This is a Streamlit-based web application that provides movie recommendations based on content similarity. The system leverages vectorization, cosine similarity, and precomputed data stored in pickle files.

• Recommends similar movies based on user input.
• Simple and interactive UI built with Streamlit.
• Backend powered by vectorization and cosine similarity for calculating movie similarity.

• Python 3.7+
• Required libraries:
  • streamlit
  • pandas
  • scikit-learn
  • numpy

Install dependencies using:

pip install -r requirements.txt

1. main.ipynb: Notebook to preprocess data and generate movies_list.pkl and similarity.pkl.
2. app.py: The main Streamlit app file.
3. Movies_data.csv: Dataset containing movie information.
4. movies_list.pkl: Pickle file storing processed movie data (not included).
5. similarity.pkl: Pickle file storing cosine similarity matrix (not included).

1. Clone the repository:

   git clone https://github.com/darshan-dalvi/Movie-Recommendation-System.git

2. Navigate to the project directory:

   cd Movie-Recommendation-System

3. Generate pickle files:

   • Open and run the main.ipynb file to process the dataset and generate movies_list.pkl and similarity.pkl.

4. Run the application:

   streamlit run app.py

• Movies_data.csv contains movie information used to compute recommendations.

1. Run the Streamlit app.
2. Enter the name of a movie in the input box.
3. Get a list of recommended movies based on similarity.

• Improve recommendation logic by including additional features.
• Support for hybrid recommendations (content + collaborative filtering).
• Include more datasets to expand movie recommendations.

Feel free to fork this repository and contribute by submitting a pull request.

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

Enjoy exploring your next favorite movie! 🍿