Predictive Modeling of Customer Churn in Telecom Industry Using Machine Learning Techniques
Table Of Contents
Chapter ONE
: Introduction
1.1 Introduction1.2 Background of Study
1.3 Problem Statement
1.4 Objectives of Study
1.5 Limitations of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms
Chapter TWO
: Literature Review
2.1 Review of Literature Item 12.2 Review of Literature Item 2
2.3 Review of Literature Item 3
2.4 Review of Literature Item 4
2.5 Review of Literature Item 5
2.6 Review of Literature Item 6
2.7 Review of Literature Item 7
2.8 Review of Literature Item 8
2.9 Review of Literature Item 9
2.10 Review of Literature Item 10
Chapter THREE
: Research Methodology
3.1 Research Design3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Analysis Methods
3.5 Research Instruments
3.6 Ethical Considerations
3.7 Data Validation Techniques
3.8 Data Analysis Software
Chapter FOUR
: Discussion of Findings
4.1 Presentation of Data4.2 Analysis of Results
4.3 Comparison with Existing Literature
4.4 Interpretation of Findings
4.5 Discussion of Implications
4.6 Recommendations for Future Research
4.7 Limitations of the Study
Chapter FIVE
: Conclusion and Summary
5.1 Summary of Findings5.2 Conclusion
5.3 Contributions to Knowledge
5.4 Practical Implications
5.5 Recommendations for Practice
5.6 Recommendations for Further Research
5.7 Conclusion Statement
Project Abstract
AbstractThe telecommunication industry is known for its highly competitive nature and the challenges associated with customer retention. Customer churn, the phenomenon of customers switching from one service provider to another, has become a critical issue for telecom companies. In order to address this challenge, predictive modeling using machine learning techniques has emerged as a promising approach to forecast and prevent customer churn. This research project aims to develop a predictive model for customer churn in the telecom industry by leveraging machine learning algorithms. The research will begin with a comprehensive review of existing literature on customer churn, machine learning techniques, and their applications in the telecom industry. This review will provide a solid foundation for understanding the factors influencing customer churn and the potential of machine learning in predicting churn behavior. The research methodology will involve collecting and analyzing historical customer data from a telecom company, including demographic information, usage patterns, and customer feedback. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be applied to build and evaluate predictive models for customer churn. The findings of this research will be presented and discussed in detail in Chapter Four, highlighting the performance of different machine learning algorithms in predicting customer churn. The factors that contribute to customer churn in the telecom industry will be identified, providing valuable insights for telecom companies to design targeted retention strategies. In conclusion, this research project will contribute to the advancement of customer churn prediction in the telecom industry by demonstrating the effectiveness of machine learning techniques. By developing accurate predictive models, telecom companies can proactively address customer churn and improve customer retention strategies, ultimately enhancing customer satisfaction and loyalty.
Project Overview
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