Employee Turnover Analytics

Overview

This Jupyter notebook is a comprehensive tool for analyzing employee turnover. It utilizes popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. The notebook begins by importing these libraries, setting up a robust environment for data manipulation and visualization.

The core of the analysis involves a dataset on human resources, which is read into a Pandas DataFrame. Initial exploration of this data is conducted through basic methods like head() and shape, providing an overview of the dataset structure and dimensions.

This tool is designed to offer insights into employee turnover by applying data analysis and machine learning techniques. It's suitable for HR departments looking to understand and predict employee turnover trends, making it an invaluable resource for strategic human resource planning.

Usage

To use this notebook:

1. Clone or download the repository containing the Jupyter notebook.
2. Ensure all required libraries are installed.
3. Open the notebook in a Jupyter environment.
4. Run the cells sequentially to replicate the analysis or modify as needed for custom analysis.

Installation

To run the "Employee Turnover Analytics V1" notebook, you need to have Python installed on your system along with the following libraries:

• NumPy: For numerical computations.
• Pandas: For data manipulation and analysis.
• Matplotlib: For creating static, interactive, and animated visualizations.
• Seaborn: For high-level data visualization based on Matplotlib.
• Scikit-Learn (sklearn): For machine learning and data processing.
• Imbalanced-Learn (imblearn): For dealing with imbalanced datasets.

You can install these libraries using pip. Run the following command in your terminal or command prompt:

pip install numpy pandas matplotlib seaborn scikit-learn imbalanced-learn

Data

The analysis is based on an HR dataset, possibly containing employee demographics, job characteristics, and turnover information. Ensure the dataset is in the correct format (e.g., Excel or CSV) and modify the data import step if the format differs.

Contributing

Contributions to this project are welcome! Please adhere to the following guidelines:

• Fork the repository.
• Create a new branch for your feature or fix.
• Submit a pull request with a clear description of your changes or enhancements.

License

This project is released under MIT License, which allows for modification and redistribution under certain conditions.

Contact

For any questions or feedback regarding this project, please contact Lisa Reed-Preston, MATM Creative https://github.com/matmcreative/.

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