FreeFuse is seeking to gain insights into the patterns and timing of customer churn within its subscription offerings. The goal of this project is to analyze existing subscription data to identify key indicators and trends that precede churn events. By understanding these patterns, FreeFuse aims to develop a proactive alert system that can notify the company when users are at risk of discontinuing their subscriptions. This project will allow learners to apply data analysis techniques and predictive modeling skills acquired in the classroom. The tasks will include data cleaning, exploratory data analysis, and the development of a predictive model. The project is designed to be completed by a team of data science students within a single academic program, ensuring a focused approach to the problem.

The project deliverables will include a comprehensive report detailing the findings of the data analysis and the predictive model developed to forecast churn events. Additionally, the team will provide a prototype of an alert system that can be integrated into FreeFuse's existing infrastructure. Deliverables will also include visualizations that illustrate key churn indicators and a presentation summarizing the project's outcomes and recommendations.