Employee attrition forecasting: Determining the optimal algorithm for predicting employee turnover
Authors
Kumari Jaya Kumar, Vinitha
Issue Date
2024-05
Degree
MSc in Business Analytics
Publisher
Dublin Business School
Rights holder
Rights
Abstract
Employee attrition is a severe organizational challenge contributing to decline in both the productivity and profitability. The research target is to identify the universal machine learning algorithm which could predict employee attrition through examination of diverse factors including employee demographics, termination reasons, and job-related specifications. The study has Logistic Regression, Random Forest, and Gradient Boosting, models that were compared in terms of their functionality in predicting an individual leaving the company. Exploratory Data Analysis (EDA) is intended in the early stages of the analysis when the dataset structure is analyzed and the main factors that cause attrition are identified. Development of models, followed by their evaluation using metrics such as accuracy, precision, recall, F1 score, area under the ROC curve, and precision recall curves takes place concurrently.
The results display that the Gradient Boosting some of the other models in terms of accuracy and recall highlighting the robustness of the model in capturing the imbalanced nature of the dataset. The fundamental condition of the reasons for attrition consist of termination causes, voluntary and involuntary types of termination, age, and length of service. The paper demonstrates the value of these factors in predicting the attrition an in addition it supplies insights into fruitful attrition management techniques.
The survey closes with recommendations for organizations to take an advanced machine learning approach and data-driven strategy in order to reduce staff turnover. Future trends encompass the discovery of the additional predictive indicators, the enhancement of the algorithm models and the implementation of those techniques across different fields to ensure the high-performance capability of the model. This research is a part of the broad field of human resources analytics techniques that confirm the applicability of machine learning to address critical workforce problems.