Contract Data Insights: Analyze anonymized athlete contract data to uncover trends and patterns that can inform smarter AI-driven contract review tools.

Task Optimization Models: Explore data-driven approaches for optimizing small task management AI agents to improve efficiency, cost-saving, and networking opportunities for athletes.

User Behavior Analysis: Examine athlete interaction data within WEVOLV to understand user preferences and recommend platform improvements, focusing on career management and lifestyle features.

Data Exploration and Cleaning:

• Perform thorough data cleaning and preprocessing on the provided datasets, ensuring accuracy and usability for analysis.
• Deliver a report detailing the data preparation process, including challenges and solutions.

Exploratory Data Analysis (EDA):

• Conduct an in-depth analysis of athlete contract trends and user behavior data.
• Provide visualizations and summaries showcasing key insights, such as common contract clauses, average values, or frequently used features in the WEVOLV app.

Predictive Modeling and Recommendations:

• Build basic predictive models using Python libraries (e.g., Scikit-Learn) to forecast outcomes such as the likelihood of contract disputes or the time saved by utilizing AI agents.
• Generate actionable recommendations for improving AI contract review tools and optimizing athlete task management features.

Final Presentation:

• Present key findings, insights, and actionable recommendations in a 15-20 minute online session.
• Share a slide deck summarizing the analysis, predictive modeling, and strategic recommendations.