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This dataset contains valuable employee information over time that can be analyzed to help optimize key HR functions. Some potential use cases include:

Attrition analysis: Identify factors correlated with attrition like department, role, salary, etc. Segment high-risk employees. Predict future attrition.

Performance management: Analyze the relationship between metrics like ratings, and salary increments. recommend performance improvement programs.

Workforce planning: Forecast staffing needs based on historical hiring/turnover trends. Determine optimal recruitment strategies.

Compensation analysis: Benchmark salaries vs performance, and experience. Identify pay inequities. Inform compensation policies.

Diversity monitoring: Assess diversity metrics like gender ratio over roles, and departments. Identify underrepresented groups.

Succession planning: Identify high-potential candidates and critical roles. Predict internal promotions/replacements in advance.

Given its longitudinal employee data and multiple variables, this dataset provides rich opportunities for exploration, predictive modeling, and actionable insights. With a large sample size, it can uncover subtle patterns. Cleaning, joining with other contextual data sources can yield even deeper insights. This makes it a valuable starting point for many organizational studies and evidence-based decision-making.

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This dataset contains information about different attributes of employees from a company. It includes 1000 employee records and 12 feature columns.

The columns are:

satisfaction_level: Employee satisfaction score (1-5 scale)
last_evaluation: Score on last evaluation (1-5 scale)
number_project: Number of projects employee worked on
average_monthly_hours: Average hours worked in a month
time_spend_company: Number of years spent with the company
work_accident: If an employee had a workplace accident (yes/no)
left: If an employee has left the company (yes/no)
promotion_last_5years: Number of promotions in last 5 years
Department: Department of the employee
Salary: Annual salary of employee
satisfaction_level: Employee satisfaction level (1-5 scale)
last_evaluation: Score on last evaluation (1-5 scale)

Tables

Hr Comma Sep

@kaggle.fahadrehman07_hr_comma_sep_csv.hr_comma_sep

64.34 KB
14999 rows
10 columns


CREATE TABLE hr_comma_sep (
  "satisfaction_level" DOUBLE,
  "last_evaluation" DOUBLE,
  "number_project" BIGINT,
  "average_montly_hours" BIGINT,
  "time_spend_company" BIGINT,
  "work_accident" BIGINT,
  "left" BIGINT,
  "promotion_last_5years" BIGINT,
  "department" VARCHAR,
  "salary" VARCHAR
);