[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122888-en":3,"doc-seo-122888-105":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},122888,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","An Approach for Loan Approval Prediction Using Machine Learning","Banking loan approval demands accurate decisions because banks must evaluate whether an applicant is genuine and capable of repaying within the required time. The paper addresses the complexity of analyzing large volumes of applicant and financial data, which can be difficult to review manually while still ensuring reliable outcomes. The objective is to analyze test data and predict loan eligibility using machine learning trained on historical examples to support faster, more informed approval decisions.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 863:868  \nAn Approach for Loan Approval Prediction Using Machine Learning Anusha G1*, K Thanusha Reddy2, G Tanmayee3, G Roopa4, Vani Krishnaswamy5  \n1,2,3,4,5School of Computing and Information Technology  \nEmail: [anushagopinath165@gmail.com](anushagopinath165@gmail.com1)[1](anushagopinath165@gmail.com1), [thanushareddykonikanti@gmail.com](thanushareddykonikanti@gmail.com2)[2](thanushareddykonikanti@gmail.com2), [tanudurgag@gmail.com](tanudurgag@gmail.com3)[3](tanudurgag@gmail.com3),  \n[gowniroopareddy22@gmail.com](gowniroopareddy22@gmail.com4)[4](gowniroopareddy22@gmail.com4), [vanikrishnaswamy@gmail.com](vanikrishnaswamy@gmail.com5)[5](vanikrishnaswamy@gmail.com5)  \n*Corresponding author’[s E-mail:](s E-mail:anushagopinath165@gmail.com)[anushagopinath165@gmail.com](s E-mail:anushagopinath165@gmail.com)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 30 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Banking sector is one such field where the company needs more accurate results after analysis. There are many people applying for bank loans from banks or other finance companies each day. But the banks cannot provide loan to every individual who is applying for loan. There is a very complex task that the bank employees do to study an analyze if the applicant is genuine or not. To find this out, there are a lot offactors to be considered. Going through this huge amount of data can be a really difficult task and yet one cannot be sure if the applicant will be able to pay back the loan within the given time or not. Objective of the paper is to make thorough analysis of the test data and make predictions if the applicant is genuine or not. For this process, we are using Machine Learning where the trained data is used to make predictions.\u003Cbr>Keywords: Banking Sector, Finance Companies, Loan, Trained Data, Test Data, Machine Learning, Prediction |\n| --- | --- |\n\n1. Introduction  \nThere are many applicants who apply for loan each day. But there is no surety that the applicants are genuine and the money is in safe hands. Even though there are a lot of people applying for loan, loan cannot be granted to every applicant who has applied for loan. There has to be a filtration process to check the details of applicants and see if the loan can be granted or not. Banks have a fixed percentage of money to be issued as loan. Hence filtration among applicants is necessary. For banks, the interest that comes from the applicants of loan serves as a huge business part. Loan is kind of an asset for the bank as the income from loan interest brings good profit to the bank. Hence the loan has to distributed after a good thorough analysis.  \nSelection of approval of loan is a major business part of all the banks and finance companies. The employees working at the banking sectors face a lot of problem to process large data. There is a necessity to filter and give accurate result whether to approve the loan or not for a particular person. Processing huge data requires more time and effort. By the implementation of this project, we can reduce the time, effort and manpower required at the banking sectors. This problem is done by mining the data collected.  \nA. Scope of the project  \nThe main contributions of this project therefore are:  \n Data Analysis  \n Dataset Pre-processing  \n Training the Model  \n Testing of Dataset  \nB. Domain overview  \nTo predict the future or to do classification on information, machine learning is popular in doing such problems. The algorithms which we use in machine learning are trained over examples or instances from past experience and historical data is also analysed. As these algorithms are trained repeatedly, they are able to find patterns, which help to make future predictions. In machine learning algorithms data is the pillar. By using historical data, we are creating much more data by training alg","cbCaicbQh6Hc16X9","https://ap.wps.com/l/cbCaicbQh6Hc16X9","pdf",397807,1,6,"English","en",105,"# Introduction\n## Project Scope and Contributions\n## Domain Overview\n# Literature Review\n## Related Works\n# Proposed System","[{\"question\":\"What problem does the paper address in loan approval decisions?\",\"answer\":\"The paper targets the need for reliable loan eligibility decisions when banks face many applicants and must ensure applicants are genuine and can repay on time. It emphasizes reducing the time and effort required to process large datasets.\"},{\"question\":\"How does the proposed approach use machine learning?\",\"answer\":\"The approach trains machine learning models on historical data to learn patterns and then uses test data to predict whether an applicant should be approved. It relies on trained data for prediction support.\"},{\"question\":\"What preparation and processing steps are included?\",\"answer\":\"The scope highlights dataset preprocessing, along with training and testing steps. The proposed system also mentions normalization, including Min-Max normalization, to transform values into a suitable range and reduce irregularities.\"}]","An Approach for Loan Approval Prediction Using Machine Learning | PDF",1785813512,15,{"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},"an-approach-for-loan-approval-prediction-using-machine-learning","",{"@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/an-approach-for-loan-approval-prediction-using-machine-learning/122888/",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","2026-08-04",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 problem does the paper address in loan approval decisions?","Question",{"text":75,"@type":76},"The paper targets the need for reliable loan eligibility decisions when banks face many applicants and must ensure applicants are genuine and can repay on time. It emphasizes reducing the time and effort required to process large datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach use machine learning?",{"text":80,"@type":76},"The approach trains machine learning models on historical data to learn patterns and then uses test data to predict whether an applicant should be approved. It relies on trained data for prediction support.",{"name":82,"@type":73,"acceptedAnswer":83},"What preparation and processing steps are included?",{"text":84,"@type":76},"The scope highlights dataset preprocessing, along with training and testing steps. 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