This repository contains an in-depth analysis of employee performance data, aimed at uncovering key insights that can help organizations improve workforce management, productivity, and employee satisfaction. The dataset includes a range of factors like employee demographics, performance scores, compensation, and tenure, among others.
The analysis was performed using Python, Power BI, DAX, Power Query with the goal of identifying patterns and correlations between performance and other key metrics.
The dataset used in this project is sourced from Kaggle and includes the following columns:
- Employee_ID: Unique identifier for each employee.
- Department: The department in which the employee works (e.g., Sales, HR, IT).
- Gender: Gender of the employee (Male, Female, Other).
- Age: Employee's age (between 22 and 60).
- Job_Title: The role held by the employee (e.g., Manager, Analyst, Developer).
- Hire_Date: The date the employee was hired.
- Years_At_Company: The number of years the employee has been working for the company.
- Education_Level: Highest educational qualification (High School, Bachelor, Master, PhD).
- Performance_Score: Employee's performance rating (1 to 5 scale).
- Monthly_Salary: The employee's monthly salary in USD, correlated with job title and performance score.
- Work_Hours_Per_Week: Number of hours worked per week.
- Projects_Handled: Total number of projects handled by the employee.
- Overtime_Hours: Total overtime hours worked in the last year.
- Sick_Days: Number of sick days taken by the employee.
- Remote_Work_Frequency: Percentage of time worked remotely (0%, 25%, 50%, 75%, 100%).
- Team_Size: Number of people in the employee's team.
- Training_Hours: Number of hours spent in training.
- Promotions: Number of promotions received during their tenure.
- Employee_Satisfaction_Score: Employee satisfaction rating (1.0 to 5.0 scale).
- Resigned: Boolean value indicating if the employee has resigned.