An end-to-end AI pipeline that scrapes LinkedIn profile and post data to predict 16-personality (MBTI) types using a RAG-enhanced LLM fine-tuned with LoRA. It automates data collection, preprocessing, storage in PostgreSQL, and personality inference from real-world behavioral and linguistic patterns.

his repository contains a robust, end-to-end machine learning pipeline for predicting personality types (Extrovert vs. Introvert) from behavioral survey data. The workflow includes advanced data cleaning, missing value imputation, class balancing, feature engineering, model selection, and ensemble learning. Built for the Kaggle Playground Series S.

An AI-powered interview automation platform that evaluates candidates through resume parsing, personality prediction, facial emotion recognition, and speech tone analysis — all in one pipeline.

Code and experiments for the paper “Navigating Pathways to Automated Personality Prediction.” Implements NLP pipelines with Hugging Face models (RoBERTa, ALBERT, DistilBERT) to predict Big Five traits, comparing accuracy, efficiency, and sustainable AI trade-offs for marketing and consumer research.

An Enterprise-Grade AI Pipeline combining LLMs (OpenAI) and Machine Learning (Random Forest) for Big Five Personality Prediction through CV analysis.

End-to-end ML pipeline for MBTI personality type classification using questionnaire data. Features leakage-safe scikit-learn Pipeline, strong multiclass Logistic Regression (~0.92 F1), model interpretability, and a Streamlit MVP app for real-time predictions.

This project predicts user personality types based on social behavior inputs using a Random Forest model. It covers data preprocessing, feature selection, model training, and evaluation, achieving 80% accuracy. The project demonstrates a complete machine learning pipeline, from data handling to deployment for reliable personality predictions.

Machine learning project for customer personality segmentation using K-Means clustering and Logistic Regression. Built with Python, FastAPI, Docker, and MongoDB, featuring an end-to-end pipeline for data processing, training, prediction, and deployment on AWS/Azure.

An end-to-end NLP pipeline for MBTI personality prediction from text, covering preprocessing, TF-IDF baselines, Word2Vec, SBERT, LSTM/BiLSTM with Attention, and BERT fine-tuning. Handles class imbalance, adds LIME/SHAP insights and ships handy visuals

This notebook presents a machine learning approach to classify personality as Introvert or Extrovert. It covers data preprocessing, building a Random Forest model pipeline, training and evaluating the model, and generating predictions, including visualizations for data insights and feature importance.