The research on the classification prediction of students' employment destinations in higher education not only opens up new application areas for classification algorithms, but also represents a new attempt to introduce machine learning algorithms into the analysis of employment guidance and the development of teaching systems. The purpose of this paper is to study the employment destination prediction of college students based on machine learning algorithms. The factors influencing the employability of university students are analysed and the XGBoost model in decision trees is explored. A graduate employment prediction algorithm based on HMIGW feature selection and XGBoost algorithm is proposed to predict the employment situation of the class of 2022 and the type of employment, and the experimental results show that the algorithm is able to obtain relatively accurate conclusions on graduate employment prediction.

Weijun Xie. Predicting the Employment Destinations of University Students Based on Machine Learning Algorithms. Machine Learning Theory and Practice (2022), Vol. 3, Issue 4: 27-35. https://doi.org/10.38007/ML.2022.030404.

[1] Ishwank Singh, A. Sai Sabitha, Tanupriya Choudhury, Archit Aggarwal, Bhupesh Kumar Dewangan: Mapping Student Performance with Employment Using Fuzzy C-Means. Int. J. Inf. Syst. Model. Des. 11(4): 36-52 (2020) https://doi.org/10.4018/IJISMD.2020100103

[2] Theodoros Lappas: Mining Career Paths from Large Resume Databases: Evidence from IT Professionals. ACM Trans. Knowl. Discov. Data 14(3): 37:1-37:38 (2020) https://doi.org/10.1145/3379984

[3] Sophia Alim, Ibrahim AlShourbaji: Professional uses of Facebook amongst university students in relation to searching for jobs: an exploration of activities and behaviours. Int. J. Soc. Media Interact. Learn. Environ. 6(3): 200-229 (2020) https://doi.org/10.1504/IJSMILE.2020.109225

[4] Alexander A. McKenzie: David Corydon Walden's Five Careers. IEEE Ann. Hist. Comput. 44(3): 70-79 (2022) https://doi.org/10.1109/MAHC.2022.3193812

[5] Andrea Delgado, Verónica G. Melesse Vergara, Andrea Schneibel: Careers in STEM: A Latina Perspective. Comput. Sci. Eng. 24(3): 81-85 (2022) https://doi.org/10.1109/MCSE.2022.3188195

[6] Nooshin Pordelan, Simin Hosseinian, Hamid Heydari, Sadaf Khalijian, Mohammad Khorrami: Consequences of teleworking using the internet among married working women: Educational careers investigation. Educ. Inf. Technol. 27(3): 4277-4299 (2022) https://doi.org/10.1007/s10639-021-10788-6

[7] João M. Fernandes, António Costa, Paulo Cortez: Author placement in Computer Science: a study based on the careers of ACM Fellows. Scientometrics 127(1): 351-368 (2022) https://doi.org/10.1007/s11192-021-04035-5

[8] Marek Kwiek, Wojciech Roszka: Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems. Scientometrics 127(6): 3543-3575 (2022) https://doi.org/10.1007/s11192-022-04363-0

[9] D. Venkata Vara Prasad, Srinivas Gumparthi, Lokeswari Y. Venkataramana, S. Srinethe, R. M. Sruthi Sree, K. Nishanthi: Prediction of Stock Prices Using Statistical and Machine Learning Models: A Comparative Analysis. Comput. J. 65(5): 1338-1351 (2022) https://doi.org/10.1093/comjnl/bxab008

[10] Praveen Lalwani, Manas Kumar Mishra, Jasroop Singh Chadha, Pratyush Sethi: Customer churn prediction system: a machine learning approach. Computing 104(2): 271-294 (2022) https://doi.org/10.1007/s00607-021-00908-y

[11] Amirpasha Mozaffari, Michael Langguth, Bing Gong, Jessica Ahring, Adrian Rojas Campos, Pascal Nieters, Otoniel José Campos Escobar, Martin Wittenbrink, Peter Baumann, Martin G. Schultz: HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction. Data Intell. 4(2): 271-285 (2022) https://doi.org/10.1162/dint_a_00131

[12] Filippos Giannakas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou, Ioannis Voyiatzis: Multi-technique comparative analysis of machine learning algorithms for improving the prediction of teams' performance. Educ. Inf. Technol. 27(6): 8461-8487 (2022) https://doi.org/10.1007/s10639-022-10900-4

[13] Muhammad Shoaib, Nasir Sayed, Nedra Amara, Abdul Latif, Sikandar Azam, Sajjad Muhammad: Prediction of an educational institute learning environment using machine learning and data mining. Educ. Inf. Technol. 27(7): 9099-9123 (2022) https://doi.org/10.1007/s10639-022-10970-4

[14] Abdolreza Nazemi, Friedrich Baumann, Frank J. Fabozzi: Intertemporal defaulted bond recoveries prediction via machine learning. Eur. J. Oper. Res. 297(3): 1162-1177 (2022) https://doi.org/10.1016/j.ejor.2021.06.047

[15] Marwah Sattar Hanoon, Alharazi Abdulhadi Abdullatif B, Ali Najah Ahmed, Arif Razzaq, Ahmed H. Birima, Ahmed El-Shafie: A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia. Earth Sci. Informatics 15(1): 91-104 (2022) https://doi.org/10.1007/s12145-021-00689-0

[16] Mohammed Zubair M. Shamim, Sattam Alotaibi, Hany S. Hussein, Mohammed Farrag, Mohammad Shiblee: Diagnostic Accuracy of Smartphone-Connected Electrophysiological Biosensors for Prediction of Blood Glucose Level in a Type-2 Diabetic Patient Using Machine Learning: A Pilot Study. IEEE Embed. Syst. Lett. 14(1): 27-30 (2022) https://doi.org/10.1109/LES.2021.3096717

[17] T. Vaisakh, R. Jayabarathi: Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction. Evol. Intell. 15(1): 235-254 (2022) https://doi.org/10.1007/s12065-020-00505-6

[18] Abdalrhman Milad, Sadaam Hadee Hussein, Ahlam R. Khekan, Mohammed Rashid, Haitham Al-Msari, Tan Huy Tran: Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model. Eng. Comput. 38(4): 3625-3637 (2022) https://doi.org/10.1007/s00366-021-01398-4