Supervisor
Kayoum Khubli
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This research addresses customer churn in the Telecom industry by utilizing Machine Learning (ML) models to predict customers at risk of leaving and provide data-driven retention strategies. The study highlights the effectiveness of ML, particularly in churn prediction, while noting the need for further exploration into the ethical implications of AI, such as potential biases towards vulnerable groups. Using the CRISP-DM framework, the study develops and compares three Supervised Learning (SL) models: Random Forests (RF), LightGBM (LGBM), and XGBoost (XGB), incorporating class resampling techniques to manage data imbalance.
The findings identified five key features as the most significant predictors of churn at Viatel Technology Group (VTG), including customer billing, service retention efforts, and product offerings. Among the models tested, LGBM-SMOTETomek delivered the best performance with a precision of 97.92%, recall of 95.25%, and an F1-score of 96.57%. The research concludes with recommendations to promote automatic payment methods, reward loyal customers, and proactively engage with customers who frequently contact the company.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Recommended Citation
Hasson, Stephen, "Evaluation and Implementation of Machine Learning Models to Predict Customer Churn in the Telecommunications Sector." (2024). ICT. 65.
https://arc.cct.ie/ict/65