available-pixel/malaria_prediction_benin

Predicting malaria cases in Benin using historical epidemiological data, climate factors, and machine learning. Interactive dashboard built with Streamlit for visualization, single-year prediction, and multi-year trend forecasting. Includes feature importance analysis and customizable climate inputs # 🦟 Malaria Prediction Dashboard (Benin) ## 🔗 Project Links - 📄 Research Proposal (DOI): Predicting Malaria Cases in… - 🌍 Live App: malaria-prediction-benin-fa… - 💻 Source Code: GitHub Repository ## 📌 Project Overview This project predicts malaria cases in Benin using machine learning and climate data such as rainfall and temperature. It demonstrates how **Artificial Intelligence can be applied to public health** to better understand and anticipate disease trends. --- ## 📄 Published Research This project is based on a published research proposal: 👉 Predicting Malaria Cases in… This publication demonstrates the application of machine learning in predicting malaria cases in Benin using climate data. ## 📄 Full Research Proposal (PDF) The full research proposal for this project is available as a PDF: Download PDF ## 📸 Application Preview ### 🔹 Dashboard ### 🔹 Prediction Result ### 🔹 Data Visualization --- ## 🚀 Features * 📊 Predict malaria cases for any future year * 📈 Forecast next 5 years automatically * 🌧️ Input rainfall & temperature * 📉 Interactive graphs and analysis * 🧠 Feature importance explanation --- ## 🧠 Machine Learning Model This project uses: * **Random Forest Regressor** (main model) * **Linear Regression** (baseline model) ### Features used: * Rainfall * Temperature * Previous year malaria cases * Year --- ## 📈 Model Performance The models were evaluated using standard regression metrics: - Mean Squared Error (MSE) - Root Mean Squared Error (RMSE) The Random Forest model outperformed Linear Regression, providing more accurate predictions of malaria cases. These results confirm that machine learning can effectively capture patterns between climate factors and malaria incidence. ## 📊 How It Works 1. Load datasets (malaria + climate) 2. Filter data for Benin 3. Merge datasets by year 4. Train machine learning model 5. Predict malaria cases 6. Display results in a Strea …