Predictive Analytics for Climate-Sensitive Sectors
A Workshop on Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
- ModeVirtual / Online
- LevelModerate
- Fee₹2,499
A Workshop on Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
Global overview of climate-sensitive sectors and their vulnerabilities
Introduction to predictive analytics: regression, classification, time series forecasting
Understanding and accessing open climate data sources (NASA EarthData, NOAA, Copernicus, IPCC)
Data preparation techniques: missing values, temporal formatting, spatial tagging
Visualizing climate trends using Python and Jupyter Notebooks
🛠️ Hands-on Tools: Python (Pandas, NumPy, Plotly), Jupyter Notebook
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)
