Personality prediction from Human Connectome Project fMRI data using BrainNetCNN deep learning model (Kawahara et al. 2016). Project with Dr. Johann Kruschwitz, Division of Mind and Brain at Charite Medical University

Code & study comparing ML (Naive Bayes) & DL (LSTM, Bi-LSTM, Bi-LSTM+Attention, DistilBERT) for MBTI personality prediction. Uses GloVe, SMOTE, & attention. Bi-LSTM+Attention achieves 96% accuracy, 13% loss. For NLP & personality research.

Twitter personality prediction using word embedding and deep learning

Personality prediction is an important application area of affective computing. The process of assessing the personality of users is proposed in this project using questionnaires answered by the user and classifying the personality according to MBTI traits. This project proposed a framework in which some questions are to be answered by the user. Every question has an option in the form of agree or disagree and then the model predicts the personality of the user from the 16 MBTI traits with a description of personality trait based on the questions answered. This project used deep learning approach. Recurrent Neural Network(RNN) along with Long Short-Term Memory units (LSTM) is used to develop this project. Accuracy of the proposed model achieved is 64.5 which could overcome the average accuracy of personality prediction classifier made while previously it was 55% using RNN. This model which makes use of Questionaries’ can be used as a feedback system in academics and organisation for improving the quality of life of students and employees.

The project aims to predict MBTI personality types based on text data using deep learning models. It leverages LSTMs, Bi-Directional LSTMs, and BERT to enhance classification accuracy.

Predict Myers-Briggs Type Indicator (MBTI)personality types from text with deep learning models. Explores two models: a BERT-based Transformer and an LSTM-based model to classify the four MBTI axes: Introversion/Extraversion (I/E), Sensing/Intuition (S/N), Thinking/Feeling (T/F), and Judging/Perceiving (J/P)—by analyzing textual cues.

Deep learning model that predicts a person’s dominant personality trait (Extraversion, Agreeableness, Conscientiousness, Neuroticism, or Openness) using sleep pattern data such as sleep duration, distance, app usage, and average sleeping time.

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Built regression models using BERT, LSTM and CNN to predict personality traits from speech transcripts

AI-powered personality prediction web app using Deep Learning, Streamlit, and MBTI-style analysis.

A deep learning-based personality prediction model using LSTM, Bi-LSTM, and DistilBERT trained on MBTI data.

Personality Prediction with Deep Learning using Streamlit: This repository hosts a user-friendly web app that predicts personality types based on the 16 Personality Types model. Powered by Artificial Neural Network

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using Multiclass Classification Models, topics: Personality Prediction System via CV Analysis.

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using Multiclass Classification Models, topics: Personality Prediction System via CV Analysis

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using Multiclass Classification Models, topics: Personality Prediction System via CV Analysis

Hybrid multimodal deep learning model using Mixture-of-Recursions (MoR) architecture for real-time MBTI personality prediction from text and facial expressions.

AI-Based Personality Prediction from Handwriting is a machine learning project that analyzes handwriting images to predict personality traits. The system uses image preprocessing, feature extraction, and deep learning to study writing patterns like slant, spacing, pressure, and baseline to estimate personality traits.

This project predicts Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) using machine learning and deep learning. The notebook covers data preprocessing, training multiple models (Logistic Regression, Random Forest, XGBoost, LSTM), evaluating results, and explaining predictions with SHAP and LIME.

Personality Prediction Using Handwriting & LLMs is an AI-powered system that analyzes handwriting samples to predict personality traits based on the Big Five model (Agreeableness, Conscientiousness, Extrav The workflow combines OCR (to detect and extract handwriting), a deep learning model (EfficientNet-B0 for classification), ersion, Neuroticism,

Personality Prediction Using Handwriting & LLMs is an AI-powered system that analyzes handwriting samples to predict personality traits based on the Big Five model (Agreeableness, Conscientiousness, Extrav The workflow combines OCR (to detect and extract handwriting), a deep learning model (EfficientNet-B0 for classification), ersion, Neuroticism,

Brain is the regulatory unit in the human body. It controls the functions such as memory, vision, hearing, knowledge, personality, problem solving, etc. Many health organizations have recognized brain tumor as the second foremost dispute that causes a large number of human deaths all around the world. Identification of brain tumor at a premature stage offers an opportunity of effective medical treatment. There are various techniques to detect brain tumors or neoplasms. Brain tumor is the growth of abnormal cells in the brain some of which may lead to cancer. The usual method to detect brain tumors is Magnetic Resonance Imaging (MRI) scans. From the MRI images information about the abnormal tissue growth in the brain is identified. When these algorithms are applied on the MRI images the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patients. Here, in our proposed work we detect the brain tumor with the help of deep learning algorithms. These predictions made using DL also helps the radiologist in making quick decisions.