/MACHINE LEARNING
Predict Employee Attrition
Built classification models (Random Forest, Logistic Regression) with SHAP/LIME explainability to support HR retention decisions.
- Tools & technologies
- Classification, EDA, Explainability, ML
- Data
- Source details to be added
- Explore
- View GitHub
A look at the project
A machine learning project focused on predicting employee attrition to help HR departments make data-driven retention decisions.
Project Overview:
- Developed multiple classification models to predict employee turnover
- Implemented explainable AI techniques for model interpretability
- Conducted comprehensive exploratory data analysis
Models Implemented:
- Random Forest Classifier
- Logistic Regression
- Feature importance analysis using SHAP
- Model explainability with LIME
Key Features:
- Comprehensive EDA with visualization
- Feature engineering and selection
- Model comparison and evaluation
- Explainable AI for business stakeholders
Business Impact:
- Designed to help explore employee attrition risk
- Provided actionable insights for retention strategies
- Improved understanding of attrition factors
