Project Abstract

Abstract
The telecommunications industry is highly competitive, and retaining customers is crucial for sustainable business growth. Customer churn, which refers to the rate at which customers discontinue services, poses a significant challenge for telecommunications companies. Predictive modeling techniques, particularly machine learning algorithms, have emerged as powerful tools for identifying customers at risk of churning. This research project aims to develop a predictive model for customer churn in the telecommunications industry using machine learning techniques. The study begins with an extensive review of the literature on customer churn, machine learning, and their applications in the telecommunications sector. The literature review highlights the importance of accurately predicting customer churn and the role of machine learning algorithms in achieving this goal. The research methodology chapter outlines the data collection process, variables selected for the analysis, modeling techniques employed, and evaluation metrics used to assess the predictive performance of the model. The methodology section also discusses the ethical considerations involved in using customer data for predictive modeling purposes. The findings chapter presents the results of the predictive modeling exercise, including the identification of key factors influencing customer churn and the performance of different machine learning algorithms in predicting churn. The discussion of findings section interprets the results, identifies patterns and trends in customer behavior, and provides insights for telecommunications companies to proactively manage customer churn. In conclusion, this research project contributes to the growing body of knowledge on customer churn prediction in the telecommunications industry. The developed predictive model offers a practical tool for telecom companies to identify at-risk customers and implement targeted retention strategies. By leveraging machine learning techniques, telecom companies can enhance customer satisfaction, reduce churn rates, and ultimately improve business performance in a competitive market environment.

Project Overview