Project Overview

This project was conceptualized by Taarifa as a competitive project among Data Scientists hosted on Drivendata platform

This project seeks to build a model that would predicts if a water-pump is functional, or needs some repairs, or totally non-functional. Prediction of one of these three classes based on a number of variables about what kind of pump is operating, when it was installed, and how it is managed. A smart understanding of which waterpoints will fail can improve maintenance operations and ensure that clean, potable water is available to communities across Tanzania.

Of all the models built, while training the models with the provided dataset, the most performing of all was Random Forest. Focus was placed more on the label class where water-pump-status seems functioning but will need repair. This will guide a sudden collapse of any waterpoint since prompt action is taken on any waterpoint that seem to have some trait of malfunctioning. I decided to keen in to where the model would predict the water-pump points that are functioning but need repair.