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Water crisis is a crucial concern around the globe. Appropriate and timelymaintenance of water pumps in drought-hit countries is vital for communitiesrelying on the well. In this paper, we analyze and apply a sequential attentivedeep neural architecture, TabNet, for predicting water pump repair status inTanzania. The model combines the valuable benefits of tree-based algorithms andneural networks, enabling end-to-end training, model interpretability, sparsefeature selection, and efficient learning on tabular data. Finally, we comparethe performance of TabNet with popular gradient tree-boosting algorithms likeXGBoost, LightGBM,CatBoost, and demonstrate how we can further uplift theperformance by choosing focal loss as the objective function while training onimbalanced data.

Pump It Up: Predict Water Pump Status using Attentive Tabular Learning