{"id":26906018,"url":"https://github.com/riciokzz/stroke-prediction","last_synced_at":"2025-04-01T10:59:38.141Z","repository":{"id":283337522,"uuid":"951447802","full_name":"Riciokzz/Stroke-Prediction","owner":"Riciokzz","description":"Stroke Prediction project.","archived":false,"fork":false,"pushed_at":"2025-03-19T17:36:53.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-19T18:35:28.484Z","etag":null,"topics":["gradient-boosting","logistic-regression","machine-learning","random-forest","stroke","stroke-prediction"],"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/Riciokzz.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":"2025-03-19T17:36:10.000Z","updated_at":"2025-03-19T17:39:55.000Z","dependencies_parsed_at":"2025-03-19T18:46:56.433Z","dependency_job_id":null,"html_url":"https://github.com/Riciokzz/Stroke-Prediction","commit_stats":null,"previous_names":["riciokzz/stroke-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Riciokzz%2FStroke-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Riciokzz%2FStroke-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Riciokzz%2FStroke-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Riciokzz%2FStroke-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Riciokzz","download_url":"https://codeload.github.com/Riciokzz/Stroke-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246628213,"owners_count":20808106,"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":["gradient-boosting","logistic-regression","machine-learning","random-forest","stroke","stroke-prediction"],"created_at":"2025-04-01T10:59:37.564Z","updated_at":"2025-04-01T10:59:38.134Z","avatar_url":"https://github.com/Riciokzz.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Stroke Prediction\n\n## Introduction\n\nAccording to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, \nresponsible for approximately 11% of total deaths.\n\nThe aim of this work is to create a machine learning model, which could predict if the patient is likely to get a \nstroke - being able to determine which patients have high stroke risk will allow your doctors to advise them and \ntheir families on how to act in case of an emergency. We will pretend to be a data analyst working in \nThe Johns Hopkins Hospital.\n\nWe will check for data quality, correlations and relation of features. We will perform statistical inference, \nform hypothesis base by what we find in our data and create machine learning models to predict patients \nwho have risk of the stroke.\n\nTo install all necessary libraries use `pip install -r requirements.txt`\n\nTo deploy model follow  the steps:\n1. Pick model from models and edit `app.py` model_name. (Default model: Logistic Regression)\n2. Run this code `uvicorn app:app --reload` in terminal.\n3. Edit `predict_data.py` file to input values.\n4. Run this code `python predict_data.py` in terminal.\n5. Answer will be printed in console: `{'prediction': 0} or {'prediction': 1}`. 0 for No and 1 for Yes\n\n\nDataset can be downloaded from \n[Kaggle](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset/data).\n\n\n## License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Contact Information\n[Email](ricardas.poskrebysev@gmail.com)\n[LinkedIn](https://www.linkedin.com/in/ri%C4%8Dardas-poskreby%C5%A1evas-665207206/)\n[GitHub](https://github.com/Riciokzz)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Friciokzz%2Fstroke-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Friciokzz%2Fstroke-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Friciokzz%2Fstroke-prediction/lists"}