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Employee Departure Prediction Dataset
Data Science and Analytics
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Free
About
This dataset is designed to assist Human Resources (HR) departments in predicting whether an employee will leave the company within the next two years. It provides various employee-related attributes to enable the development of predictive models for employee future prospects within an organisation. The data can be used for exploratory data analysis (EDA) to derive insights into factors influencing employee departure.
Columns
- Education: Details the education level of the employee, categorised as Bachelors, Masters, or Other.
- JoiningYear: Represents the year the employee joined the company, ranging from 2012 to 2018.
- City: Indicates the city office where the employee is posted, with primary locations being Bangalore and Pune, alongside other cities.
- PaymentTier: Specifies the employee's payment tier, where 1 signifies the highest tier, 2 is mid-level, and 3 is the lowest.
- Age: The current age of the employee, spanning from 22 to 41 years.
- Gender: Identifies the gender of the employee as Male or Female.
- EverBenched: A boolean flag indicating whether the employee has been kept out of projects for one month or more.
- ExperienceInCurrentDomain: Measures the employee's experience in their current field, ranging from 0 to 7 years.
- LeaveOrNot: The target variable, indicating whether the employee leaves the company in the next two years (1 for leaving, 0 for not leaving).
Distribution
The dataset is provided in a CSV format and has a file size of 195.25 kB. It comprises 9 columns and contains 4653 valid records across all fields, with no mismatched or missing values reported for any column.
Usage
This dataset is ideal for:
- Building predictive models to forecast employee attrition.
- Performing exploratory data analysis (EDA) to uncover key insights related to employee retention.
- Developing binary classification models to identify employees at risk of leaving.
- Supporting HR initiatives focused on talent retention and workforce planning.
Coverage
The dataset includes employee information from various city offices (Bangalore, Pune, and others). The joining years range from 2012 to 2018, with predictions focused on the subsequent two years. Demographic coverage includes various education levels (Bachelors, Masters, Other), ages (22-41), genders (Male, Female), and payment tiers (1, 2, 3).
License
CC0: Public Domain
Who Can Use It
- HR Departments: To proactively identify employees at risk of leaving and implement retention strategies.
- Data Scientists and Analysts: For developing and testing machine learning models for employee churn prediction.
- Organisational Researchers: To study factors influencing employee tenure and loyalty.
- Business Intelligence Professionals: For integrating workforce insights into business strategy.
Dataset Name Suggestions
- Employee Departure Prediction Dataset
- HR Employee Churn Data
- Workforce Attrition Forecast
- Employee Retention Analytics Data
- Staff Turnover Prediction
Attributes
Original Data Source: Employee Departure Prediction Dataset
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