A Comprehensive Examination of Machine Learning Models in Predicting 16 Personality Traits

This study applies several kinds of machine learning frameworks to predict personality traits according to the Myers-Briggs Type Indicator (MBTI). The dataset used in this study includes demographic data as well as self-identification information. Prior analysis, the dataset underwent processing to convert categorical characteristics, such as gender, into numerical representations. To solve the multinomial classification test, multinomial logistic regression was used, incorporating variables such as age, gender, and recall scores derived from the 16 MBTI personality traits. Model training includes tweaking parameters while running the Newton-CG solver to enhance accuracy and convergence. The model's efficacy was assessed utilizing testing data from demographic data providers and also the MBTI 16 personality traits. The core objective of this study is to estimate individual traits while presenting the outcome in a way that is understandable. The study's findings demonstrate the effectiveness of logistic regression used with the MBTI's 16 personality types in accurately predicting personality attributes.