XYZ is a courier company. As we appreciate that human capital plays an important role in collection, transportation and delivery. The company is passing through genuine issue of Absenteeism. The company has shared it dataset and requested to have an answer on the following areas: 1. What changes company should bring to reduce the number of absenteeism? 2. How much losses every month can we project in 2011 if same trend of absenteeism continues?

The dataset used in this project is the Employee Face Recognition (EFG) dataset, which contains over 13,000 labeled images of 5,749 individuals. The images, captured from the different employees(men and women), making them ideal for testing face recognition systems. The faces are divided into individual folders, each named after the person depicted. The dataset consists of various image conditions, including different lighting, poses, and expressions, providing a challenging but realistic environment for face recognition. It is commonly used for training and benchmarking facial recognition models, particularly in machine learning and computer vision research.

This dataset is created by Datarspectrum Technology Training Center and contains structured information about employees in an organization. It includes 500 employee records with details such as personal information, department, designation, salary, and work mode.

The dataset is clean and complete (no missing values), making it suitable for practicing data analysis, SQL queries, machine learning preprocessing, and HR analytics use cases.

🔑 Features

firstName, middleName, lastName—Employee full name details

email – Official email address

Aadhar Card—Unique identity proof number

designation—job designation (Developer, Analyst, Manager, etc.)

role—Specific role/responsibility in the company

phone – Contact number

department—Assigned department (HR, IT, Sales, Data Science, etc.)

dateOfJoining – Date when employee joined the company

status—Current status (active, inactive, terminated)

salary—Monthly salary in INR

workMode – Work arrangement (remote, office, hybrid)

🎯 Possible Use Cases

HR Analytics (employee performance & retention trends)

Salary distribution and workforce diversity studies

SQL practice on employee database queries

Machine learning preprocessing for classification (e.g., predict employee status)

Data visualization and dashboard building practice

A curated dataset featuring 25 top employee experience platforms, including features, pros, cons, use cases, and pricing indicators.

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. 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 for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High)

Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

The Office of Personnel Management requires government agencies, at a minimum, to query employees on job satisfaction, organizational assessment and organizational culture. VHA maintains response data for all census surveys such as the Voice of VA as well as the VA Entrance and Exit surveys.

Explore demographic data on the Massachusetts executive branch workforce. Track our progress toward our goals to reflect the diversity of the people we serve, and to stand out as an employer of choice.

Predicting Employee Churn at Dunder Mifflin Paper Company

In the quaint town of Scranton, Pennsylvania, lies the regional branch of the Dunder Mifflin Paper Company, a well-established and somewhat quirky paper company. Dunder Mifflin has been a staple of the local community for years, providing paper products to businesses and individuals alike. However, the company is facing a unique challenge: employee churn.

The regional manager, Michael Scott, is deeply concerned about the high turnover rate among the employees. He believes that by understanding the factors contributing to employee churn, the company can take steps to improve employee satisfaction and retention.

Dataset Features: 1. EmployeeID: A unique identifier for each employee. 2. Tenure: The number of years the employee has been with the company. 3. Salary: The employee's annual salary. 4. Department: The department in which the employee works (e.g., Sales, Accounting, Customer Service). 5. JobSatisfaction: The employee's self-reported job satisfaction level (on a scale from 1 to 5, with 5 being highly satisfied). 6. WorkLifeBalance: The employee's self-reported work-life balance rating (on a scale from 1 to 5, with 5 being excellent). 7. CommuteDistance: The distance the employee commutes to work (e.g., Short, Medium, Long). 8. MaritalStatus: The marital status of the employee (e.g., Single, Married, Divorced). 9. Education: The highest level of education attained by the employee (e.g., High School, Bachelor's, Master's). 10. PerformanceRating: The employee's performance rating (on a scale from 1 to 5, with 5 being excellent). 11. TrainingHours: The number of hours of training the employee has received. 12. OverTime: Whether the employee works overtime or not. 13. NumProjects: The number of projects the employee is currently working on. 14. YearsSincePromotion: The number of years since the employee's last promotion. 15. EnvironmentSatisfaction: The employee's self-reported environment satisfaction (on a scale from 1 to 5, with 5 being highly satisfied). 16. Branch: The "Branch" feature represents the geographic location of each employee within one of the 12 Dunder Mifflin branches across the United States.

Classes (Target Variable): Employees will be classified into four classes based on their likelihood to leave the company: - Class A: Highly likely to leave. - Class B: Moderately likely to leave. - Class C: Slightly likely to leave.

This classification problem can help Dunder Mifflin Paper Company identify key factors contributing to employee turnover and implement strategies to improve employee retention and workplace satisfaction, all in a setting reminiscent of the beloved TV show "The Office."

Usage Note:

This fictional dataset is intended solely for educational and illustrative purposes. While it may resemble real-world data in some aspects, it should not be used for making real business decisions or drawing conclusions about real employees or organizations.

