Feature/notebook 03 carbon analysis#64
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#29) - Add governance module with SHAPExplainer class - Implement band-level and spatial attribution using DeepExplainer - Add /api/explain endpoint for SHAP-based explanations - Create 06_explainability.ipynb with visualization examples - Add shap>=0.42.0 to requirements.txt Closes #22 Co-authored-by: Linda Oraegbunam <obielinda@gmail.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* Add SECURITY.md for security policy and reporting Added a security policy document outlining supported versions, vulnerability reporting, scope, and disclosure policy. * chore: add PR template for contributor guidance Add a pull request template to guide contributors. * chore: add CODEOWNERS assigning @Goldokpa as default reviewer Added CODEOWNERS file to define code ownership and review assignments. * chore: add Dependabot config for pip, npm, and GitHub Actions Configured Dependabot for Python, GitHub Actions, and npm dependencies with specified schedules and reviewers. * chore: add CHANGELOG.md following Keep a Changelog format Document notable changes, additions, modifications, and removals for ClimateVision. * chore: add CITATION.cff to enable GitHub Cite this repository button Added citation file for ClimateVision software. * fix: replace #email placeholder in CODE_OF_CONDUCT with Security Advisory link This change updates the Code of Conduct document by removing the original content and replacing it with a new version that includes various sections on community standards, enforcement responsibilities, and guidelines. * chore: remove SETUP_COMPLETE.md (internal artifact not suited for public repo) * chore: remove internal team_docs (Francis_Umo_Role.pdf) from public repo * chore: remove internal team_docs (Olufemi_Taiwo_Role.pdf) from public repo
End-to-end notebook that loads (or simulates) a biomass-labelled spectral dataset, trains a Random Forest BiomassRegressor, evaluates RMSE/MAE/R^2/MAPE, converts biomass predictions to carbon and CO2e using IPCC defaults, plots feature importances, and persists the model + metrics for the analytics API and the model-card pipeline. Falls back to a synthetic dataset when the labelled parquet file is not present, so the notebook is runnable in CI.
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