Predictive Modeling of Customer Churn in Telecommunication Industry using Machine Learning Techniques

Thesis Abstract

Abstract
The telecommunication industry is characterized by intense competition and high customer turnover rates, making the prediction and prevention of customer churn a critical challenge for service providers. This thesis focuses on developing a predictive modeling framework for identifying customers at risk of churn using machine learning techniques. The study aims to leverage historical customer data to build accurate churn prediction models that can help telecommunication companies proactively address customer retention strategies. The research begins with a comprehensive review of existing literature on customer churn prediction, machine learning algorithms, and their applications in the telecommunication sector. This literature review provides a foundation for understanding the current state of research in the field and identifies gaps that this study seeks to address. The methodology chapter outlines the research design, data collection process, feature selection techniques, model development, and evaluation methods employed in this study. The research methodology combines data preprocessing, feature engineering, model training, and validation to create robust predictive models for customer churn prediction. The findings chapter presents the results of the predictive modeling experiments, including the performance metrics of the developed models, feature importance analysis, and model interpretation. The discussion delves into the implications of these findings for telecommunication companies and provides insights into how the developed models can be integrated into existing business operations. In conclusion, this thesis highlights the significance of leveraging machine learning techniques for customer churn prediction in the telecommunication industry. The study contributes to the existing body of knowledge by demonstrating the effectiveness of predictive modeling in identifying customers at risk of churn and enabling proactive retention strategies. The research findings provide valuable insights for telecommunication companies seeking to improve customer retention rates and enhance overall business performance. Keywords Customer Churn, Telecommunication Industry, Machine Learning, Predictive Modeling, Retention Strategies