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Introduction

The popularity of mobile gaming apps has been increasing exponentially over the past decade, with millions of users engaging with these apps on a daily basis. As the competition in the mobile gaming industry continues to grow, developers are now faced with the challenge of predicting customer engagement in order to optimize user experience and increase revenue. This thesis aims to explore the use of user behavior data and machine learning techniques to predict customer engagement for mobile gaming apps.

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 mobile gaming industry
2.2 Customer engagement in mobile gaming apps
2.3 User behavior data analysis
2.4 Machine learning in predicting customer behavior
2.5 Previous studies on customer engagement prediction
2.6 Factors influencing customer engagement
2.7 Predictive modeling techniques
2.8 Data collection methods
2.9 Data preprocessing techniques
2.10 Evaluation metrics for predictive models

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 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of user behavior data
4.2 Results of predictive models
4.3 Comparison of different prediction techniques
4.4 Implications for mobile gaming industry
4.5 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Limitations of the study
5.4 Future research directions
5.5 Conclusion

Thesis Overview: Predicting customer engagement for mobile gaming apps using user behavior data and machine learning

The purpose of this thesis is to investigate how user behavior data and machine learning techniques can be used to predict customer engagement in mobile gaming apps. The first chapter provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. The second chapter presents a comprehensive literature review on the mobile gaming industry, customer engagement, user behavior data analysis, machine learning for prediction, previous studies, influencing factors, predictive modeling techniques, data collection, preprocessing, and evaluation metrics.

Chapter three discusses the research methodology, including research design, data collection, preprocessing, feature selection, model selection, training, evaluation, and ethical considerations. Chapter four delves into the discussion of findings, analyzing user behavior data, presenting results of predictive models, comparing techniques, and providing implications and recommendations for the mobile gaming industry and future research. The final chapter summarizes the findings, discusses contributions and limitations, suggests future research directions, and concludes the thesis.

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