Burnout-Detector

How to use

1. Clone the repository in your local system.
2. Open Chrome and go to Extension.
3. Turn on Developer Mode and load the extension from the src folder in the repository.
4. Run app.py in your terminal.
5. Enter your details in localhost and after spending time on the work url, click on the burn rate result on the bottom right.

How to contribute

1. Fork the repository.
2. Make a branch with the feature you have added. Eg. For feature 'foo' make a branch foo and add your changes.
3. Send a pull request.

Project Description

This is a ML based project which helps track the burnout rate of an employee based on the time spent on the web browser working in their job's url. It uses a chrome extension to track the time spent on the work url and uses a simple test and some details to periodically track the burnout rate in an entire work day.

The objectives of this project:

• Alarm the user(employee) about the signs of incoming burnout.
• Integrating technology to help mental health of employees, and of all kinds of users in future.

Features

• Although the project is still under development, it will use an extension to track the time spent on websites which will be avaliable on the Chrome Marketplace.
• We also aspire to add some features to make sure the prediction is more accurate and requires less input from the user's side.

Data

• The data is taken from the Hackerearth competition on kaggle about employee burnout, which involved predict the burnout rate of an employee based on details provided by them.
• The data involves the following features in it.

Tech

The libraries and frameworks used in this project till now are:

• Pandas - Used for data analysis
• NumPy - Awesome mathematical library for python
• Matplotlib - For data visualization.
• Scikit-learn - For implementing ML algorithms.
• Jupyter - For making python notebook.
• Flask - For making web application.

In future we will add more features, hence other tech used in the project will be also mentioned.

To Dos

• Improve the model.
• Create an interactive UI and build a real-time prediction mechanism through the Chrome Extension.
• Take inputs without requiring the user to explicitly enter them.
• Deploy the extension on marketplace.

Contributors

• Siddharth Sharma
• Ankit Oscar Xalxo
• Tanishq Dubey
• Sadeep Nanda

Versions:

• 1.0.0 - Only some basic files, will deploy the chrome extension and integrate the website with it.

Free Software, Hell Yeah!