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Introduction

In the telecommunications industry, customer churn prediction plays a crucial role in retaining customers and maximizing revenue. The ability to accurately predict when a customer is likely to churn can help telecom companies implement targeted retention strategies and ultimately improve customer satisfaction and loyalty. Call detail records (CDRs) contain valuable information about customer behavior and interactions with the network, making them a rich data source for predicting churn. Machine learning techniques have been increasingly used to analyze CDRs and make accurate predictions about customer churn.

This thesis aims to investigate the use of CDRs and machine learning for customer churn prediction in the telecommunications industry. By analyzing historical CDR data and applying advanced machine learning algorithms, this study seeks to develop a predictive model that can accurately identify customers at risk of churning. The findings of this research will provide valuable insights for telecom companies to proactively manage customer churn and enhance customer retention efforts.

Table of Contents
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 2: Literature Review
2.1 Introduction to Customer Churn Prediction
2.2 Overview of Machine Learning Techniques
2.3 Previous Studies on Customer Churn Prediction in Telecom Industry
2.4 Use of Call Detail Records in Customer Churn Prediction
2.5 Benefits of Predicting Customer Churn
2.6 Challenges in Customer Churn Prediction
2.7 Integration of Machine Learning and CDRs
2.8 Evaluation Metrics for Churn Prediction Models
2.9 Case Studies of Successful Churn Prediction Models
2.10 Gaps in Existing 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 Model Training
3.7 Model Evaluation
3.8 Performance Metrics
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Data Analysis
4.2 Model Performance
4.3 Feature Importance
4.4 Comparison with Existing Models
4.5 Implications for Telecom Companies
4.6 Recommendations for Future Research
4.7 Practical Applications
4.8 Limitations of the Study

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview

The telecommunications industry is highly competitive, with companies constantly striving to retain customers and reduce churn rates. Customer churn prediction is a crucial task for telecom companies, as it allows them to identify customers who are likely to switch to competitors and take proactive measures to retain them. Call detail records (CDRs) contain valuable information about customer behavior and interactions with the network, making them a rich data source for predicting churn. By using machine learning algorithms to analyze CDR data, telecom companies can develop accurate predictive models to improve customer retention efforts.

This thesis explores the use of CDRs and machine learning for customer churn prediction in the telecommunications industry. The study aims to investigate the potential of CDR data for predicting churn and to develop a predictive model that can accurately identify at-risk customers. By analyzing historical CDR data and applying advanced machine learning techniques, the research seeks to provide valuable insights for telecom companies to enhance their customer retention strategies.

The thesis is structured into five main chapters. Chapter 1 provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter 2 reviews relevant literature on customer churn prediction, machine learning techniques, the use of CDRs, benefits and challenges of predicting churn, and previous studies in the field. Chapter 3 describes the research methodology, including research design, data collection, preprocessing, feature selection, model selection, training, evaluation, and ethical considerations.

Chapter 4 discusses the findings of the study, including data analysis, model performance, feature importance, comparison with existing models, implications for telecom companies, recommendations for future research, and limitations. Chapter 5 presents the conclusion and summary of the thesis, highlighting the key findings, contributions to the field, practical implications, future research directions, and a conclusive remark.

Overall, this thesis aims to contribute to the existing literature on customer churn prediction in the telecommunications industry by exploring the use of CDRs and machine learning techniques. The findings of this study are expected to provide valuable insights for telecom companies to improve customer retention efforts and reduce churn rates.

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