Rokhaya8/health-africa-etl
Domaine:
healthcareCréateur:
Rok
Hôte:
ETL pipeline analyzing health indicators in Africa using Python, Pandas, SQLite and Matplotlib
# Health Indicators ETL Pipeline - Africa
End-to-end ETL pipeline that extracts, transforms, loads and analyzes World Bank
health data across 54 African countries, with a focus on Senegal.
## Overview
This project builds a complete data pipeline from a raw World Bank dataset to
SQL-queryable health indicators and visualizations. It covers the four ETL stages
and demonstrates data cleaning, reshaping, storage in a relational database, and
analysis with SQL.
## Pipeline
- **Extract** - Dataset downloaded programmatically from Kaggle via the Kaggle API
(World Bank Health Nutrition and Population Statistics).
- **Transform** - Cleaning and restructuring with Pandas: column normalization,
wide-to-long reshaping (`melt`), type conversion, removal of empty records, and
filtering to the 54 African countries.
- **Load** - Storage in a SQLite database (`sante_afrique.db`).
- **Analyze** - SQL queries and visualizations with Matplotlib.
## Selected Indicators
The analysis focuses on four public-health indicators:
- Life expectancy at birth, total (years)
- Mortality rate, infant (per 1,000 live births)
- Improved water source (% of population with access)
- Health expenditure per capita (current US$)
## Sample Results
**Top 10 life expectancy in Africa (2014)**
**Life expectancy trend in Senegal (2000–2014)**
## Key Insights
- Algeria has the highest life expectancy in Africa (74.8 years in 2014).
- Senegal's life expectancy increased by +8.6 years between 2000 and 2014.
- Infant mortality in Senegal dropped by nearly 40% between 2000 and 2014.
## Tools
Python, Pandas, SQLite, Matplotlib, Jupyter Notebook
## How to Run
1. Clone the repository.
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
3. Set up your Kaggle API credentials (guide) —
place your `kaggle.json` file in `~/.kaggle/` (or set the `KAGGLE_CONFIG_DIR`
environment variable to its folder).
4. Open and run the notebook:
```bash
jupyter notebook HETL.ipynb
```
Run all cel …