An AI-powered climate risk analytics platform developed to predict rice productivity and provide explainable climate adaptation insights using Machine Learning, Explainable AI (SHAP), and decision-support analytics.
Try the interactive dashboard:
Open Climate Food Security Dashboard
- Rice yield prediction using Machine Learning
- Time-based model validation
- SHAP Explainable AI analysis
- Climate risk assessment
- Policy decision support
- Interactive Streamlit dashboard
Climate & Agricultural Data → Data Processing → Machine Learning → Prediction → SHAP Explanation → Policy Decision Support
Algorithm: Random Forest Regression
Validation Strategy: Time-based split
Training Period: 2000-2019
Testing Period: 2020-2022
Metrics: - MAE - RMSE - R²
- Python
- Pandas
- Scikit-learn
- SHAP
- Streamlit
- Plotly
Install dependencies:
pip install -r requirements.txt
Run dashboard:
streamlit run app.py
This project demonstrates how AI-based analytics can support climate adaptation, food security analysis, and evidence-based decision support.