SQL-based investigation of national water access, quality, and infrastructure, transforming data analysis into actionable decisions.
# Maji Ndogo — SQL Data Investigation & Decision-Driven Analysis
## 📌 Project Overview
This project presents a comprehensive SQL-based analysis of national water
access, quality, and infrastructure challenges in **Maji Ndogo**.
The objective is not only to explore data, but to:
- Ensure data integrity
- Identify access bottlenecks
- Detect inconsistencies through audit comparison
- Translate analysis into actionable repair priorities
The project follows a structured, multi-phase analytical approach,
moving from exploration to investigation and finally to decision-making.
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## 🧠 Key Questions Addressed
- Where are the most severe water access challenges?
- Which water sources affect the largest populations?
- How do queue times reflect infrastructure pressure?
- Are recorded water quality scores reliable?
- Do audit findings align with internal survey data?
- How should limited resources be prioritized for maximum impact?
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## 🗂️ Project Structure
```text
data/ → Raw data, audit reports, SQL schema
notebooks/ → Step-by-step SQL analysis (Part 1–4)
slides/ → Original project reference materials
```
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## 🔍 Analysis Breakdown
### **Part 1 — Data Exploration & Quality Checks**
* Database structure understanding
* Water source classification
* Visit patterns and queue analysis
* Pollution data correction
### **Part 2 — Analytical Prioritization**
* Employee contribution analysis
* Geographic coverage assessment
* Population impact by source type
* Infrastructure bottleneck identification
### **Part 3 — Data Investigation & Audit Comparison**
* ERD construction
* Integration of independent auditor reports
* Linking records to responsible entities
* Evidence-based discrepancy detection
### **Part 4 — Final Analysis & Action Plan**
* Cross-table aggregation
* Identification of high-risk, high-impact sources
* Executive-level summary
* Practical, phased repair strategy
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## 🛠️ Tools & Technologies
* **SQL (MySQL)**
* **Jupyter Note …