A rule-based machine learning model for career selection through MBTI personality

Noureen Fatima; Sana Gul; Javed Ahmed; Zahid Hussain Khand; Ghulam Mujtaba

doi:10.22581/muet1982.2202.18

Noureen Fatima Center of Excellence for Robotics, Artificial Intelligence & Blockchain, Department of Computer Science, Sukkur IBA University Pakistan
Sana Gul Center of Excellence for Robotics, Artificial Intelligence & Blockchain, Department of Computer Science, Sukkur IBA University Pakistan
Javed Ahmed Center of Excellence for Robotics, Artificial Intelligence & Blockchain, Department of Computer Science, Sukkur IBA University Pakistan
Zahid Hussain Khand Center of Excellence for Robotics, Artificial Intelligence & Blockchain, Department of Computer Science, Sukkur IBA University Pakistan
Ghulam Mujtaba Center of Excellence for Robotics, Artificial Intelligence & Blockchain, Department of Computer Science, Sukkur IBA University Pakistan

DOI: https://doi.org/10.22581/muet1982.2202.18

Abstract

Career selection is one of the most important decisions in everyone's life. Being a student it's quite difficult to find the right career as the world is moving so fast and the competition level is too high so it is tough to choose the right job for anyone. According to the Council of Scientific and Industrial Research (CSIR) survey report, 40% of students are a bit confused about their career options. The productivity of human resources may reduce if someone chooses the wrong career. Therefore, we need an intelligent system that chooses your career based on a person's personality type. This study creates a personality profile of the person and suggests their best careers list according to their personality type after analysing the text written by the user such as a blog post, essay, or tweet. The main purpose of this project is to work on the text classification method, pre- processing of that dataset and convert it into main features and then train the model on a best-performed classification model. After applying various feature vector combinations and machine learning models that are detailed in this work, accuracies up to 93% were achieved.