Spotify Songs Clustering based on Audio Features:

This repository contains the code, research paper, dataset, and presentation slides for the project "Spotify Songs Clustering based on Audio Features". The project aims to analyze and cluster songs from the Spotify music library using audio features to identify patterns and similarities among the songs.

Introduction:

In this project, we explore the audio features provided by the Spotify API and use them to cluster songs based on their acoustic properties. By applying clustering algorithms to the dataset, we aim to identify groups of songs with similar characteristics, such as tempo, energy, danceability, and more. The clustering analysis can provide insights into the relationships between different musical genres, artist styles, and listener preferences.

Dataset:

The dataset used for this project is included in the repository as a CSV file. It contains a collection of songs from various genres, each annotated with audio features extracted from Spotify. The dataset provides information such as song title, artist, genre, and a range of audio features that describe the song's acoustic attributes.

Research Paper:

The research paper included in this repository presents the methodology, analysis, and findings of the project. It discusses the data preprocessing techniques, feature extraction, clustering algorithms used, and the interpretation of the clustering results. The paper also provides insights into the implications of the findings and potential future directions for research in this field.

Code:

The code for this project is implemented in a Jupyter Notebook file (.ipynb). The notebook contains the step-by-step process of data preprocessing, feature extraction, clustering, and evaluation. It also includes visualizations of the clustering results and provides a clear understanding of the methodology used.

Presentation:

The presentation slides included in this repository provide a concise overview of the project, highlighting the main objectives, methodology, key findings, and conclusions. The presentation aims to deliver a clear and visually appealing summary of the project to facilitate easy understanding and knowledge sharing.

Results:

Usage:

To use the code in this repository, follow these steps:

• Clone or download the repository to your local machine.
• Ensure you have the required dependencies installed (specified in the notebook).
• Open the Jupyter Notebook file (spotify_songs_clustering.ipynb) in Jupyter Notebook or JupyterLab.
• Execute the code cells sequentially to reproduce the analysis and obtain the clustering results.
• Modify the code and experiment with different clustering algorithms or parameters as desired.