This is a content-based movie recommendation system that suggests similar movies based on their content features. It utilizes vectorization techniques to analyze the characteristics of movies and recommend similar ones. The system is implemented as a web application using Streamlit and has been deployed for online access.

• Recommends movies based on content features such as genre, cast, and plot keywords.
• Utilizes vectorization methods to represent movies as numerical vectors.
• Provides a user-friendly interface for users to input their preferences and receive recommendations.
• Deployed online for easy access.

• Python
• scikit-learn for vectorization
• Streamlit for web application development

Since the application is deployed and accessible online, there's no need to install anything locally. Simply visit the following URL to access the movie recommendation system: Movie Recommendation System

1. Open the Movie Recommendation System link in your web browser.
2. Enter your preferences, such as favorite movie from the options and click on the "Recommendation" button
3. Receive movie recommendations based on your input.

Here's a screenshot of the movie recommendation system interface:

This project was developed by [shravya pamu].

This project is licensed under the MIT License. Movie Recommendation System