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GitHub / McGill-MMA-EnterpriseAnalytics / DataCo_Supply_Chain

This project focuses on leveraging the DataCo Smart Supply Chain dataset from Kaggle to build predictive models that serve critical business functions: forecasting monthly demand, detecting fraudulent orders, and clustering orders to identify common traits in fraud cases.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/McGill-MMA-EnterpriseAnalytics%2FDataCo_Supply_Chain

Stars: 0
Forks: 4
Open Issues: 0

License: None
Language: Jupyter Notebook
Repo Size: 202 MB
Dependencies: 390

Created: 3 months ago
Updated: 17 days ago
Last pushed: 17 days ago
Last synced: 9 days ago

Topics: fraud-detection, machine-learning-algorithms, supply-chain, time-series

Files

Readme

Dependencies

result/deploy/pyproject.toml pypi

fastapi ^0.110.3
h2o ^3.46.0.1
ipykernel ^6.29.4
jupyter ^1.0.0
matplotlib ^3.8.4
mlflow ^2.12.1
numpy ^1.26.4
pandas ^2.2.2
plotly ^5.21.0
python ^3.9
scikit-learn ^1.4.2
seaborn ^0.13.2
uvicorn ^0.29.0
xgboost ^2.0.3

models/mlruns/0/43693950abc7462c98e2e308df51d6b1/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

models/mlruns/0/832a2448e6994fd780934dd0f1f8e752/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/832a2448e6994fd780934dd0f1f8e752/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/a2db9dccf4ed4e499cb12ca32a9af34d/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/a2db9dccf4ed4e499cb12ca32a9af34d/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/a86b5a98bc144e9bbcb9d57712b0315b/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/a86b5a98bc144e9bbcb9d57712b0315b/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
numpy ==1.26.4
packaging ==23.2
pandas ==1.5.3
requests ==2.31.0
wcwidth ==0.2.5

models/mlruns/0/d2b2025b3ae14b64959b1009c94c7451/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

models/mlruns/0/d2b2025b3ae14b64959b1009c94c7451/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

models/mlruns/0/ee7885c186944d0a9f0f879f07376379/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

models/mlruns/0/ee7885c186944d0a9f0f879f07376379/artifacts/model/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

result/Docker-PoC-Test/Dockerfile docker

python 3.9-slim build

result/deploy/Dockerfile docker

python 3.9-slim build

result/deploy/Flask-Test/Dockerfile docker

python 3.9-slim build

models/mlruns/133278343132896536/003eb13a95cb44b9bad8d31d6f0a0257/artifacts/model/metadata/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/003eb13a95cb44b9bad8d31d6f0a0257/artifacts/model/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/2b7c8136ccbc4644be825fe46ecdf572/artifacts/model/metadata/requirements.txt pypi

cloudpickle ==3.0.0
mlflow ==2.12.1
numpy ==1.23.5
packaging ==23.2
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.0.2
scipy ==1.10.1

models/mlruns/133278343132896536/2b7c8136ccbc4644be825fe46ecdf572/artifacts/model/requirements.txt pypi

cloudpickle ==3.0.0
mlflow ==2.12.1
numpy ==1.23.5
packaging ==23.2
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.0.2
scipy ==1.10.1

models/mlruns/133278343132896536/bdb772115db540a780011a97e7d9ecfd/artifacts/model/metadata/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/bdb772115db540a780011a97e7d9ecfd/artifacts/model/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/fb03a57f38d549378451524330ac7d15/artifacts/model/metadata/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/fb03a57f38d549378451524330ac7d15/artifacts/model/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
numpy ==1.24.3
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
scipy ==1.11.3

models/mlruns/133278343132896536/fcc9b4e7b32d433fa02e861ed2cf3910/artifacts/model/metadata/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
typing-extensions ==4.7.1

models/mlruns/133278343132896536/fcc9b4e7b32d433fa02e861ed2cf3910/artifacts/model/requirements.txt pypi

cloudpickle ==2.0.0
lz4 ==4.3.2
mlflow ==2.12.1
packaging ==23.1
pathlib ==1.0.1
psutil ==5.9.0
pyyaml ==6.0.1
scikit-learn ==1.4.2
typing-extensions ==4.7.1

result/Docker-PoC-Test/environment.yml pypi

scikit-learn ==1.4.2

result/Docker-PoC-Test/requirements.txt pypi

matplotlib ==3.5.1
numpy ==1.26.3
pandas ==2.1.4
plotly ==5.9.0
scikit-learn ==1.4.2
seaborn ==0.12.2

result/deploy/Flask-Test/environment.yml pypi

result/deploy/Flask-Test/requirements.txt pypi

Werkzeug ==2.0.2
flask ==2.0.3
h2o ==3.46.0.1
matplotlib ==3.5.1
mlflow ==2.12.1
numpy ==1.26.3
pandas ==1.2.1
plotly ==5.9.0
scikit-learn ==1.2.2
seaborn ==0.12.2

models/mlruns/0/43693950abc7462c98e2e308df51d6b1/artifacts/model/metadata/requirements.txt pypi

cffi ==1.15.1
defusedxml ==0.7.1
h2o ==3.46.0.1
matplotlib ==3.6.2
mlflow ==2.12.1
pandas ==1.5.3
wcwidth ==0.2.5

result/deploy/poetry.lock pypi

160 dependencies