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Title
Cross Project Model For Churn Prediction In Telecom Sector
Author(s)
Meer Wali-Ur-Rehman Khan
Abstract
Customer churn is an important and critical issue in telecommunication sector. With acquiring new customers, the high cost is associated, so due to this customer churn prediction is one of the most important activities for a project manager and has indispensable part of industry’s strategic decision making and planning process. Unlike traditional customer churn prediction models that identify customer churn, cross projects just in time prediction is relative new and more practical alternate to traditional churn prediction techniques, providing immediate feedback while design decisions are still fresh in the minds of the project managers. The proposed model requires a large size of training data, usually such amount of data not available when the companies are at initial stage. To address this challenge in traditional churn prediction, prior studies have proposed cross-project models (CPM). Cross Project Model learned from previous projects of same nature with sufficient history. However, only few studies have focused on transferring prediction models from one project to another. This research do an early attempt which makes the use of just-in-time approach needed for customer churn prediction with cross-project model. Along with this there is always the problem of accuracy in CPM which are addressed by embedding ensemble technique. Ensemble application has shown tremendous increase in the accuracy of prediction for customer churn. With ensemble technique, genetic algorithm outperforms other classifiers by achieving an optimized accuracy of 68% which is 11% more than the previous technique that is without ensemble technique for cross project model.
Type
Thesis/Dissertation MS
Faculty
Engineering and Computer Science
Department
Computer Science
Language
English
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eea69b3689.pdf
2018-11-05 11:35:29
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