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Employee Attrition Prediction Dataset
Data Science and Analytics
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About
This dataset is designed for predicting employee attrition, focusing on the human resources (HR) domain. It provides crucial insights for HR departments, particularly within large insurance companies, to anticipate whether employees are likely to leave the organisation. The problem statement highlights the increasing importance of HR in fostering growth and competitive advantage, with employee retention being a key factor. High attrition negatively impacts morale and incurs higher costs for hiring new staff compared to retaining existing ones. Utilising this dataset can help identify factors contributing to attrition and aid in strategic staffing decisions.
Columns
- Emp_ID: A unique identifier for each employee.
- Age: The age of the employee.
- Gender: The gender of the employee.
- City: The city where the employee is located.
- Date of Joining: The date when the employee commenced employment.
- Last Working Date: The last date an employee worked, relevant for those who have left.
- Designation: The employee's job role or title.
- Salary: The employee's monthly income.
- Quarterly Rating: Historical performance ratings for the employee on a quarterly basis.
- Monthly Business Acquired: Data on the business generated by the employee each month.
- Target: The variable to be predicted, indicating whether an employee will leave the organisation.
Distribution
The dataset comprises 19,104 instances, representing individual employees. It contains monthly information for a segment of employees covering the years 2016 and 2017. While the exact total size of the primary dataset is not specified in kilobytes, a sample submission file (
sample_submission.csv) is provided, which is 4.89 kB. Data files are typically in CSV format. Specific numbers for total rows/records beyond the 19,104 instances are not detailed for the main dataset.Usage
This dataset is ideal for several applications, including:
- Predicting Employee Turnover: Forecasting whether a current employee will depart the organisation within specific future quarters (e.g., Q1 and Q2 2018).
- HR Analytics: Performing in-depth analyses to understand patterns and drivers of employee attrition.
- Staffing Optimisation: Aiding HR departments in making informed staffing decisions and reducing attrition rates.
- Factor Identification: Uncovering key factors that contribute to employees leaving the company.
- Comparative Analysis: Exploring questions such as 'show me a breakdown of distance from home by job role and attrition' or 'compare average monthly income by education and attrition'.
Coverage
The dataset focuses on the demographic and performance aspects of employees.
- Time Range: It includes monthly historical data for employees for the years 2016 and 2017, with the objective of predicting attrition for the period of 01 January 2018 to 01 July 2018.
- Demographic Scope: Features include employee demographics such as age, gender, and city. It specifically pertains to employees, likely within a sales team context of a large insurance company.
- Geographic Scope: While not explicitly stated, the context implies a corporate setting within an insurance company.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly valuable for:
- Data Scientists: Especially those working with HR departments to build predictive models for attrition.
- HR Analysts: To gain insights into workforce dynamics, identify retention challenges, and inform HR strategies.
- Organisational Leaders: To understand the financial and operational impact of attrition and develop strategies for staff retention.
- Researchers: Studying employee behaviour, motivation, and turnover within corporate environments.
- Business Intelligence Professionals: To generate reports and dashboards on employee retention metrics.
Dataset Name Suggestions
- Employee Attrition Prediction Dataset
- HR Workforce Retention Analytics
- Sales Team Attrition Forecast 2018
- Employee Churn Prediction for Insurance Sector
- Workforce Turnover Analysis Data
Attributes
Original Data Source: Employee Attrition Prediction Dataset
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