Machine learning system to predict household-level corn production in Kenya, supporting food security decisions and resource allocation.
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predictive service deployed in cloud run and url is adjusted in testing ipynb.
a88357aView on GitHubsolution deployment instructions was included in readme.md file.
d1d4ed7View on GitHubtest_clean.py file refactored to work with ci.yml workflow and the mock data it creates.
2a94d6bView on GitHubclean.py file refactored into a modular, function-based approach.
b971404View on GitHubbest practices section included in readme.md file.
84682aaView on GitHubdocker creation and tests are not needed because the artifacts are created in GCS buckets.
17dc59bView on GitHubtest_clean.py skipped because clean.py file is a script that executes immediately, not a module with callable functions.
e01b989View on GitHubtest_registry_fetch.py refactored to Mocks the modules before import.
c8eab71View on GitHubPre-test script that validates and fixes the JSON created.
763bc3aView on GitHubjson files adjusted for to evoid getting None values.
85ed470View on GitHubcreates target_val.npy and file structure with 20+ mock files.
115c645View on GitHub