Predicting HR Churn with Python and Machine Learning

Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity loss are analysed to explain when and why employees are prone to leave. The project aims to empower companies to take pre-emptive measures for retention. Contributing to HR Analytics, it provides a methodological framework applicable to various machine learning problems, optimizing human resource management, and enhancing overall workforce stability. This research contributes not only to predicting turnover but also proposes policies and strategies derived from the model's results. By understanding the root causes and timing of employee departures, companies can proactively implement measures to mitigate turnover, thereby minimizing the associated financial and operational burdens

Decoding the Workplace & EOR: An Employee Survey Analysis by Data Science Techniques and Visualization

This research study explores the new dynamics of employee-organi-zation relationships (EOR) [6] using advanced data science methodologies and presents findings through accessible visualizations. Leveraging a dataset pro-cured from a comprehensive nationwide big employee survey, this study employs innovative strategy for theoretical researcher by using our state-of-the-art visual-ization. The results present insightful visualizations encapsulating demographic analysis, workforce satisfaction, work environment scrutiny, and the employee's view via word cloud interpretations and burnout predictions. The study underscores the profound implications of data science across various management sectors, enhancing understanding of workplace dynamics and pro-moting mutual growth and satisfaction. This multifaceted approach caters to a diverse array of readers, from researchers in sociology and management to firms seeking detailed understanding of their workforce's satisfaction, emphasizing on practicality and interpretability. The research encourages proactive measures to improve workplace environ-ments, boost employee satisfaction, and foster healthier, more productive organ-izations. It serves as a resourceful tool for those committed to these objectives, manifesting the transformative potential of data science in driving insightful nar-ratives about workplace dynamics and employee-organization relationships. In essence, this research unearths valuable insights to aid management, HR profes-sionals, and companiesComment: Accepted in XXV INTERNATIONAL CONFERENCE "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2023

From Big Data to Deep Data to support people analytics for employee attrition prediction

In the era of data science and big data analytics, people analytics help organizations and their human resources (HR) managers to reduce attrition by changing the way of attracting and retaining talent. In this context, employee attrition presents a critical problem and a big risk for organizations as it affects not only their productivity but also their planning continuity. In this context, the salient contributions of this research are as follows. Firstly, we propose a people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity. In fact, this deep data-driven approach is based on a mixed method to construct a relevant employee attrition model in order to identify key employee features influencing his/her attrition. In this method, we started thinking ‘big’ by collecting most of the common features from the literature (an exploratory research) then we tried thinking ‘deep’ by filtering and selecting the most important features using survey and feature selection algorithms (a quantitative method). Secondly, this attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated human resources datasets and then a real small-sized dataset from a total of 450 responses. Our approach achieves higher accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. Finally, while rewards and payments are generally considered as the most important keys to retention, our findings indicate that ‘business travel’, which is less common in the literature, is the leading motivator for employees and must be considered within HR policies to retention.publishedVersio

Employee Attrition Prediction based on Grey Wolf Optimization and Deep Neural Networks

Despite the constructive application of promising technologies such as Neural Networks, their potential for predicting human resource management outcomes still needs to be explored. Therefore, the primary aim of this paper is to utilize neural networks and meta-heuristic technologies to predict employee attrition, thereby enhancing prediction model performance. The conventional Grey Wolf optimization optimization (GWO) has gained substantial attention notice because of its attributes of robust convergence, minimal parameters, and simple implementaton. However, it encounter problems with slow convergence rates and susceptibility to local optima in practical optimization scenarios. To address these problems, this paper introduces an enhanced Grey Wolf Optimization algorithm incorporating the utilization of Cauchy-Gaussian mutation, which contributes to enhancing diversity within the leader wolf population and enhances the algorithm's global search capabilities. Additionally, this work preserves exceptional grey wolf individuals through a greedy selection of 2 mechanisms to ensure accelerated convergence. Moreover, an enhanced exploration strategy is suggested to expand the optimization possibilities of the algorithm and improve its convergence speed. The results shows that the proposed model achieved the accuarcy of 97.85%, precision of  98.45%, recall of 98.14%, and f1-score of 97.11%. Nevertheless, this paper extends its scope beyond merely predicting employee attrition probability and activities to enhance the precision of such predictions by constructing an improved model employing a Deep Neural Network (DNN).

