Movie Recommendation Engine

This project demonstrates the creation of a basic movie recommendation system using PySpark. It processes user ratings to identify similar movies through collaborative filtering techniques and generates personalized recommendations based on a user’s viewing history. The system also highlights trending movies, analyzes rating patterns, and uncovers insights into user preferences. Along the way, the project covers advanced data processing concepts including joins and self-joins, pivot tables, and statistical functions such as correlation and standard deviation, providing a comprehensive understanding of building recommendation engines from raw data.