Thesis Abstract

Abstract
The telecommunication industry is highly competitive, with customer churn being a significant challenge for service providers. Customer churn refers to the phenomenon where customers switch from one service provider to another or discontinue services altogether. Predicting and understanding customer churn is crucial for telecommunication companies to improve customer retention strategies and enhance overall business performance. This study focuses on developing a predictive modeling framework for customer churn in the telecommunication industry using machine learning algorithms. The research begins with a comprehensive literature review, which examines existing studies on customer churn prediction, machine learning algorithms, and their applications in the telecommunication sector. The study then outlines the research methodology, including data collection, preprocessing, feature selection, model development, and evaluation metrics. Using real-world customer data from a telecommunication company, the study applies various machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting to build predictive models for customer churn. The models are trained and tested on historical data to predict future customer churn accurately. The findings reveal that machine learning algorithms can effectively predict customer churn in the telecommunication industry, with certain algorithms outperforming others in terms of predictive accuracy and performance metrics. The study discusses the implications of these findings for telecommunication companies, emphasizing the importance of leveraging predictive modeling to proactively manage customer churn. In conclusion, this research contributes to the existing literature on customer churn prediction by demonstrating the efficacy of machine learning algorithms in the telecommunication industry. The study provides valuable insights for telecommunication companies seeking to enhance customer retention strategies and reduce churn rates. Overall, the predictive modeling framework developed in this study offers a data-driven approach to address the challenges posed by customer churn in the telecommunication sector.

Thesis Overview