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CUSTOMER CHURN PREDICTION USING ML MODELS
编号：98 访问权限：仅限参会人 更新：2025-12-23 13:10:40 浏览：111次 拓展类型1

报告开始：2025年12月29日 14:30（Asia/Amman）

报告时间：15min

所在会场：[S5] Track 5: Emerging Trends of AI/ML [S5-1] Track 5: Emerging Trends of AI/ML

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摘要
Predicting customer churn is an essential part of retention strategy for telecom companies so as to maximize revenue. In this paper, four machine learning models, Random Forest, Gradient Boosting, Logistic Regression, and K-Nearest Neighbors are compared to predict customer churn using a telecom dataset. We use SMOTE-Tomek to cope with class imbalance and optimize models by using GridSearchCV, Optuna, and Grey Wolf Optimizer. Our optimized Random Forest has 85.9% of accuracy beating other models. The study reveals the main churn factors such as type of contract and the usage of services, which are useful in developing targeted retention strategies for telecom providers..
关键词
Customer churn, machine learning, Random Forest, SMOTE, hyperparameter optimization
报告人
Waleed Alomoush
Associate professor Plekhanov Russian University of Economics in Dubai.； Dubai Knowledge Park； Dubai； UAE

稿件作者
Abeer Moazzam School of Computing, Skyline University College, P.O. Box 1797, Sharjah, UAE
Muzhda Rahimi School of Computing, Skyline University College, P.O. Box 1797, Sharjah, UAE
Waleed Alomoush Plekhanov Russian University of Economics in Dubai.； Dubai Knowledge Park； Dubai； UAE
Ayat Alrosan Artificial Intelligence Center for Humanities and social science research, Alwasl University, Dubai, UAE
Osama A Khashan Research and Innovation Centers, Rabdan Academy, Abu Dhabi, P.O. Box 114646, United Arab Emirates
Zakaria Che muda INTI-IU University
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重要日期

  • 会议日期

    12月29日

    2025

    至

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 02月10日 2026

    初稿截稿日期

  • 02月10日 2026

    注册截止日期

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