The project aims to analyze user data from MyEdMaster LLC's longevity software to understand how effectively users adhere to their self-created health plans. The primary goal is to identify patterns and factors that contribute to high adherence levels. By examining user engagement and plan completion rates, the project seeks to uncover insights that can enhance user experience and improve the software's effectiveness. The analysis will focus on identifying demographic, behavioral, and contextual factors that correlate with adherence. This project provides an opportunity for learners to apply data analysis techniques and statistical methods to real-world user data, enhancing their understanding of data-driven decision-making in health technology.

The deliverables for this project include a comprehensive report detailing the findings of the data analysis, including key factors associated with high adherence. Additionally, a presentation summarizing the insights and recommendations for improving user adherence will be prepared. The team will also provide visualizations that illustrate adherence trends and patterns.