Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis

Murat Urfalıoğlu

doi:10.29130/dubited.1830199

TR EN

Random Forest Algoritması ile Kariyer Memnuniyeti Tahmini: Öznitelik Önemi ve Performans Analizi

Abstract

Bu araştırmanın amacı, bireylerin kariyer memnuniyeti düzeylerini çok boyutlu bir veri yapısı üzerinden makine öğrenmesi tabanlı bir regresyon modeli ile tahmin etmek ve bu tahminde etkili olan özniteliklerin önem düzeylerini belirlemektir. Çalışmada, Kaggle platformunda yayımlanan ve 2018–2022 yılları arasında mezun olan 400 üniversite mezununa ait veri seti kullanılmıştır. Veri setine veri ön işleme yapıldıktan sonra Random Forest regresyon algoritması uygulanmış ve model performansı 10 katlı çapraz doğrulama ile değerlendirilmiştir. Elde edilen bulgular, modelin kariyer memnuniyetini tahmin etmede oldukça yüksek bir performans sergilediğini göstermektedir (R = 0.99, MAE = 0.02, RMSE = 0.08, RAE = %1.77, RRSE = %6.13). Öznitelik önem analizi sonuçlarına göre kariyer memnuniyetinin başlıca belirleyicileri; terfiye kadar geçen süre, mevcut iş düzeyi, sertifika sayısı , iş teklifleri ve tamamlanan stajlar gibi kariyer çıktılarıyla ilişkili değişkenlerdir. Yaş ve cinsiyet gibi demografik değişkenlerin önem düzeyleri ise oldukça düşüktür. Bulgular, kariyer memnuniyetinin geliştirilebilir nitelikteki mesleki deneyimler ve kariyer çıktılarıyla daha yakından ilişkili olduğunu ortaya koymaktadır. Bu bulgular kariyer danışmanlığı, insan kaynakları yönetimi ve yükseköğretimde kariyer hizmetlerine yönelik veri temelli karar alma süreçlerine katkı sunmaktadır.

Keywords

Kariyer memnuniyeti, Makine öğrenmesi, Random Forest, Regresyon

Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis

Abstract

Career satisfaction is a dynamic and multifaceted phenomenon shaped by professional development, individual experiences, and career-related outcomes. This study aims to predict individuals’ career satisfaction using a machine learning-based regression framework with multidimensional features. The dataset consists of 400 observations, including demographic, academic, professional, and social attributes. Six machine learning algorithms, namely Linear Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB), were employed and comparatively evaluated. To enhance methodological rigor, potential data leakage risk was assessed through correlation analysis, and a restricted feature set was constructed by excluding variables highly correlated with the target variable. Model performance was evaluated using both cross-validation and train–test split approaches. Hyperparameter optimization was performed using GridSearchCV, and model performance was evaluated with coefficient of determination (R²), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE) metrics. The results indicate that tree-based methods tend to outperform other models. After optimization, the GB model achieved the best performance on the held-out test set, with an R² value of 0.9213. Feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that individual and career-related variables had a stronger influence on predictions. Overall, this study provides a methodologically transparent framework for predicting career satisfaction and supports data-driven decision-making in career-related contexts.

Keywords

Career satisfaction, Machine learning, Regression analysis, Feature importance, SHAP

Supporting Institution

This research received no external funding.

Ethical Statement

This study used a publicly available and anonymized secondary dataset. The author did not directly collect data from human participants; therefore, institutional ethics committee approval was not required.

Thanks

The author declares that there are no acknowledgements to be made regarding any individual or institution.

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Details

Primary Language

English

Subjects

Supervised Learning, Machine Learning Algorithms, Classification Algorithms

Journal Section

Research Article

Authors

Murat Urfalıoğlu ^*
0000-0002-2893-8135
Türkiye

Publication Date

July 24, 2026

Submission Date

November 25, 2025

Acceptance Date

April 19, 2026

Published in Issue

Year 2026 Volume: 14 Number: 3

DOI

https://doi.org/10.29130/dubited.1830199

IZ

https://izlik.org/JA27YG69KN

APA

Urfalıoğlu, M. (2026). Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis. Duzce University Journal of Science and Technology, 14(3), 759-770. https://doi.org/10.29130/dubited.1830199

AMA

1.Urfalıoğlu M. Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis. DUBİTED. 2026;14(3):759-770. doi:10.29130/dubited.1830199

Chicago

Urfalıoğlu, Murat. 2026. “Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis”. Duzce University Journal of Science and Technology 14 (3): 759-70. https://doi.org/10.29130/dubited.1830199.

EndNote

Urfalıoğlu M (July 1, 2026) Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis. Duzce University Journal of Science and Technology 14 3 759–770.

IEEE

[1]M. Urfalıoğlu, “Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis”, DUBİTED, vol. 14, no. 3, pp. 759–770, July 2026, doi: 10.29130/dubited.1830199.

ISNAD

Urfalıoğlu, Murat. “Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis”. Duzce University Journal of Science and Technology 14/3 (July 1, 2026): 759-770. https://doi.org/10.29130/dubited.1830199.

JAMA

1.Urfalıoğlu M. Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis. DUBİTED. 2026;14:759–770.

MLA

Urfalıoğlu, Murat. “Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis”. Duzce University Journal of Science and Technology, vol. 14, no. 3, July 2026, pp. 759-70, doi:10.29130/dubited.1830199.

Vancouver

1.Murat Urfalıoğlu. Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis. DUBİTED. 2026 Jul. 1;14(3):759-70. doi:10.29130/dubited.1830199

Random Forest Algoritması ile Kariyer Memnuniyeti Tahmini: Öznitelik Önemi ve Performans Analizi

Abstract

Keywords

Career Satisfaction Prediction Using Machine Learning: Feature Importance and Performance Analysis

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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