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Workforce Turnover Predictor
Data Science and Analytics
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About
Provides key features related to employee characteristics, satisfaction levels, and mobility, alongside a binary indicator of whether they ultimately left the organisation. It is designed to facilitate the creation of predictive models aimed at mitigating staff turnover and safeguarding human capital investments. Understanding the factors that drive employee separation is crucial for workforce planning and management efficiency.
Columns
The dataset contains 8 distinct columns, typically found within a CSV file structure:
- Gender: Indicates the sex of the employee (e.g., Male is the most common at 65%, Female is 35%).
- Satisfaction: Reflects the employee's self-reported satisfaction with their work environment and the company (e.g., High is 47%, Medium is 32%).
- Business Travel: Denotes how frequently the employee travels for work (e.g., Rare is the most frequent response at 76%).
- Department: Specifies the employee’s primary department within the company (e.g., R&D is 71%, Sales is 29%).
- EducationField: Identifies the area of the employee's educational qualification (e.g., Engineering is 44%, Medical is 32%).
- Salary: Categorises the employee’s salary bracket (e.g., High is 42%, Medium is 37%).
- Home-Office: Measures the distance between the employee's residence and the office (e.g., Near is 67%, Far is 33%).
- Attrition: A Boolean flag indicating if the employee ultimately left the company (18% True, 82% False).
Distribution
The data is delivered in a standard file format, usually CSV (e.g.,
Employee Data.csv). The file structure consists of 8 attributes. The provided samples show 100 valid records across all metrics; however, the total number of rows in the full product is not specified. The dataset is static, as its expected update frequency is 'Never'.Usage
This data is ideal for:
- Building Machine Learning (ML) models focused on predicting employee turnover.
- Conducting statistical analysis to determine the primary drivers of attrition within an organisation.
- Developing targeted interventions and retention programmes for high-risk employee segments.
Coverage
The scope covers demographic factors such as employee gender and educational backgrounds, alongside professional metrics like department and salary level. Specific geographical or precise time range coverage details are not available in the sources.
License
CC0: Public Domain
Who Can Use It
- HR Professionals and Analysts: Utilising the data to forecast workforce losses and justify investment in retention programmes.
- Data Scientists: Developing and refining classification models to accurately predict turnover.
- Business Executives: Gaining insights into human capital risk management and organisational efficiency.
Dataset Name Suggestions
- Employee Attrition Drivers
- Workforce Turnover Predictor
- Predicting Employee Turnover
Attributes
Original Data Source: Workforce Turnover Predictor
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