https://github.com/akashkg03/spotify-recommendation-system

This notebook analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.
https://github.com/akashkg03/spotify-recommendation-system

clustering matplotlib nonnegative-matrix-factorization pandas python

Last synced: 8 days ago
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This notebook analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.

• Host: GitHub
• URL: https://github.com/akashkg03/spotify-recommendation-system
• Owner: Akashkg03
• Created: 2024-02-08T11:07:52.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-02-13T07:32:09.000Z (9 months ago)
• Last Synced: 2024-02-14T08:34:11.067Z (9 months ago)
• Topics: clustering, matplotlib, nonnegative-matrix-factorization, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 3.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Spotify-Recommendation-System

### Project Overview

- This project analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.

### Dataset

-  The dataset contains the number of songs heard by each user. Each record represents the number of times a user has listened to a particular song.

### Approach

Our approach involved the following steps:

- Imported necessary libraries for data processing and model building.

- Applied NMF to factorize the feature matrix into two non-negative matrices.

- Clustered songs based on their latent factors obtained from NMF.

- Generated recommendations based on the clustered results and user listening behavior.

### Results:

The system successfully recommended songs based on user listening behavior.

### Technologies Used:

Python, pandas, scikit-learn, Jupyter Notebook.

### Skills Demonstrated:

Clustering, Dimensionality reduction.