. KU MUHAMMAD NA’IM BIN KU KHALIF  \nName of Academic Tutor Date: 21st July 2023  \n______________________  \n(Industry Coach’s Signature)  \nMR. DAVID CHONG TEAK WEI Name of Industry Coach  \nDate: 21st July 2023  \n_______________________  \nNOTE : * If the thesis is CONFIDENTIAL or RESTRICTED, please attach a thesis declaration letter.  \nSUPERVISOR’S DECLARATION  \nWe hereby declare that we have checked this project report, and in our opinion, this final report of Data Science Project is adequate in terms of scope and quality for the award of the Bachelor of Applied Science in Data Analytics with Honours.  \n(Academic Tutor’s Signature)  \nFull Name : DR. KU MUHAMMAD NA’IM BIN KU KHALIF  \nPosition : Senior Lecturer, Universiti Malaysia Pahang  \nDate : 21st July 2023  \n_______________________________  \n(Industry Coach’s Signature)  \nFull Name : MR. DAVID CHONG TEAK WEI  \nPosition : Head of AI, Ever AI Holding Sdn Bhd  \nDate : 21st July 2023  \nSTUDENT’S DECLARATION  \nI hereby declare that the work in this project report is based on my original work.  \n_______________________________  \n(Student’s Signature)  \nFull Name : NIK NUR AIN BINTINIK JID  \nID Number :  \nDate : 21st July 2023  \nINCREASE THE EFFICIENCY OF LOAN PREDICTION USING MACHINE  \nLEARNING APPROACH  \nNIK NUR AIN BINTI NIK JID  \nData Science Project Report submitted in fulfilment of the requirements for the award of the degree of  \nBachelor of Applied Science in Data Analytics with Honours  \nCentre for Mathematical Sciences  \nUNIVERSITI MALAYSIA PAHANG  \nJULY 2023  \nACKNOWLEDGEMENTS  \nبِ سْمِ اللهِ الرَّ حْ مٰ نِ الرَّ حِ يْمِ  \nIn the name of Allah, the Most Gracious and Most Merciful  \nAll praise be to Allah SWT for easing my journey in completing this Data Science Project. My deepest gratitude and appreciation go to several people who have assisted me in completing this project. Without them, I would never have been able to finish it.  \nFirst and foremost, I would like to express my gratitude to my academic tutors, Dr. Chuan Zun Liang and Dr. Ku Muhammad Na’im bin Ku Khalif, for dedicating their time and sharing their valuable knowledge to guide me throughout the completion of this project. I am also sincerely appreciative of the guidance provided by the internal examiner of my project, Dr. Wan Nur Syahidah binti Wan Yusoff, who offered impactful comments that helped me make necessary corrections before the final submission. Additionally, I extend my heartfelt appreciation to all my lecturers at the Centre for Mathematical Sciences, Universiti Malaysia Pahang, who have shared their wisdom and knowledge with me during my years of study.  \nI am also grateful to Mr. David Chong Teak Wei from Ever AI Holdings Sdn Bhd, who provided gu","cbCaio4piDKvsQpW","https://ap.wps.com/l/cbCaio4piDKvsQpW","pdf",2850951,2,1,100,"English","en",105,"# Acknowledgements\n# Abstrak\n## Background and problem statement\n## Study objective and approach\n## Prediction basis and benefits","[{\"question\":\"What is the main problem addressed by the study?\",\"answer\":\"The study addresses the ongoing challenge of individual loan approval faced by financial institutions, especially as applications increase and repayment failures can occur.\"},{\"question\":\"What is the purpose of using predictive models in this research?\",\"answer\":\"The purpose is to identify the best predictive model to help institutions determine the likelihood of loan approval or rejection.\"},{\"question\":\"Which machine learning methods are used to train the loan prediction models?\",\"answer\":\"The study uses machine learning methods including logistic regression, decision trees, support vector machines, random forest, gradient booster, and artificial neural networks.\"}]","INCREASE THE EFFICIENCY OF LOAN PREDICTION USING MACHINE LEARNING APPROACH - 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