This project analyzes employee attrition data to understand the factors that contribute to employees leaving an organization. By leveraging data analysis and machine learning techniques, the goal is to identify patterns and predictors of attrition, helping HR departments take proactive measures to retain talent and improve employee satisfaction.
The dashboard provides a visual representation of key attrition trends based on different employee attributes. It includes:
- Attrition by Marital Status: Highlights the correlation between marital status and employee attrition rates, showing which groups are most affected.
- Attrition by Education: Displays how education levels impact attrition, revealing trends among different educational backgrounds.
- Other Key Metrics: The dashboard may also include insights on department-wise attrition, salary impact, job satisfaction scores, and more, helping HR teams make data-driven decisions.
The dataset contains the following key features:
- Employee Demographics: Age, Gender, Marital Status, etc.
- Job-Related Features: Job Role, Department, Job Satisfaction, etc.
- Financial Features: Monthly Income, Salary Hike, Stock Options, etc.
- Attrition: Whether the employee left the organization (Yes/No).
- Explore the dataset to understand key trends and patterns.
- Identify factors that contribute to employee attrition.
- Build predictive models to estimate the likelihood of attrition.
- Provide actionable insights to reduce attrition rates.
- Python
- Libraries:
pandas,numpy,matplotlib,seaborn,scikit-learn - Jupyter Notebook
- Git/GitHub
data/- Contains the dataset file(s).notebooks/- Jupyter notebooks for data cleaning, analysis, and modeling.scripts/- Python scripts for automation (if applicable).visualizations/- Saved plots and charts.README.md- Project overview and instructions.