🎬 Movie Recommendation System using TMDB 5000 Movies Dataset 🤖

This repository contains a movie recommendation system web application developed using the TMDB 5000 Movies Dataset. 🌐

📁 Dataset: The TMDB 5000 Movies Dataset, sourced from Kaggle, serves as the foundation for the movie recommendation system. It includes information such as movie titles, genres, overview, cast, and crew. 🎥

Read More Here👉: https://medium.com/@afaqueumer/tmdb-streamlit-build-your-own-movie-recommendation-system-f2ffbca63d11

⚙️ Technologies Used:

• Python 🐍
• Streamlit 🚀
• scikit-learn 📊
• Pandas 🐼
• NumPy 🔢

📝 Description: The movie recommendation system utilizes the concept of cosine similarity and word vectorization. By employing word embeddings, such as Word2Vec or GloVe, it transforms movie overviews into dense vectors and calculates their similarity using cosine similarity. This approach helps identify similar movies based on their textual descriptions. 📚

📊 Features:

• Movie search: Users can search for movies based on title or keywords.
• Movie recommendations: Users receive personalized movie recommendations based on their input.
• Movie details: Users can explore detailed information about movies, including cast, crew, genres, and an overview.

💻 Usage:

1. Install the required dependencies listed in the requirements.txt file.
2. Run the Streamlit web application by executing the command: streamlit run app.py.
3. Access the application via the provided URL, usually http://localhost:8501.

🌟 Feel free to explore different movies, discover recommendations, and enjoy your cinematic journey! 🎉

📚 References:

• TMDB API: https://www.themoviedb.org/documentation/api
• TMDB 5000 Movies Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

📜 License: This project is licensed under the MIT License. Please see the LICENSE file for more details.

🤝 Contributing: Contributions to this project are welcome! Feel free to open issues or submit pull requests.

📧 Contact: For any inquiries or questions, feel free to reach out to [afaqueumer33@gmail.com].

🎥 Happy movie browsing and discovering new favorites! 🍿