A MACHINE LEARNING APPROACH TO PREDICTING CAREER DECISION-MAKING DIFFICULTIES

Abstract

Data-driven approaches have become increasingly effective in understanding human behavior and making predictions for the future with advancing technologies. In particular, machine learning techniques are highly successful in analyzing, classifying, and predicting complex relationships through large and multivariate datasets. This study aims to predict the difficulties faced by individuals in the career decision-making process with machine learning methods. In this context, prediction models were created based on individuals’ career decision-making difficulties, decision-making styles, and personality traits. The data was obtained from students studying at a state university through an online survey method. The data were analyzed using the Python language and PyCaret library, and different algorithms were tested. As a result of the comparisons, the highest accuracy value of 84,44% was obtained with the logistic regression model. The results of the analysis show that especially lack of information and inconsistency are effective variables in the decision-making difficulties. These findings point to the importance of approaches that support cognitive awareness and decision-making skills in career counselling.

Keywords

• Machine Learning
• Career Decision-Making
• Personality Traits
• Decision-Making Styles

Project Number

SGA-2025-2028

MAKİNE ÖĞRENMESİ YAKLAŞIMIYLA KARİYER KARAR VERME GÜÇLÜKLERİNİN ÖNGÖRÜLMESİ

Abstract

Gelişen teknolojilerle birlikte, veri odaklı yaklaşımlar insan davranışlarını anlamada ve geleceğe yönelik öngörülerde bulunmada giderek daha etkili hale gelmiştir. Özellikle makine öğrenmesi teknikleri, büyük ve çok değişkenli veri kümeleri aracılığıyla karmaşık ilişkileri analiz etme, sınıflandırma ve öngörmede yüksek başarı sağlamaktadır. Bu çalışma, bireylerin kariyer kararı verme sürecinde karşılaştıkları zorlukların makine öğrenmesi yöntemleriyle öngörülmesini amaçlamaktadır. Bu bağlamda çalışmada, kariyer karar verme güçlükleri, bireylerin karar verme stilleri ve kişilik özellikleri temel alınarak öngörü modelleri oluşturulmuştur. Veriler, bir devlet üniversitesinde öğrenim gören öğrencilerden çevrim içi anket yöntemiyle elde edilmiştir. Elde edilen veriler Python dili ve PyCaret kütüphanesi aracılığıyla analiz edilmiş ve farklı algoritmalar test edilmiştir. Yapılan karşılaştırmalar sonucunda %84,44 ile en yüksek doğruluk değeri lojistik regresyon modeli ile elde edilmiştir. Analiz sonuçları, bilgi eksikliği ve tutarsızlığın, karar verme sürecindeki zorluklarda etkili değişkenler olduğunu göstermektedir. Bu sonuçlar, kariyer danışmanlığı süreçlerinde bireylerin bilişsel farkındalığını ve karar verme becerilerini geliştirmeye yönelik veri destekli müdahale stratejilerinin önemine işaret etmektedir.

Keywords

• Makine Öğrenimi
• Kariyer Kararı
• Kişilik Tipleri
• Karar Verme Stilleri.

Supporting Institution

Bursa Uludağ Üniversitesi

Project Number

SGA-2025-2028

Ethical Statement

Bu çalışma 24.09.2024 tarihli ve 2024-09 sayılı oturumda Bursa Uludağ Üniversitesi Etik Komitesi tarafından onaylanmıştır.

Thanks

Bu çalışma, Bursa Uludağ Üniversitesi Bilimsel Araştırmalar Projeleri birimi tarafından desteklenmiştir (Proje Numarası: SGA-2025-2028)

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