This project explores how different living arrangements impact self-reported happiness, stress levels, and overall wellbeing.
As a data analytics professional, I approached a real-life question through an analytical lens:
“Does living arrangement (e.g., living with parents, renting privately, or student housing) influence wellbeing?”
Instead of relying on opinions, I used data to uncover measurable patterns and insights.
How does living arrangement relate to:
- Self-reported happiness
- Stress levels
- Sleep quality
- Physical activity
- Source: Kaggle
- Dataset: Integrated Lifestyle, Sleep, and Mental Health Dataset
- Description: Combined dataset capturing lifestyle, physiological, and psychological indicators
- Total Records: 375
- Total Variables: 44
- Duplicate Rows Removed: 16
- Data Types: Numerical and Categorical
- Psychological wellbeing indicators
- Lifestyle and health behaviors
- Sleep patterns and disorders
- Demographic and social variables
- Microsoft SQL Server (SSMS) – Data cleaning & transformation
- SQL (T-SQL) – Data querying and preparation
- Tableau Public – Data visualization and dashboard creation
- Removed duplicate records
- Standardized categorical values for consistency
- Validated data types and handled missing values
- Prepared dataset for visualization and analysis
An interactive dashboard was developed to visualize relationships between living arrangements and wellbeing indicators.
- Happiness scores by living arrangement
- Stress and sleep quality comparison
- Physical activity levels across housing types
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Higher Happiness at Home: Individuals living at home (with parents) reported higher happiness levels compared to those in privately rented or university housing.
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Higher Stress Despite Higher Happiness: Those living at home also showed slightly higher stress levels, potentially linked to lower sleep quality and reduced sleep duration.
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Physical Activity Trends: Individuals in privately rented housing had the highest physical activity levels (58.6), followed by those living at home (57.8), and university housing (52.5).
- Cleaning and structuring real-world datasets using SQL in SSMS
- Identifying meaningful patterns across lifestyle and wellbeing variables
- Building dashboards in Tableau to communicate data-driven insights
- Applying analytical thinking to real-world questions
Living at home may provide emotional support (higher happiness), but could also introduce environmental or social factors that increase stress and reduce sleep quality.