Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (8): 536-546.doi: 10.23940/ijpe.23.08.p6.536546

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An Advanced Machine Learning Approach for Student Placement Prediction and Analysis

K. Eswara Raoa*(), Bala Murali Pydib, T. Panduranga Vitala, P. Annan Naidua, U. D. Prasanna, and T. Ravikumara   

  1. aDepartment of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, India
    bDepartment of Electrical and Electronics Engineering, Aditya Institute of Technology and Management, Tekkali, India
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As there are job opportunities worldwide, the graduates who are being produced in large numbers from various backgrounds are constantly trying to get them. Moreover, the management of graduate colleges gives proper training to the students to get those opportunities. Every student has their skills, unique creative outlook, studying, and good academic skills that help them get placed in various companies and also have a chance to get reputed positions, but most of the graduates are still failing to get the opportunity because they cannot find what skills to acquire. For this reason, in this paper, we gathered information from students who have finished their courses at different colleges. Collected information by communication and asked them about their academics, performance, families, skills, personal information, habits, etc., and what prevented them from taking the opportunity. Then, we made a dataset with all the factors that affected a student's career and used that to create a model with synthetic data. Student Placement Prediction can also benefit colleges and universities by providing valuable observations of student career outcomes. By understanding the factors influencing student job placement, colleges can conduct services and programs to help their students be better prepared for their careers. Accuracy and precision were used to evaluate the eXtreme Gradient Boost (XGBoost) machine learning model's performance compared to standard classification techniques. According to the results, the proposed algorithm is vastly superior to the alternatives.

Key words: career prediction, traditional machine learning classifiers, crossfold Validation, boosting methods, XG Boost