  This paper presents an optimized logistic regression machine learning model
that predicts the occupancy of an Electric Vehicle (EV) charging station given
the occupancy of neighboring stations. The model was optimized for the time of
day. Trained on data from 57 EV charging stations around the University of
California San Diego campus, the model achieved an 88.43% average accuracy and
92.23% maximum accuracy in predicting occupancy, outperforming a persistence
model benchmark.
