An Optimized early churn prediction for food industry by deep neural decision forests

dc.contributor.advisorThayasivam, U
dc.contributor.authorBelendran, K
dc.date.accept2023
dc.date.accessioned2025-06-09T09:35:05Z
dc.date.issued2023
dc.description.abstractCustomer retention is one of the main goals in large-scale food industries. This is achieved by predicting churn customers in advance and satisfying their needs. This research focuses on providing a higher accurate model than the other previous models. The deep neural decision forest model is a combination of Random Forest and Convolutional Neural Network models that help in fulfilling the objective. The dataset is taken from a US food industry to train and test the models. This model has achieved 92% accuracy in predicting the churn customers.
dc.identifier.accnoTH5314
dc.identifier.citationBelendran, K. (2023). An Optimized early churn prediction for food industry by deep neural decision forests [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. https://dl.lib.uom.lk/handle/123/23625
dc.identifier.degreeMSc in Computer Science
dc.identifier.departmentDepartment of Computer Science & Engineering
dc.identifier.facultyEngineering
dc.identifier.urihttps://dl.lib.uom.lk/handle/123/23625
dc.language.isoen
dc.subjectCHURN PREDICTION
dc.subjectDEEP NEURAL DECISION FOREST
dc.subjectFOOD SERVICE INDUSTRY
dc.subjectCOMPUTER SCIENCE & ENGINEERING - Dissertation
dc.subjectINFORMATION TECHNOLOGY - Dissertation
dc.subjectMSc in Computer Science
dc.titleAn Optimized early churn prediction for food industry by deep neural decision forests
dc.typeThesis-Abstract

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