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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Research Objectives
1.4 Research Questions
1.5 Scope of the Study
1.6 Significance of the Study
1.7 Definitions of Key Terms

Chapter 2: Literature Review
2.1 Overview of Customer Churn Prediction
2.2 Machine Learning Techniques for Customer Churn Prediction
2.3 Previous Studies on Customer Churn Prediction
2.4 Gaps in the Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics
4.2 Inferential Statistics
4.3 Data Visualization
4.4 Interpretation of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Recommendations for Future Research
5.4 Practical Implications

Brief Overview:

The thesis on “Machine Learning in Customer Churn Prediction” aims to explore the use of machine learning techniques in predicting customer churn in the telecommunications industry. The research will focus on utilizing historical customer data to build predictive models that can help identify customers who are at risk of leaving a service provider.

The study will begin with an introduction that outlines the background, problem statement, objectives, and scope of the research. A comprehensive literature review will be conducted to review previous studies on customer churn prediction and identify gaps in the existing literature.

The research methodology will detail the research design, data collection methods, data analysis techniques, and ethical considerations. The findings of the study will be discussed in Chapter 4, including descriptive statistics, inferential statistics, data visualization, and interpretation of findings.

The final chapter will summarize the research findings, draw conclusions, make recommendations for future research, and discuss the practical implications of the study. The thesis aims to contribute to the field of customer churn prediction and provide valuable insights for businesses looking to improve customer retention strategies.

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