{"id":26496914,"url":"https://github.com/philiptitus/stroke-prediction","last_synced_at":"2026-05-13T05:42:48.867Z","repository":{"id":281475924,"uuid":"945396547","full_name":"philiptitus/Stroke-Prediction","owner":"philiptitus","description":"This Project utilizes 3 Decision Tree Algorithms to make  stroke Prediction models","archived":false,"fork":false,"pushed_at":"2025-03-09T10:24:08.000Z","size":182,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-09T11:23:10.761Z","etag":null,"topics":["decision-tree-classifier","decision-trees","hyperparameter-tuning","random-forest-classifier","sickit-learn","supervised-learning","xgboost","xgboost-algorithm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/philiptitus.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-09T10:21:52.000Z","updated_at":"2025-03-09T10:25:49.000Z","dependencies_parsed_at":"2025-03-09T11:33:42.620Z","dependency_job_id":null,"html_url":"https://github.com/philiptitus/Stroke-Prediction","commit_stats":null,"previous_names":["philiptitus/stroke-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philiptitus%2FStroke-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philiptitus%2FStroke-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philiptitus%2FStroke-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philiptitus%2FStroke-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/philiptitus","download_url":"https://codeload.github.com/philiptitus/Stroke-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244618417,"owners_count":20482316,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["decision-tree-classifier","decision-trees","hyperparameter-tuning","random-forest-classifier","sickit-learn","supervised-learning","xgboost","xgboost-algorithm"],"created_at":"2025-03-20T13:00:08.483Z","updated_at":"2026-05-13T05:42:48.828Z","avatar_url":"https://github.com/philiptitus.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 Stroke Prediction Models  \n\nThis project utilizes Kaggle's stroke prediction dataset to develop and compare three decision tree-based machine learning models:  \n\n1. **XGBoost**  \n2. **Random Forest**  \n3. **Decision Tree (scikit-learn)**  \n\n## 📂 Project Structure  \n\n- 📜 **`model.ipynb`** – Jupyter Notebook containing the implementation and comparison of the three models.  \n- 📜 **`README.md`** – Project documentation.  \n- 📜 **`requirements.txt`** – List of dependencies required to run the project.  \n\n## ⚙️ Installation  \n\nEnsure you have Python installed, then install the required dependencies using:  \n\n```bash\npip install -r requirements.txt\n```  \n\n## 🚀 Usage  \n\n1. Open **`model.ipynb`** in **Jupyter Notebook** or **JupyterLab**.  \n2. Run the notebook cells to train, evaluate, and compare the models.  \n3. Analyze the results and accuracy metrics.  \n\n## 📊 Conclusion  \n\nBased on the accuracy results:  \n\n✅ **XGBoost** provides the highest accuracy.  \n✅ **Random Forest** performs well but is slightly less accurate than XGBoost.  \n⚠️ **Decision Tree** has the lowest accuracy among the three models.  \n\n## 📁 Dataset  \n\nThe dataset used in this project is the **Stroke Prediction Dataset** from Kaggle. You can access it [here](https://www.kaggle.com).  \n\n## 📜 License  \n\nThis project is licensed under the **MIT License**.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphiliptitus%2Fstroke-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphiliptitus%2Fstroke-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphiliptitus%2Fstroke-prediction/lists"}