- This project aims to develop a predictive model using machine learning techniques to predict burnout rate of a Employee based on various factors.
- By understanding the factors contributing to burnout and developing prediction models, we aim to identify at-risk employees and provide targeted support.
Download the dataset for custom training data.
This dataset contains employee information, including no.of designations, work-from-home availability, and other relevant attributes.
The project is organized into the following directories and files:
- Data: The data folder contains both raw and processed data used in this project.
- Notebooks: This folder contains Jupyter notebooks with code covering data exploration, model building, and evaluation.
- Models: This folder houses a collection of trained machine learning models.
- Reports: This folder contains project reports, such as a Power BI data analysis report and a presentation.
- images: This folder contains all the relevant images used in this project, such as those used in document preparation, presentation materials, and visual aids to enhance understanding
- Static: This folder includes static files used in the project, such as images, stylesheets.
- Templates: The templates folder contains HTML templates used for rendering web pages.
- app.py: This is the main application file that runs the project's web application.
- Requirements: This requirements text file contains all the required dependencies that we need to install to run the project.
To get started with the project, follow these steps:
- Clone this repository to your local machine:
git clone https://github.com/usmanbvp/Employees-Burnout-Analysis-and-Prediction.git
- Install the project dependencies by running the following command:
pip install -r requirements.txt
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Explore the project's directories and files to become familiar with its structure.
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To run the project, execute follwoing commad:
python app.py
Once you'have successfully installed and run the project, you can use it to predict the burnout rate of Emoloyees. Here's how to get started:
- Open your web browser and navigate to
http://127.0.0.1:5000/
- You will be presented with a user-friendly web app interface. Explore the available features and prediction options.
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Follow the on-screen instructions to input relevant employee data.Utilize the prediction feature to predict future burnout outcomes for employees.
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The web app should provide you with results based on your input.
- Based on the results, you can take any necessary actions, make decisions, or utilize the project for your specific use case.
To deploy this project, follow these steps:
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Choose a hosting platform or service for your web application. Popular choices include Heroku, AWS, Azure, or PythonAnywhere.
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Set up an account on the selected hosting platform if you don't already have one.
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Prepare your project for deployment by making sure it meets the requirements of the chosen hosting service. This may include adjusting configuration files, environment variables, or dependencies.
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Deploy your project to the hosting platform using the platform's provided deployment tools or instructions.
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Once deployed, you can access your project by navigating to the URL provided by the hosting platform.
For more detailed deployment instructions specific to your chosen hosting service, refer to their official documentation and guidelines.
Enjoy using the deployed version of the Employees Burnout Analysis and Prediction project!
This project is licensed under the MIT License - see the LICENSE file for details.
The MIT License is a permissive open source license that allows you to use, modify, and distribute this project for both commercial and non-commercial purposes.
If you have any feedback, suggestions, or questions regarding the project, please create an issue in the repository or contact me at usman.bvp@gmail.com.
Your star is a great way to let us know you appreciate our work and find value in this project. Thank you! ⭐
Happy analyzing and predicting❤️!