Personality refers to individual differences in characteristic patterns of thinking, feeling, and behaving. In today’s world, most people have social media accounts and post hundreds of messages per day. Often, people use social media to express themselves on certain issues related to their lives and family well beings, psychology, financial issues, interaction with societies and environment, as well as politics. In some cases, these expressions can be used to characterize the individual behavior and personality. In this project, we attempted to predict the personality of a person based on social media content. For this, we are using a Myers-Briggs personality type indicator (MBTI) to classify people’s personalities under various categories. MBTI is one of the most popular personality tests in the world. The dataset contains 16 personality types across 4 characteristic traits, namely, Introversion (I) - Extroversion (E), Intuition (N) - Sensing (S), Thinking (T) - Feeling (F), and Judging (J) - Perceiving (P). When a person is found to be introverted, intuitive, sensible, and judging, he/she will be labeled as INSJ personality type. We have built four machine learning models, which include Logistic Regression, Support Vector Machines (SVM), Naïve Bayes, and Random Forest. Finally, we compare and contrast the results obtained from the machine learning models and conclude which one is best based on the results from evaluation metrics (accuracy score, geometric mean score, ROC-AUC score). The final model uses Logistic Regression, since it performed well when compared to remaining classification models.