This repository contains a Power BI dashboard created using HR analytics datasets sourced from Kaggle. The goal of the project is to analyze various HR-related factors such as employee attrition, satisfaction, promotion rates, and other key performance indicators (KPIs).
The dashboard provides insights into data-driven HR decision-making and offers visualizations to help understand trends, patterns, and opportunities within an organization’s workforce.
Employee Attrition Analysis: Identify trends and factors contributing to employee attrition. Performance Evaluation: Analyze performance ratings and their impact on promotion and salary increments. Satisfaction Levels: Explore employee satisfaction across departments, roles, and locations. Workforce Demographics: Insights into gender, age, and other workforce-related demographics. Promotion and Retention: Examine promotion history and retention rates.
The dataset used in this project is sourced from Kaggle and contains various HR metrics such as employee job satisfaction, performance, tenure, salary, and demographics. This data is used to create meaningful and actionable insights that can assist HR departments in decision-making.
Power BI: For creating interactive visualizations and dashboards. Excel: For preprocessing and cleaning the dataset.
The dashboard includes several visualizations:
Attrition by Department: Breakdown of employee attrition across various departments. Satisfaction Heatmap: Heatmap showing satisfaction levels. Promotion History: Visual timeline of employee promotions. Salary vs Performance: Scatter plot visualizing the relationship between salary and performance ratings.
To view the Power BI dashboard:
Download the hr_analytics_dashboard.pbix file from this repository. Open the file using Power BI Desktop. Explore the dashboard to interact with the visuals and analysis. Repository Structure bash
. ├── hr_analytics_dashboard.pbix # Power BI dashboard file ├── README.md # Project overview (this file) └── Data # Raw or cleaned data files (CSV)
Expand analysis to cover employee recruitment trends. Add machine learning models for predictive analysis of employee attrition. Explore additional visualizations and metrics such as diversity and inclusion.