• Introduction
• Project Structure
• Installation
• Dataset
• Methods
• Results
• Contributing

Clustering_Music_in_Spotify is a machine learning project aimed at clustering songs from Spotify based on their audio features. This project leverages unsupervised learning techniques to group similar songs, helping users discover new music that fits their taste.

To install the necessary dependencies for this project, run the following command:

pip install -r requirements.txt

For an interactive exploration, you can use the provided Jupyter notebooks in the notebooks/ directory.

The dataset used in this project consists of audio features for a variety of songs from Spotify. These features include attributes such as danceability, energy, loudness, tempo, and more.

The following machine learning methods are utilized in this project:

• Data Preparation: Cleaning and normalizing the data.
• Clustering: Using algorithms like K-Means, DBSCAN, and Hierarchical Clustering to group similar songs.
• Evaluation: Validating the clustering results with metrics such as silhouette score and Davies-Bouldin index.

The results of the clustering analysis include visualizations of the clusters and insights into the characteristics of each cluster. These results can be used to recommend similar songs to users.

Contributions are welcome! Please follow these steps to contribute:

1. Fork this repository.
2. Create a new branch with your feature or bug fix.
3. Commit your changes.
4. Push to the branch.
5. Create a pull request.