This project focuses on analyzing employee attrition patterns using HR data and Machine Learning techniques.
The goal is to identify the key factors that influence employees to leave an organization and provide data-driven insights for improving employee retention.
The project uses an HR Employee Attrition dataset containing employee information such as:
- Age
- Department
- Job Role
- Monthly Income
- Work-Life Balance
- Years at Company
- Job Satisfaction
- Attrition Status
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization
- Feature Analysis
- Machine Learning Model Building
- Model Evaluation
Models used:
- Logistic Regression
- Random Forest Classifier
Evaluation metrics:
- Accuracy
- Confusion Matrix
- ROC Curve
- Feature Importance
- Identified important factors affecting employee attrition.
- Analyzed attrition trends across departments and job roles.
- Found patterns related to income, work-life balance, and experience.
- HR should focus on employees with higher attrition risk.
- Improve work-life balance and employee satisfaction.
- Create better retention strategies for critical roles.
Shreya Maurya