Predictive Analysis of Bank Marketing Data for Customer Response

Predictive Analysis of Bank Marketing Data for Customer Response | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Analysis of Bank Marketing Data for Customer Response Yash Mishra, Kedarnath senapati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8603966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research analyzes a bank's telemarketing campaign data to identify key factors influencing a customer's response. Using a dataset containing demographic information, contact history, and campaign details, the study explores the relationships between various attributes and the likelihood of deposit acceptance. We performed exploratory data analysis (EDA) to visualize trends, revealing that age, job type, marital status, and education significantly affect a customer's propensity to subscribe. Furthermore, we found a strong positive correlation between call duration and deposit acceptance. The project applies several supervised machine learning models, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine (SVC), Decision Tree, Random Forest, and XGBoost, to predict the outcome of a campaign. The models were evaluated using accuracy and confusion matrices, with XGBoost and Random Forest classifiers achieving the highest accuracy, demonstrating the effectiveness of ensemble learning for this predictive task. The findings provide actionable insights for banks to optimize their marketing strategies, enabling them to target potential customers more effectively and increase campaign success rates. Artificial Intelligence and Machine Learning Deep learning Machine learning Data analysis Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8603966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574701647,"identity":"d2a02377-2a36-4682-ab83-03ce2bf9b269","order_by":0,"name":"Yash Mishra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYHACNoaPfyR4+OUPHwByJGSI0sI4s8FGTnIGWwJICw9RWph5G9KMDW7wGIB4hLUYHD/+7AHvjsOJDbd7Pr+6UWPBw8B++OgGvFrO5JgbSJ45nNg45+w265xjQIfxpKXdwKvlQA6bhAHb4cRmhtxtxkA2D9A7Zvi1nH/+TCIBqKWNIeeZcc4/YrTcSDCTONiWZswjkcP8OLeNCC2SN96YSTacsZGT4DlmxpzbJ8HDRsgvfOfTn0n/qZDgsT/e/Phzzrc6OX72w8fwalE4gGCzSYBJfMpBQL4BwWb+QEj1KBgFo2AUjEwAAKmlTQZsW6kyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5004-1797","institution":"Nitk Surathkal","correspondingAuthor":true,"prefix":"","firstName":"Yash","middleName":"","lastName":"Mishra","suffix":""},{"id":574701728,"identity":"96971582-eb16-4e15-91b1-4bb819bb8db9","order_by":1,"name":"Kedarnath senapati","email":"","orcid":"","institution":"Nitk Surathkal","correspondingAuthor":false,"prefix":"","firstName":"Kedarnath","middleName":"","lastName":"senapati","suffix":""}],"badges":[],"createdAt":"2026-01-14 17:08:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8603966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8603966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100549023,"identity":"66a4ba1f-eb0e-4f99-8ca4-cc8cd08673ef","added_by":"auto","created_at":"2026-01-19 08:22:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":857720,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchP4NitkMacsThisresearchanalyzesabankstelemarketingcampaigndata1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8603966/v1_covered_6b6cd685-66d9-46fe-968f-a8015afd9012.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePredictive Analysis of Bank Marketing Data for\u003c/p\u003e\n\u003cp\u003eCustomer Response\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nitk surathkal","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Machine learning, Data analysis ","lastPublishedDoi":"10.21203/rs.3.rs-8603966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8603966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research analyzes a bank's telemarketing campaign data to identify key factors influencing a customer's response. 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