Publication

Abstract : Existing methods for assessing an individual’s personality are very cumbersome due to those tests being very monotonous and erroneous due to a self-report bias. Whereas data-driven, automatic personality deduction raises privacy concern due to them needing external personal information. In our model, a person’s personality can be classified based on the 16 categories of Myers-Briggs personality types. With the availability of a huge amount of data on human behavior and personality traits, a machine learning model can be trained, and it can predict the personality trait of a person. A MBTI dataset is used to analyze and train the model. Upon analyzing the dataset, we found out that the classes are not balanced. To overcome this problem, we used binary classification. Next, we used the NLTK library to pre-process the data. We selected four machine learning models and trained our data in those models. Furthermore, we compared the results of four different machine learning models and chose the most accurate model. This research can be used to personalize advertisements, coaching for students and companies can use this research to attract specific types of audience.