mind-metrics 0.9.4

Workplace Mental Health Analytics - A comprehensive data analysis application for mental health survey data

Mind Metrics

Mind Metrics is a comprehensive data analysis application for workplace mental health survey data. It provides insights into burnout patterns, stress factors, and workplace well-being metrics through interactive visualizations and statistical analysis.

Features

• 📊 Comprehensive Analytics: Analyze burnout levels, job satisfaction, stress patterns, and productivity metrics
• 📈 Interactive Visualizations: Generate insightful plots and charts for mental health data
• 🔧 Flexible Data Input: Support for CSV and Excel files with automatic data validation
• 🖥️ Command Line Interface: Easy-to-use CLI built with Click framework
• 📋 Detailed Reporting: Generate comprehensive reports on workplace mental health trends
• 🧪 Robust Testing: Comprehensive test suite ensuring data integrity and reliability

Installation

From PyPI (Recommended)

pip install mind-metrics

From Source

git clone https://codebase.helmholtz.cloud/tud-rse-pojects-2025/group-7.git
cd group-7
pip install uv
uv sync
uv pip install -e . 

Quick Start

Basic Usage

# Analyze a mental health dataset
mind-metrics --source dataset/mental_health_workplace_survey.csv

# Run in text-only mode (no GUI plots)
mind-metrics --source dataset/mental_health_workplace_survey --text-only

# Enable verbose logging
mind-metrics --source dataset/mental_health_workplace_survey -vv

# Get help
mind-metrics --help

Example Analysis

# Comprehensive analysis with debug output
mind-metrics --source https://example.com/survey.csv -vvv --text-only

Data Format

Mind Metrics expects datasets with the following key columns:

• EmployeeID: Unique identifier for employees
• Age: Employee age
• Gender: Employee gender
• BurnoutLevel: Burnout score (0-10 scale)
• JobSatisfaction: Job satisfaction score (0-10 scale)
• StressLevel: Stress level (0-10 scale)
• WorkLifeBalanceScore: Work-life balance rating
• HasMentalHealthSupport: Whether employee has mental health support

For a complete list of supported columns, see our data schema documentation.

Example Dataset

Mind Metrics includes an example dataset (mental_health_workplace_survey.csv) for testing and demonstration purposes. This dataset is based on the "Mental Health and Burnout in the Workplace" dataset by Khushi Yadav, available on Kaggle.

Dataset Attribution

The example dataset used in this project is derived from:

• Title: Mental Health and Burnout in the Workplace
• Author: Khushi Yadav
• Source: Kaggle
• URL: https://www.kaggle.com/datasets/khushikyad001/mental-health-and-burnout-in-the-workplace
• License: [Check Kaggle dataset page for license information]

We thank the original dataset creator for making this valuable data available for research and development purposes.

Command Line Options

Options:
  --source TEXT           URL or path to dataset (CSV/Excel)  [required]
  -v, -vv, -vvv          Verbose output (WARNING/INFO/DEBUG levels)
  --text-only            Suppress graphical output
  --output-dir TEXT      Directory for output files [default: ./output]
  --format [json|csv]    Output format for reports [default: json]
  --help                 Show this message and exit.

Output

Mind Metrics generates several types of output:

1. Summary Statistics: Key metrics and trends in your data
2. Visualizations: Charts showing burnout patterns, correlations, and distributions
3. Reports: Detailed analysis reports in JSON or CSV format
4. Recommendations: Data-driven insights for improving workplace mental health

Development

Setting up Development Environment

# Clone the repository
git clone https://codebase.helmholtz.cloud/tud-rse-pojects-2025/group-7.git
cd group-7

# Install dependencies
uv sync

# Install pre-commit hooks
pre-commit install

# Run tests
uv run pytest

# Run linting
uv run ruff check
uv run mypy group-7/

Project Structure

group-7/
├── CITATION.cff
├── CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE.md
├── dataset/
│   ├── mental_health_workplace_survey.csv
├── pyproject.toml
├── README.md
├── src/
│   ├── __init__.py
│   ├── __main__.py         # Main entry point
│   ├── cli.py              # Command line interface
│   ├── data/
│   │   ├── __init__.py
│   │   ├── loader.py       # Data loading and validation
│   │   └── cleaner.py      # Data cleaning utilities
│   ├── analysis/
│   │   ├── __init__.py
│   │   ├── burnout.py      # Burnout analysis
│   │   ├── correlations.py # Correlation analysis
│   │   └── statistics.py   # Statistical computations
│   ├── visualization/
│   │   ├── __init__.py
│   │   ├── plots.py        # Plotting functions
│   │   └── dashboard.py    # Dashboard generation
│   └── utils/
│       ├── __init__.py
│       ├── logger.py       # Logging configuration
│       └── validation.py   # Data validation utilities
├── tests/
│   ├── conftest.py
│   ├── integration/
│   │   ├── test_cli.py
│   │   └── test_end_to_end.py
│   └── unit/
│       ├── test_burnout_analyzer.py
│       ├── test_data_cleaner.py
│       ├── test_data_loader.py
│       └── test_validation.py
└── uv.lock

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=src

# Run specific test file
uv run pytest tests/test_data_loader.py

Contributing

We welcome contributions! Please see our Contributing Guide for details on how to get started.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for your changes
5. Run the test suite
6. Commit your changes (git commit -m 'feat: add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Merge Request

Documentation

• User Guide
• Developer Guide

Citation

If you use Mind Metrics in your research, please cite it:

@software{mind_metrics,
  title = {Mind Metrics: Workplace Mental Health Analytics},
  author = {Group 7},
  year = {2025},
  url = {https://codebase.helmholtz.cloud/tud-rse-pojects-2025/group-7},
  version = {1.0.0}
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• Mental health research community for inspiration
• Khushi Yadav for the "Mental Health and Burnout in the Workplace" dataset from Kaggle
• Open source contributors and maintainers
• Survey participants who make this research possible

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitLab Issues
• 📖 Documentation: Project Documentation

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Made with ❤️ for better workplace mental health