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Introduction

In recent years, the telecommunications industry has become increasingly competitive due to the rapid advancements in technology and the rise of new players in the market. One of the key challenges faced by telecom companies is customer churn, which refers to the phenomenon of customers switching from one service provider to another. Customer churn can have a significant impact on a company’s bottom line, as it can lead to a loss of revenue and market share. Therefore, it is crucial for telecom companies to be able to predict customer churn and take proactive measures to retain their customers.

This thesis aims to explore the use of predictive analytics techniques to predict customer churn in the telecom industry. By analyzing customer data and identifying patterns and trends, telecom companies can develop strategies to retain customers and improve customer satisfaction. The study will focus on the application of machine learning algorithms and data mining techniques to predict customer churn, and will evaluate the effectiveness of these techniques in improving customer retention rates.

Chapter 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 Introduction to Customer Churn in Telecom Industry
2.2 Factors Influencing Customer Churn
2.3 Predictive Analytics in Telecom Industry
2.4 Machine Learning Algorithms for Customer Churn Prediction
2.5 Data Mining Techniques for Customer Churn Prediction
2.6 Previous Studies on Customer Churn Prediction
2.7 Customer Retention Strategies
2.8 The Impact of Customer Churn on Telecom Companies
2.9 Challenges in Customer Churn Prediction
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sample Size and Sampling Techniques
3.5 Data Preprocessing
3.6 Model Development
3.7 Model Evaluation
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics
4.2 Customer Churn Prediction Models
4.3 Model Performance Evaluation
4.4 Factors Influencing Customer Churn
4.5 Comparison of Machine Learning Algorithms
4.6 Recommendations for Telecom Companies
4.7 Implications for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions to Knowledge
5.4 Implications for Practice
5.5 Limitations of the Study
5.6 Suggestions for Future Research

Thesis Overview on Customer Churn Prediction in Telecom Industry

With the increasing competition in the telecom industry, customer churn prediction has become a critical issue for telecom companies. Predictive analytics techniques offer a promising solution to this problem by enabling companies to analyze customer data and identify patterns that can help predict customer behavior. This thesis aims to explore the application of machine learning algorithms and data mining techniques in predicting customer churn in the telecom industry.

The literature review section will provide an overview of previous studies on customer churn in the telecom industry, factors influencing customer churn, predictive analytics techniques, and customer retention strategies. The research methodology section will outline the research design, data collection methods, data analysis techniques, and ethical considerations involved in the study.

The discussion of findings section will present the results of the analysis, including descriptive statistics, customer churn prediction models, model performance evaluation, and recommendations for telecom companies. The conclusion and summary section will summarize the findings, draw conclusions, discuss the contributions to knowledge, and suggest directions for future research.

Overall, this thesis aims to contribute to the growing body of knowledge on customer churn prediction in the telecom industry and provide valuable insights for telecom companies looking to improve customer retention rates.

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