oumaouma20/malaria-prediction-east-africa-ml

A machine learning project predicting malaria incidence in Kenya, Uganda, and Tanzania using malaria incidence data. Includes model comparison (Linear Regression, Decision Tree, Random Forest), visualizations, and an optional Streamlit web app # 🦟 Malaria Incidence Prediction in East Africa Using Machine Learning ## 📌 Project Overview This project uses machine learning to predict malaria incidence in three East African countries — **Kenya, Uganda, and Tanzania** — based on environmental and historical data. The study compares three models: **Linear Regression**, **Decision Tree Regressor**, and **Random Forest Regressor** to determine which performs best in capturing and forecasting malaria trends. The final goal is to assist public health officials with better forecasting tools and enable data-driven malaria control strategies. --- ## 🎯 Objectives - Predict annual malaria incidence using weather and time-based features. - Compare the performance of multiple machine learning models. - Visualize and interpret model predictions versus actual reported cases. - Recommend the best-performing model for real-world deployment. - Optionally deploy the final model as a Streamlit web app. --- ## 🧠 Technologies & Tools - **Python** (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Joblib) - **Jupyter Notebook** (for data analysis and model training) - **Streamlit** (for optional web app deployment) - **Git & GitHub** (for version control and collaboration) --- ## 📊 Models Used - Linear Regression - Decision Tree Regressor - Random Forest Regressor Performance was evaluated using: - Mean Squared Error (MSE) - R² Score - Visual comparison of predicted vs actual malaria cases over time --- ## 📂 Project Structure 📦 malaria-prediction-east-africa-ml/ ├── data/ # Raw and cleaned data files ├── notebooks/ # Jupyter notebooks for EDA and modeling ├── models/ # Saved .pkl models using joblib ├── app/ # Optional Streamlit app code ├── plots/ # Visualizations generated from analysis ├── README.md # Project documentation └── requirements.txt # Python dependencies --- ## 📈 Visualizations - Correlation heatmaps - Boxplots and distribution plots - Actual vs predicted malaria incidence line plots (per model) - M …