https://github.com/muneeb706/patient-no-show
Patient No Show Predictive Modeling Using RIPPER and Hoeffding Trees Algorithms
https://github.com/muneeb706/patient-no-show
binary-classification hoeffding-trees interpretable-machine-learning predictive-modeling ripper shap
Last synced: 2 months ago
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Patient No Show Predictive Modeling Using RIPPER and Hoeffding Trees Algorithms
- Host: GitHub
- URL: https://github.com/muneeb706/patient-no-show
- Owner: muneeb706
- Created: 2021-04-21T07:59:52.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-17T04:06:47.000Z (about 4 years ago)
- Last Synced: 2025-01-31T10:42:15.095Z (4 months ago)
- Topics: binary-classification, hoeffding-trees, interpretable-machine-learning, predictive-modeling, ripper, shap
- Language: Jupyter Notebook
- Homepage:
- Size: 12.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Patient No Show Predictive Modeling
This project contains [notebook](https://github.com/muneeb706/patient-no-show/blob/main/Patient_NoShow_Predictive_Modeling.ipynb) and [final report](https://github.com/muneeb706/patient-no-show/blob/main/Muneeb_Final_Report.pdf) that explains all the steps followed for evaluating performance of RIPPER and Hoeffding Tree Algorithm in solving patient no-show problem. For this project, SHAP (SHapley Additive exPlanations) has been used for the feature selection despite the fact that this method does not have any correlation with the methods used for prediction but, I wanted to study, how this method helps us in achieving interpretability in feature selection process regardless of the type of predictive models.
I have used [Kaggle dataset](https://www.kaggle.com/joniarroba/noshowappointments) for this study.
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