Water Pump Functionality Prediction

Project Overview

This proejct develops machine learning models to predict the functionality status of water pumps across Tanzania based on a variety of features including location, water quality, management structure, and technical specifications.

This project addresses a critical infrastructure challenge: identifying which water pumps are functional, which need repairs, and which are non-functional to improve maintenance operations and ensure communities have access to clean water.

Project Summary

Summary of our work here: Final presentation

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Project Goals

• Develop classification models to predict three target classes:
  • Functional water pumps
  • Water pumps that need repair
  • Non-functional water pumps
• Analyze which factors most strongly influence water pump functionality
• Create geospatial visualizations to communicate findings effectively
• Deploy a simple dashboard to showcase models and insights

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Data Description

Dataset Source

The data comes from Taarifa and the Tanzanian Ministry of Water, which aggregates information about water points across Tanzania. Data is avaialable here: DrivenData - Pump it Up: Data Mining the Water Table

Features Overview

The dataset contains 40+ columns representing:

• Geographic information: coordinates, region, basin
• Technical specifications: pump type, extraction method
• Management details: installer, funder, payment type
• Water characteristics: quality, quantity, source
• Temporal information: construction year, recording date

Target Variable

status_group: The operating condition of the waterpoint with three possible values:

• functional → operational with no repairs needed
• functional needs repair → operational but needs repairs
• non functional → not operational

Acknowledgements

We would like to express our sincere gratitude to the following:

• ReDI School of Digital Integration for providing the opportunity, guidance, and learning environment that made this project possible.
• DrivenData for organizing the Pump it Up: Data Mining the Water Table competition and providing an engaging platform to apply data science for social good.
• Taarifa and the Tanzanian Ministry of Water for making the dataset available, enabling research and innovation aimed at improving water infrastructure.
• The broader open-source community for providing the tools, libraries, and frameworks that make projects like this possible.
• Our peers, mentors, and collaborators for their valuable feedback, support, and encouragement throughout this project.