This document provides an overview of a movie recommendation system project. It discusses using a movie dataset containing 5000 movies from TMDB. The project will use libraries like NumPy, Pandas, and Streamlit to preprocess the data, create a model for movie recommendations, and deploy the model through a Streamlit application. The scope of the project is to build a recommendation system that can predict movie ratings and provide personalized movie suggestions to users. The problem statement is to recommend movies to users based on their previous ratings and integrate social media analysis to improve recommendations.