Built an LSTM (Long Short-Term Memory) neural network to predict electric vehicle charger demand across New York City. The project involved processing large-scale spatial-temporal data, implementing one-hot encoding for categorical variables, and establishing a performance baseline using Facebook Prophet. I documented the end-to-end development—from data engineering to model architecture—to share my learning journey and insights.
NYC EV Charger Demand Modeling
Focus: Applied Machine Learning & Urban Tech
