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Introduction

Customer churn prediction is a crucial task for companies in the subscription-based services industry as retaining customers is key to the long-term success and profitability of the business. With the rise of subscription-based models in various industries such as telecommunications, software as a service, and media streaming platforms, the ability to accurately predict and prevent customer churn has become increasingly important.

Ensemble methods have shown promise in improving the predictive accuracy of customer churn models by combining multiple base classifiers to create a strong learner. By leveraging the diversity of individual classifiers, ensemble methods can effectively capture complex patterns in the data and provide more reliable predictions.

This thesis aims to explore the use of ensemble methods for customer churn prediction in the subscription-based services industry. The study will investigate how different ensemble techniques, such as random forests, gradient boosting, and stacked ensembles, can be applied to improve the accuracy and reliability of churn prediction models.

Chapter 1: Introduction
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 Overview of Customer Churn Prediction
2.2 Subscription-Based Services Industry
2.3 Ensemble Methods in Predictive Modeling
2.4 Previous Studies on Customer Churn Prediction
2.5 Factors Influencing Churn in Subscription Services
2.6 Evaluation Metrics for Churn Prediction Models
2.7 Challenges in Customer Churn Prediction
2.8 Ensemble Techniques for Churn Prediction
2.9 Comparison of Ensemble Methods
2.10 Theoretical Framework

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Ensemble Model Development
3.6 Model Evaluation
3.7 Performance Metrics
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Model Performance Comparison
4.3 Feature Importance Analysis
4.4 Interpretation of Results
4.5 Implications for Practitioners
4.6 Recommendations for Future Research

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

Thesis Overview

The aim of this thesis is to investigate the use of ensemble methods for customer churn prediction in the subscription-based services industry. The study will explore different ensemble techniques and evaluate their performance in predicting customer churn. The research will involve data collection, preprocessing, feature selection, model development, and evaluation using various performance metrics. The findings from this study will contribute to the existing literature on customer churn prediction and provide insights for practitioners in the subscription-based services industry.

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