Abstract
In the higher education institute or universities employability of the student is the key factor that decides the quality of the education. One of the outcomes of education acquired is employability according to students’ perspective. To sustain in this competitive world a higher education along with different hard and soft skills are required. Being a computer engineer many skills are required for employability. This study aimed to determine the employment status depending upon the competencies related to the courses that were learned during the engineering education are considered. As well as some factors related to the students’ competitive exams, some factors related to their primary, secondary education, location and student profile were also examined. For this study, the survey was conducted with 14 different parameters. From these parameters, it is predicted that whether the students will be employed or not? These parameters are considered as a very high contribution for placement. To predict whether the student will get a placement or not we have applied Artificial Neural Network (ANN) and Logistic Regression. Our Finding shows that 87.5% accuracy, we got by using Artificial Neural Network (ANN) and 62.5% by Logistic Regression.
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References
Aravind T, Reddy BS, Avinash S, Jeyakumar G (2019) A comparative study on machine learning algorithms for predicting the placement information of under graduate students. In: 2019 third international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE, pp 542–546
Joy LC, Raj A (2019) A review on student placement chance prediction, pp 542–545
Lundberg GM, Krogstie BR, Krogstie J (2020) Becoming fully operational: employability and the need for training of computer science graduates, pp 644–651
Marling C, Juedes D (2016) CS0 for computer science majors at Ohio University, pp 138–143
Mavani U, Lobo VB, Pednekar A, Pereira NC, Mishra R, Ansari N (2020) Naïve Bayes classification on student placement data: a comparative study of data mining tools. Information and communication technology for sustainable development. Springer, Germany, pp 363–372
Rao AS, Aruna Kumar S, Jogi P, Chinthan Bhat K, Kuladeep Kumar B, Gouda P (2019) Student placement prediction model: a data mining perspective for outcome-based education system. Int J Recent Technol Eng (IJRTE) 8:2497–2507
Shukla M, Malviya AK (2019) Modified classification and prediction model for improving accuracy of student placement prediction
Soumya M, Sugathan T, Bijlani K (2017) Improve student placement using job competency modeling and personalized feedback, pp 1751–1755
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Laddha, M.D., Kiwelekar, A.W., Netak, L.D., Mahajan, P.C. (2022). To Predict Employability of Student by Using Artificial Neural Network. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_61
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DOI: https://doi.org/10.1007/978-981-16-3690-5_61
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