Personality Prediction using Machine Learning

Personality traits significantly influence human behavior, social interactions, and individual potential across various domains including psychology and business. This paper presents a Flask-based web application that predicts personality traits by leveraging psychological and demographic features through advanced machine learning models: LightGBM and CatBoost. The system accepts user inputs such as gender, age, and the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) via an intuitive web interface, and provides real-time personality type predictions. Utilizing a large-scale dataset with efficient categorical encoding, the models perform multi-class classification with high accuracy and consistency. The application also integrates administrative functionalities for user management, health tips dissemination, and query handling, fostering interactive communication between users and administrators. This work demonstrates the effective combination of data science and web development to build scalable, user-friendly tools for personality analysis, with potential applications in personal development, recruitment, and psychological research.