https://github.com/eduardoedubox/health_data_analysis
Health data analysis using Jupyter Notebook
https://github.com/eduardoedubox/health_data_analysis
data-analysis data-science database jupyter-notebook pandas python
Last synced: 8 months ago
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Health data analysis using Jupyter Notebook
- Host: GitHub
- URL: https://github.com/eduardoedubox/health_data_analysis
- Owner: EduardoEduBox
- Created: 2025-05-28T06:18:32.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-05-30T02:40:33.000Z (9 months ago)
- Last Synced: 2025-06-07T15:06:10.323Z (8 months ago)
- Topics: data-analysis, data-science, database, jupyter-notebook, pandas, python
- Language: Jupyter Notebook
- Homepage: https://www.canva.com/design/DAGoti_X5z4/zCvTn9m2Zs78SP6bxYUVhA/edit?utm_content=DAGoti_X5z4&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton
- Size: 324 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sleep Health Statistical Analysis
## Project Overview
This 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.
## Dataset
- **Filename:** `sleep_health.csv`
- **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).
## Notebooks
- **`Eduardo.ipynb`**: Data import, cleaning, correlation visualization, ANOVA analyses.
- **`Jolei.ipynb`**: Data cleaning, correlation strength function, chi-square tests for categorical associations.
## Requirements
- Python 3.7+
- pip
Install dependencies:
```powershell
pip install pandas numpy scipy matplotlib seaborn plotly
```
## Usage
1. Open a terminal in this project folder.
2. Install required packages.
3. Launch Jupyter Notebook:
```powershell
jupyter notebook
```
4. Run cells in `Eduardo.ipynb` and `Jolei.ipynb` to reproduce analysis.
## Insights and Next Steps
- Extend analysis with predictive modeling or external factors.
- Develop interactive dashboards or detailed reports to communicate findings.
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_Project completed May 28, 2025_