Forecasting Employees’ Promotion Based on Personal Indicators by Using a Machine Learning Algorithm

Abstract

Promotion is a tool to motivate employees to improve themselves and take on the burden and responsibility of the position assigned to them. Due to the fairness and measurability of promotions conducted by traditional methods needing to be quantifiable, different methods are required. In recent years, with the widespread use of information systems in companies, much information, such as performance data of employees, has started to be stored digitally. Additionally, with the development of data sciences and their application in many fields, machine learning and artificial intelligence algorithms in evaluating this data have become widespread. This study aims to establish a robust framework to predict employee promotions based on various features. These features include but are not limited to the number of training sessions attended, previous year ratings, tenure, awards received, and average training scores. The study aims to provide organizations with a reliable tool to make informed promotion decisions and demonstrate that this framework can be generalized to other prediction problems. Experimental results show that the XGBoost model is the most efficient in terms of accuracy. XGBoost is considered a superior algorithm with 94% accuracy, 94% ROC AUC, 94% sensitivity, and 94% precision, excelling in memory usage efficiency, accuracy, and runtime.

Keywords

• Employee Promotion
• Employee Promotion Prediction
• XGBoost
• Machine Learning
• Supervised Learning.

Makine Öğrenimi Algoritması Kullanarak Kişisel Göstergelere Dayalı Çalışan Teşviklerinin Tahmini

Abstract

Terfi, çalışanın kendini geliştirmesi ve işin yükünü ve sorumluluğunu, kendisine yüklenen pozisyonla birlikte taşıma isteği için motive etmenin bir aracı olarak hareket eder. Geleneksel yöntemler ile yapılan terfilerin hakkaniyeti ve ölçülebilirliği nicel olarak ölçülemediği için farklı yöntemlere ihtiyaç duyulmaktadır. Son yıllarda şirketlerde bilgi sistemlerinin kullanımın yaygınlaşması ile çalışanlara ait performans bilgileri gibi birçok bilgi dijital ortamda tutulmaya başlandı. Yine ver bilimlerinin gelişmesi ve birçok alana uygulanması ile birlikte çalışanlara ait bu verilerin değerlendirmesinde makine öğrenmesi ve yapay zekâ algoritmalarının kullanımı yaygınlaştı. Bu çalışma, çeşitli özelliklere dayalı olarak bir kuruluş içindeki çalışanların terfilerini tahmin etmek için sağlam bir çerçeve oluşturmayı amaçlamaktadır. Bu özellikler, eğitim sayısını, önceki yıl derecelendirmelerini, hizmet süresini, kazanılan ödülleri ve ortalama eğitim puanını içermekle birlikte bunlarla sınırlı değildir. Çalışmanın amacı, kuruluşların bilinçli terfi kararları almaları için güvenli bir araç sağlamak ve bu çerçevenin diğer tahmin problemlerine genelleştirilebileceğini göstermektir. Deneysel sonuçlar XGBoost modelinin doğruluk açısından en verimli model olduğunu göstermektedir. XGBoost, %94 doğruluk ve ROC AUC, %94 duyarlılık ve %94 hassasiyetle bellek kullanımı verimliliği, doğruluk ve çalışma süresi açısından üstün bir algoritma olarak kabul edilmektedir.

Keywords

• Çalışan Terfisi
• Çalışan Terfi Tahmin Çerçevesi
• XGBoost
• Makine Öğrenimi
• Denetimli Öğrenme.

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