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This MSc research project develops and evaluates four machine learning approaches—logistic regression, random forests, gradient boosting, and LSTM—to forecast churn while prioritizing interpretability. Explainability tools SHAP and LIME are used to identify churn drivers, with results highlighting “Tenure” and “Monthly Charges.” Gradient boosting achieves the best accuracy and AUC-ROC, and limitations are discussed for LSTM due to time-agnostic data.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/customer-segmentation-and-churn-prediction-in-telecommunication-using-machine-learning-msc-research-project-abstract-introduction/290875/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/customer-segmentation-and-churn-prediction-in-telecommunication-using-machine-learning-msc-research-project-abstract-introduction/290875.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in the telecommunications sector?","Question",{"text":113,"@type":114},"The study addresses customer churn, focusing on predicting which customers will stop using telecom services to support retention and business growth.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models are evaluated for churn prediction?",{"text":118,"@type":114},"The research evaluates logistic regression, random forests, gradient boosting, and LSTM networks to predict customer churn.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the study make the models interpretable?",{"text":122,"@type":114},"Explainability techniques SHAP and LIME are applied to identify which features contribute to churn, helping stakeholders understand model reasoning.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290875,1789642859,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Customer Segmentation and Churn Prediction in Telecommunication Using  \nMachine Learning  \nMSc Research Project MSc. Data Analytics  \nSusanth Kammili  \nStudent ID : X22248064  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Professor. Noel Cosgrave  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nSusanth Kammili  \nStudent    \nName:  \nStudent    \nID: x22248064    \n…. .……  \nProgramme:  \nMSc. Data Analytics Year:  \n2023-  \n2024    \nModule:  \nResearch Project  \nSupervisor: Prof. Noel Cosgrave  \nSubmission Due Date:  \n03-01-2025  \nProject Title:  \nCustomer Segmentation and Churn Prediction in Telecommunication Using Machine  \nLearning  \nWord Count:  \n7642  \n  Page Count   26   .   . .  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nSignature:   Susanth Kammili    \n03-01-2025  \nDate:    \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nCustomer Segmentation and Churn Prediction in Telecommunication Using Machine Learning  \nSusanth Kammili  \nStudent ID: x22248064  \nAbstract  \nCustomer churn is a significant challenge for businesses, particularly in the telecommunications sector, where customer retention plays a crucial role in profitability and growth. This study addresses the problem of predicting customer churn using machine learning models, with an emphasis on both accuracy and interpretability. The research aims to develop and evaluate four machine learning models—logistic regression, random forests, gradient boosting, and LSTM networks—to predict customer churn. Further, the present research incorporates explainability tools like SHAP and LIME that would help in identifying what the algorithms look as the cause of churn to make the models efficient as well as to help the business executives understand them. The results show that the gradient boosting delivers the highest accuracy of 0.83% and the AUC-ROC of 0.90%; nevertheless, random forest and logistic regression models are also important and precise. While LSTM networks are quite effective, they failed because of the time agnostic data set used in the study. The implementation of SHAP and LIME also indicated that “Tenure,” and “Monthly Charges” were important features to distinguish churn. In this research, the following advancements were made 1) Moving from binary PAM and model performance metrics to show an integrated solution with an interval of interpretability and performance. The study also points to the lack of dynamic and temporal data as the way forward in improving the performance of churn models in practice.  \n1 Introduction  \nCustomer attrition or the instances where the customers are no longer using a given telecom product or service is one of the most important problems that companies face, and due to the nature of the telecom business, its importanc","cbCaihgoCNbSD9Gh","https://ap.wps.com/l/cbCaihgoCNbSD9Gh","pdf",553799,26,"English","# Abstract\n# 1 Introduction","[{\"question\":\"What problem does the study address in the telecommunications sector?\",\"answer\":\"The study addresses customer churn, focusing on predicting which customers will stop using telecom services to support retention and business growth.\"},{\"question\":\"Which machine learning models are evaluated for churn prediction?\",\"answer\":\"The research evaluates logistic regression, random forests, gradient boosting, and LSTM networks to predict customer churn.\"},{\"question\":\"How does the study make the models interpretable?\",\"answer\":\"Explainability techniques SHAP and LIME are applied to identify which features contribute to churn, helping stakeholders understand model reasoning.\"}]","Customer Segmentation and Churn Prediction in Telecommunication Using Machine Learning - MSc Research Project - Abstract & Introduction | PDF",66]