https://github.com/sevdanurgenc/diabetes-health-indicators-ml-and-qml
Compare classical machine learning and quantum machine learning techniques for feature selection and classification on the Diabetes Health Indicators dataset using Cirq and Scikit-Learn.
https://github.com/sevdanurgenc/diabetes-health-indicators-ml-and-qml
cirq quantum-computing quantum-machine-learning
Last synced: 7 months ago
JSON representation
Compare classical machine learning and quantum machine learning techniques for feature selection and classification on the Diabetes Health Indicators dataset using Cirq and Scikit-Learn.
- Host: GitHub
- URL: https://github.com/sevdanurgenc/diabetes-health-indicators-ml-and-qml
- Owner: SevdanurGENC
- License: mit
- Created: 2024-08-01T10:10:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-01T14:30:00.000Z (about 1 year ago)
- Last Synced: 2025-01-29T03:37:42.870Z (9 months ago)
- Topics: cirq, quantum-computing, quantum-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 4.73 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Diabetes-Health-Indicators-ML-And-QML
Compare classical machine learning and quantum machine learning techniques for feature selection and classification on the Diabetes Health Indicators dataset using Cirq and Scikit-Learn.
## Links to the study and download file referencing the dataset:
- https://www.cdc.gov/pcd/issues/2019/19_0109.htm
- https://www.kaggle.com/code/alexteboul/diabetes-health-indicators-dataset-notebook/output
- https://www.kaggle.com/datasets/alexteboul/diabetes-health-indicators-dataset/data