JoelKy-coder/Water_Pump_Functionality-_Prediction

Predicting whether water pumps in Tanzania are working, broken, or need repair built as an end-to-end ML project covering data cleaning, EDA, model comparison (Logistic Regression, Random Forest, Gradient Boosting), and a simple Flask app for live predictions. # Water Pump Functionality Prediction ## Overview Access to clean water is a fundamental human right and critical development indicator. In Tanzania, millions of citizens depend on waterpoints for daily consumption, yet many are in poor operational condition or non-functional. The absence of reliable water infrastructure directly impacts public health, educational outcomes, and economic productivity. This project addresses the water accessibility crisis in Tanzania by leveraging supervised machine learning to predict the operational condition of waterpoints. By accurately classifying waterpoints into functional, needs repair, and non-functional categories, government agencies and non-governmental organizations can prioritize maintenance efforts, allocate resources efficiently, and improve water accessibility for rural communities. The ability to predict waterpoint failure before it occurs enables: - Preventive maintenance scheduling - Strategic resource allocation - Reduced downtime and service interruptions - Improved community water security - Data-driven infrastructure planning This end-to-end machine learning project demonstrates the complete workflow from raw data to production deployment, utilizing historical infrastructure, geographic, management, and water source data to build an accurate predictive model. ## Problem Statement **Business Context**: Tanzania's water infrastructure serves millions of citizens, yet maintenance and repair resources are limited and often reactive rather than proactive. Without predictive insight into waterpoint functionality, maintenance teams respond to failures after they occur, resulting in prolonged service interruptions and increased operational costs. **Technical Challenge**: Develop a supervised multiclass classification model capable of accurately predicting whether a waterpoint will remain functional, require repair, or become non-functional based on available historical and contextual features. **Objective …