Thesis Abstract

Abstract
This thesis presents a comprehensive study on the application of machine learning algorithms for predictive modeling of customer churn in the telecommunication industry. Customer churn, the phenomenon of customers switching from one service provider to another, poses a significant challenge for telecommunication companies. The objective of this research is to develop predictive models that can accurately identify customers at risk of churn, enabling proactive retention strategies to be implemented. The study focuses on leveraging historical customer data to train and evaluate various machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks. The research begins with a detailed introduction that outlines the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. A comprehensive literature review in Chapter Two examines existing research on customer churn prediction, machine learning algorithms, and their applications in the telecommunication industry. Chapter Three details the research methodology, including data collection, preprocessing, feature selection, model training, evaluation metrics, and validation techniques. The findings presented in Chapter Four highlight the performance of different machine learning algorithms in predicting customer churn. The results demonstrate the effectiveness of certain algorithms in accurately identifying customers likely to churn, providing valuable insights for telecommunication companies to proactively address customer retention. The discussion also includes insights into the key factors influencing customer churn and the implications for strategic decision-making within the industry. In conclusion, this thesis summarizes the key findings and contributions of the research, emphasizing the significance of predictive modeling in addressing customer churn challenges in the telecommunication industry. The study underscores the importance of leveraging machine learning algorithms to enhance customer retention strategies and improve overall business performance. Finally, recommendations for future research and practical implications for industry practitioners are discussed, paving the way for further advancements in customer churn prediction using machine learning techniques.

Thesis Overview