Predictive Analysis of Internship and Job Placement Success in Computer Science Education

The growing competition in the job market calls for a more in-depth comprehension of the factors that affect successful internship and job placement results in computer science education. This study provides a thorough examination of 25 machine learning classification models in order to forecast the job prospects of computer science students. Out of these models, the Random Forest Classifier stood out as the best performer, with an accuracy, precision, recall, and F1 score of 82.90%, 82.90%, 100.00%, and 90.65% respectively. The research investigates the influence of different factors like CGPA, course completion, specialization, project attachments, and extracurricular activities on job placement outcomes. Moreover, the study highlights the significance of model calibration, leading to enhanced performance of the Random Forest Classifier. The results provide useful information for schools, enabling them to provide focused assistance for students and improve their curriculum. Future efforts will be directed towards enhancing the model, incorporating current industry data in real-time, and experimenting with advanced methods to enhance the accuracy of predictions. This research seeks to enhance predictive analytics in the education sector, specifically by enhancing placement results for computer science graduates.