Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering

TL;DR

This paper introduces a comprehensive data-driven framework combining machine learning and data engineering to predict and mitigate employee attrition, providing actionable insights for organizations.

Contribution

It presents a novel integrated approach that handles data collection, feature engineering, model calibration, and interpretation specifically for employee attrition prediction.

Findings

Effective handling of imbalanced datasets improves prediction accuracy.

Model interpretation with SHAP values offers actionable insights.

Calibrated models enable proactive retention strategies.

Abstract

This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu