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Kachawa, Ananya (2023) Customer Churn Prediction in Telecom Industry through Applied Machine Learning Approaches. Masters thesis, Dublin, National College of Ireland.
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Abstract
The objective of this research is to develop and assess predictive models for customer attrition in the telecommunications industry through the utilization of sophisticated data analysis and machine learning techniques. The objective is to recognise key components that impact customers and give recommendations to assist mobile companies in progressing their approach to customer retention. Exploratory data analysis was conducted on a variety of data sets to understand the characteristics and behaviours of mobile phone customers. An assessment was conducted to decide the viability of machine learning models such as logistic regression, random forest, and gradient boosting in predicting customer churn.
The study also sought to examine the basic components that impact customer inclinations and behaviours. A comprehensive performance assessment of the diverse models was performed, centring on F1 score, accuracy, accuracy, recall, and fatigue expectation. The discoveries give practical counsel for telcos to diminish customer disarray and construct client loyalty.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Hafees, Taimur UNSPECIFIED |
| Uncontrolled Keywords: | Customer Churn; Predictive Modelling; Customer Retention; Machine Learning; Telecommunication; Random Forest; Gradient Boosting; Logistic Regression |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Telecommunications Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 09 May 2025 10:24 |
| Last Modified: | 09 May 2025 10:24 |
| URI: | https://norma.ncirl.ie/id/eprint/7538 |
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