Covid-19-Epidermic-data

🦠 COVID-19 Epidemic Spread Prediction CodeCure AI Hackathon 📌 Overview A machine learning pipeline that predicts COVID-19 epidemic spread and visualizes outbreak risk across countries using multi-source epidemiological data.

🚀 Features Prophet 30-day case forecasting per country Per-country risk classification (High / Medium / Low) Interactive Folium risk map with heatmap layer Multi-source data pipeline (OWID + JHU + Vaccination + Testing) Streamlit dashboard with 5 interactive tabs Random Forest feature importance analysis 🗂️ Project Structure ├── prediction.py # ML pipeline — run this first ├── dashboard.py # Streamlit dashboard — run this second ├── ml_risk_map.html # Pre-generated interactive risk map ├── requirements.txt # All dependencies └── README.md

⚙️ How to Run

1. Install dependencies pip install -r requirements.txt

2. Download datasets (see below) and place in project folder

3. Run ML pipeline first python prediction.py

4. Launch dashboard python -m streamlit run dashboard.py

📥 Dataset Download Links OWID COVID-19 Data Johns Hopkins CSSE Our World in Data — Vaccination Our World in Data — Testing 🛠️ Tech Stack Python · Streamlit · Prophet · Scikit-learn · Plotly · Folium · Pandas · NumPy · OpenPyXL

📊 Models Used | Model | Purpose | | Facebook Prophet | 30-day case forecasting | | Linear Regression | 14-day per-country prediction | | Random Forest | Feature importance analysis |