ML-Based Disease Outbreak Prediction System 📌 Overview
The Disease Outbreak Prediction System is a machine learning–based application that analyzes epidemiological, environmental, and demographic data to predict potential outbreaks. The system helps public health authorities in early detection, risk assessment, and decision-making for disease prevention and control.
🚀 Features
🗂️ Data preprocessing from epidemiological and environmental sources
📈 Time-series forecasting for disease trends
🌍 Visualization of outbreak hotspots via dashboards
🤖 Machine learning models (Random Forest, SVM, LSTM) for prediction
🔔 Real-time alerts for outbreak risks
🛠️ Tech Stack
Programming: Python
Libraries: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib, Plotly
Database: PostgreSQL / MongoDB
Visualization: Streamlit / Dash
📂 Project Structure DiseaseOutbreakML/ │── data/ # Raw and processed datasets │── notebooks/ # Jupyter notebooks for experiments │── models/ # Trained ML models │── src/ # Source code for preprocessing, training, evaluation │── dashboard/ # Visualization & web app code │── requirements.txt # Dependencies │── README.md # Project documentation