Analysing Impact of Internship on Career Readiness – A Deep Learning Framework Approach

Internships bridge classroom learning with real-world job skills, providing hands-on experience and industry exposure. However, their impact on career readiness is often assumed rather than measured. This project applies deep learning models to predict career readiness based on key internship factors. Student data was collected through surveys covering internship duration, mentorship quality, industry exposure, and skill development. After preprocessing with techniques like normalization and feature selection, three models MLP, LSTM, and CNN were trained. CNN outperformed the others, achieving the highest accuracy in predicting career readiness by capturing complex data patterns. The findings confirm that strong mentorship, real tasks, and industry-specific learning significantly enhance career preparedness. These insights can help universities and organizations improve internship programs. Future work can expand this approach by integrating real-time workplace feedback and long-term career tracking.