Thesis Abstract

Abstract
The telecommunications industry is highly competitive, and customer churn poses a significant challenge for service providers. Predictive modeling using machine learning techniques offers a promising approach to identify customers at risk of churn and implement proactive retention strategies. This thesis explores the application of machine learning algorithms to predict customer churn in the telecommunications industry. The study focuses on developing predictive models that can accurately forecast customer churn based on historical data and customer behavior patterns. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review, covering ten key areas related to customer churn prediction, machine learning algorithms, and telecommunications industry trends. Chapter 3 outlines the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and evaluation techniques. The chapter also discusses the dataset used for the analysis, the selection of machine learning algorithms, and the performance metrics used to assess the predictive models. In Chapter 4, the findings of the predictive modeling analysis are presented and discussed in detail. The chapter includes an evaluation of the performance of different machine learning algorithms in predicting customer churn, as well as an exploration of the key factors influencing customer churn in the telecommunications industry. The discussion also highlights the strengths and limitations of the predictive models developed in this study. Chapter 5 concludes the thesis by summarizing the key findings, implications of the research, and recommendations for future studies. The conclusion emphasizes the importance of leveraging machine learning techniques for customer churn prediction in the telecommunications industry and provides insights for service providers to enhance customer retention strategies. Overall, this thesis contributes to the existing body of knowledge on customer churn prediction in the telecommunications industry by demonstrating the effectiveness of machine learning techniques in identifying customers at risk of churn. The study underscores the value of proactive churn management strategies and highlights the potential for improved customer retention through predictive modeling.

Thesis Overview