Thesis Abstract

Abstract
Customer churn is a critical challenge faced by telecommunication companies worldwide. This thesis focuses on the application of machine learning algorithms for predictive modeling of customer churn in the telecommunication industry. The primary objective is to develop an accurate and efficient model that can predict customer churn based on historical data and relevant features. The study begins with a comprehensive introduction to the problem of customer churn in the telecommunication sector, emphasizing its impact on business performance and the need for effective churn prediction models. The literature review presents an in-depth analysis of existing research on customer churn prediction, machine learning algorithms, and their applications in the telecommunication industry. This chapter provides insights into the various techniques and methodologies employed by researchers to address similar problems and highlights the significance of machine learning in improving predictive accuracy and model performance. The research methodology chapter outlines the data collection process, feature selection, model building, and evaluation techniques used in this study. The methodology includes data preprocessing steps, such as data cleaning, feature engineering, and normalization, to prepare the dataset for model training. Various machine learning algorithms, including logistic regression, decision trees, random forests, and neural networks, are applied to develop predictive models for customer churn. The discussion of findings chapter presents the results of the predictive modeling experiments conducted in this study. The performance of different machine learning algorithms is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The findings highlight the strengths and limitations of each model and provide insights into the factors that influence customer churn in the telecommunication industry. In conclusion, this thesis contributes to the field of customer churn prediction by demonstrating the effectiveness of machine learning algorithms in developing accurate and efficient predictive models. The study emphasizes the importance of proactive churn management strategies for telecommunication companies to reduce customer attrition and enhance customer retention. The findings of this research can inform decision-making processes and help businesses optimize their customer relationship management practices. Keywords Customer Churn, Telecommunication Industry, Machine Learning Algorithms, Predictive Modeling, Data Analysis, Decision Making, Customer Retention.

Thesis Overview

The project, "Predictive Modeling of Customer Churn in Telecommunication Industry Using Machine Learning Algorithms," aims to address the critical issue of customer churn in the telecommunication industry by leveraging advanced machine learning techniques. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for telecommunication companies due to its negative impact on revenue and customer retention. The research will focus on utilizing machine learning algorithms to develop predictive models that can forecast customer churn, enabling telecommunication companies to proactively identify at-risk customers and implement targeted retention strategies. By analyzing historical customer data, such as usage patterns, billing information, and service interactions, the project seeks to identify key factors that contribute to customer churn and build accurate predictive models to anticipate customer behavior. Through a comprehensive literature review, the project will explore existing research on customer churn prediction, machine learning algorithms, and their applications in the telecommunication industry. This review will provide a solid foundation for the research methodology, guiding the selection of appropriate algorithms, data preprocessing techniques, and model evaluation methods. The research methodology will involve collecting and preprocessing a large dataset of customer information, including demographics, service usage, and churn status. Various machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, will be implemented and compared to identify the most effective approach for customer churn prediction. The findings from the predictive models will be thoroughly analyzed and discussed in Chapter Four of the thesis. The discussion will highlight the key insights gained from the analysis, including the most influential factors contributing to customer churn, the performance of different machine learning algorithms, and the practical implications for telecommunication companies. In conclusion, the project will provide valuable insights into the application of machine learning algorithms for predicting customer churn in the telecommunication industry. By developing accurate predictive models, telecommunication companies can enhance their customer retention efforts, improve service quality, and ultimately increase customer satisfaction and loyalty.