This project provides a comprehensive analysis of human resources data, leveraging Python, and Tableau to extract meaningful insights.
The goal is to assist HR managers, executives, and analysts in understanding workforce trends, employee performance, salary distribution, and attrition rates.
Modern HR departments need data-driven decision-making to optimize workforce planning, employee retention, and diversity strategies.
This dashboard offers interactive visualizations that help HR professionals:
- Monitor key workforce metrics (e.g., employee demographics, attrition rates, salary trends)
- Identify patterns affecting productivity, employee satisfaction, and promotions
- Improve talent acquisition and retention strategies
✅ Clean, preprocess, and analyze HR employee data using Python
✅ Perform feature engineering to extract valuable insights
✅ Develop an interactive Tableau dashboard for HR professionals
✅ Identify key trends in workforce demographics, salaries, and attrition
✅ Enable data-driven decision-making for talent retention and workforce planning
This dashboard allows users to:
✔ Filter data by department, gender, salary range, and experience level
✔ Compare attrition rates across different teams and job roles
✔ Analyze salary distributions based on gender and job experience
✔ Assess promotion rates and performance trends
The dataset used in this project was synthetically generated using a combination of ChatGPT prompts and the Python Faker library.
It simulates real-world HR data typically found in HR systems, covering various aspects such as:
🔹 Demographics: Age, gender, marital status, education level
🔹 Job Information: Department, job role, work experience, promotions
🔹 Salary Data: Compensation breakdown and salary trends
🔹 Performance & Attrition: Employee performance scores and attrition factors
🔹 Work-Life Balance: Leave history, overtime records, and job satisfaction
While this dataset is artificial, it is designed to mimic real HR trends, making it a valuable resource for analysis and visualization.
📌 Dataset Source: Synthetic Data Generated Using Python (Faker Library & ChatGPT)
✔ Used Faker library to generate realistic HR employee records
✔ Handled missing values and standardized categorical data
✔ Normalized salary figures for better comparison
✔ Created new features such as attrition risk scores
✔ Segmented employees based on age, gender, department, and job roles
✔ Examined salary trends across different levels of experience
✔ Identified factors influencing attrition rates
✔ Built an interactive dashboard with department-wise insights
✔ Designed filters for workforce demographics, salaries, and attrition
✔ Enabled real-time insights for HR decision-making
📌 Attrition Rate: 15% of employees left the company in the past year, with the highest attrition in sales and support roles
📌 Gender Salary Gap: Males earn 10% more on average than females in technical roles
📌 Top Performing Departments: IT and R&D departments show the highest productivity scores
📌 Job Satisfaction Trends: Employees with higher work-life balance scores are 50% less likely to leave
📌 Promotion Rates: Employees with 5+ years of experience have a higher promotion rate
🔍 Additional Insights:
- Employees with remote work options report higher job satisfaction
- Younger employees (aged 22-30) have the highest turnover rates
- Employees earning above $80,000 have the lowest attrition rate
📌 HR departments play a crucial role in business success. With data-driven insights, they can:
- Improve employee retention strategies
- Optimize hiring and promotion policies
- Address salary disparities
- Enhance workforce diversity and inclusion