Any analysis or modeling performed on this dataset should be considered fictional and should not be extrapolated to real-world scenarios.

Please keep in mind that the dataset is purely fictional and is meant to provide a lighthearted and relatable context for learning and practicing data analysis and machine learning techniques.

Pay Information for calendar year 2023 for the employees of the State of Missouri by their Agency of employment, Position Title or Employee name.

The Employer Costs for Employee Compensation (ECEC) is a measure of the cost of labor. The compensation series includes wages and salaries plus employer costs for individual employee benefits. Employee benefit costs are calculated as cents-per-hour-worked for individual benefits ranging from employer payments for Social Security to paid time off for holidays. The survey covers all occupations in the civilian economy, which includes the total private economy (excluding farms and households), and the public sector (excluding the Federal government). Statistics are published for the private and public sectors separately, and the data are combined in a measure for the civilian economy. For information and data, visit: https://www.bls.gov/ncs/ect/

ABOUT THE CITY OF TEMPE EMPLOYEE SURVEY REPORTS DATASETThis data set includes the results from the Tempe Employee Survey, conducted every other year, to gather input from employees about issues in six major areas: professional development and career mobility; organizational support; supervisions and working environment; compensation and benefits; employee engagement; and peer relationships. Participation in the survey is voluntary and confidential. Employees are able to complete the survey during work hours or at home, with surveys directly returned to the vendor conducting the survey.PERFORMANCE MEASURESData collected in this survey applies directly to the following Performance Measures for the City of Tempe:1. Safe & Secure Communities1.11 Feeling Safe in City Facilities2. Strong Community Connections2.13 Employee Engagement2.25 Employee Work-Related NeedsThe City of Tempe Employee Survey was first conducted in 2016 and will occur every two years.Additional InformationSource: Employee SurveyContact (author): Aaron PetersonContact E-Mail (author): aaron_peterson@tempe.govContact (maintainer): Aaron PetersonContact E-Mail (maintainer): aaron_peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: BiennialPublish Method: ManualData Dictionary

This horizontal bar chart displays employees (people) by employee type using the aggregation sum in Richland. The data is about companies.

Employee payroll data for all Cook County employees excluding Forest Preserves, indicating amount of base salary paid to an employee during the County fiscal quarter. Salaries are paid to employees on a bi-weekly basis. Any pay period that extended between quarters will be reported to the quarter of the Pay Period End Date. (e.g. If a Pay Period runs 02/21-03/05, that pay period would be reported in the Q2 period, as the end of the pay period falls in March - Q2)

The county fiscal quarters are: Q1: December - February Q2: March - May Q3: June - August Q4: September - November

The Employee Unique Identifier field is a unique number assigned to each employee for the purpose of this data set, that is not their internal employee ID number, and allows an employee to be identified in the data set over time, in case of a name change or other change. This number will be consistent within the data set, but we reserve the right to regenerate this number over time across the data set.

ISSUE RESOLVED: As of 4/19/2018 there was an issue regarding employee FY2016 and FY2017 payroll in which records were duplicated in the quarterly aggregation, resulting in inflated base pay amounts. Please disregard any data extracted from this dataset prior to the correction date and use this version moving forward.

KNOWN ISSUE: Several records are missing Bureau and Office information. We are working on correcting this and will update the dataset when this issue has been resolved.

For data prior to Fiscal Year 2016, see datasets at https://datacatalog.cookcountyil.gov/browse?tags=payroll

Employee Activtiy

## Overview

Employee Activtiy is a dataset for object detection tasks - it contains Humans Things JbA0 annotations for 514 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Turnover rates for State of Oklahoma classified employees beginning in fiscal year 2000.

United States Private Employee: DBP: Union Workers data was reported at 66.000 % in 2017. This records an increase from the previous number of 65.000 % for 2016. United States Private Employee: DBP: Union Workers data is updated yearly, averaging 67.000 % from Mar 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 72.000 % in 2005 and a record low of 65.000 % in 2016. United States Private Employee: DBP: Union Workers data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G076: Employee Benefits Survey: Private Industry.

Costco Wholesale Corporation's annual net income per employee was $23.75 K in fiscal year 2025. The net income per employeeincreased$1.63 Kfrom $22.12 K(in 2024) to $23.75 K (in 2025), representing a 7.36% year-over-year growth.

Pay Information for calendar year 2010 for the employees of the State of Missouri by their Agency of employment, Position Title or Employee name.

This dataset is about companies in Marondera. It has 1 row. It features 3 columns: employees, and employee type.

United States Private Employee: DCB: Workers in Service Producing Industries data was reported at 30.000 % in 2017. This records a decrease from the previous number of 31.000 % for 2016. United States Private Employee: DCB: Workers in Service Producing Industries data is updated yearly, averaging 33.000 % from Mar 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 34.000 % in 2010 and a record low of 28.000 % in 2000. United States Private Employee: DCB: Workers in Service Producing Industries data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G076: Employee Benefits Survey: Private Industry.