{"id":28476098,"url":"https://github.com/shivsharcode/diabetes-prediction-new","last_synced_at":"2026-04-12T13:38:08.067Z","repository":{"id":296757309,"uuid":"994379880","full_name":"shivsharcode/Diabetes-Prediction-New","owner":"shivsharcode","description":"DIABETES PREDICTION MODEL","archived":false,"fork":false,"pushed_at":"2025-06-01T21:17:06.000Z","size":49712,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-01T23:36:26.650Z","etag":null,"topics":["flask","jupyter-notebook","logistic-regression","machine-learning","matplotlib","numpy","pandas","python","scikit-learn"],"latest_commit_sha":null,"homepage":"https://diabetes-prediction-new-uyn8.onrender.com","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/shivsharcode.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":"2025-06-01T20:13:16.000Z","updated_at":"2025-06-01T21:17:10.000Z","dependencies_parsed_at":"2025-06-02T06:28:09.043Z","dependency_job_id":null,"html_url":"https://github.com/shivsharcode/Diabetes-Prediction-New","commit_stats":null,"previous_names":["shivsharcode/diabetes-prediction-new"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shivsharcode/Diabetes-Prediction-New","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shivsharcode%2FDiabetes-Prediction-New","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shivsharcode%2FDiabetes-Prediction-New/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shivsharcode%2FDiabetes-Prediction-New/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shivsharcode%2FDiabetes-Prediction-New/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shivsharcode","download_url":"https://codeload.github.com/shivsharcode/Diabetes-Prediction-New/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shivsharcode%2FDiabetes-Prediction-New/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266904553,"owners_count":24004128,"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","status":"online","status_checked_at":"2025-07-24T02:00:09.469Z","response_time":99,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["flask","jupyter-notebook","logistic-regression","machine-learning","matplotlib","numpy","pandas","python","scikit-learn"],"created_at":"2025-06-07T15:06:02.804Z","updated_at":"2026-04-12T13:38:08.013Z","avatar_url":"https://github.com/shivsharcode.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🩺 Diabetes Prediction Web App\n\nThis is a **Flask-based** web application that predicts whether a person is diabetic or non-diabetic based on input medical parameters. The prediction is powered by a machine learning model trained on the Pima Indians Diabetes Dataset.\n\n## ✨ Features\n\n- 🖥️ User-friendly web interface for inputting medical data\n- 🤖 Predicts diabetes status using a trained ML model\n- 📊 Visual feedback and result display\n- 🖼️ Reference images and design assets included\n\n## 🚀 Deployment\n\n**🌐 Deployed Project:**  \n🔗 [https://diabetes-prediction-new-uyn8.onrender.com](https://diabetes-prediction-new-uyn8.onrender.com)\n\n## 📁 Project Structure\n\n```\napp.py\ndbpred.py\ndiabetes_model.pkl\nscaler.pkl\nrequirements.txt\nrender.yaml\nML-MODEL/\nstatic/\ntemplates/\n```\n\n- `app.py`: Main Flask application.\n- `dbpred.py`: Loads the ML model and scaler, and provides the prediction function.\n- `diabetes_model.pkl`: Trained machine learning model.\n- `scaler.pkl`: Scaler used for preprocessing input data.\n- `requirements.txt`: Python dependencies.\n- `render.yaml`: Deployment configuration for Render.com.\n- `static/`: Static files (CSS, JS, images).\n- `templates/`: HTML templates.\n\n## 🛠️ Setup Instructions\n\n1. **Clone the repository**\n\n   ```sh\n   git clone https://github.com/shivsharcode/Diabetes-Prediction-New.git\n   cd Diabetes-Prediction-New/project\n   ```\n\n2. **Install dependencies**\n\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n3. **Ensure model files are present**\n\n   Make sure `diabetes_model.pkl` and `scaler.pkl` are in the project root.\n\n4. **Run the application**\n\n   ```sh\n   python app.py\n   ```\n\n5. **Open in browser**\n\n   Visit [http://localhost:5000](http://localhost:5000) to use the app.\n\n## 📜 License\n\nThis project is for educational purposes.\n\n---\n\n**👤 Author:** [shivsharcode](https://github.com/shivsharcode)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshivsharcode%2Fdiabetes-prediction-new","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshivsharcode%2Fdiabetes-prediction-new","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshivsharcode%2Fdiabetes-prediction-new/lists"}