{"id":27736723,"url":"https://github.com/vishalgaud17/stroke","last_synced_at":"2026-04-14T04:01:44.682Z","repository":{"id":290346144,"uuid":"974125071","full_name":"VishalGaud17/Stroke","owner":"VishalGaud17","description":"A simple Streamlit web app that predicts stroke risk based on user input features like age, BMI, glucose level, and lifestyle factors, using a pre-trained machine learning model.","archived":false,"fork":false,"pushed_at":"2025-04-28T09:54:44.000Z","size":218,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-28T10:49:59.283Z","etag":null,"topics":["machine-learning","numpy","pandas","python","scikit-learn","streamlit"],"latest_commit_sha":null,"homepage":"https://stroke-awz8xzb8pnmmtekkn2yd4e.streamlit.app/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/VishalGaud17.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-04-28T09:41:28.000Z","updated_at":"2025-04-28T10:00:06.000Z","dependencies_parsed_at":"2025-04-28T10:50:03.689Z","dependency_job_id":"c97717aa-2107-4e52-b147-4ebbb87c7d37","html_url":"https://github.com/VishalGaud17/Stroke","commit_stats":null,"previous_names":["vishalgaud17/stroke"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VishalGaud17%2FStroke","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VishalGaud17%2FStroke/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VishalGaud17%2FStroke/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VishalGaud17%2FStroke/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VishalGaud17","download_url":"https://codeload.github.com/VishalGaud17/Stroke/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251329947,"owners_count":21572211,"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":["machine-learning","numpy","pandas","python","scikit-learn","streamlit"],"created_at":"2025-04-28T14:29:46.591Z","updated_at":"2026-04-14T04:01:39.661Z","avatar_url":"https://github.com/VishalGaud17.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 Stroke Risk Prediction App\n\nAn interactive Streamlit web app that predicts the risk of stroke based on user health and lifestyle information.  \nIt uses a machine learning model trained on healthcare data to provide instant predictions.\n\n---\n\n## 🚀 Features\n\n- Predicts stroke risk based on:\n  - Age\n  - BMI\n  - Average glucose level\n  - Gender\n  - Smoking status\n  - Hypertension\n  - Heart disease\n  - Marital status\n  - Residence type\n  - Work type\n- Friendly and simple user interface.\n- Instant feedback with health advisory messages.\n- Error handling for model loading.\n\n---\n\n## 🛠️ Tech Stack\n\n- Python\n- Streamlit\n- Pickle (for loading ML model)\n- NumPy\n\n---\n\n## 📥 Setup Instructions\n\n1. **Clone this repository:**\n   ```bash\n   git clone https://github.com/VishalGaud17/Stroke.git\n   cd Stroke\n   ```\n\n2. **Install dependencies:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Run the Streamlit app:**\n   ```bash\n   streamlit run stroke.py\n   ```\n\n---\n\n## 📂 Project Structure\n\n```plaintext\nStroke/\n├── stroke.py         # Main Streamlit application\n├── stroke.pkl        # Pre-trained machine learning model\n└── README.md         # Project documentation (you are reading it)\n```\n\n---\n\n## 🤔 How It Works\n\n1. Enter your details such as age, BMI, glucose level, etc.\n2. The app encodes the inputs appropriately.\n3. Inputs are passed to the machine learning model.\n4. The model predicts whether there is a stroke risk.\n5. An advisory message is displayed based on the prediction.\n\n---\n\n## 👨‍💻 Author\n\n- [Vishal Gaud](https://github.com/VishalGaud17)\n\n---\n\n## ⭐ Support\n\nIf you find this project useful, please consider giving it a ⭐ on GitHub!\n\n---\n\n## ⚠️ Disclaimer\n\nThis app is for educational and informational purposes only.  \nIt is not a substitute for professional medical advice, diagnosis, or treatment.  \nAlways seek the advice of your physician or other qualified health provider for any medical condition.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishalgaud17%2Fstroke","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvishalgaud17%2Fstroke","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishalgaud17%2Fstroke/lists"}