Thesis Abstract

Abstract
The telecommunications industry is highly competitive, with companies constantly striving to retain customers and minimize churn rates. In this context, predictive modeling using machine learning techniques has emerged as a valuable tool for identifying customers at risk of churn. This thesis focuses on the application of machine learning algorithms to predict customer churn in the telecommunications industry. The research begins with a comprehensive literature review to explore existing studies on customer churn prediction, machine learning techniques, and their applications in the telecommunications sector. The methodology section outlines the data collection process, feature engineering, model selection, and evaluation criteria used in developing the predictive models. Using a dataset from a leading telecommunications company, this study applies various machine learning algorithms such as logistic regression, random forest, and neural networks to predict customer churn. The results are analyzed and compared to identify the most effective model for predicting churn in the telecommunication industry. The findings of this research provide valuable insights into the factors that influence customer churn in the telecommunications sector and demonstrate the effectiveness of machine learning techniques in predicting and preventing customer churn. The study also highlights the importance of proactive customer retention strategies based on predictive modeling to reduce churn rates and improve customer satisfaction. Overall, this thesis contributes to the growing body of knowledge on customer churn prediction in the telecommunications industry and provides practical implications for telecommunication companies seeking to enhance customer retention strategies through the application of machine learning techniques.

Thesis Overview