Predictive Analytics for Climate-Sensitive Sectors

• Machine learning techniques for climate data: ARIMA, Random Forest, XGBoost

• Predictive modeling for rainfall, temperature, and crop yield

• Introduction to geospatial data: raster vs vector, spatial layers, map overlays

• Climate zoning and risk area detection (e.g., drought, floods)

• Model development using real datasets, including training, testing, and validation

🛠️ Hands-on Tools: Scikit-learn, GeoPandas, Folium, QGIS, Google Earth Engine

• Review of global case studies: agriculture forecasting, hydrology, health risk models

• Building a full predictive pipeline: data ingestion → modeling → evaluation → visualization

• Model evaluation metrics: MAE, RMSE, R², bias assessment

• Designing interactive climate dashboards using Streamlit

🛠️ Hands-on Tools: Streamlit, GitHub, Python (Seaborn, Altair, Matplotlib)

Take your research to the next level with NanoSchool.