This project is a content-based movie recommendation system developed using text classification, cosine similarity, and vectorization techniques. The system allows users to input a movie and receive five similar movie recommendations based on textual features.
- Content Filtering: Utilizes movie descriptions, genres, and metadata for recommendation generation.
- Cosine Similarity: Measures textual similarity between movies for accurate recommendations.
- Streamlit Interface: Interactive interface for users to input movie preferences and view recommendations.
- Jupyter Notebook: Utilized for model development, data preprocessing, and analysis.
- Python: Language used for data processing, model development, and system implementation.
- Jupyter Notebook: Platform for developing and running machine learning models.
- Streamlit: Framework for creating the user interface.
- TMDB Database / Kaggle: Sources for movie-related datasets.
- Clone the repository:
git clone <repository-url> - Install dependencies:
pip install -r requirements.txt - Run the Streamlit app:
streamlit run app.py
Hello.py: Main file containing the Streamlit app code.Main.ipynb: Contains code for text classification, vectorization, and cosine similarity.data/: Directory containing datasets used for training and testing.
- Fork the repository.
- Create a new branch:
git checkout -b feature-new-feature - Make changes and commit:
git commit -am 'Add new feature' - Push to the branch:
git push origin feature-new-feature - Submit a pull request.