DeepMBTI is an AI-powered MBTI full-stack predictor application. The app includes an interactive front-end that links to an NLP Log Reg ML Model back-end, utilizing HTML/CSS, Python, Flask, PostgreSQL, and Sklearn.

No description available

MBTI Personality Prediction – Predict users’ MBTI personality types from text using Machine Learning and NLP. Includes code and detailed report for full reproducibility.

In my Data Science class (CS 427), my partner and I created this as our final project. This project aims to predict a person's MBTI personality type based on their text posts using Natural Language Processing (NLP) and machine learning.

Developed a chat interface to collect and store user text messages, leveraging NLP techniques for data preprocessing. Implemented a logistic regression model to predict personality traits based on the MBTI 16 personalities, resulting in detailed evaluation reports generated in PDF format.

MBTI Personality Predictor using NLP and Scikit-learn

NLP based model to predict MBTI Personality types using social media contents(textual).

Multi-class NLP classification project predicting MBTI personality types using TF-IDF and LinearSVC.

Beginner NLP project applying machine learning to predict MBTI personality types from text posts.

HuggingFace-based NLP classifier that predicts MBTI personality types from textual input using transformer models.

This project predicts MBTI personality types from users' recent 50 posts using NLP and ML techniques.

ConnectAI is a web app that uses the Myers-Briggs Type Indicator (MBTI) dataset and NLP techniques to predict personality types and connect users with compatible matches. It aims to foster meaningful relationships based on personality compatibility through machine learning and personalized recommendations

This project uses NLP and Machine Learning to predict a person's MBTI personality type based on their written text.

Predict Myers-Briggs (MBTI) personality types based on user-written text using Natural Language Processing (NLP) and Machine Learning techniques.

This project has been built to predict a user's MBTI Personality type using their social media posts using NLP.

Natural Language Processing (NLP) techniques to predict a person’s MBTI (Myers-Briggs Type Indicator) personality type based on their written text.

Developed a multi-class classification model using NLP techniques (TF-IDF, LSTM) to predict Myers-Briggs (MBTI) personality types from unstructured text data, achieving 85% accuracy on a dataset of 8,675 samples.Developed a multi-class classification model using NLP techniques (TF-IDF, LSTM) to predict Myers-Briggs (MBTI) personality type

A full end-to-end NLP project that analyzes and predicts MBTI (Myers-Briggs Type Indicator) personality types based on user-generated text. This project includes EDA, NLP preprocessing, clustering, and classification.

This project leverages NLP to analyze the text content of a resume and predict the candidate's personality type using Myers-Briggs Type indicator(MBTI) model.

End-to-end NLP pipeline predicting MBTI personality types from social media text. TF-IDF + spaCy tokenization, SMOTETomek resampling, Logistic Regression & XGBoost classifiers. Includes honest error analysis, unit tests, and modular src/ package.

Categorise people according to the characteristics that make up their personalities Uses NLP and machine learning algorithms to predict the HEXACO personality traits and MBTI personality type based on the input given by the user

This project builds a machine learning pipeline to predict personality types (like MBTI) based on user-written text data (e.g., forum posts or messages). It uses Natural Language Processing (NLP) techniques and Logistic Regression for multi-class classification.

An end-to-end mental health risk prediction system using NLP and personality traits. It predicts Big Five traits from text using a DistilBERT regressor, then classifies mental health risk (Low/Mid/High) via a BiLSTM model. Built with PyTorch, Transformers, and real-world datasets (MBTI, CLPsych).