Implementing Convolutional Neural Networks for Career Prediction: A Case Study on Twelfth Grade Students

In today’s fast-paced job market, accurate career prediction models are essential for personalized guidance and efficient hiring processes. This study explores the innovative application of Convolutional Neural Networks (CNNs) to enhance the precision and personalization of career prediction models, particularly for twelfth-grade students. CNN algorithms effectively process diverse data inputs, like educational backgrounds and skills, improving the accuracy of personalized recommendations. Traditional methods often struggle to capture intricate relationships between academic performance, aptitude assessments, and personal interests. Leveraging deep learning, this research aims to revolutionize career guidance by addressing the multidimensional aspects influencing career choices. The study fills a gap by presenting a detailed case study on implementing CNNs tailored to the unique challenges faced by twelfth-grade students.