Project Abstract

Abstract
The telecommunications industry is highly competitive, with service providers constantly striving to retain customers and minimize churn rates. In this research study, we focus on applying machine learning algorithms to develop predictive models for customer churn in the telecommunications industry. The primary objective is to leverage historical customer data to identify patterns and factors that contribute to customer churn, ultimately enabling service providers to proactively address customer retention strategies. The research begins with an in-depth exploration of the background of customer churn in the telecommunications sector, highlighting the significance and challenges faced by service providers in managing churn rates. A comprehensive review of existing literature on customer churn prediction and machine learning techniques is conducted to identify relevant methodologies and best practices. The research methodology section outlines the approach taken to build and evaluate predictive models for customer churn. Data preprocessing techniques, feature selection methods, and model evaluation criteria are discussed in detail. The research methodology also includes a description of the dataset used, the selection of machine learning algorithms, and the process of model training and evaluation. The findings from the predictive modeling process are presented and discussed in Chapter Four. The results of the analysis highlight the key predictors of customer churn in the telecommunications industry and the performance of different machine learning algorithms in predicting churn. Insights gained from the analysis provide valuable information for service providers to enhance customer retention strategies and reduce churn rates. In conclusion, the research study emphasizes the importance of leveraging machine learning algorithms for predicting customer churn in the telecommunications industry. By developing accurate predictive models, service providers can identify at-risk customers, implement targeted retention strategies, and improve overall customer satisfaction. The study contributes to the existing body of knowledge on customer churn prediction and provides practical recommendations for industry practitioners to enhance customer retention efforts.

Project Overview