This project involves creating an HR analytics dashboard using a synthetic dataset generated with Faker and pandas. The dashboard provides insights into various aspects of HR metrics, including employee demographics, income and hiring trends, performance ratings, and more. The final visualizations were created using Tableau.
The dashboard is divided into four main sections:
- Overview: General statistics and summaries of the dataset.
- Gender Insights: Insights into gender distribution and related metrics within the company.
- Income and Hiring Insights: Analysis of salary distributions, hiring trends, and attrition rates.
- Employee HR Metrics: Detailed metrics related to employee performance, job satisfaction, training, and promotions.
- Python: Used for generating the dataset and initial data processing.
- Pandas: Utilized for data manipulation and cleaning.
- Faker: Library used to generate synthetic data.
- Figma: Used for layout design and structuring.
- Tableau: Used for creating interactive visualizations and the final dashboard.
The dataset for this HR analytics project was generated using the Faker library in Python, which allows for the creation of realistic synthetic data. Faker was employed to simulate a variety of employee attributes such as employee ID, name, gender, age, job role, department, salary, date of hire, education level, marital status, and more. Additional features like the number of projects, last promotion date, work location, training hours, overtime status, job satisfaction, monthly income, years at the company, years in the current role, years since last promotion, and years with the current manager were also included. The data was processed and organized using the Pandas library. This synthetic dataset provides a comprehensive foundation for creating meaningful HR analytics and visualizations.