Collaborative Filtering Recommendation System

In this project, we'll build a movie recommendation system using collaborative filtering techniques and the IMDB movie dataset, then deploy it as an interactive web application with Streamlit. Collaborative filtering is a widely-used recommendation algorithm that predicts user preferences by analyzing patterns from similar users' behaviors, powering personalized experiences in platforms like Netflix, Amazon, and Spotify. We'll implement user-based collaborative filtering (user-user similarity) using scikit-learn to find similar users and recommend movies they've enjoyed.

We'll start by importing the IMDB dataset with user ratings and exploring it using Pandas for data analysis. We'll create a user-item interaction matrix that captures all user-movie rating relationships, then generate a similarity matrix using cosine similarity or Pearson correlation to measure how alike different users are based on their rating patterns. Using this similarity scoring, we'll build a recommendation engine that identifies the top similar users and suggests movies they've rated highly but the target user hasn't seen yet.

Finally, we'll create a production-ready recommender application using Streamlit with an interactive interface where users can select their profile and view personalized movie recommendations with details. By the end, you'll have a complete recommendation system demonstrating collaborative filtering algorithms, similarity computation, matrix operations with NumPy, Pandas data manipulation, and Streamlit web app development applicable to any personalized recommendation scenario.