Use of Artificial Neural Network in Developing a Personality Prediction Model for Career Guidance: A Boon for Career Counselors

  • Abhishek S. Rao, Bola Sunil Kamath, Ramya R, Shreya Chowdhury, Shreya A Pattan, Raveena Krishna Kundar

Abstract

Choosing the right career is the most important decision one makes in their life. If job profiles are not selected based on their personality, it might lead to many other problems like shifting of jobs, work stress, occupational infirmity, reduced productivity, and manual error. The use of MBTI categorization questionnaires was the only available system for career counselors which were a tedious task and may lead to manual errors. The availability of various career opportunities due to the change in technological trends has made career guidance a challenging task for counselors. Hence, there is a need for automated technology that will try to learn the personality of a person and predict a suitable career goal in a sophisticated manner. Hence the present study has focused on the use of the Artificial Neural Network (ANN) technique to build a predictive model. For data collection, a web-based questionnaire was designed using Google forms which comprised of 36 questions as described by MBTI categorization. To validate the model, confusion matrix and K-Folds Cross-Validation were used to measure the performance of the model. The data was fed 20 times (epochs) to the model for better prediction so that the error rate was minimized.  Evaluation matrices like sensitivity, specificity, precision, and accuracy for all the MTBI categorizations were evaluated by the model which showed values above 92% in all the cases.

Published
2020-05-18
How to Cite
Abhishek S. Rao, Bola Sunil Kamath, Ramya R, Shreya Chowdhury, Shreya A Pattan, Raveena Krishna Kundar. (2020). Use of Artificial Neural Network in Developing a Personality Prediction Model for Career Guidance: A Boon for Career Counselors. International Journal of Control and Automation, 13(4), 391 - 400. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/16455
Section
Articles