Movies Recommender System App

A compact movie recommendation system that analyzes The Movies Dataset to deliver personalized suggestions. The project explores three main recommendation strategies: Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering—and evaluates multiple algorithms across a dataset of 45,000 movies and 270,000 user ratings. Key steps include data cleaning, integration, preprocessing, and model benchmarking to identify effective approaches for predicting user preferences.