Khadijarogo24/Utilizing-Random-Forest-for-Predictive-Modeling-of-Malaria-Incidence-in-Africa

Malaria Risk Prediction in Africa uses machine learning to estimate malaria risk based on key health indicators. like cases, bed net usage, water access, and sanitation. A Random Forest model and Streamlit app enable simple, interactive prediction across African countries. ***Malaria Risk Prediction in Africa** is a data science and machine learning project that predicts malaria incidence risk using key public health indicators such as malaria cases, bed net usage, water access, and sanitation. It leverages a **Random Forest model** trained on sample malaria data and a **Streamlit web interface** for user-friendly visualization and prediction of malaria risk across African countries. # 🦟 Malaria Risk Prediction in Africa This project models and predicts **malaria incidence risk** in African countries using a **Random Forest Classifier**. It combines **data science**, **machine learning**, and **public health analytics** with an interactive **Streamlit web app** for easy data input and risk visualization. ## 🌍 Overview Malaria remains one of Africa’s most persistent health challenges. This project aims to **predict malaria risk** based on factors such as: - Incidence rate (per 1,000 population at risk) - Reported malaria cases - Use of insecticide-treated bed nets - Access to antimalarial treatment - Preventive treatment in pregnancy (IPT) - Access to clean water and sanitation The system outputs a **risk classification** (High or Low) and provides **recommendations** for public health intervention. ## 🧠 Features - 🧩 **Random Forest Model** for malaria risk classification - 🌐 **Interactive Streamlit App** for real-time predictions - 📊 **Input parameters** include key malaria-related health and infrastructure indicators - 💡 **Actionable insights** with preventive recommendations for high-risk regions - 🗺️ **Country selection** for prediction across African nations --- ## 🚀 Getting Started ### Prerequisites Make sure you have **Python 3.8+** installed. Then install the required libraries: ```bash pip install streamlit pandas numpy scikit-learn ```` --- ## ▶️ Running the App 1. Clone the repository: ```bash git clone github.com cd malaria-risk-prediction ``` 2. Run th …