Data Tube, a company specializing in data analytics solutions, is seeking to understand and mitigate customer churn. The project aims to identify patterns and factors contributing to customer attrition by analyzing historical customer data. By leveraging statistical and machine learning techniques, the team will explore key questions such as: What are the primary indicators of customer churn? Are there specific customer segments more prone to leaving? How do customer interactions and service usage correlate with churn rates? The ultimate goal is to provide actionable insights that will support Data Tube in developing strategies to enhance customer retention and satisfaction. This project will allow learners to apply their classroom knowledge in data analysis, statistical modeling, and machine learning.

The deliverables for this project include a comprehensive report detailing the findings of the churn analysis, including visualizations and statistical models. The team will also provide a presentation summarizing key insights and recommendations for Data Tube's management team. Additionally, a set of actionable strategies to reduce churn based on the analysis will be proposed. These deliverables will demonstrate the learners' ability to apply data-driven decision-making in a real-world context.