ifedayofakayode/Maji-Ndogo-Data-Analysis-Project

This project showcases a data-driven analysis of the water crisis in Maji Ndogo using SQL queries within a Jupyter Notebook. The goal was to uncover data inconsistencies, expose corruption, and provide actionable insights to improve water access and quality. # Maji-Ndogo-Data-Analysis-Project ## Table of Contents - Maji Ndogo Part I - Maji Ndogo Part II - Maji Ndogo Part III - Maji Ndogo Part IV ## Maji Ndogo Part I ## 📘 Overview In this initial phase of the Maji Ndogo water project, our mission was to **explore, validate, and clean a national water survey database** consisting of 60,000+ records. This foundational work set the stage for subsequent analysis and data-driven decisions aimed at resolving the water access crisis in the fictional country of Maji Ndogo. --- ## 🛠 Tools & Technologies - **SQL** (MySQL Workbench) - **Python** (Jupyter Notebook) - **Relational Database**: `md_water_services` - **Tables Used**: `employee`, `location`, `visits`, `water_quality`, `water_source`, `well_pollution`, `data_dictionary` --- ## 🎯 Project Goals - Understand the data structure and table relationships - Identify types of water sources and their usage - Examine visit patterns and queue times - Assess subjective water quality scores - Investigate pollution data and correct inconsistencies --- ## 📊 Key Tasks & Insights ### 1. Data Discovery - Used `SHOW TABLES` to list all tables. - Ran `SELECT * FROM [table] LIMIT 5;` to examine sample records. - Located and reviewed the embedded `data_dictionary` table for metadata on columns. ### 2. Water Source Types - Identified five key water source types: - `tap_in_home` - `tap_in_home_broken` - `shared_tap` - `well` - `river` - Noted that household data was aggregated into single records (e.g., 956 people served = approx. 160 homes). ### 3. Visit Log Analysis - Reviewed the `visits` table to analyze: - Repeated visits - Time of day - `time_in_queue` metrics - Found extreme queue times > 500 minutes at some shared taps. ### 4. Water Quality Evaluation - Explored `water_quality` table: - Scores ranged from 1 to 10. - Unexpected: 218 home taps rated 10 were visited multiple times (likely incorrect). ### 5. Pollution Data Validation - Reviewed the `well_pollution` table: - …