Preventing Customer Churn: A Supervised Learning Approach to Identifying At-Risk Customers and Analyzing Retention Factors
In this project, using supervised learning models, we identified cell phone service customers who are more likely to stop using the service in the future and create a model that can predict if a certain customer will drop the service.
Furthermore, it would be enlightening to analyze the top factors that influence user retention to help the company prevent user churn.
The dataset contains information on customers' plans and usage of the service, as well as whether or not they stopped using the service eventually.