An advanced system leveraging Machine Learning (ML) and Natural Language Processing (NLP) for accurate student answer evaluation with 89% accuracy.

A Python-based system that recommends movies based on user preferences using vectorization and cosine similarity. The project leverages natural language processing (NLP) techniques to convert movie descriptions into numerical vectors and compute similarity scores for personalized recommendations.