Machine Learning Technique: Holistic Personality Analysis and Guidance

Machine learning techniques for personality analysis have been exploited as a key method in the education, mental health and human resource management fields. In this project, I develop such a holistic personality analysis system using state of the art machine learning models to infer and predict personality traits from behavioral, psychological and social data. The system uses trickling along of both traditional and deep learning algorithms to craft a personality output based on personality frameworks like the Five Factor Model and Carl Jung’s cognitive functions for a holistic personality profile. Moreover, hybrid models involving the hybridization of collaborative and content filtering techniques are used to match mentor and mentee and aimed to improve such selection process through personality compatibility. To show the adaptability of the model across domains, the research involves examining various data sources including handwriting analysis, social media behavior as well as educational performance. Further in this paper, personalized guidance and decision making in education, workplace dynamics and personal development are further brought forward from a broader view. The suggested approach improves both the accuracy of personality predictions and offers useful information for tailored guidance in a variety of professional and educational settings. Future work will be to combine generative models for personalized learning, as well as the ethical implications of personality detection.