Logistic Regression for Employability Prediction
S. Celine1, M. Maria Dominic2, M. Savitha Devi3
1S. Celine*, Research Scholar, Department of Computer Science, Sacred Heart College, Tirupattur, Tamil Nadu, India.
2M. Maria Dominic, Assistant Professor, Department of Computer Science, Sacred Heart College, Tirupattur, Tamil Nadu, India.
3M. Savitha Devi, Assistant Professor, Department of Computer Science, Periyar University Constitution College for Arts and Science, Harur, Tamil Nadu, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 2471-2478 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8170019320/2020©BEIESP | DOI: 10.35940/ijitee.C8170.019320
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction is a conjecture about something which may happen. Prediction need not be based upon the previous knowledge or experience on the unknown event of interest in the future. But it is a necessity for mankind to foresee and make the right decisions to live better. Every person does predictions but the quality of the predictions differs and that differentiates successful persons and unsuccessful persons. In order to automate the prediction process and to make quality predictions available to every person, machines are trained to make predictions and such field comes under machine learning and later on deep learning algorithms. Various fields such as health care, weather forecasting, natural calamities, and crime prediction are some of the applications of prediction. The researchers have applied the field of prediction to see whether a model can predict the employability of a candidate in a recruitment process. Organizations use human expertise to identify a skilled candidate for employment based on various factors and now these organizations are trying to migrate to automated systems by harnessing the benefits of the exponential growth in the area of machine learning and deep learning. This investigation presents the development of a model to predict the employability by using Logistic Regression. A set of candidates was tested in the proposed model and results are discussed in this paper.
Keywords: Machine Learning Algorithm, Support Vector Machine, Decision Trees, Clustering, K-Means Classification, Logistic Regression Model, Artificial Intelligence.
Scope of the Article: Classification