Customer Behavior in Telecom: A Machine Learning Approach to Predictive Modeling and Analysis of Churn with Logistic Regression

Abstract

The telecommunications industry operates in a dynamic landscape marked by rapid technological advancements
and intense market competition. One of the persistent challenges faced by telecom service providers is the
phenomenon of customer churn, where subscribers terminate their services, impacting revenue and market
share. This research paper investigates the nuanced aspects of customer behaviour within the telecom sector,
aiming to shed light on the underlying factors driving churn. The study employs a cutting-edge machine
learning approach to develop predictive models and conduct in-depth analyses of churn patterns. Specifically,
the research focuses on logistic regression, a powerful statistical technique capable of revealing intricate
relationships between variables. By leveraging this method, the paper seeks to unravel the complexities of
customer churn and provide actionable insights for telecom companies to enhance customer retention strategies.