The term "personality" refers to the combination of characteristics and attributes that contribute to an individual’s distinct personality, such as thinking, emotion, and behavior. Texts on social networking sites can automatically detect an individual’s personality qualities. The Myers-Briggs Type Indicator (MBTI) dataset is being used in our research paper, consisting of 16 categories of postings with an amount of 8675 posts. We classified four personality traits from the input text, namely Introversion-Extroversion (I-E), Intuitions-Sensing (N-S), Feeling-Thinking (F-T), and Judging-Perceiving (J-P). We have initially adopted several preprocessing techniques to fit these text data into our proposed machine learning model. Text preprocessing methods include tokenization, word stemming, stop words deletion. After preprocessing, features like tf-idf and countvector were extracted. The model was then trained with six state-of-the-arts classifiers. After evaluating the model, it was found that the SVM outperformed all other classifiers and proved better than the other existing works also.