Predicting Customer Churn in a Telecommunications Company Using Machine Learning Models

Chapter ONE

INTRODUCTION

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

Chapter TWO

LITERATURE REVIEW

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

Chapter THREE

RESEARCH METHODOLOGY

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

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

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

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

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