Spotify Song Analysis In music and movie business, due to the high cost of copyr

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Spotify Song Analysis
In music and movie business, due to the high cost of copyr

Spotify Song Analysis
In music and movie business, due to the high cost of copyright, the distributor should explore the song or movie’s popularity potential before purchasing it. In this project, with a dataset of Spotify from Kaggle.com, we will explore the features that affect the song’s popularity. With the machine learning models, we try to:
• predict the popularity of songs based on song attributes such as key, danceability, and acousticness. artist, track_id etc. will not be included.
• Cluster the songs based on some features
Dataset spotify_data.csv Description:
This dataset contains a comprehensive list of the most famous songs of Jan 4th ~ May 4th 2023 as listed on Spotify. The dataset offers a wealth of features beyond what is typically available in similar datasets. It provides insights into each song’s attributes, popularity, and presence on various music platforms. The dataset includes information such as track name, artist(s) name, release date, Spotify playlists and charts, streaming statistics, Apple Music presence, Deezer presence, Shazam charts, and various audio features.
Some features in the dataset are:
• track_name: Name of the song
• artist(s)_name: Name of the artist(s) of the song
• artist_count: Number of artists contributing to the song
• bpm: Beats per minute, a measure of song tempo
• key: Key of the song
• mode: Mode of the song (major or minor)
• danceability_%: Percentage indicating how suitable the song is for dancing
• valence_%: Positivity of the song’s musical content
• energy_%: Perceived energy level of the song
• acousticness_%: Amount of acoustic sound in the song
• instrumentalness_%: Amount of instrumental content in the song
• liveness_%: Presence of live performance elements
• speechiness_%: Amount of spoken words in the song

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