Predicting MBTI Personality type with K-means Clustering and Gradient Boosting

A way to analyze the user's data posted on social media by combining two existing machine learning algorithms, such as K-Means Clustering and Gradient Boosting, in order to predict user personality type is proposed. Personality refers to a characteristic pattern of thoughts, behavior, and feelings that makes a person unique. Asking users to fill a questionnaire to get their personality insights could be inaccurate because the users are conscious and try to take a careful approach when filling the survey. However, when it comes to social media, users do not take any consideration before posting their opinions on social media. Therefore, the data obtained from social media could be precious to determine the user personality type. In this paper, we propose a way to analyze the user's data posted on social media by combining two existing machine learning algorithms, such as K-Means Clustering and Gradient Boosting, in order to predict user personality type. Moreover, this research helps to analyze the empirical relation between the user's data posted on social media and the user's personality. In this paper, we used The Myer-Briggs Type Indicator (MBTI) introduced by Swiss psychiatrist Carl Jung. MBTI is based on sixteen personality types, and they act as a valuable reference point to understand a person's unique personality. The technique of combining these two machine learning algorithms gave accurate results than the traditional naive Bayes classification and other algorithms. Results of this study can help bloggers and social media users to know what type of personality they are showing on the social media with the data they posted on the internet.

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PDF) Predicting MBTI Personality type with K-means Clustering and Gradient Boosting

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GitHub - devinaa1604/Myers_Briggs_Personality_Predictor: The project aims to predict personality types using Natural Language Processing on the social media presence of more than 8600 individuals and implement various machine learning algorithms on a