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Introduction:

The growth of the e-learning industry in recent years has transformed the way educational content is delivered to students across the globe. With the increasing popularity of online courses and virtual classrooms, e-learning platforms have become an integral part of the education landscape. However, a common challenge faced by e-learning platforms is customer churn, which refers to the phenomenon of students discontinuing their use of the platform. Customer churn can have significant financial implications for e-learning providers, as it leads to a loss of revenue and can impact the overall success of the platform.

To address this challenge, the use of machine learning algorithms for customer churn prediction has gained prominence in recent years. By analyzing student engagement data, e-learning platforms can identify patterns and trends that indicate a student is likely to churn, allowing them to take proactive measures to retain these students. This thesis aims to explore the use of machine learning techniques for customer churn prediction in the e-learning industry, specifically focusing on student engagement data as a key predictor of churn.

Table of Contents:

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 E-Learning Industry
2.2 Customer Churn in E-Learning
2.3 Machine Learning for Customer Churn Prediction
2.4 Student Engagement Data
2.5 Previous Studies on Customer Churn Prediction in E-Learning
2.6 Factors Influencing Customer Churn in E-Learning
2.7 Importance of Retaining Customers in E-Learning
2.8 Challenges in Customer Churn Prediction
2.9 Current Trends in E-Learning Industry
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Machine Learning Models
3.6 Evaluation Metrics
3.7 Validation Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Student Engagement Data
4.2 Performance of Machine Learning Models
4.3 Key Predictors of Customer Churn
4.4 Comparison of Different Machine Learning Algorithms
4.5 Implications for E-Learning Providers
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 and Future Directions
5.5 Conclusion

Thesis Overview:

Customer churn prediction is a critical issue in the e-learning industry, as it directly impacts the revenue and sustainability of e-learning platforms. This thesis focuses on the use of machine learning techniques for predicting customer churn in the e-learning industry by analyzing student engagement data. By leveraging advanced machine learning algorithms, e-learning providers can identify at-risk students and implement targeted retention strategies to improve student retention rates.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. Chapter 2 presents a comprehensive literature review on the e-learning industry, customer churn, machine learning for churn prediction, student engagement data, and previous studies on customer churn prediction in e-learning.

Chapter 3 describes the research methodology, including research design, data collection, preprocessing, feature selection, machine learning models, evaluation metrics, validation techniques, and ethical considerations. Chapter 4 discusses the findings of the study, including descriptive analysis of student engagement data, performance of machine learning models, key predictors of customer churn, and implications for e-learning providers.

Chapter 5 concludes the thesis by summarizing the findings, discussing contributions to the literature, practical implications, limitations, and future directions for research in customer churn prediction in the e-learning industry using student engagement data and machine learning.

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