{"id":28476154,"url":"https://github.com/eduardoedubox/health_data_analysis","last_synced_at":"2026-05-07T06:34:05.902Z","repository":{"id":296322242,"uuid":"991776955","full_name":"EduardoEduBox/Health_Data_Analysis","owner":"EduardoEduBox","description":"Health data analysis using Jupyter Notebook","archived":false,"fork":false,"pushed_at":"2025-05-30T02:40:33.000Z","size":332,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-07T15:06:10.323Z","etag":null,"topics":["data-analysis","data-science","database","jupyter-notebook","pandas","python"],"latest_commit_sha":null,"homepage":"https://www.canva.com/design/DAGoti_X5z4/zCvTn9m2Zs78SP6bxYUVhA/edit?utm_content=DAGoti_X5z4\u0026utm_campaign=designshare\u0026utm_medium=link2\u0026utm_source=sharebutton","language":"Jupyter Notebook","has_issues":true,"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/EduardoEduBox.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}},"created_at":"2025-05-28T06:18:32.000Z","updated_at":"2025-05-31T01:40:54.000Z","dependencies_parsed_at":"2025-05-30T03:31:35.674Z","dependency_job_id":"23ed0a81-a31c-4744-841b-3b843d8e0d21","html_url":"https://github.com/EduardoEduBox/Health_Data_Analysis","commit_stats":null,"previous_names":["eduardoedubox/health_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/EduardoEduBox/Health_Data_Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EduardoEduBox%2FHealth_Data_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EduardoEduBox%2FHealth_Data_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EduardoEduBox%2FHealth_Data_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EduardoEduBox%2FHealth_Data_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EduardoEduBox","download_url":"https://codeload.github.com/EduardoEduBox/Health_Data_Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EduardoEduBox%2FHealth_Data_Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263024998,"owners_count":23401693,"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":["data-analysis","data-science","database","jupyter-notebook","pandas","python"],"created_at":"2025-06-07T15:06:09.646Z","updated_at":"2026-05-07T06:34:05.829Z","avatar_url":"https://github.com/EduardoEduBox.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sleep Health Statistical Analysis\n\n## Project Overview\n\nThis project investigates the relationships between demographic, lifestyle, and health factors and various sleep outcomes. By leveraging real-world sleep health data, we perform exploratory data analysis, correlation studies, and statistical tests to uncover meaningful insights into sleep patterns and their determinants.\n\n## Dataset\n\n- **Filename:** `sleep_health.csv`\n- **Description:** Records of individuals including personal details (age, gender, occupation), sleep metrics (duration, quality), lifestyle measures (physical activity level, daily steps), and health-related indicators (stress level, heart rate, BMI category, blood pressure, and sleep disorder diagnosis).\n\n## Notebooks\n\n- **`Eduardo.ipynb`**: Data import, cleaning, correlation visualization, ANOVA analyses.\n- **`Jolei.ipynb`**: Data cleaning, correlation strength function, chi-square tests for categorical associations.\n\n## Requirements\n\n- Python 3.7+\n- pip\n\nInstall dependencies:\n\n```powershell\npip install pandas numpy scipy matplotlib seaborn plotly\n```\n\n## Usage\n\n1. Open a terminal in this project folder.\n2. Install required packages.\n3. Launch Jupyter Notebook:\n\n```powershell\njupyter notebook\n```\n\n4. Run cells in `Eduardo.ipynb` and `Jolei.ipynb` to reproduce analysis.\n\n## Insights and Next Steps\n\n- Extend analysis with predictive modeling or external factors.\n- Develop interactive dashboards or detailed reports to communicate findings.\n\n---\n\n_Project completed May 28, 2025_\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feduardoedubox%2Fhealth_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feduardoedubox%2Fhealth_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feduardoedubox%2Fhealth_data_analysis/lists"}