NithishaMarripelly/Analyzing-Malnutrition-in-Africa-A-Data-Driven-Policy-Insight-Project
Domain:
healthcaresocioeconomicRecord type:
project
Creator:
Nit
Host:
Used World Bank data and Python (Pandas, Matplotlib) to explore the impact of GDP, food prices, maternal education, and immunization on malnutrition in Africa. Applied statistical correlation analysis to derive policy-relevant insights
# 🌍 Tackling Malnutrition in Africa – A Data-Driven Policy Insight Project
This project explores the key socio-economic and health-related factors influencing malnutrition in African countries. Using data from the World Bank and Python-based statistical analysis, we validated four hypotheses through correlation analysis and data visualization to uncover actionable insights for policymakers.
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## 🧠 Objective
To investigate how variables like GDP, food affordability, maternal education, and immunization rates affect malnutrition levels across African countries and to provide data-backed insights for improving public health and policy planning.
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## 📊 Dataset
- **Source**: World Bank Open Data
- **Format**: `.csv` files from the World Development Indicators & Africa Development Indicators
- **Variables Studied**:
- Malnutrition Index (avg. of stunting, wasting, underweight)
- GDP per capita
- Cost of healthy diet per capita
- Immunization coverage (BCG, DPT, Polio, Measles)
- Female education (25+ years, upper secondary attainment)
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## 📌 Hypotheses Tested
1. **Negative correlation** between GDP per capita and population unable to afford a healthy diet
2. **Positive correlation** between food prices and malnutrition
3. **Negative correlation** between immunization rates and malnutrition
4. **Negative correlation** between maternal education and malnutrition
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## 📈 Tools & Technologies
- **Language**: Python
- **Libraries**: `Pandas`, `Matplotlib`, `NumPy`, `SciPy`
- **Methods Used**:
- Pearson Correlation Coefficient
- Spearman Correlation Coefficient
- p-value testing for statistical significance
- Scatter plots for visual analysis
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## 📉 Results Summary
| Relationship | Pearson | Spearman | p-value | Conclusion |
|--------------|---------|----------|---------|------------|
| GDP vs Food Affordability | -0.79 | -0.74 | 4.5e-9 | Strong negative correlation |
| Food Price vs Malnutrition | 0.75 | 0.73 | 1.2e-7 | Strong positive correlation |
| …