HR Classification for Promotion

This project focuses on analyzing HR-related data to classify employees who are potentially eligible for promotion. The goal is to provide insights into factors influencing promotions within the company and to create a predictive model using linear regression to identify employees with a high likelihood of being promoted.

Table of Contents

1. Data Analytics Insights
2. Linear Regression Model
3. Usage
4. Contributing
5. License

Data Analytics Insights

In this section, we analyze various aspects of the employee dataset to uncover insights related to department headcounts, attrition, employee demographics, training performance, and more. Some of the insights include:

• Key departments contributing significantly to headcounts.
• Analysis of attrition and new hires.
• Gender diversity and inclusiveness efforts.
• Age distribution and employee demographics.
• Employee preferences for training courses.

Linear Regression Model

The linear regression model aims to predict employees who are potential candidates for promotion. The model considers factors such as KPI assessment, training scores, and awards. While the model's prediction accuracy is 91%, it's important to note that model predictions should be used as a reference rather than the sole basis for decision-making.

Usage

Provide instructions here on how to set up and run the project locally, including any dependencies that need to be installed. Also, mention how to use the linear regression model to make predictions for potential promotions.

Contributing

We welcome contributions to enhance the HR Classification for Promotion project. If you want to contribute, follow these steps:

1. Fork the repository to your GitHub account.
2. Create a new branch for your changes: git checkout -b feature/your-feature-name.
3. Make your changes and commit them with descriptive commit messages: git commit -m "Add feature".
4. Push your changes to your branch: git push origin feature/your-feature-name.
5. Open a pull request from your branch to the main branch of this repository.

Please ensure that your contributions align with the project's goals and follow the existing coding style and guidelines.

License

This project is licensed under the MIT license. For more details, please see the LICENSE.txt file.