AI-based Career Recommendation System for Youth Employment Using Machine Learning
Keywords:
Artificial intelligence, Career guidance, Career recommendation, Decision tree, Machine learning, Random forest, Student profiling, Youth employmentAbstract
Choosing a career is a difficult decision for students because a useful choice depends on more than knowing which occupations are popular. Students may have a reasonable idea of what they enjoy, yet still struggle to translate their abilities into a realistic career direction. This study develops a machine-learning-based career recommendation system that uses structured student skill information to predict a suitable career category. The dataset contains 3,600 student records with attributes representing linguistic ability, logical reasoning, spatial intelligence, and interpersonal skill. The records were prepared through data cleaning, treatment of missing and duplicate entries, categorical encoding, and feature selection before model training. Two supervised classification approaches, Decision Tree and Random Forest, were implemented in Python using Scikit-learn in Google Colab. The data were divided into training and testing subsets using an 80:20 split, and the models were assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The Decision Tree achieved 97.50% accuracy, while Random Forest reached 97.77%. Precision, recall, and F1-score for the Random Forest model were approximately 0.98. The results indicate that the proposed approach can learn a strong relationship between the selected skill attributes and the career labels contained in the dataset. The system is intended as a decision-support tool rather than a replacement for counselors, since career choice also depends on interests, academic history, context, opportunities, and changing labour-market conditions. The work demonstrates a simple and scalable foundation that can be extended with richer student and labour-market information.
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