{"id":27471109,"url":"https://github.com/swish78/diabeto-predictor","last_synced_at":"2025-04-16T01:39:11.098Z","repository":{"id":259099711,"uuid":"876312196","full_name":"Swish78/Diabeto-Predictor","owner":"Swish78","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-21T19:15:23.000Z","size":259,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-10-22T11:35:03.545Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"JavaScript","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/Swish78.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":"2024-10-21T18:53:45.000Z","updated_at":"2024-10-21T19:15:27.000Z","dependencies_parsed_at":"2024-10-22T17:17:26.610Z","dependency_job_id":null,"html_url":"https://github.com/Swish78/Diabeto-Predictor","commit_stats":null,"previous_names":["swish78/diabeto-predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Swish78%2FDiabeto-Predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Swish78%2FDiabeto-Predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Swish78%2FDiabeto-Predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Swish78%2FDiabeto-Predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Swish78","download_url":"https://codeload.github.com/Swish78/Diabeto-Predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249182790,"owners_count":21226123,"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":[],"created_at":"2025-04-16T01:39:10.505Z","updated_at":"2025-04-16T01:39:11.073Z","avatar_url":"https://github.com/Swish78.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diabeto-Predictor\n\nDiabeto-Predictor is a Flask-based API with a React frontend that predicts the likelihood of diabetes based on various input features. It provides data visualizations and predictions powered by a machine learning model. The backend is deployed on AWS EC2, with data stored on AWS RDS (MySQL) and static files hosted on AWS S3.\n\n## Features\n- **Diabetes Prediction API**: Get predictions on diabetes risk based on input features.\n- **Data Visualization**: Explore visualizations like histograms, scatter plots, and box plots.\n- **Machine Learning Model**: Powered by a RandomForest and GradientBoosting voting classifier.\n- **AWS Integration**:\n  - EC2 for backend deployment.\n  - RDS (MySQL) for database management.\n  - S3 for frontend hosting.\n\n## Technology Stack\n- **Backend**: Flask, Pandas, Joblib\n- **Frontend**: React, Tailwind CSS\n- **Machine Learning**: Scikit-learn, Imbalanced-learn (SMOTE), Voting Classifier (RandomForest, GradientBoosting)\n- **Data Visualization**: Plotly, Seaborn, Matplotlib\n- **AWS**:\n  - EC2 for Flask server\n  - RDS (MySQL) for database\n  - S3 for static assets and frontend\n\n## Prerequisites\n- Python 3.x\n- Node.js and npm\n- AWS account with access to EC2, RDS, and S3 services\n- MySQL installed locally or accessible via RDS\n\n## Model Pipeline\nThe machine learning model is trained on the following features:\n- **Numerical**: Age, BMI, FBS, HbA1c\n- **Categorical**: Gender, Blood Pressure, Family History of Diabetes, Smoking, Diet, Exercise\n\nThe model uses a voting classifier composed of:\n- RandomForestClassifier\n- GradientBoostingClassifier\n\n## License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswish78%2Fdiabeto-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fswish78%2Fdiabeto-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswish78%2Fdiabeto-predictor/lists"}