  Water crisis is a crucial concern around the globe. Appropriate and timely
maintenance of water pumps in drought-hit countries is vital for communities
relying on the well. In this paper, we analyze and apply a sequential attentive
deep neural architecture, TabNet, for predicting water pump repair status in
Tanzania. The model combines the valuable benefits of tree-based algorithms and
neural networks, enabling end-to-end training, model interpretability, sparse
feature selection, and efficient learning on tabular data. Finally, we compare
the performance of TabNet with popular gradient tree-boosting algorithms like
XGBoost, LightGBM,CatBoost, and demonstrate how we can further uplift the
performance by choosing focal loss as the objective function while training on
imbalanced data.
