Movie Recommendation System

Welcome to the Movie Recommendation System! This project uses Streamlit to provide personalized movie recommendations based on user preferences and similarity.

Table of Contents

• Introduction
• Features
• Technologies Used

Introduction

This project aims to recommend movies to users based on their interests. The recommendation system leverages content-based filtering techniques to suggest movies that the user is likely to enjoy.

Features

• Movie Recommendations: Provides personalized movie recommendations.
• Search Functionality: Enables users to search for movies.
• Movie Details: Displays detailed information about each movie.

Technologies Used

• Python: Programming language
• Streamlit: Web framework for interactive applications
• Pandas: Data manipulation library
• NumPy: Numerical computing library
• Scikit-learn: Machine learning library
• TMDB API: For fetching movie data