OchiengGideon/Tanzania-Water-Wells-Project

# Predictive Analytics for Waterpoint Operational Status in Tanzania Tanzania relies heavily on waterpoints to provide clean and accessible water to its population. However, many waterpoints are either non-functional or in need of repair, leading to inefficiencies and hardships for communities that depend on them. This project seeks to address this issue by building a machine learning model to predict the operational status of waterpoints, enabling better resource allocation and proactive maintenance. ## Table of Contents 1. Overview 2. Business Understanding - Objectives - Stakeholders - Success Criteria - Key Questions - Constraints - Potential Impact 3. Data Understanding - Dataset - Data Details 4. Workflow - Business Understanding Step - Data Understanding Step - Data Preparation - Modeling - Evaluation 5. Rationale 6. Results 7. Limitations 8. Recommendations 9. Tableau Dashboard --- ## Overview This project aims to predict the operational status of waterpoints in Tanzania using machine learning techniques. It addresses critical challenges in water resource management by identifying whether a waterpoint is functional, needs repair, or is non-functional, enabling better planning and allocation of resources. --- ## Business Understanding ### Objectives The primary goal of this project is to predict the operational status of waterpoint in Tanzania. By accurately predicting whether it is fuctional, needs repair or is non-fuctional, it can help the Tanzania Ministry of water and other stakeholders in optimizing operations ansd ensure a reliable supply of clean water to communities. ### Stakeholders - **Tanzania Ministry of Water:** Responsible for infrastructure and resource allocation. - **Local Communities:** Rely on waterpoints for daily needs. - **Maintenance Teams:** Tasked with repairs and operational upkeep. ### Success Criteria - **Accuracy:** The model should have a high accuracy in predicting the status of waterpoints. - **Actionable Insight:** The …