Employee Well-Being Analysis: A Capstone Project

Overview

This project explores key factors affecting employee well-being, such as stress levels, work-life balance, productivity, and mental health conditions, across various job roles, work environments, and regions. Using Python and interactive visualizations built with Bokeh, the project uncovers actionable insights to help organizations improve employee satisfaction and workplace health.

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Key Features

• Interactive Visualizations: Explore stress levels, productivity, and satisfaction trends through intuitive charts and tabbed layouts.
• Data-Driven Insights:
  • Relationship between work hours and stress.
  • Impact of work-life balance on well-being.
  • Access to mental health resources by job roles.
  • Regional differences in stress and remote work satisfaction.
• Actionable Takeaways: Suggestions for improving employee mental health, productivity, and satisfaction.

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Tools and Technologies

• Google Colab: For code execution and analysis in a cloud-based environment.
• Python: Core language used for data processing and visualization.
• Bokeh: Visualization library for creating dynamic and interactive charts.
• Pandas: Data manipulation and preprocessing.
• Tab-Based Navigation: Organized multiple visualizations using Bokeh's TabPanel and Tabs.

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Visualizations

1. Stress Count vs. Hours Worked:
   • Shows how longer work hours lead to increased stress levels.
2. Balance vs. Stress:
   • Highlights the impact of work-life balance on stress.
3. Location vs. Productivity:
   • Analyzes productivity trends across hybrid, remote, and onsite work setups.
4. Mental Health Condition Distribution:
   • Provides insights into common mental health conditions like anxiety, burnout, and depression.
5. Access to Mental Health Resources:
   • Displays the proportion of employees with and without access to mental health support.
6. Mental Health Access by Job Role:
   • Highlights disparities in resource access across different job roles.
7. Region Satisfaction with Remote Work:
   • Examines remote work satisfaction across regions.
8. Region Stress Levels:
   • Compares stress levels across different geographic regions.

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Data Source

The data for this project was sourced from Kaggle, specifically the Remote Work & Mental Health dataset, which includes information on employee stress levels, productivity, and job satisfaction across different roles and regions. https://www.kaggle.com/datasets/waqi786/remote-work-and-mental-health

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Results and Insights

The project provides valuable insights into:

• The strong correlation between longer work hours and high stress.
• The uneven distribution of mental health resource access across job roles.
• Regional trends in remote work satisfaction and stress levels.

These findings emphasize the need for targeted interventions to improve employee well-being and productivity.