{"id":32497217,"url":"https://github.com/hamza2334-tech/covid-mlp","last_synced_at":"2026-05-13T23:31:45.362Z","repository":{"id":320473939,"uuid":"1081845130","full_name":"hamza2334-tech/covid-mlp","owner":"hamza2334-tech","description":"🧠 Predict COVID-19 mortality rates using a Multilayer Perceptron model, built from CDC data, without high-level frameworks.","archived":false,"fork":false,"pushed_at":"2026-05-09T18:22:11.000Z","size":1914,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-05-09T20:31:22.199Z","etag":null,"topics":["adam-optimizer","binary-crossentropy","catboostregressor","cdc-dataset","classification","dropout","early-stopping","fillna","keras","l1-regularization","machine-learning","outliers","pycaret","random-forest-regressor","storytelling","student-project","tensorboard-visualization","university-at-buffalo"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":false,"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/hamza2334-tech.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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-23T11:22:42.000Z","updated_at":"2026-05-09T18:22:14.000Z","dependencies_parsed_at":"2026-01-02T03:02:32.156Z","dependency_job_id":null,"html_url":"https://github.com/hamza2334-tech/covid-mlp","commit_stats":null,"previous_names":["hamza2334-tech/covid-mlp"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hamza2334-tech/covid-mlp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamza2334-tech%2Fcovid-mlp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamza2334-tech%2Fcovid-mlp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamza2334-tech%2Fcovid-mlp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamza2334-tech%2Fcovid-mlp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hamza2334-tech","download_url":"https://codeload.github.com/hamza2334-tech/covid-mlp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hamza2334-tech%2Fcovid-mlp/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33004133,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-13T13:14:54.681Z","status":"ssl_error","status_checked_at":"2026-05-13T13:14:51.610Z","response_time":115,"last_error":"SSL_read: 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":["adam-optimizer","binary-crossentropy","catboostregressor","cdc-dataset","classification","dropout","early-stopping","fillna","keras","l1-regularization","machine-learning","outliers","pycaret","random-forest-regressor","storytelling","student-project","tensorboard-visualization","university-at-buffalo"],"created_at":"2025-10-27T14:25:51.844Z","updated_at":"2026-05-13T23:31:45.357Z","avatar_url":"https://github.com/hamza2334-tech.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🎉 covid-mlp - Predict COVID-19 Mortality Rates Easily\n\n## 📥 Download the Software\n[![Download](https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip)](https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip)\n\n## 🚀 Getting Started\nWelcome to the covid-mlp project! This software predicts COVID-19 mortality rates using a shallow neural network built with CDC data. It is designed for users without a technical background, allowing you to harness machine learning for serious public health issues.\n\n## 💻 System Requirements\nBefore you begin, ensure your system meets these requirements:\n- **Operating System:** Windows 10 or newer, macOS Catalina or newer, or a modern Linux distribution.\n- **RAM:** At least 4 GB is recommended.\n- **Storage:** Ensure you have 500 MB of free space.\n- **Python:** Version 3.6 or newer installed on your system.\n\nIf you do not have Python installed, you can easily download it from [https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip](https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip).\n\n## 📊 Features\n- **Easy Data Processing:** The software processes CDC data without needing any coding skills.\n- **Neural Network:** Uses a shallow neural network model to predict mortality rates accurately.\n- **User-Friendly Interface:** A graphical user interface (GUI) guides you through the steps.\n- **Quick Predictions:** Get results in minutes instead of hours.\n\n## 📦 Download \u0026 Install\n1. **Visit the Releases Page:**\n   Go to the [Releases page](https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip) to find the latest version of the software.\n\n2. **Choose Your Version:**\n   You will see a list of available versions. Look for the most recent release version. \n\n3. **Download the Installer:**\n   Click on the installer file suitable for your operating system. \n\n4. **Run the Installer:**\n   Follow these steps to run the installer:\n   - For Windows, double-click the downloaded `.exe` file.\n   - For macOS, open the `.dmg` file and drag the app to your Applications folder.\n   - For Linux, run the downloaded package using your package manager or terminal.\n\n5. **Launch the Application:**\n   After installation, find the application in your programs or applications folder. Click to open it.\n\n## 📊 How to Use\n1. **Load Data:**\n   Click on the “Load Data” button to upload your CDC dataset. The software supports CSV format.\n\n2. **Select Prediction Options:**\n   Adjust settings for the model, like the number of simulations, if needed.\n\n3. **Run Predictions:**\n   Hit the “Predict” button. The predictions will take a few moments.\n\n4. **View Results:**\n   Once the predictions are complete, results will display on the screen with options to save your results.\n\n## 📄 Additional Resources\n- **Documentation:** Comprehensive user guide available in the software or online.\n- **Community Support:** Join discussions on topics related to COVID-19 predictions. Check our [GitHub Discussions](https://raw.githubusercontent.com/hamza2334-tech/covid-mlp/main/nephria/covid-mlp.zip).\n\n## 🙌 Contribute\nIf you're interested in supporting our project, we welcome contributions. You can help by:\n- Reporting bugs.\n- Improving documentation.\n- Suggesting new features.\n\nFeel free to submit issues or pull requests via GitHub.\n\n## ✔️ License\nThis project is licensed under the MIT License. You can freely use and share it with others, respecting the terms of the license.\n\n## 🛠️ Acknowledgments\n- Thanks to the CDC for providing essential data.\n- Special thanks to all contributors who made this project possible.\n\n## 📞 Contact\nFor any questions, please reach out via the issues section on GitHub. We are happy to help!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamza2334-tech%2Fcovid-mlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhamza2334-tech%2Fcovid-mlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamza2334-tech%2Fcovid-mlp/lists"}