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Introduction

Customer churn prediction is a critical task in the banking sector as retaining customers is essential for sustainable growth and profitability. Understanding customer behavior and predicting churn can help banks identify at-risk customers and implement targeted retention strategies. With the advancement in technology and data analytics, banks can now leverage machine learning algorithms to analyze customer data and predict churn accurately. This thesis aims to explore the application of machine learning techniques in predicting customer churn in the banking sector.

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 Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Customer churn in the banking sector
2.2 Factors influencing customer churn
2.3 Traditional methods of churn prediction
2.4 Machine learning techniques in customer churn prediction
2.5 Challenges in customer churn prediction
2.6 Best practices in customer retention
2.7 Case studies on customer churn prediction in banking sector
2.8 Comparison of different machine learning algorithms
2.9 Ethical considerations in customer churn prediction
2.10 Future trends in customer churn prediction

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection
3.5 Model development
3.6 Model evaluation
3.7 Performance metrics
3.8 Validation techniques

Chapter Four: Discussion of Findings
4.1 Descriptive analysis of customer data
4.2 Feature importance in churn prediction
4.3 Model comparison
4.4 Interpretation of model results
4.5 Recommendations for churn prevention
4.6 Practical implications for banks
4.7 Implementation challenges
4.8 Future research directions

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for the banking sector
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview

Customer churn prediction is a crucial task for banks to retain customers and enhance their profitability. In this thesis, we explore the application of machine learning techniques in predicting customer churn in the banking sector. The introduction provides an overview of the importance of customer churn prediction and outlines the structure of the thesis.

The literature review in Chapter Two covers various aspects of customer churn in the banking sector, factors influencing churn, traditional and machine learning techniques in churn prediction, challenges, best practices, case studies, comparison of algorithms, and ethical considerations.

Chapter Three discusses the research methodology, including research design, data collection, preprocessing, feature selection, model development, evaluation, performance metrics, and validation techniques. Chapter Four presents a detailed discussion of findings, including descriptive analysis of customer data, feature importance, model comparison, interpretation of results, recommendations for churn prevention, practical implications, implementation challenges, and future research directions.

The conclusion in Chapter Five summarizes the key findings, contributions, implications for the banking sector, limitations of the study, recommendations for future research, and concludes the thesis. Overall, this thesis aims to provide insights into customer churn prediction in the banking sector and offer practical recommendations for banks to improve customer retention strategies.

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