{"id":30360528,"url":"https://github.com/ksganni/diabetes-prediction-using-machine-learning","last_synced_at":"2026-04-28T12:02:15.491Z","repository":{"id":308989947,"uuid":"1034776150","full_name":"ksganni/DIABETES-PREDICTION-USING-MACHINE-LEARNING","owner":"ksganni","description":"🔬 A machine learning project that predicts diabetes likelihood using patient health metrics and multiple classification algorithms.","archived":false,"fork":false,"pushed_at":"2025-08-09T03:46:58.000Z","size":497,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-19T14:40:04.522Z","etag":null,"topics":["jupyter-notebook","matplotlib","numpy","pandas","python","scikitlearn-machine-learning","seaborn"],"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/ksganni.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,"zenodo":null}},"created_at":"2025-08-09T01:03:18.000Z","updated_at":"2025-08-09T03:58:36.000Z","dependencies_parsed_at":null,"dependency_job_id":"ff8a6a50-be04-46f1-beb1-b1fdc1dce9c6","html_url":"https://github.com/ksganni/DIABETES-PREDICTION-USING-MACHINE-LEARNING","commit_stats":null,"previous_names":["ksganni/diabetes-prediction-using-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ksganni/DIABETES-PREDICTION-USING-MACHINE-LEARNING","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ksganni%2FDIABETES-PREDICTION-USING-MACHINE-LEARNING","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ksganni%2FDIABETES-PREDICTION-USING-MACHINE-LEARNING/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ksganni%2FDIABETES-PREDICTION-USING-MACHINE-LEARNING/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ksganni%2FDIABETES-PREDICTION-USING-MACHINE-LEARNING/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ksganni","download_url":"https://codeload.github.com/ksganni/DIABETES-PREDICTION-USING-MACHINE-LEARNING/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ksganni%2FDIABETES-PREDICTION-USING-MACHINE-LEARNING/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32379629,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-28T11:25:28.583Z","status":"ssl_error","status_checked_at":"2026-04-28T11:25:05.435Z","response_time":56,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["jupyter-notebook","matplotlib","numpy","pandas","python","scikitlearn-machine-learning","seaborn"],"created_at":"2025-08-19T14:23:00.837Z","updated_at":"2026-04-28T12:02:15.486Z","avatar_url":"https://github.com/ksganni.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🩺 Diabetes Prediction Project\n\nA beginner-friendly machine learning project that predicts the likelihood of diabetes using the Pima Indians Diabetes Database. This project demonstrates a complete ML workflow from data exploration to model deployment.\n\n---\n\n## 📋 Project Overview\n\nThis project aims to predict diabetes occurrence using various patient health metrics. The workflow includes data preprocessing, model training, evaluation, and comparison of multiple classification algorithms. \n\n---\n\n## 🎯 Goals\n\n- Demonstrate end-to-end machine learning workflow\n- Compare performance of multiple classification algorithms\n- Provide clear visualizations and interpretable results\n- Create a foundation for more advanced diabetes prediction models\n\n---\n\n## 🛠️ Tech Stack\n\n- **Python** 3.8+\n- **Data Manipulation**: pandas, numpy\n- **Machine Learning**: scikit-learn\n- **Visualization**: matplotlib, seaborn\n- **Environment**: Jupyter Notebook\n\n---\n\n## 📁 Project Structure\n\n```\ndiabetes-prediction/\n├── diabetes_prediction.ipynb    # Main notebook with complete workflow\n├── README.md                    # Project documentation\n├── requirements.txt             # Python dependencies\n├── diabetes.csv                 # Dataset file\n└── results/                     # Generated outputs\n    ├── feature_histograms.png\n    ├── correlation_heatmap.png\n    ├── model_accuracy_comparison.png\n    ├── roc_curves.png\n    └── best_model.pkl\n```\n\n---\n\n## 📊 Dataset\n\n**Source**: Pima Indians Diabetes Database from [Kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n\n**Features**:\n- `Pregnancies`: Number of times pregnant\n- `Glucose`: Plasma glucose concentration\n- `BloodPressure`: Diastolic blood pressure (mm Hg)\n- `SkinThickness`: Triceps skin fold thickness (mm)\n- `Insulin`: 2-Hour serum insulin (mu U/ml)\n- `BMI`: Body mass index (weight in kg/(height in m)^2)\n- `DiabetesPedigreeFunction`: Diabetes pedigree function\n- `Age`: Age in years\n\n**Target**: `Outcome` (0 = No diabetes, 1 = Diabetes)\n\n---\n\n## 🚀 Getting Started\n\n---\n\n### Installation\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/yourusername/diabetes-prediction.git\n   cd diabetes-prediction\n   ```\n\n2. **Create a virtual environment**\n   ```bash\n   python -m venv venv\n   \n   # On Windows\n   venv\\Scripts\\activate\n   \n   # On macOS/Linux\n   source venv/bin/activate\n   ```\n\n3. **Install dependencies**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Download the dataset**\n   - Download `diabetes.csv` from [Kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n   - Place it in the project root directory\n\n---\n\n### Running the Project\n\n1. **Launch Jupyter Notebook**\n   ```bash\n   jupyter notebook\n   ```\n\n2. **Open and run the notebook**\n   - Open `diabetes_prediction.ipynb`\n   - Run cells sequentially from top to bottom\n   - Results will be saved in the `results/` directory\n\n---\n\n## 📈 Model Performance\n\nThe project implements and compares three machine learning algorithms:\n\n| Model | Accuracy | Precision | Recall | F1 Score | ROC AUC |\n|-------|----------|-----------|--------|----------|---------|\n| **Random Forest** | 77.9% | 0.717 | 0.611 | 0.66 | 0.818 |\n| **K-Nearest Neighbors** | 75.3% | 0.660 | 0.611 | 0.635 | - |\n| **Logistic Regression** | 70.8% | 0.600 | 0.500 | 0.545 | 0.813 |\n\n---\n\n## 📊 Key Visualizations\n\nThe notebook generates several insightful visualizations:\n\n- **Feature Distribution Histograms**: Understanding data distributions\n- **Correlation Heatmap**: Feature relationships and multicollinearity\n- **Model Accuracy Comparison**: Side-by-side performance metrics\n- **ROC Curves**: Model discrimination capability\n- **Feature Importance Plot**: Most influential predictors\n\n---","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksganni%2Fdiabetes-prediction-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fksganni%2Fdiabetes-prediction-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fksganni%2Fdiabetes-prediction-using-machine-learning/lists"}