This project analyzes the relationship between digital lifestyle behaviors and mental wellness among Gen Z individuals. Using Python, Excel, and Tableau, the study explores how factors such as sleep duration, screen time, burnout risk, and exercise frequency influence wellbeing and anxiety levels.
🛠 Tools & Technologies
--Python (Pandas, NumPy)
--Jupyter Notebook
--Excel (Pivot Tables)
--Tableau (Interactive Dashboard)
📊 Dataset
Synthetic dataset containing information on:
--Gender
--Country
--Daily Sleep Hours
--Screen Time Hours
--Study Hours
--Exercise Frequency
--Anxiety Score
--Wellbeing Index
--Burnout Risk
--Sleep Category
🔎 Exploratory Data Analysis (Python)
Performed:
--Data cleaning and validation
--Correlation analysis
--Group-based comparisons
--Statistical summaries
Key Correlations:
--Sleep vs Wellbeing: Strong positive correlation (r ≈ 0.70)
--Screen Time vs Anxiety: Weak positive correlation (r ≈ 0.09)
📈 Excel Analysis
Created Pivot Tables to analyze:
--Sleep Category vs Average Wellbeing
--Country vs Average Study Hours
Key Finding: Individuals with higher sleep duration show significantly higher wellbeing scores compared to low sleep groups.
📊 Tableau Dashboard
Built interactive dashboard including:
--Burnout Risk vs Wellbeing (Bar Chart)
--Sleep vs Wellbeing (Scatter + Trend Line)
--Screen Time vs Anxiety (Scatter + Trend Line)
--Dashboard visually demonstrates how lifestyle factors impact mental health outcomes.
💡 Key Insights
--Sleep duration is the strongest predictor of wellbeing.
--High burnout risk individuals have the lowest average wellbeing.
--Screen time has minimal impact on anxiety compared to lifestyle factors.
--Healthy habits (sleep, exercise) correlate positively with mental wellness.
🚀 Skills Demonstrated
--Exploratory Data Analysis (EDA)
--Data Cleaning & Preparation
--Correlation & Statistical Interpretation
--Business Insight Generation
--Dashboard Development
📌 Conclusion
This project highlights the importance of lifestyle balance in improving mental wellness among Gen Z. Through structured analysis and visualization, it demonstrates how data can be used to derive actionable insights in behavioral analytics.