Exploratory Data Analysis of malaria trends across Africa using Python — incidence patterns, geographic distribution, and public health correlations.
# 🦟 Malaria in Africa: Exploratory Data Analysis
> Uncovering malaria trends, geographic patterns, and public health correlations across Africa using Python.
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## 📌 Project Overview
This project performs an end-to-end exploratory data analysis on malaria trends across African countries, focusing on incidence patterns, prevention indicators, and the relationship between infrastructure access and malaria burden.
**Dataset:** Malaria in Africa — Kaggle
**Notebook:** View on Kaggle
**Status:** ✅ Completed
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## 🛠️ Tech Stack
| Tool | Purpose |
|------|---------|
| Python | Core analysis |
| Pandas | Data manipulation |
| Matplotlib | Visualizations |
| Seaborn | Correlation heatmap |
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## 📈 Key Findings
| # | Finding |
|---|---------|
| 1 | Nigeria recorded a **34% reduction** in malaria incidence between 2008 and 2017 |
| 2 | **Burkina Faso** has the highest average malaria incidence in Africa |
| 3 | Rural populations show a weak positive correlation (**+0.3**) with malaria incidence |
| 4 | Access to clean water shows a moderate negative correlation (**-0.5**) with malaria |
| 5 | Infrastructure access is a stronger driver of malaria burden than location alone |
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## 📊 Dashboard Preview
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## 🗂️ Repository Structure
malaria-in-africa-eda/
│
├── kpi_cards.png # KPI dashboard
├── nigeria_trend.png # Nigeria malaria trend chart
├── continental_comparison.png # Top 15 countries comparison
├── rural_vs_malaria.png # Rural population vs malaria
├── water_vs_malaria.png # Water accessibility vs malaria
├── heatmap.png # Correlation heatmap
├── Malaria_in_Africa_EDA.ipynb # Full Python notebook
└── README.md
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## 💡 Recommendations
1. **Invest in clean water infrastructure** — strongest correlation with reduced malaria
2. **Focus on rural healthcare** — rural areas carry the highest burden
3. **Scale up bed net distribution** — particula …