Abstract:
The term “customer churn” is used to indicate those customers who are about to
leave for a competitor or end their subscription. Customer churn or customer attrition has
become an important issue for organizations particularly in subscription-based
businesses, where customers have a contractual relationship which must be ended.
According to numerous studies, acquiring new clients is significantly more expensive
than keeping the ones that already have. As a result, businesses are concentrating on
creating precise and trustworthy predictive models to pinpoint potential clients who will
churn in the near future. This model uses data from telecom companies on a range of
aspects, including customers who left within the last month, services that each client has
signed up for, demographic data about customers, and customer account information. The
model is presented using machine learning techniques, particularly Logistic Regression
(LR) and Decision Tree (DT), to forecast churn for telecoms companies. Comparisons are
made to determine the algorithm's efficacy using the provided dataset. The results from a
strategy based on Logistic Regression (LR) can predict the telecom market better than
Decision Tree (DT) techniques.
