silverback0/Maji-Ndogo-Water-Analysis

# Maji Ndogo Water Crisis: Turning 27,000+ Field Records into an Action Plan **ALX Data Analytics Capstone** · SQL + Power BI > Maji Ndogo is a fictional nation used in the ALX Data Analytics program to simulate a real-world water crisis. This project takes raw survey data from over 27,000 water access points and turns it into a decision-ready dashboard for national and provincial leaders. --- ## The Problem Millions of people in Maji Ndogo don't have reliable access to clean water. Long queues at shared taps and wells aren't just an inconvenience — they cost time, expose people to unsafe water, and put women and children at risk of crime while they wait. Leadership needed answers to three questions before they could act: 1. **Where** is water access worst, and why? 2. **Which** water sources are contaminated, and how badly? 3. **When and where** are people most at risk of crime while collecting water? ## What I Did Starting from raw MySQL survey data, I cleaned and modeled the dataset, then built a Power BI dashboard so stakeholders could explore the crisis themselves instead of reading a static report. - **SQL** — queried and validated the source data, joining field visits, water quality tests, and crime logs across five tables - **Power Query (M)** — standardized inconsistent labels (e.g., `F`/`M`/`C` → `Female`/`Male`/`Child`) and cleaned whitespace/formatting issues - **DAX** — built calculated columns and measures to classify contamination severity and roll up crime and queue metrics - **Power BI** — designed an interactive report with drill-through by province and town ## Key Findings **Water quality** - ~51% of tested wells are clean; **~28% show chemical contamination** and **~21% show biological contamination** - Sokoto and Hawassa provinces have the most severe biological contamination, making them top priorities for UV filtration and chlorine dosing **Safety at water sources** - Women make up the largest share of crime victims at collection p …