{"id":30714710,"url":"https://github.com/shervinnd/blood-donor-availability-predictor","last_synced_at":"2025-10-11T04:16:49.654Z","repository":{"id":311459209,"uuid":"1043766962","full_name":"shervinnd/Blood-Donor-Availability-Predictor","owner":"shervinnd","description":"A deep learning model to predict blood donor availability using TensorFlow and sklearn. Features data preprocessing, neural network training, and ROC curve visualization. Achieve high accuracy in predicting donor status! 🩺💉","archived":false,"fork":false,"pushed_at":"2025-08-24T15:38:01.000Z","size":60,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-03T05:20:02.739Z","etag":null,"topics":["binary-classification","blood-donation","blood-donor-prediction","data-preprocessing","deep-learning","healthcare-ai","machine-learning","medical-data-analysis","neural-network","predictive-modeling","python","roc-curve","scikit-learn","tensorflow"],"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/shervinnd.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":"2025-08-24T15:22:05.000Z","updated_at":"2025-08-25T05:19:39.000Z","dependencies_parsed_at":"2025-08-24T20:28:01.915Z","dependency_job_id":null,"html_url":"https://github.com/shervinnd/Blood-Donor-Availability-Predictor","commit_stats":null,"previous_names":["shervinnd/blood-donor-availability-predictor"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shervinnd/Blood-Donor-Availability-Predictor","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shervinnd%2FBlood-Donor-Availability-Predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shervinnd%2FBlood-Donor-Availability-Predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shervinnd%2FBlood-Donor-Availability-Predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shervinnd%2FBlood-Donor-Availability-Predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shervinnd","download_url":"https://codeload.github.com/shervinnd/Blood-Donor-Availability-Predictor/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shervinnd%2FBlood-Donor-Availability-Predictor/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006247,"owners_count":26084060,"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":"2025-10-11T02:00:06.511Z","response_time":55,"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":["binary-classification","blood-donation","blood-donor-prediction","data-preprocessing","deep-learning","healthcare-ai","machine-learning","medical-data-analysis","neural-network","predictive-modeling","python","roc-curve","scikit-learn","tensorflow"],"created_at":"2025-09-03T05:14:55.751Z","updated_at":"2025-10-11T04:16:49.607Z","avatar_url":"https://github.com/shervinnd.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Blood Donor Availability Predictor 🩺💉\n\nWelcome to the **Blood Donor Availability Predictor** project! This\nrepository contains a deep learning model built with TensorFlow to\npredict whether blood donors are available to donate based on their\ndonation history and blood group. 🚀\n\n## 📖 Project Overview\n\nThis project uses a neural network to classify blood donors as available\n(`Yes`) or unavailable (`No`) for donation. The dataset includes\nfeatures like months since first donation, number of donations, pints\ndonated, and blood group. The model is trained with advanced techniques\nlike batch normalization, dropout, and learning rate scheduling to\nachieve high accuracy. 📊\n\n### Key Features\n\n-   **Data Preprocessing**: Handles categorical data (blood group) with\n    label encoding and scales numerical features. 🛠️\n-   **Neural Network**: A deep learning model with multiple layers,\n    batch normalization, and dropout for robust predictions. 🧠\n-   **Evaluation**: Includes accuracy metrics and ROC curve\n    visualization for model performance. 📈\n-   **Visualization**: Plots ROC curves to assess the model's\n    discriminatory power. 🎨\n\n## 🛠️ Installation\n\nTo run this project, ensure you have Python 3.8+ installed. Follow these\nsteps:\n\n1.  Clone the repository:\n\n    ``` bash\n    git clone https://github.com/shervinnd/blood-donor-predictor.git\n    cd blood-donor-predictor\n    ```\n\n2.  Install dependencies:\n\n    ``` bash\n    pip install -r requirements.txt\n    ```\n\n3.  Ensure you have the dataset (`blood_donor_dataset.csv`) in the\n    project directory.\n\n## 📋 Requirements\n\n-   pandas\n-   numpy\n-   scikit-learn\n-   tensorflow\n-   matplotlib\n\nInstall them using:\n\n``` bash\npip install pandas numpy scikit-learn tensorflow matplotlib\n```\n\n## 🚀 Usage\n\n1.  **Prepare the Dataset**: Place `blood_donor_dataset.csv` in the\n    project root.\n2.  **Run the Notebook**: Open `donor.ipynb` in Jupyter Notebook or\n    Google Colab.\n3.  **Train the Model**: Execute the cells to preprocess data, train the\n    model, and evaluate predictions.\n4.  **Visualize Results**: Check the ROC curve and prediction\n    comparisons in the output.\n\n## 📊 Model Details\n\n-   **Input Features**:\n    -   `months_since_first_donation`\n    -   `number_of_donation`\n    -   `pints_donated`\n    -   `blood_group` (encoded)\n-   **Target**: `availability` (Yes/No)\n-   **Architecture**:\n    -   Dense layers: 256, 128, 64, 32 units with ReLU activation\n    -   Batch normalization and dropout for regularization\n    -   Sigmoid output for binary classification\n-   **Training**:\n    -   Optimizer: Adam (learning rate = 0.001)\n    -   Loss: Binary Crossentropy\n    -   Epochs: 200 with early stopping and learning rate reduction\n\n## 📈 Results\n\nThe model achieves high accuracy on the test set, with detailed\npredictions compared to actual values. The ROC curve visualizes the\ntrade-off between true positive and false positive rates, with AUC\nindicating model performance. 📉\n\n## 🤝 Contributing\n\nContributions are welcome! Feel free to: - Open issues for bugs or\nfeature requests 🐛 - Submit pull requests with improvements 🔧 -\nSuggest enhancements to the model or preprocessing pipeline 🌟\n\n## 📜 License\n\nThis project is licensed under the MIT License. See the\n[LICENSE](LICENSE) file for details.\n\n## 📧 Contact\n\nFor questions or feedback, reach out via GitHub Issues or email at\nshervindanesh8282@gmail.com\n\nHappy coding and predicting! 🚀🩺\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshervinnd%2Fblood-donor-availability-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshervinnd%2Fblood-donor-availability-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshervinnd%2Fblood-donor-availability-predictor/lists"}