[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117085-en":3,"doc-seo-117085-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117085,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","LOAN ELIGIBILITY CLASSIFICATION USING MACHINE LEARNING APPROACH","Machine learning is increasingly vital for loan eligibility classification because it supports analysis of large datasets, builds predictive models, incorporates new information, and automates decision processes. This study compares Logistic Regression, Random Forest, and Decision Tree using Python and Jupyter Notebook for data analysis and model development. Models are evaluated on a testing set with Accuracy, Precision, Recall, and F1-Score to determine the most effective algorithm. Results indicate the Logistic Regression model provides the strongest overall performance, with reported Accuracy 82% and F1-Score 79%.","LOAN ELIGIBILITY CLASSIFICATION USING MACHINE LEARNING APPROACH  \nPAUL LAW LIK PAO  \nBachelor of Computer Science (Software Engineering)  \nUNIVERSITI MALAYSIA PAHANG  \nUNIVERSITI MALAYSIA PAHANG  \n\n| DECLARATION OF THESIS AND COPYRIGHT\u003Cbr>Author’s Full Name :  PAUL LAW LIK PAO  Date ofBirth\u003Cbr>Title : LOAN ELIGIBILITY CLASSIFICATION USING\u003Cbr>MACHINE LEARNING APPROACH\u003Cbr>_____________________________________________________\u003Cbr>Academic Session :  SEMESTER 2, SESSION 2022/2023 \u003Cbr>I declare that this thesis is classified as: |  |  |\n| --- | --- | --- |\n| 􀂅 CONFIDENTIAL\u003Cbr>􀂅 RESTRICTED\u003Cbr>􀀻 OPEN ACCESS | (Contains confidential information under the Official Secret Act 1997)*\u003Cbr>(Contains restricted information as specified by the organization where research was done)*\u003Cbr>I agree that my thesis to be published as online open access (Full Text) |  |\n| I acknowledge that Universiti Malaysia Pahang reserves the following rights:\u003Cbr>1. The Thesis is the Property of Universiti Malaysia Pahang\u003Cbr>2. The Library of Universiti Malaysia Pahang has the right to make copies of the thesis for the purpose of research only.\u003Cbr>3. The Library has the right to make copies of the thesis for academic exchange. |  |  |\n| Certified by:\u003Cbr>(Student’s Signature)\u003Cbr>New IC/Passport Number Date: 18 MAY 2023 |  | (Supervisor’s Signature)\u003Cbr>TS. DR MOHD ARFIAN BIN ISMAIL\u003Cbr>Name of Supervisor Date: 18 MAY 2023 |\n| * |  |  |\n\nNOTE : If the thesis is CONFIDENTIAL or RESTRICTED, please attach a thesis declaration letter.  \nSUPERVISOR’S DECLARATION  \nI hereby declare that I have checked this thesis and in my opinion, this thesis is adequate in terms of scope and quality for the award of the degree of Bachelor of Computer Science (Software Engineering) with Honours.  \n(Supervisor’s Signature)  \nFull Name : TS. DR MOHD ARFIAN BIN ISMAIL  \nPosition : SENIOR LECTURER  \nDate : 18 MAY 2023  \nSTUDENT’S DECLARATION  \nI hereby declare that the work in this thesis is based on my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously or concurrently submitted for any other degree at Universiti Malaysia Pahang or any other institutions.  \n(Student’s Signature) Full Name : PAUL LAW IK PAO  \nID Number : CB20025  \nDate : 18 MAY 2023  \nLOAN ELIGIBILITY CLASSIFICATION USING MACHINE LEARNING  \nAPPROACH  \nPAUL LAW LIK PAO  \nThesis submitted in fulfillment of the requirements for the award of the degree of  \nBachelor of Computer Science (Software Engineering) with Honours  \nFaculty of Computing  \nUNIVERSITI MALAYSIA PAHANG  \nMAY 2023  \nACKNOWLEDGEMENTS  \nI would like to express my heartfelt thanks and immense appreciation to the divine providence for granting me the opportunity and favor to successfully accomplish my Final Year Project titled \"Loan Eligibility Classification using Machine Learning Approach\" within the designated timeframe.  \nFirst and foremost, I am immensely grateful to my supervisor, Ts. Dr Mohd Arfian Bin Ismail, for his guidance, support, and invaluable expertise throughout the entire duration of this project. His insightful feedback, encouragement, and continuous assistance have been instrumental in shaping and refining the direction of my research.  \nI would also like to extend my sincere appreciation to the lecturers of the Faculty of Computing at Universiti Malaysia Pahang for their exceptional teaching and providing me with a solid foundation in the field of machine learning. Their dedication to education and willingness to share their knowledge have been pivotal in developing my skills and understanding in this area.  \nI am indebted to the researchers and authors whose work and publications I extensively studied to gain a comprehensive understanding of different machine learning algorithms. Their contributions to the field have been instrumental in shaping the theoretical framework of this project.  \nLast but not least, I would like to thank my family and friends for their unwa","cbCaipybzjCseluA","https://ap.wps.com/l/cbCaipybzjCseluA","pdf",2617244,1,100,"English","en",105,"# Acknowledgements\n# Abstrak\n# Abstract","[{\"question\":\"Which machine learning algorithms are compared for loan eligibility classification?\",\"answer\":\"The study compares Logistic Regression, Random Forest, and Decision Tree models for loan eligibility classification.\"},{\"question\":\"What tools and platforms are used to build and evaluate the models?\",\"answer\":\"Python and Jupyter Notebook are used for data analysis and model development, followed by evaluation on a testing set.\"},{\"question\":\"Which model performs best according to the reported metrics?\",\"answer\":\"The Logistic Regression (LR) model is reported as the most effective, achieving Accuracy 82%, Precision 81%, Recall 82%, and F1-Score 79%.\"}]","LOAN ELIGIBILITY CLASSIFICATION USING MACHINE LEARNING APPROACH | PDF",1785673666,252,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"loan-eligibility-classification-using-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/loan-eligibility-classification-using-machine-learning-approach/117085/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared for loan eligibility classification?","Question",{"text":75,"@type":76},"The study compares Logistic Regression, Random Forest, and Decision Tree models for loan eligibility classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What tools and platforms are used to build and evaluate the models?",{"text":80,"@type":76},"Python and Jupyter Notebook are used for data analysis and model development, followed by evaluation on a testing set.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best according to the reported metrics?",{"text":84,"@type":76},"The Logistic Regression (LR) model is reported as the most effective, achieving Accuracy 82%, Precision 81%, Recall 82%, and F1-Score 79%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]