<![CDATA[This study examines the use of Tree-Based Models for classifying Myers- Briggs Type Indicator (MBTI) personality types. Data was collected through questionnaires referencing the 16Personalities test, followed by preprocessing steps to handle missing values, split MBTI labels, and analyze statistical summaries. Seven algorithms, including Decision Tree, Random Forest, Extra Trees, and Gradient Boosting, were applied to evaluate classification accuracy across MBTI dimensions (E-I, S-N, T-F, J-P). Random Forest emerged as the best model with an accuracy of 72.5% when analyzing high-correlation questions, highlighting its robustness in managing data interactions. This research emphasizes the effectiveness of machine learning in personality classification and provides practical insights for integrating MBTI assessments into applications. Future work suggests leveraging larger, more diverse datasets and exploring deep learning integrations for enhanced model performance.]]>
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