Chapter ONE

: Introduction 1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms

Chapter TWO

: Literature Review 2.1 Overview of Customer Churn in Telecommunications Industry
2.2 Factors Influencing Customer Churn
2.3 Machine Learning Applications in Predicting Customer Churn
2.4 Previous Studies on Customer Churn Prediction
2.5 Importance of Customer Retention Strategies
2.6 Evaluation Metrics for Churn Prediction Models
2.7 Data Collection Methods for Customer Churn Analysis
2.8 Role of Data Preprocessing in Churn Prediction
2.9 Supervised and Unsupervised Learning Algorithms for Churn Prediction
2.10 Comparative Analysis of Churn Prediction Techniques

Chapter THREE

: Research Methodology 3.1 Research Design
3.2 Data Collection Procedures
3.3 Sampling Techniques
3.4 Data Preprocessing Methods
3.5 Feature Selection and Engineering Techniques
3.6 Machine Learning Model Selection
3.7 Model Training and Evaluation
3.8 Performance Metrics for Model Evaluation

Chapter FOUR

: Discussion of Findings 4.1 Descriptive Analysis of Customer Churn Data
4.2 Model Performance Evaluation Results
4.3 Feature Importance Analysis
4.4 Comparison of Different Machine Learning Models
4.5 Interpretation of Predictive Patterns
4.6 Implications of Findings on Customer Retention Strategies
4.7 Future Research Directions

Chapter FIVE

: Conclusion and Summary 5.1 Summary of Key Findings
5.2 Conclusions Drawn from the Study
5.3 Contributions to Existing Knowledge
5.4 Practical Implications of the Study
5.5 Recommendations for Future Research
5.6 Conclusion