For our machine learning final project, we decided to predict Myers Briggs personality types(MBTI) using a dataset we found on Kaggle. The dataset consists of posts users made on a personality forum: https://www.kaggle.com/datasnaek/mbti-type

We first cleaned up the data and did feature engineering, then we fit different models suchas Multinomial Naive Bayes, Logistic Regression, Random Forest and eXtreme Gradient Boosting.

We used metrics such as AUC, F1 score and accuracy to judge the performance of our models. We then fine tuned our models and finally we used our model to predict the MBTI personality type of politicians and celebrities like Obama and Lady Gaga.

The data was heavily imbalanced, with most people identifying as introverted (I) and Intuitive (N) rather than extroverted (E) and Sensitive (S). Because of this, all the models we tried (Logarithmic Regression, Random Forest, Multinomial Naive Bayes, SVM, LightGBM, and XGBoost) had trouble classifying extroversion vs introversion and intuition vs sensitivity. However, when we made a Voting Classifier using Logarithmic Regression, Random Forest, LightGBM, and XGBoost, we were able to achieve the best AUC-ROC score and f-scores.

Ben Khuong
Donya Fozoonmayeh
Nan Lin
Tomohiko Ishihara
Zack Pan