https://devpost.com/software/club-compatibility

• 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

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