An Optimized early churn prediction for food industry by deep neural decision forests
dc.contributor.advisor | Thayasivam, U | |
dc.contributor.author | Belendran, K | |
dc.date.accept | 2023 | |
dc.date.accessioned | 2025-06-09T09:35:05Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Customer 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.accno | TH5314 | |
dc.identifier.citation | Belendran, 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.degree | MSc in Computer Science | |
dc.identifier.department | Department of Computer Science & Engineering | |
dc.identifier.faculty | Engineering | |
dc.identifier.uri | https://dl.lib.uom.lk/handle/123/23625 | |
dc.language.iso | en | |
dc.subject | CHURN PREDICTION | |
dc.subject | DEEP NEURAL DECISION FOREST | |
dc.subject | FOOD SERVICE INDUSTRY | |
dc.subject | COMPUTER SCIENCE & ENGINEERING - Dissertation | |
dc.subject | INFORMATION TECHNOLOGY - Dissertation | |
dc.subject | MSc in Computer Science | |
dc.title | An Optimized early churn prediction for food industry by deep neural decision forests | |
dc.type | Thesis-Abstract |
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