- api: https://developer.spotify.com/documentation/web-api
- https://open.spotify.com/playlist/7fmD71MFQJNVwWFAho5aPV?si=c1fcd3cc2b3f4f25
- Make Dataset
- find features we want to use
- make data viz
- find chart ideas
- make model to predit whether song wouuld make a good club hit (Club compataiblity index)
- use LLM
- pass in the features and embeddings
- "will it make a good club hit" look at probability of yes
- look at cover art
- use LLM
Control: Top 20 artists of club banger genre songs Experimental: Club hits
Playlist IDs
- 37i9dQZF1EIezLFyG0SSkJ
- 4vKtucORuHzl3bhnUWMMbq
- 54nOF0dt7yuHMBK1cLjFCk
- 1q4yqfWq8DQ9k8xvpwjSGb
- 66bd2w90q5RXlfUnTIF1W3
- 2Hy1V8q07RL4ygQtatadve
- 33P9WHZFJArsEEb0q8zwJD
- 3EoStzWMnc93ktTEZYsSD4
- 5KlUhhSR7sZOdl8Hxy3Guz
- 6ebBexShOwJOfK9UIdzNIm
Non-bangers dataset
Dataset 1: In-Genre
- Retrieve top 20 artists for the most common club genres
- Get all the songs for the picked artists
- Filter out songs that are part of the bangers dataset
- Take out non-club songs with more than 60% popularity
- Get the audio features
Dataset 2: Out-of-Genre
- Out-of-Genre: Country, classical, reggae, jazz, blues
- Find playlists (1-2) for the genres
- Get the audio features
API Feature Analysis acousticness, danceability, duration_ms, energy, instrumentalness, liveness, loudness, speechiness, tempo, valence, key, time_signature