Chijudy/Dorathy-s-Predicting-Malaria-Risk-Using-Weather-Data-in-Nigeria

🦟 Predicting Malaria Risk Using Weather Data in Nigeria 📌 Project Overview This project leverages machine learning and weather data to predict malaria risk in Nigeria. By analyzing climatic variables such as rainfall, temperature, and humidity, the model identifies patterns that influence malaria transmission. Malaria remains a major public health challenge, and this project demonstrates how data-driven insights can support early warning systems and health intervention planning. 🎯 Objectives 📊 Predict malaria risk using weather data 🌦️ Identify key environmental drivers of malaria transmission ⚡ Build an early warning system for outbreaks 🏥 Support public health decision-making and resource allocation 📂 Dataset The dataset combines: 🌦️ Weather Data Rainfall (mm) Temperature (°C) Humidity (%) 🏥 Malaria Data Confirmed malaria cases Time variables (Month, Year) 🛠️ Tech Stack Python Pandas & NumPy – Data processing Matplotlib & Seaborn – Visualisation Scikit-learn – Machine learning models 🔍 Methodology 1️⃣ Data Collection Meteorological data sources Health surveillance records 2️⃣ Data Preprocessing Missing value handling Feature engineering Data normalization 3️⃣ Exploratory Data Analysis (EDA) Seasonal malaria trends Correlation between weather variables and malaria cases 4️⃣ Model Development Models used include: Linear Regression Random Forest Gradient Boosting Support Vector Machines 5️⃣ Model Evaluation RMSE (Root Mean Square Error) MAE (Mean Absolute Error) R² Score 📈 Results & Insights 🌧️ Rainfall significantly impacts mosquito breeding 🌡️ Temperature affects parasite development 📊 Machine learning models improve prediction accuracy 🔄 Seasonal patterns strongly influence malaria outbreaks 🚀 Applications 🧭 Early warning systems for malaria outbreaks 🏥 Health resource planning and allocation 🌍 Policy-making and intervention strategies 📱 Potential integration into digital health platforms 🔮 Future Improvements 🔗 Integration with real-time weather …