This project studies a dataset of employees to understand why they leave the company. We explore patterns, salary differences, job roles, and years at the company. The goal is to give insights to help reduce employee turnover.
- Source:https://www.kaggle.com/datasets/itssuru/hr-employee-attrition/data
- The dataset has information about employees such as:
- Department and Job Role
- Monthly Income / Salary
- Years at Company / Tenure
- Attrition Status (Yes/No)
- Age, Gender, and other details
- Python 3.x
- Libraries:
- pandas
- matplotlib
- seaborn
- streamlit
-Notebook: Open notebook/Project_notebook.py in Google Colab, Run all the cells step by step to see the analysis, charts, and insights.
-Streamlit Dashboard: Go to the streamlit folder: cd (location of streamlit file) streamlit run app.py The dashboard shows:
- Data preview
- Summary statistics
- Interactive visualizations
- Filters (dropdown, slider, multiselect)
- Insights section
- Low income is the strongest and most consistent factor related to employee attrition.
- Department culture, experience level, and early career engagement also play major roles.
- Improving compensation, onboarding, and (department-level conditions)—especially in HR and R&D—may help reduce turnover.