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https://github.com/dmunasingha/spotify-mix-algorithm

Music Recommendation System: A Python-based music recommendation system using collaborative and content-based filtering techniques. Provides personalized song suggestions based on user preferences and song features.
https://github.com/dmunasingha/spotify-mix-algorithm

algorithm filtering music python recommendation-system spotify

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Music Recommendation System: A Python-based music recommendation system using collaborative and content-based filtering techniques. Provides personalized song suggestions based on user preferences and song features.

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README

        

You can name the file something descriptive like `music_recommendation_system.py`. Here’s a `README.md` to explain how to use the script:

### `README.md`

```markdown
# Music Recommendation System

This project demonstrates a simple music recommendation system using both collaborative filtering and content-based filtering techniques. It provides recommendations based on user listening history and music features.

## File Structure

- `music_recommendation_system.py`: Contains the implementation of the recommendation algorithms.

## Prerequisites

Before running the script, ensure you have the necessary Python libraries installed. You can install them using pip:

```bash
pip install numpy pandas scikit-learn
```

## Usage

1. **Clone the Repository**: Clone the repository or download the `music_recommendation_system.py` file to your local machine.

2. **Run the Script**: Execute the script using Python.

```bash
python music_recommendation_system.py
```

3. **View Recommendations**: The script will output the recommendations based on collaborative filtering and content-based filtering.

## How It Works

### Collaborative Filtering

- Calculates similarity between users based on their listening history.
- Recommends songs that similar users have enjoyed but the current user hasn't listened to yet.

### Content-Based Filtering

- Uses track features such as genre and mood to build a profile of the user's preferences.
- Recommends songs that match the user's profile based on feature similarity.

## Example Output

The script will print recommendations from both methods:

```
Collaborative Filtering Recommendations:
Song1: 4.5
Song5: 3.0

Content-Based Filtering Recommendations:
Song1
Song3
```

## Customization

You can customize the data in the `user_item_matrix` and `track_features` variables to suit your needs. For a real-world application, consider using a larger dataset and more advanced techniques.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Acknowledgments

This project uses the following libraries:

- [NumPy](https://numpy.org/)
- [Pandas](https://pandas.pydata.org/)
- [Scikit-learn](https://scikit-learn.org/)
```

### Notes:

- **Customization**: If you use different data sources or formats, adjust the `user_item_matrix` and `track_features` accordingly.
- **Advanced Features**: For more advanced implementations, consider integrating with real data sources and using additional techniques like matrix factorization or deep learning models."# spotify-mix-algorithm" git init git add README.md git commit -m "initial commit" git branch -M main git remote add origin https://github.com/dmunasingha/spotify-mix-algorithm.git git push -u origin main
"# spotify-mix-algorithm" git init git add README.md git commit -m "initial commit" git branch -M main git remote add origin https://github.com/dmunasingha/spotify-mix-algorithm.git git push -u origin main
"# spotify-mix-algorithm"

```
---

## Contact

For any inquiries, please contact [[email protected]](mailto:[email protected]).