🔍 The Problem
Following the 2015 Gorkha earthquake in Nepal, emergency response teams needed a way to rapidly assess which buildings were most likely to suffer severe damage, enabling faster allocation of limited relief resources.
🧭 Approach
Worked with a dataset of 260,000+ buildings, engineering features from structural properties, geographical data, and construction materials. Designed and evaluated multiple ML models from logistic regression to Neural Networks and XGBoost.
⚙️ Solution
Built a multi-class classification model achieving top-tier accuracy on the DrivenData competition benchmark. Leveraged Cursor AI to accelerate the development pipeline.
📈 Impact
Drove a 14% improvement in predictive performance. Demonstrated how predictive analytics can accelerate humanitarian response by prioritising high-risk structures.