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[In-person]CUSTOMER CHURN PREDICTION USING ML MODELS
CUSTOMER CHURN PREDICTION USING ML MODELS
ID：98
Submission ID：142 View Protection：ATTENDEE
Updated Time：2025-12-23 13:10:40 Hits：403
In-person
Start Time：2025-12-29 14:30 （Asia/Amman）
Duration：15min
Session：[S5] Track 5: Emerging Trends of AI/ML » [S5-1] Track 5: Emerging Trends of AI/ML
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Abstract
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..
Keywords
Customer churn, machine learning, Random Forest, SMOTE, hyperparameter optimization
Speaker
Waleed Alomoush
Associate professor Plekhanov Russian University of Economics in Dubai.； Dubai Knowledge Park； Dubai； UAESubmission Author
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