https://github.com/shuyib/pimamlsklearn

Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.
https://github.com/shuyib/pimamlsklearn

cross-validation-score datascienceresearch diabetes machine-learning machine-learning-algorithms matplotlib-figures numpy pima-indians-dataset predictive-modeling python-3-5 roc-auc scikit-learn seaborn supervised-learning-estimators test-split-accuracy

Last synced: 7 days ago
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Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.

• Host: GitHub
• URL: https://github.com/shuyib/pimamlsklearn
• Owner: Shuyib
• Created: 2017-06-01T05:23:12.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-09-17T07:16:06.000Z (over 2 years ago)
• Last Synced: 2024-12-22T14:57:39.595Z (7 days ago)
• Topics: cross-validation-score, datascienceresearch, diabetes, machine-learning, machine-learning-algorithms, matplotlib-figures, numpy, pima-indians-dataset, predictive-modeling, python-3-5, roc-auc, scikit-learn, seaborn, supervised-learning-estimators, test-split-accuracy
• Language: Jupyter Notebook
• Size: 1.25 MB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Security: SECURITY.md

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README

        
# pimaMLsklearn

Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset. 

Begin by reviewing the package requirements in requirements.txt

Train - test split accuracy with SVM(Support Vector Classifier) 0.77

Train - test split accuracy with Random Forest Classifier 0.76

Accuracy mean cross-validation score: 0.83 scoring "roc_auc" for Random forest Classifier

Accuracy mean cross-validation score: 0.69 scoring "roc_auc" for Support Vector Classfier

Original Owners:

National Institute of Diabetes and Digestive and Kidney Diseases

# How to run the docker image

Build docker image

```bash

docker build -t pimaMLsklearn .

```

Run the Docker image

```bash

docker run -it -p 9999:9999 pimaMLsklearn:latest

```