Movie-Recommendation-System

This project is a content-based movie recommendation system that suggests 5 similar movies based on a user-selected input. It processes a database of over 5000 movies to provide personalized movie recommendations.

Project Demo

Movie.recommendation.video.mp4

Features

• Recommends 5 similar movies based on user selection.

• Uses Bag-of-Words for text vectorization.

• 

• Employs cosine similarity to identify movie similarities.

• 

• Average recommendation speed of 3-4 seconds.

How It Works

• Data Processing: A dataset of 5000+ movies is processed.
• Text Vectorization: The Bag-of-Words technique is applied to movie overviews and metadata.
• Cosine Similarity: Movie similarities are calculated based on text vectors.
• Recommendations: When a user selects a movie, the system suggests 5 similar movies.

Technologies Used

• Python: Core programming language.
• Pandas: For data manipulation.
• Scikit-learn: For Bag-of-Words and cosine similarity computation.
• Streamlit: For building the user interface (optional).

Dataset

The dataset contains over 5000 movie entries with features like:

1. Movie title
2. Overview
3. Genres
4. Cast and crew

Future Enhancements

• Integrating additional filtering options such as genre, director, or actors.
• Improving recommendation speed.
• Enhancing the user interface with a more dynamic experience.