Advancing Personality Insight Automation: A Hybrid Machine Learning Strategy - Gupta, Neha; Dadheech, Shalu; Bansal, Ashish (Journal of Neonatal Surgery, 2025)
This academic paper investigates how to improve automated personality prediction by using machine learning techniques. The core of this study is the use of the Myers-Briggs Type Indicator (MBTI) dataset obtained from Kaggle, with the goal of improving the prediction accuracy of the Judging-Perceiving (J/P) dichotomy. Previous studies have highlighted significant obstacles to predicting this factor, often due to issues like data loss and model inefficiencies. This examination looks at several machine learning algorithms, including Random Forest, XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, and LightGBM, to see how they can overcome these obstacles. The results show that traditional single-model techniques are not as reliable or precise as ensemble models, particularly LightGBM. To overcome data imbalance, further sophisticated data preparation techniques are added, such as TF-IDF vectorization and SMOTE. By amalgamating these diverse modelling techniques, this research establishes a sturdy framework for automated personality forecasting, supplying valuable insights for fields such as psychology, marketing, and human resources. By bridging critical gaps in existing methodologies and proposing innovative remedies for more precise and dependable personality prediction, this paper makes a significant contribution to the academic domain.