Movie Recommender System with Machine Learning

This project implements a movie recommender system using Streamlit, leveraging machine learning techniques to recommend movies based on user selection.

Overview

The Movie Recommender System allows users to:

• Select a movie from a dropdown menu or by typing its name.
• Receive recommendations of similar movies based on precomputed similarity scores.
• View recommended movies along with their posters fetched from The Movie Database (TMDb) API.

How It Works

Data Preparation and Model Training

• The system is pre-trained with a dataset containing movie titles, genres, plot summaries, and user ratings.
• A machine learning model computes similarity scores between movies based on these features.

Recommendation Process

• When a user selects a movie, the system retrieves precomputed similarity scores to find similar movies.
• It fetches movie posters using real-time API calls to TMDb for visual appeal.

User Interface

• Users interact with the system through a Streamlit web interface.
• They select a movie from a dropdown menu and click "Recommend" to receive movie suggestions.

Live Demo

Check out the live demo of the Movie Recommender System here.