This project focuses on analyzing employee attrition using an interactive Power BI dashboard.
The goal is to identify attrition patterns across departments, job roles, gender, and overtime, and to present key HR insights in a clear and business-friendly format.
The dashboard is built using basic Power BI measures and standard data modeling techniques.
- Power BI Desktop
- DAX (Basic Measures)
- Microsoft Excel
- Git & GitHub
The dataset was cleaned and prepared using Power BI and Excel:
- Checked for missing and inconsistent values
- Standardized department and job role names
- Ensured correct data types for measures and calculations
Basic DAX measures were created to calculate:
- Total Employees
- Employees Left
- Attrition Rate (%)
(Only simple aggregation and percentage measures were used in this project.)
- Total Employees
- Employees Left
- Attrition Rate (%)
- Department (Human Resources, Research & Development, Sales)
- Gender (Male, Female)
- Attrition by Department – Identifies departments with higher attrition
- Attrition by Job Role – Highlights roles most affected by attrition
- Overtime vs Attrition – Compares attrition trends based on overtime status
- Sales department shows the highest attrition rate
- Attrition is notably higher among certain job roles such as Sales Executive
- Employees working overtime show higher attrition compared to those who do not
- Gender-based filtering provides additional insight into attrition patterns
- HR_Attrition_Analysis.pbix – Power BI dashboard file
- hr_analytics_data.xlsx / .csv – Dataset used for analysis
- README.md – Project documentation
This project focuses on analyzing employee attrition using an interactive Power BI dashboard.
The goal is to identify attrition patterns across departments, job roles, gender, and overtime, and to present key HR insights in a clear and business-friendly format.
The dashboard is built using basic Power BI measures and standard data modeling techniques.
- Power BI Desktop
- DAX (Basic Measures)
- Microsoft Excel
- Git & GitHub
- Dataset Type: HR Analytics Dataset
- Source: Public HR Attrition dataset (commonly used for analytics practice)
- Granularity: Employee-level data
The dataset was cleaned and prepared using Power BI and Excel:
- Checked for missing and inconsistent values
- Standardized department and job role names
- Ensured correct data types for measures and calculations
Basic DAX measures were created to calculate:
- Total Employees
- Employees Left
- Attrition Rate (%)
(Only simple aggregation and percentage measures were used in this project.)
- Total Employees
- Employees Left
- Attrition Rate (%)
- Department (Human Resources, Research & Development, Sales)
- Gender (Male, Female)
- Attrition by Department – Identifies departments with higher attrition
- Attrition by Job Role – Highlights roles most affected by attrition
- Overtime vs Attrition – Compares attrition trends based on overtime status
- Sales department shows the highest attrition rate
- Attrition is notably higher among certain job roles such as Sales Executive
- Employees working overtime show higher attrition compared to those who do not
- Gender-based filtering provides additional insight into attrition patterns
- HR_Attrition_Analysis.pbix – Power BI dashboard file
- hr_analytics_data.xlsx / .csv – Dataset used for analysis
- README.md – Project documentation
- Download the .pbix file from the repository
- Open it using Power BI Desktop
- Refresh data if required
- Use slicers to explore attrition insights interactively
- Download the .pbix file from the repository
- Open it using Power BI Desktop
- Refresh data if required
- Use slicers to explore attrition insights interactively
This project is created for learning, practice, and portfolio purposes only.
The dataset is publicly available and does not represent real organizational data.