A project created in fulfillment for our DATA102 class in De La Salle Univeristy (AY 2020-2021).
Authors:
- Lorenzo Mercado
- Nathan Roleda
- Bea Teope
- James Candelario
The project aims to predict the next top 10 Spotify songs based on Spotify Top 50 Charts and Music Features data. The charts data used in this project came from SpotifyCharts.com extracted using BeautifulSoup. While the music features of each song were extracted from Spotify's WEBAPI via Spotipy. The original dataset contained Top200 songs but were cut by the authors of this repository for the purpose of reducing computing requirements.
The project was developed in a virtual environment using virtualenv. The authors highly suggest the usage of a virual environment to run this project.
The project was developed using the following libraries:
jupyter
cloudscraper
spotipy
beautifulsoup4
yellowbrick
scikit-learn
pandas
numpy
seaborn
matplotlib
plotly
To install the libraries, run the following command:
pip install -r requirements.txt
Run the following command to scrape the data from SpotifyCharts.com and Spotify's WEBAPI:
python3 main.py
You may view how we explored the data and made predictions using through spotify_eda_prediction.ipynb.
- Giridih, J. (2020). Spotify Analysis (Starman). Retrieved from https://www.kaggle.com/darkstardream/spotify-analysis-starman
- Surat, Gu. (2021). Music Recommendation System using Spotify Dataset. Retrieved from https://www.kaggle.com/vatsalmavani/music-recommendation-system-using-spotify-dataset