Predicting employee attrition using data science and machine learning techniques. This project is based on the IBM HR Analytics Employee Attrition & Performance dataset, which provides valuable insights into employee turnover and performance factors.

• Introduction
• Dataset
• Installation
• Usage
• Features
• Contributing
• License

Employee attrition, the loss of employees through voluntary or involuntary separation, can have a significant impact on an organization's productivity and overall success. This repository contains code and models that aim to predict employee attrition and identify factors that contribute to it. By leveraging data science and machine learning, we can gain valuable insights into employee retention strategies.

The project is built around the IBM HR Analytics Employee Attrition & Performance dataset, which includes various features related to employees, such as age, job role, job satisfaction, and more. You can find the dataset here.

To get started with this project, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/ruturaj0626/Employee-Attrition-Predictor.git

2. Start exploring the code and Jupyter notebooks provided in the repository.

The repository offers several Jupyter notebooks and Python scripts to help you:

• Explore and preprocess the dataset.
• Train and evaluate machine learning models for attrition prediction.
• Visualize important insights related to employee attrition.

Feel free to customize the code to suit your specific needs and datasets.

• Data exploration and preprocessing scripts.
• Machine learning models for attrition prediction.
• Visualization tools for understanding employee attrition factors.
• Clear and well-structured codebase for easy customization.

Contributions are welcome! If you have any suggestions, bug reports, or improvements to the codebase, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push to your fork and submit a pull request to the main repository.

This project is licensed under the MIT License - see the LICENSE file for details.