Malaria Outbreak Prediction

Overview

This project predicts malaria outbreaks using a machine learning model based on Support Vector Machines (SVM). The model forecasts outbreaks 15–20 days in advance with high accuracy, leveraging historical data and environmental factors.

Features

• Predicts malaria outbreaks up to 20 days in advance.
• Utilizes climate and health data for forecasting.
• Implements an SVM-based machine learning model.
• Provides visualization of prediction results.

Tech Stack

• Programming Language: Python
• Machine Learning Model: Support Vector Machine (SVM)
• Libraries Used: Pandas, NumPy, Scikit-learn, Matplotlib

Installation

git clone https://github.com/rahatbhambri/Malaria_outbreak.git
cd Malaria_outbreak
pip install -r requirements.txt

Usage

1. Prepare the dataset (ensure data is in the expected format).
2. Run the model:

   python main.py

3. Review the predictions and visualization outputs.

Results

• Model accuracy and performance metrics.
• Key findings from prediction analysis.

Future Enhancements

• Improve model accuracy with additional features.
• Implement deep learning models for better predictions.
• Develop a web or mobile application for real-time alerts.

Contributing

Contributions are welcome! Feel free to submit issues and pull requests.