richardcornall / Ensemble-Machine-Learning-Stroke-risk-prediction Public

Notifications You must be signed in to change notification settings
Fork 0
Star 0

98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis.

0 stars 0 forks Branches Tags Activity

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
LICENSE		LICENSE
README.md		README.md
Stroke Machine learning Model.ipynb		Stroke Machine learning Model.ipynb
healthcare-dataset-stroke-data.csv		healthcare-dataset-stroke-data.csv

Repository files navigation

Stroke-risk-prediction

Stroke risk prediction dataset using machine learning

About

98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis.

python machine-learning random-forest machine-learning-algorithms ensemble-learning smote roc-auc precision-recall-curve stroke-prediction stratified-k-fold

Report repository

Releases

No releases published

Packages

No packages published

Languages

Jupyter Notebook 100.0%