Weather Disease Prediction System
End-to-end ML pipeline predicting disease outbreak risk from weather patterns using time-series models and geospatial data.
Weather conditions significantly influence the spread of many infectious diseases. This system integrates historical weather data (temperature, rainfall, humidity) with disease surveillance records to build predictive models that forecast outbreak risks. The pipeline includes data ingestion, feature engineering, model training with LSTM and XGBoost, and a dashboard for visualizing risk maps.