Geomagnetic storms driven by solar activity pose risks to power grids, satellites, and communication systems. In this project, we developed a data-driven solar-to-ground proxy model that predicts near-term geomagnetic activity using solar wind data. The final model is an XGBoost Classifier tuned to maximize correctly predicting when a storm occurs subject to keeping the false positive rate at an acceptable level.