Churn Prediction of Employees Using Machine Learning Techniques

Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate

Analysing Theoretical Models for Predicting Employee Attrition: A Comparative Study in the FMCG Sector

  Employee attrition—the voluntary or involuntary departure of employees—causes business problems. Increasing attrition can hurt productivity, knowledge retention, and financial performance. This study compares the Herzberg Theory, Employee Equity Model, Expectancy Theory, and Job Embeddedness Theory to discover the best turnover predictor in FMCG. 53 FMCG workers completed a questionnaire survey for the study. The participants' opinions on the company, career objectives, employment engagement, and happiness were valuable. The survey data was analyzed using descriptive statistics, normality testing, and correlation analyses to determine which theoretical model better described FMCG turnover. Career problems were a fundamental cause of employee turnover. Many respondents seek more challenging and career-focused workplace positions. Many workers felt their jobs offered neither intellectual challenge nor professional progress. Attrition concerns increased due to a heavy workload and project deadlines. Most workers were satisfied with their salaries and non-cash benefits, suggesting that financial incentives did not drive attrition. Employees were proud of their company and communicated well with management. Comparing theoretical models, the Herzberg Theory, which prioritizes career growth and job happiness, predicted FMCG turnover the greatest. Model of Employee Equity and Expectations The Job Embeddedness Theory helped explain attrition dynamics but did not match attrition patterns. FMCG and other knowledge-intensive companies can use our findings to reduce personnel churn. Career development, job enrichment, and intellectually stimulating workplaces can boost employee retention

Identifying and characterizing employee groups by turnover risk using predictive analytics

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis project presents a predictive analytics project developed in a European multinational to understand and predict the turnover of its employees. It analyses the Human Resources current challenges, such as the increasing global competition for talent, where players compete for scarce skillsets such as technology and data science, and the new strategies necessary to deal with this scenario. The study explores the literature review of these contextual matters and of the studies of variables that influence turnover, generating insights and input for applying techniques aligned with the new mindset of identifying ‘flight-risk’ groups and developing targeted actions instead of only one-size-fits-all solutions. The project gathered data from different sources of the organization, designed variables, based on a literature review and internal brainstorms, treated data quality issues, transformed the data and applied three different machine learning algorithms to develop a classification predictive model. The study evaluated 46 input variables and selected a set of 26 that had higher impact on the turnover which were used in the models. Finally, it applied clustering techniques to divide employees in clusters, and identified two containing more extreme turnover behaviors (“Loyal” and “Flight risk”) and described them accordingly to their main characteristics contributing with practical insights to support potential decisions

Comparison of Classification Algorithms and Undersampling Methods on Employee Churn Prediction: A Case Study of a Tech Company

Churn prediction is a common data mining problem that many companies face across industries. More commonly, customer churn has been studied extensively within the telecommunications industry where there is low customer retention due to high market competition. Similar to customer churn, employee churn is very costly to a company and by not deploying proper risk mitigation strategies, profits cannot be maximized, and valuable employees may leave the company. The cost to replace an employee is exponentially higher than finding a replacement, so it is in any company’s best interest to prioritize employee retention. This research combines machine learning techniques with undersampling in hopes of identifying employees at risk of churn so retention strategies can be implemented before it is too late. Four different classification algorithms are tested on a variety of undersampled datasets in order to find the most effective undersampling and classification method for predicting employee churn. Statistical analysis is conducted on the appropriate evaluation metrics to find the most significant methods. The results of this study can be used by the company to target individuals at risk of churn so that risk mitigation strategies can be effective in retaining the valuable employees. Methods and results can be tested and applied across different industries and companies

The Explanation Matters: Enhancing AI Adoption in Human Resource Management

Artificial intelligence (AI) has ubiquitous applications in companies, permeating multiple business divisions like human resource management (HRM). Yet, in these high-stakes domains where transparency and interpretability of results are of utmost importance, the black-box characteristic of AI is even more of a threat to AI adoption. Hence, explainable AI (XAI), which is regular AI equipped with or complemented by techniques to explain it, comes in. We present a systematic literature review of n=62 XAI in HRM papers. Further, we conducted an experiment among a German sample (n=108) of HRM personnel regarding a turnover prediction task with or without (X)AI-support. We find that AI-support leads to better task performance, self-assessment accuracy and response characteristics toward the AI, and XAI, i.e., transparent models allow for more accurate self-assessment of one’s performance. Future studies could enhance our research by employing local explanation techniques on real-world data with a larger and international sample