[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125532-en":3,"doc-seo-125532-105":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},125532,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Predicting Reaction Based on Customer Transaction Using Machine Learning Approaches","Bank advertising plays a key role in targeting customers for fixed-term deposit offers and related subscription deals through online or media promotions. Banks and telecommunications firms store historical customer data to maintain relationships, select suitable offers, and improve the likelihood of deposit recovery. This study builds a binary classification prediction model to determine whether a customer will subscribe to term-deposit offers using four machine learning classifiers. Results show the decision tree achieving 91% accuracy and SVM reaching 89%.","Predicting reaction based on customer's transaction using machine learning approaches  \nIsraa M. Hayder1, Ghazwan Abdul Nabi Al Ali2,3, Hussain A. Younis2,4  \n1Department of Computer Systems Techniques, Qurna Technique Institute, Qurna, Iraq 2School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia 3Department of Computer Science (Educational Science), University of Basrah, Basrah, Iraq 4College of Education for Women, University of Basrah, Basrah, Iraq  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Jan 20, 2022 Revised Aug 9, 2022 Accepted Sep 5, 2022  \nKeywords:  \nBank marketing  \nDecision tree  \nK-nearest neighbors’ algorithm Naive Bayes  \nSupport vector machines  \nCorresponding Author:  \nBanking advertisements are important because they help target specific customers on subscribing to their packages or other deals by giving their current customers more fixed-term deposit offers. This is done through promotional advertisements on the Internet or media pages, and this task is the responsibility of the shopping department. In order to build a relationship with them, offer them the best deals, and be appropriate for the client with the company's assurance to recover these deposits, many banks or telecommunications firms store the data of their customers. The Portuguese bank increases its sales by establishing a relationship with its customers. This study proposes creating a prediction model using machine learning algorithms, to see how the customer reacts to subscribe to those fixed-term deposits or offers made with the aid of their past record. This classification is binary, i.e. , the prediction of whether or not a customer will embrace these offers. Four classifiers that include k-nearest neighbor (k-NN) algorithm, decision tree, naive Bayes, and support vector machines (SVM) were used, and the best result was obtained from the classifier decision tree with an accuracy of 91% and the other classifier SVM with an accuracy of 89% .  \nThis is an open access article under the CC BY-SA license.  \nHussain A. Younis  \nSchool of Computer Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia [Email: hussain.younis@uobasrah.edu.iq](Email: hussain.younis@uobasrah.edu.iq)  \n1. INTRODUCTION  \nBanking advertising comprises advertisements by financial institutions. This category includes, in addition to advertising directed at bank clients, business reports and information pamphlets; statements about the payment of new shares, reports on investment program outcomes, as well as several additional financial announcements may also be included [1]–[3] . Many banks or telecommunication companies store their customers' data to establish a relationship with customers and provide them with the best offers and at the sametime be appropriate for the customer with the guarantee that the company will recover their deposits. The Portuguese bank increases its sales by establishing a cordial relationship with its customers. Transaction predictions use k-nearest neighbors’ (k-NN) algorithm [4], [5], decision tree [6]–[10], naive Bayes [6],[11]–[13], and support vector machines (SVM) [14] to bank marketing. This study proposes creating a prediction model using machine learning algorithms to see how the customer reacts to subscribing to those fixed-term deposits or offers made through their past data [15]–[19] . This classification is binary, i.e., the prediction of whether or not a customer will participate in these offers. Four classifiers k-NN, decision tree, SVM, and naive Bayes were used.  \n2. LITERATURE REVIEW  \nAs the number of Internet users and businesses grows, many clusters of e-commerce applications appear not to be physically linked to each other in the system but are inter-related in business. Online banking has been in practice since the 1980s, when it was first introduced by four major banks in New York [20], [21] . The study of commercial financial transactions made a short-term expectation using a logistic","cbCaijNBUMwX0SsV","https://ap.wps.com/l/cbCaijNBUMwX0SsV","pdf",984599,1,11,"English","en",105,"# Introduction\n## Banking advertising and customer data\n## Machine learning approach for binary prediction\n# Literature Review\n## Online banking and transaction prediction models\n## Related work on security and fraud detection\n## Dataset description and prior studies","[{\"question\":\"What is the main goal of the prediction model in this study?\",\"answer\":\"To predict customer reaction in a binary way, determining whether a customer will subscribe to fixed-term deposit offers based on historical transaction/customer data.\"},{\"question\":\"Which classifiers are used and how do their results compare?\",\"answer\":\"Four classifiers are evaluated: k-nearest neighbors, decision tree, naive Bayes, and support vector machines. The decision tree achieves the best accuracy at 91%, while SVM reaches 89%.\"},{\"question\":\"What dataset is referenced for building and evaluating the model?\",\"answer\":\"The dataset is described as being downloaded from the UCI machine learning repository and associated with the Portuguese bank’s direct marketing campaign, including multiple customer records and variables.\"}]","Predicting Reaction Based on Customer Transaction Using Machine Learning Approaches | PDF",1785899699,28,{"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},"predicting-reaction-based-on-customer-transaction-using-machine-learning-approaches","",{"@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/predicting-reaction-based-on-customer-transaction-using-machine-learning-approaches/125532/",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","2026-08-05",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 prediction model in this study?","Question",{"text":75,"@type":76},"To predict customer reaction in a binary way, determining whether a customer will subscribe to fixed-term deposit offers based on historical transaction/customer data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which classifiers are used and how do their results compare?",{"text":80,"@type":76},"Four classifiers are evaluated: k-nearest neighbors, decision tree, naive Bayes, and support vector machines. 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