This project analyzes the Sleep Health and Lifestyle Dataset to uncover physiological markers for sleep quality.
The Core Insight: Contrary to the stigma that higher BMI correlates with "laziness," our analysis of 35 individuals in the "Overweight" category suggests a story of metabolic efficiency. These individuals often possess highly efficient biological systems that, when managed correctly (specifically through stress reduction), outperform other groups in sleep metrics.
For the Overweight demographic, Stress Management is the single highest lever for health improvement.
- High Stress (>7/10): Sleep Quality averages 6.0/10
- Low Stress (<7/10): Sleep Quality jumps to 7.9/10
- Impact: A 31.7% improvement in recovery outcomes.
(Biochemical Context: This group's efficient metabolism requires a switch from the cortisol-driven 'fight or flight' state to the 'rest and digest' state to unlock recovery.)
Walking (Daily Steps) was identified as the primary regulator for cortisol in this group. Higher step counts correlated directly with the ability to maintain the "Low Stress" state.
- Python: Data Manipulation & Analysis
- Pandas: Data Cleaning & Aggregation
- Matplotlib/Seaborn: Visualization & Reporting
Tinyuka_2025_Project.ipynb: The main analysis notebook.Sleep_health_dataset.csv: The raw data.images/: Contains visualization exports.
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Clone the repository.
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Install dependencies:
pip install -r requirements.txt -
Launch Jupyter Lab:
jupyter lab