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Table of Contents

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

Chapter 2: Literature Review
2.1 Overview of Credit Scoring
2.2 Traditional Methods of Credit Scoring
2.3 Machine Learning Techniques in Credit Scoring
2.4 Previous Studies on Machine Learning for Credit Scoring
2.5 Gaps in the Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Performance Comparison of Machine Learning Models
4.2 Feature Importance Analysis
4.3 Interpretation of Results
4.4 Comparison with Traditional Credit Scoring Methods

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

Thesis Overview: Machine Learning for Credit Scoring

Machine Learning techniques have revolutionized the field of credit scoring by providing more accurate and efficient models for assessing credit risk. This thesis aims to explore the potential of Machine Learning algorithms in improving credit scoring accuracy and efficiency.

The introduction chapter provides a background of the study, statement of the problem, research questions, objectives, significance, limitations, and scope of the study. The literature review chapter discusses traditional credit scoring methods, machine learning techniques in credit scoring, and previous studies on machine learning for credit scoring.

The research methodology chapter details the research design, data collection, preprocessing, feature selection, model selection, and evaluation metrics used in the study. The discussion of findings chapter presents the performance comparison of machine learning models, feature importance analysis, interpretation of results, and comparison with traditional credit scoring methods.

Finally, the conclusion and summary chapter summarizes the findings of the study, discusses the implications, provides recommendations for future research, and concludes the thesis. This thesis aims to contribute to the growing body of knowledge on the application of machine learning in credit scoring and provide insights for improving credit risk assessment in financial institutions.

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