Crop yield predictor

The brief

Planners and insurers need yield estimates before harvest. Build regression models that predict yield from rainfall, temperature, pesticide use and crop, compare them, and show which factors matter. Good looks like: validation on later years, models compared with a baseline, and clear drivers.

What you will build

1. Explore and clean the yield data
2. Engineer weather and input features
3. Train linear, tree-based and boosted models
4. Validate on later years
5. Explain the main drivers

Free, open data to build it with

• Crop yield prediction dataset ↗ Kaggle

Check each dataset's licence before you publish your work. Your mentor gives your team its own version of the brief (data, scope or region), so every team's project is its own.