Predicting Malaria Cases in Benin Using Climate Data and Machine Learning

This project presents a machine learning approach to predicting malaria cases in Benin using historical epidemiological data and climate factors such as rainfall and temperature.

The study applies models including Random Forest and Linear Regression to identify patterns and forecast future malaria trends. Results are presented through an interactive Streamlit dashboard.

This work demonstrates how data science and artificial intelligence can support public health decision-making in developing countries.