Truck Income Risk ML Mentor

I’m building a web platform that estimates how risky a truck’s income is and I need a seasoned machine-learning mentor to steer me through the full pipeline. The goal is to create a Python/XGBoost model that can simultaneously deliver three perspectives on risk:

• the chance that income falls below a chosen amount
• the expected drop in income over a future period
• an overall risk label (low / medium / high)

To reach that point I first need help locating solid public datasets for one specific country or region and, where gaps emerge, generating realistic synthetic data that will stand up to academic scrutiny. The model is expected to draw on:

• trip details (distance, routes, delivery type)
• operating costs (fuel, repairs, driver wages)
• market conditions (demand, freight rates, season)

You’ll meet with me regularly, review my code and experiments, explain the rationale behind each step, and make sure I can defend every decision in my final report. I’ll handle the hands-on coding and writing; you supply the strategic guidance, best practices, and critical feedback.

When you reply, please outline:
• relevant experience with Python, XGBoost, and data synthesis
• past mentoring or advisory work
• your hourly rate and the number of hours per week you can commit

Looking forward to working closely with a mentor who can make this graduation project rigorous, insightful, and submission-ready.