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.