Synthetic Employee Attrition Classification Dataset

Dataset Features:

• Employee ID: Unique identifier for each employee.
• Age: Age of the employee in years.
• Gender: Gender of the employee (e.g., "Male," "Female").
• Years at Company: Total number of years the employee has worked at the company.
• Job Role: Employee's role within the company (e.g., "Finance," "Healthcare").
• Monthly Income: Employee's monthly income in USD.
• Work-Life Balance: Perceived work-life balance categorized as "Poor," "Fair," "Good," or "Excellent."
• Job Satisfaction: Job satisfaction level categorized as "Low," "Medium," "High," or "Very High."
• Performance Rating: Employee's performance rating (e.g., "Below Average," "Average," "High").
• Number of Promotions: Total number of promotions the employee has received during their tenure.
• Overtime: Whether the employee works overtime ("Yes" or "No").
• Distance from Home: Distance (in miles) between the employee's home and the workplace.
• Education Level: Employee's highest level of education (e.g., "High School," "Bachelor’s Degree," "Master’s Degree," "PhD").
• Marital Status: Employee's marital status (e.g., "Single," "Married," "Divorced").
• Number of Dependents: Total number of dependents the employee supports.
• Job Level: Hierarchical level of the employee's job (e.g., "Entry," "Mid," "Senior").
• Company Size: Size of the company categorized as "Small," "Medium," or "Large."
• Remote Work: Whether the employee works remotely ("Yes" or "No").
• Leadership Opportunities: Whether the employee has leadership opportunities ("Yes" or "No").
• Innovation Opportunities: Whether the employee has opportunities to innovate in their role ("Yes" or "No").
• Company Reputation: Perception of the company’s reputation ("Poor," "Fair," "Good," "Excellent").
• Employee Recognition: Degree of employee recognition for contributions ("Low," "Medium," "High").
• Attrition: Whether the employee left ("Left") or stayed ("Stayed") at the company.

Distribution:

Usage:

• Attrition Prediction: Build machine learning models to predict whether an employee is likely to leave or stay based on their attributes.
• Employee Engagement Analysis: Identify factors that influence job satisfaction, performance, and loyalty.
• HR Strategy Development: Use insights to improve work-life balance, employee recognition, and leadership opportunities.
• Compensation Benchmarking: Analyze the correlation between income and retention to adjust salary policies.
• Diversity and Inclusion: Examine patterns based on gender, marital status, and dependents to ensure workplace equity.

Coverage:

License:

Who Can Use It:

• Data Scientists and Analysts: To practice classification, clustering, and predictive modelling.
• HR Professionals: To simulate strategies and evaluate retention policies.
• Students and Educators: For academic projects, research, and coursework in data analysis and machine learning.