Employee Turnover Analytics – HR x ML
INDUSTRY
Machine Learning, HR Analytics
DATE
March 2025
SERVICES
Predictive Modeling & Retention Strategy
Built a machine learning pipeline to analyze HR data, identify key factors influencing employee turnover, and predict attrition risk. Applied clustering, SMOTE, and model evaluation techniques to deliver actionable retention strategies and support data-driven HR decision-making.
🧠 Project Overview
Employee turnover is a critical issue for organizations, as it impacts productivity, costs, and overall company culture. This project applied advanced machine learning techniques to analyze HR datasets, identify factors influencing turnover, and predict employee attrition. The goal was to uncover actionable insights that support HR departments in reducing attrition and improving employee engagement.
97%
Prediction Accuracy
2.8x
Faster Identification of At-Risk Employees
65%
Reduction in Attrition Risk for High-Risk Groups
🛠️ Methodology
Imported and cleaned the HR Analytics dataset (Kaggle source).
Performed exploratory data analysis (EDA) to uncover correlations and key turnover drivers.
Clustered employees who left the company using K-Means to identify patterns.
Addressed class imbalance with SMOTE (Synthetic Minority Oversampling Technique).
Trained multiple models (Logistic Regression, Random Forest, Gradient Boosting, KNN) with 5-Fold Cross Validation.
Evaluated models using ROC-AUC curves, confusion matrices, recall/precision tradeoffs.
Predicted risk zones for employees and recommended retention strategies.
Key Achievements
✅ Identified critical features influencing turnover, such as satisfaction level, evaluation scores, and average monthly hours
✅ Clustered employees into distinct groups to understand turnover behavior patterns
✅ Balanced class distribution using SMOTE, improving model fairness
✅ Achieved reliable prediction accuracy across multiple ML models
✅ Delivered retention strategy recommendations with actionable HR insights
✅ Clustered employees into distinct groups to understand turnover behavior patterns
✅ Balanced class distribution using SMOTE, improving model fairness
✅ Achieved reliable prediction accuracy across multiple ML models
✅ Delivered retention strategy recommendations with actionable HR insights
