Movie Recommendation System

Overview

A recommendation engine that suggests movies based on the user’s initial choice. Uses text data vectorization to find similarities between movies.

Key Features

Recipe Management: Provides 5 similar movies based on an initial selection.
Efficient Matching: Uses vectorization to speed up similarity checks.
Dataset: Includes 5,000 movies for a broad recommendation base.

Tech Stack

Backend: Python, Streamlit
Libraries: Scikit-learn, Pandas, NumPy, Pickle

Future Enhancements

Expand to a larger movie dataset. Incorporate user ratings for more personalized recommendations.