Employee Attrition Prediction

The Employee Attrition Prediction System uses machine learning and statistical analysis to predict employee turnover. Designed for HR professionals, it facilitates proactive decision-making to reduce attrition and enhance workforce management strategies.

Technologies Used

• Backend: Flask (Python)
• Frontend: HTML5, CSS3, JavaScript
• Containerization: Docker
• Machine Learning: Scikit-learn, XGBoost, Scipy, Seaborn, Matplotlib, Pandas, NumPy

Application Demo

Installation

Prerequisites

Ensure the following are installed on your local machine:

• Python 3.8+
• Docker (optional for containerized deployment)

Clone the Repository

git clone [email protected]:abdullahashfaq-ds/Employee-Attrition-Prediction.git
cd Employee-Attrition-Prediction

Method 01: Virtual Environment Setup

python -m venv venv
# On Windows, use:
venv\Scripts\activate
# On Linux/MacOS, use:
source venv/bin/activate
# To set up the production environment:
pip install -r requirements.txt
# To set up the development environment:
pip install -r requirements.dev.txt
# To run the project:
python app.py

Method 02: Docker Setup

For a containerized environment, build and run the container:

docker build -t employee-attrition .  
docker run -p 5000:5000 employee-attrition

Access the application at http://localhost:5000

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For inquiries or support, please open an issue on GitHub or contact [email protected].