Fusionflix

Fusionflix is a movie recommendation system that provides movie recommendations based on the content of movies. It leverages a movie dataset, processes the data to extract meaningful tags, and uses vectorization and the bag-of-words model to generate recommendations. Additionally, Fusionflix integrates with the TMDB API to fetch movie images, and the frontend is built using Streamlit.

Features

• Content-Based Recommendations: Uses movie metadata to provide recommendations based on content similarity.
• Data Preprocessing: Preprocesses movie data to extract tags for better recommendation accuracy.
• Vectorization and Bag-of-Words: Utilizes vectorization and the bag-of-words technique to compute movie similarities.
• TMDB API Integration: Fetches movie images using the TMDB API.
• Streamlit Frontend: Interactive user interface built with Streamlit.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Fusionflix.git
   cd Fusionflix

2. Install the dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the root directory of the project and add your TMDB API key:

   API_KEY=your_tmdb_api_key

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Interact with the application: Open your browser and go to http://localhost:8501 to start using Fusionflix. Enter a movie name to get personalized recommendations and view movie images fetched from the TMDB API.

Recommendation Engine

The recommendation engine works by:

1. Calculating Similarities: Compute cosine similarities between movie vectors.
2. Generating Recommendations: Recommend movies based on the highest similarity scores.

License

This project is licensed under the MIT License - see the LICENSE file for details.