Straton12/Turkana-water-point-optimization

# 💧 Spatial Optimization of Water Point Allocation in Turkana County ### *A Machine Learning Approach Integrating Infrastructure Reliability and Demand Equity* --- ## 📋 Table of Contents 1. Project Overview 2. The Problem 3. Research Objectives 4. Research Questions 5. Key Results & Model Performance 6. Project Structure 7. Tech Stack 8. Data Sources 9. Analytical Pipeline 10. Machine Learning Models 11. Feature Importance 12. Top Optimal Locations Identified 13. Deployment Architecture (Django REST Framework) 14. Installation & Local Setup 15. Running the Django Application 16. API Endpoints 17. Coordinate Reference Systems 18. Data Integrity Policy 19. Reproducibility 20. Adding the README and Pushing to GitHub 21. Contributing 22. License --- ## 🌍 Project Overview Turkana County is Kenya's **largest and most arid region**, covering approximately **77,000 km²** in the northwest. Despite decades of international and government investment in water infrastructure, the county continues to face severe water access challenges. This project builds a **machine learning framework** deployed via a **Django REST Framework** web application that spatially optimizes water point allocation — enabling data-driven, equitable, and sustainable water resource planning. The system accepts GPS coordinates, extracts multi-source environmental and demographic features, applies an ensemble ML model, and returns real-time suitability predictions with plain-language explanations. The entire research follows the **CRISP-DM methodology**: Business Understanding → Data Acquisition → Preparation → Modeling → Evaluation → Deployment. --- ## ❗ The Problem | Statistic | Value | |-----------|-------| | Total water points analyzed | **1,059** | | Operational water points | **57.7%** | | Non-operational water points | **~42.3%** | | People underserved (beyond WHO 30-min walk) | **~320,000** | | County area | **~77,000 km²** | Tradition …