Abstract
The high rates of unemployment in India can be battled by increasing the employability of people. The 20–24 age group is one of the largest groups of unemployed people, of which college graduates constitute a big portion. Colleges can drastically reduce the number of unemployed graduates by introducing courses and changing the curriculum to help develop the skills that employers look for in graduates. We built a system that helps analyze the difference in the skill sets of placed and not placed students. It predicts whether a student with a given skill set would be able to secure a job or not. It uses not only technical skills but also takes into consideration other soft skills which are essential to land a job. The accuracy obtained is 87% and 90% for the SVM model and XGBoost model, respectively. We found that the technical skills, projects, certified courses are taken, and the internships of the student are the most important parameters. The results are promising and sure to improve placement rates in colleges.
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References
P.K. Nanda, 1.5 mn Indians lost jobs in aug as unemployment rate soars Sept 2021
M. Vyas, The real unemployment challenge, Jan 2021
P. Deka, Unemployment in India, Jun 2021
P. Manvitha, N. Swaroopa, Campus placement prediction using supervised machine learning techniques. Int. J. Appl. Eng. Res. 14(9) (2019)
S. Nagamani, K.M. Reddy, U. Bhargavi, S.R. Kumar, Student placement analysis and prediction for improving the education standards by using supervised machine learning algorithms. J. Crit. Rev. 7(14), 854–864 (2020)
T. Patel, A. Tamrakar, A data mining techniques for campus placement pre-diction in higher education. Indian J. Sci. Res. 14 (2017)
N. Premalatha, D.S. Sujatha, A comparative study on students placement performance using data mining algorithms. Int. J. Sci. Technol. Res. 8 (2019)
Y. Bao, Y. Peng, C. Wu, Deep learning-based job placement in distributed machine learning clusters 505–513 (2019)
S.H. Linsey, Predicting employment through machine learning, May 2019
H. Shreyas, P. Aksha, H.S. Suma, A. Suraksha, M. Tojo, Student placement prediction using machine learning. Int. Res. J. Eng. Technol. 6(4) (2019)
S.K. Thangavel, P.D. Bkaratki, A. Sankar, Student placement analyzer: A recommendation system using machine learning, 1–5 (2017)
C. Chinmay, T. Kunal, S. Siddhant, R.T. Neha, Placement prediction by mining student’s information, in Proceedings of the International Conference on Recent Advances in Computational Techniques (IC-RACT)) (2020)
T.J. Irene, R. Daibin, A.A. Jeebu, J. Joel, T.V. Mereen, Placement prediction using various machine learning models and their efficiency comparison. Int. J. Innovative Sci. Res. Technol. 5(5) (2020)
J. Samtha, D. Manjusha, B. Pooja, A. Usha, Student placement chance pre-diction. J. Emerg. Technol. Innovative Res. 7 (2020)
R. Ishizue, H. Sakamoto, K. Washizaki, Student placement and skill ranking predictors for programming classes using class attitude, psychological scales, and code metrics. Res. Pract. Technol. Enhanced Learn. 7 (2018)
M. Shahil, Campus placement analysis and prediction, May 2020
D. Muthusenthil, V.S. Mugesh, D. Thansh, R. Subaash, Predictive analysis tool for predicting student performance and placement performance using ml algorithms. Int. J. Adv. Res. Innovative Ideas Educ. 6 (2020)
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Shah, J., Kochrekar, S., Kale, N., Patil, S., Godbole, A. (2022). Campus Placement Prediction. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_33
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DOI: https://doi.org/10.1007/978-981-19-1324-2_33
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