- This project analyzes Spotify song data and builds a recommendation system to suggest songs based on user listening behavior.
- 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.
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.
The system successfully recommended songs based on user listening behavior.
Python, pandas, scikit-learn, Jupyter Notebook.
Clustering, Dimensionality reduction.