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INTRODUCTION

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

CHAPTER TWO: LITERATURE REVIEW
2.1 Customer Churn in the Insurance Industry
2.2 Ensemble Methods in Customer Churn Prediction
2.3 Previous Studies on Customer Churn Prediction
2.4 Factors Influencing Customer Churn
2.5 Machine Learning Techniques in Customer Churn Prediction
2.6 Applications of Ensemble Methods in Customer Churn Prediction
2.7 Challenges in Customer Churn Prediction
2.8 Evaluation Metrics in Churn Prediction
2.9 Theoretical Framework
2.10 Summary of Literature Review

CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Research Design
3.2 Data Collection
3.3 Variable Selection
3.4 Data Preprocessing
3.5 Ensemble Methods Used
3.6 Model Evaluation
3.7 Validation Techniques
3.8 Data Analysis Techniques

CHAPTER FOUR: DISCUSSION OF FINDINGS
4.1 Descriptive Statistics
4.2 Model Performance Evaluation
4.3 Feature Importance Analysis
4.4 Comparison of Ensemble Methods
4.5 Interpretation of Results
4.6 Practical Implications
4.7 Managerial Implications
4.8 Recommendations for Future Research

CHAPTER FIVE: CONCLUSION AND SUMMARY
5.1 Summary of Findings
5.2 Contribution to Literature
5.3 Practical Applications
5.4 Limitations and Future Directions
5.5 Conclusion

THESIS OVERVIEW

Customer churn is a critical issue in the insurance industry, as retaining customers is essential for the long-term success and profitability of insurance companies. Predicting customer churn allows insurance companies to take proactive measures to prevent customers from leaving and to optimize their marketing strategies. Ensemble methods have gained popularity in customer churn prediction due to their ability to combine multiple models to improve predictive accuracy.

This thesis aims to investigate customer churn prediction in the insurance industry using ensemble methods. The introduction provides an overview of the research, discussing the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis.

The literature review explores previous studies on customer churn prediction, factors influencing churn, machine learning techniques, applications of ensemble methods, challenges, and evaluation metrics. The research methodology outlines the research design, data collection, variable selection, preprocessing, ensemble methods used, model evaluation, and data analysis techniques.

The discussion of findings includes descriptive statistics, model performance evaluation, feature importance analysis, comparison of ensemble methods, interpretation of results, practical and managerial implications, and recommendations for future research.

Finally, the conclusion and summary chapter provides a summary of findings, contribution to literature, practical applications, limitations, future directions, and conclusion. Overall, this thesis aims to contribute to the understanding of customer churn prediction in the insurance industry and provide valuable insights for insurance companies to improve customer retention strategies.

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