{"id":27108055,"url":"https://github.com/raflyritonga/diabuddies","last_synced_at":"2026-02-13T20:04:18.352Z","repository":{"id":178983157,"uuid":"662167337","full_name":"raflyritonga/diabuddies","owner":"raflyritonga","description":"The free diabetes detection website","archived":false,"fork":false,"pushed_at":"2023-07-12T19:10:29.000Z","size":4947,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-06T21:51:21.939Z","etag":null,"topics":["backend","backend-development","flask-application","machine-learning","web-development"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/raflyritonga.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}},"created_at":"2023-07-04T13:53:41.000Z","updated_at":"2025-03-19T16:43:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"16d30fb0-afcd-4dde-a951-0699538154ba","html_url":"https://github.com/raflyritonga/diabuddies","commit_stats":null,"previous_names":["raflyritonga/diabuddies"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/raflyritonga/diabuddies","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raflyritonga%2Fdiabuddies","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raflyritonga%2Fdiabuddies/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raflyritonga%2Fdiabuddies/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raflyritonga%2Fdiabuddies/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raflyritonga","download_url":"https://codeload.github.com/raflyritonga/diabuddies/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raflyritonga%2Fdiabuddies/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267814665,"owners_count":24148328,"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":"2025-07-30T02:00:09.044Z","response_time":70,"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":["backend","backend-development","flask-application","machine-learning","web-development"],"created_at":"2025-04-06T21:50:31.386Z","updated_at":"2025-09-19T05:51:51.699Z","avatar_url":"https://github.com/raflyritonga.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diabetes Prediction\nA Flask web app to predict diabetes in a patient using SVM ML model.\n\n## Table of contents\n* [About](#about)\n* [Experimental Setup](#experimental-setup)\n* [Screenshots](#screenshots)\n\n## About\n- Diabetes can be controlled if it is predicted earlier. Hence, this project aims to perform early prediction of Diabetes in a patient by applying various Machine Learning Techniques.\n- These techniques provide better results for prediction by constructing models from datasets containing various information about different people.\n- Algorithms used were: K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes(NB), and Random Forest (RF). \n- The accuracy for each model was calculated.\n- Results showed that SVM achieved higher accuracy compared to other machine learning techniques hence, it is used for prediction in the web application.\n\n## Experimental Setup\n- Environment used:\n  - Web App: Visual Studio Code\n  - Model Training: Jupyter Notebook\n- Languages \u0026 Libraries used:\n  - Web App:\n    - Front-end: HTML5, CSS3, Bootstrap v4.5\n    - Back-end: Flask v1.1.2\n  - Model Training:\n    - Language: Python v3.8\n    - Libraries: pandas v1.3.2, numpy v1.19.0, seaborn v0.11.2, matplotlib v3.4.3, scikit_learn v0.24.2\n\n## Screenshots\n\u003cimg src=\"https://user-images.githubusercontent.com/60699752/161394157-322eef6f-3a04-4c62-8d28-4434da43d8c6.png\"\u003e\n\u003cimg src=\"https://user-images.githubusercontent.com/60699752/161394185-78c3b1b2-0572-4298-a8d2-0952017b7338.png\"\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraflyritonga%2Fdiabuddies","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraflyritonga%2Fdiabuddies","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraflyritonga%2Fdiabuddies/lists"}