Supervisor
Taufique Ahmed
Programme
HDIP in Data Analytics for Business
Abstract
This project focuses on predicting customer churn in the telecommunications sector using machine learning. A public dataset was analysed through exploratory data analysis, data cleaning, feature encoding, and scaling to prepare it for modelling. A Logistic Regression model was trained and optimised to identify customers likely to leave the service, achieving a ROC-AUC score of 0.861, with 79% accuracy, 82.3% recall, 51.9% precision, and an F1-score of 0.637. The analysis highlighted key factors influencing churn, including fibre-optic internet, month-to-month contracts, and electronic cheque payments, while longer tenure, two-year contracts, and usage of support services correlated with retention. These insights can guide targeted retention strategies and inform future improvements in predictive modelling and customer management.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Mendes Martins, A.
(2025) Predicting Customer Churn Using Machine Learning: A Data-Driven Approach CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.75