Employee attrition is the gradual reduction in employee numbers. Employee attrition happens when the size of your workforce diminishes over time. This means that employees are leaving faster than they are hired. Employee attrition happens when employees retire, resign, or simply aren't replaced. Although employee attrition can be company-wide, it may also be confined to specific parts of a business.

Employee attrition can happen for several reasons. These include unhappiness about employee benefits or the pay structure, a lack of employee development opportunities, and even poor conditions in the workplace. This project will help you predict employee attrition.

Dependencies:

• json: 2.0.9
• numpy: 1.18.1
• pandas: 1.0.1
• seaborn: 0.10.0
• matplotlib: 3.5.3
• sklearn: 0.22.1

At the end of the project, you will be able to

• explore the employee attrition dataset
• apply CatBoost and XgBoost on the dataset
• tune the model hyperparameters to improve accuracy
• evaluate the model using suitable metrics

If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Please adhere to our Code of Conduct in all your interactions with the project.

This project is licensed under the MIT License.

For questions or inquiries, feel free to contact me on Linkedin.

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.