Abstract
The retail sector is facing operational inefficiencies and high recruitment costs due to the increasing turnover rate. This project focuses on identifying and analyzing the key factors leading to the departure of qualified personnel. The data discussed is a real-life HR analytical case of a company working in the retail industry. Using advanced machine learning algorithms such as LGBM, XGBoost, AdaBoost, and CatBoost, the study seeks to reveal the relationship between variables like education level, city, and age. The project provides actionable insights that can inform strategic decisions by using both qualitative and quantitative data sources. According to the results, the most successful model is discovered as CatBoost. The findings indicate that the employee’s average sales and its coefficient of variation, trends of sales, and age of employee play crucial roles in employee churn. To interpret these, an increase in an employee's sales rates correlates with a higher likelihood of retaining their position. Actions taken in light of the project's findings can contribute to companies predicting employee churn in advance, thereby reducing turnover rates and improving operational costs.
Original language | English |
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Title of host publication | Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference |
Editors | Cengiz Kahraman, Sezi Cevik Onar, Selcuk Cebi, Basar Oztaysi, Irem Ucal Sari, A. Cagrı Tolga |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 109-116 |
Number of pages | 8 |
ISBN (Print) | 9783031671913 |
DOIs | |
Publication status | Published - 2024 |
Event | International Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey Duration: 16 Jul 2024 → 18 Jul 2024 |
Publication series
Name | Lecture Notes in Networks and Systems |
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Volume | 1090 LNNS |
ISSN (Print) | 2367-3370 |
ISSN (Electronic) | 2367-3389 |
Conference
Conference | International Conference on Intelligent and Fuzzy Systems, INFUS 2024 |
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Country/Territory | Turkey |
City | Canakkale |
Period | 16/07/24 → 18/07/24 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keywords
- AdaBoost
- ANOVA
- Catboost
- Employee Churn
- HR Analytics
- LGBM
- Machine Learning
- Statistical Tests
- Turnover
- XGBoost