Project Abstract

Abstract
Customer churn remains a critical challenge faced by companies in the telecommunications industry, impacting revenue and profitability. This research project focuses on developing a predictive modeling framework to identify potential churners using machine learning algorithms. The study aims to leverage historical customer data to build models that can accurately predict customer churn, thereby enabling proactive retention strategies. The research begins with a comprehensive review of existing literature on customer churn, machine learning algorithms, and their application in the telecommunications sector. This literature review highlights the significance of predictive modeling in reducing churn rates and improving customer retention efforts. The methodology section outlines the data collection process, feature selection techniques, model training, and evaluation methods employed in this study. Utilizing a real-world dataset from a telecommunications company, the research applies various machine learning algorithms such as logistic regression, random forest, and neural networks to develop predictive models for customer churn prediction. The findings from the study reveal the effectiveness of machine learning algorithms in accurately identifying customers at risk of churning. The discussion section delves into the key insights gained from the predictive models, including the most important features influencing churn prediction and the comparative performance of different algorithms. In conclusion, this research project emphasizes the importance of leveraging advanced analytics and machine learning techniques to address customer churn challenges in the telecommunications industry. The predictive modeling framework developed in this study provides valuable insights for telecom companies to proactively identify and retain customers at risk of churning, ultimately enhancing customer satisfaction and business profitability. Keywords Customer churn, Telecommunications industry, Predictive modeling, Machine learning algorithms, Retention strategies, Data analytics.

Project Overview