This is a machine learning-powered movie recommendation system that suggests the top 10 movies similar to a user's selected title. Built using Python and a Streamlit frontend, the application allows users to quickly discover movies they might enjoy based on their preferences.
Search any movie from the dataset and receive real-time recommendations
Content-based filtering using machine learning to find similar movies
Interactive UI built with Streamlit for a smooth user experience
Posters and movie titles displayed in a clean, responsive layout
Deployed on Render, accessible from any browser without local setup
Python – Core programming language for backend logic and data handling
Pandas & Scikit-learn – For data manipulation and building the recommendation model
Streamlit – Used for building the user-facing web application
Render – For deploying the application in the cloud
TMDB API – Used to fetch movie posters, titles, and metadata
The recommendation system is built using a movie metadata dataset sourced from The Movie Database (TMDB). It relies on the TMDB API to:
Retrieve movie details
Display high-quality movie posters
Enhance the recommendations with real-world metadata
Note : A valid TMDB API key is required to access the movie data. The key is securely managed in the backend and is not exposed publicly.
The user selects or searches for a movie from the dropdown list.
The ML model computes cosine similarity based on movie metadata.
The app returns 10 visually rich recommendations (with posters and names), in a 2-row layout.
Live Project 🎥 Try it out here: 👉 https://movie-recommender-system-1-h7uj.onrender.com