[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117608-en":3,"doc-seo-117608-105":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},117608,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Bank Loan Prediction Using Machine Learning Techniques - Research Slides","Bank loan prediction addresses default risk by estimating an applicant’s likelihood of repayment through predictive modeling. The research applies multiple machine learning algorithms to loan approval decision-making, using a dataset with 148,670 instances and 37 attributes. Applications are classified into “Approved” and “Denied” groups, and models are trained and evaluated across Decision Tree, AdaBoosting, Random Forest, SVM, and GaussianNB. AdaBoosting delivers the strongest performance at 99.99% accuracy, demonstrating the value of ensemble learning for accurate, efficient financial credit risk assessment.","BANK LOAN PREDICTION USING MACHINE LEARNING TECHNIQUES  \nF M Ahosanul Haque  \nDept. Computer Science And Engineering  \nDaffodil international university Dhaka,Bangladesh. [ahosanul15-13856@diu.edu.bd](ahosanul15-13856@diu.edu.bd)  \nMd. Mahedi Hassan  \nLecturer ,Department ofCSE Daffodil International University Dhaka,Bangladesh.  \n[mhassan.cse@diu.edu.bd](mhassan.cse@diu.edu.bd)  \nAbstract— Banks are important for the development of economies in any financial ecosystem through consumer and business loans. Lending, however, presents risks; thus, banks have to determine the applicant's financial position to reduce the probabilities of default. A number of banks have currently, therefore, adopted data analytics and state-of-the-art technology to arrive at better decisions in the process. The probability of payback is prescribed by a predictive modeling technique in which machine learning algorithms are applied. In this research project, we will apply several machine learning methods to further improve the accuracy and efficiency of loan approval processes. Our work focuses on the prediction of bank loan approval; we have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The target property segregates the loan applications into \"Approved\" and \"Denied\" groups. various machine learning techniques have been used, namely, Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB. Following that, the models were trained and evaluated. Among these, the best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99% . The results therefore show how ensemble learning works effectively to improve the prediction skills of loan approval decisions. The presented work points to the possibility of achieving extremely accurate and efficient loan prediction models that provide useful insights for applying machine learning to financial domains.  \nKeywords— Bank Loan Prediction, Machine Learning, AdaBoosting, Credit Risk Assessment, Financial Modeling, Ensemble Learning, Predictive Analytics.  \n1. Introduction  \nBank Loan Prediction: With the increasing complexity of financial transactions, coupled with the growing demand for speed and accuracy in decision-making processes, bank-loan prediction has been driven towards machine learning approaches. The paper inspects the utilization of powerful predictive modeling in the assessment and prediction of loan application approval or rejection. In this project, the central dataset contains 148,670 rows and 37 columns, each of which represents meaningful factors impacting loan choices. This paper investigates the powers of prediction for five famous machine learning algorithms: AdaBoosting, GaussianNB, RandomForestClassifier, DecisionTreeClassifier, and SVM. The target attribute, therefore, has binary classes of\"Approved\" and \"Denied.\" [1] Lightly sophisticated machine learning models are very important in managing risk and complying with regulations in the context of commercial banks. The various algorithms applied to this research provide enormous insight into how different tactics affect precision and effectiveness in forecasts of loan acceptance. It is outranked only by AdaBoosting, which boasts an incredible 99.99% accuracy. That shows the resilience of ensemble learning methods, how in a somewhat challenging domain like financial decision-making, they can outperform conventional models. [2] The beginning introduces  \nthe importance of good loan prediction models to set a platform for reducing risks and optimizing the whole process of lending. It thereby sets grounds for offering an in-depth analysis of the important role of machine learning in the banking industry. Further sections would give more information about the dataset and approaches used, as well as a conclusion to this paper, bringing out specific performance aspects of each algorithm and their implications on the larger financial scene. [3] Th","cbCaicOhciIzbcpZ","https://ap.wps.com/l/cbCaicOhciIzbcpZ","pdf",681419,1,10,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"What is the main goal of the bank loan prediction project?\",\"answer\":\"To predict bank loan approval decisions by estimating whether applicants should be approved or denied, thereby reducing default risk in lending.\"},{\"question\":\"What dataset size and features are used in the study?\",\"answer\":\"The study uses a dataset of 148,670 instances with 37 attributes representing factors that influence loan decisions.\"},{\"question\":\"Which machine learning algorithm achieved the best results?\",\"answer\":\"AdaBoosting achieved the best performance, reaching an accuracy of 99.99% in the reported evaluation.\"}]","Bank Loan Prediction Using Machine Learning Techniques - Research Slides | PDF",1785677261,25,{"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},"bank-loan-prediction-using-machine-learning-techniques-research-slides","",{"@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/bank-loan-prediction-using-machine-learning-techniques-research-slides/117608/",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},"What is the main goal of the bank loan prediction project?","Question",{"text":75,"@type":76},"To predict bank loan approval decisions by estimating whether applicants should be approved or denied, thereby reducing default risk in lending.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset size and features are used in the study?",{"text":80,"@type":76},"The study uses a dataset of 148,670 instances with 37 attributes representing factors that influence loan decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithm achieved the best results?",{"text":84,"@type":76},"AdaBoosting achieved the best performance, reaching an accuracy of 99.99% in the reported evaluation.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]