Unveling Personality Patterns: K-Means Clustering and Dimesion Reduction Analysis for Behavioural Traits Categorization

A person's personality is made up of features of their behavioral patterns, psychological state, motivation to succeed, and cognition personality traits. Individuals particular personalities do not constitute the sole objective in common personality test results, however, the identity that others who interact with them connect to them. Conventional personality descriptors are frequently referring to the Big Five personality traits. In this study, we performed a partitioning approach such as k-means clustering techniques in personality traits categorization which produces the final calculation of cluster centroids and arranging each point in its appropriate cluster. Our goal was to determine the most significant behavioral variables using data dimension reduction methods such as Principal Component Analysis. The total number of extracted principal component is 51 and the total explained variance ratio in the personality dataset is 24.33%.