Malaria Prevention Analysis in Africa

This project analyzes the effectiveness of preventive measures against malaria in African countries using machine learning techniques.

Project Structure

malaria/
├── malaria_analysis.py    # Main analysis script
├── requirements.txt       # Python dependencies
├── DatasetAfricaMalaria.csv  # Input dataset (not included)
└── output/               # Generated visualizations
    ├── prevention_effectiveness_heatmap.png
    └── prediction_accuracy_plot.png

Setup and Installation

1. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install required packages:

   pip install -r requirements.txt

Usage

1. Place your DatasetAfricaMalaria.csv file in the project root directory

2. Run the analysis script:

   python malaria_analysis.py

3. Check the output directory for generated visualizations:

   • prevention_effectiveness_heatmap.png: Shows correlation between preventive measures and malaria incidence
   • prediction_accuracy_plot.png: Displays model prediction accuracy with R² and RMSE metrics

Dataset Format

The input CSV file should contain the following columns:

• country: Name of the African country
• bed_nets: Data about bed net usage
• antimalarial_medication: Data about antimalarial medication usage
• malaria_incidence: Target variable showing malaria cases

Output

The script generates:

1. A heatmap showing the effectiveness of different prevention methods across countries
2. A prediction accuracy plot comparing actual vs. predicted malaria incidence
3. Printed performance metrics (R² and RMSE)

Error Handling

The script includes error handling for:

• Missing input file
• Data loading issues
• Visualization creation errors
• Model training problems