Introduction

This project is about the study of hit songs analysis. It revolves around the captivating intersection of music and data science, aiming to unravel the secrets behind hit songs. Hit songs are the lifeblood of the music industry, shaping popular culture and defining an artist’s success. Leveraging machine learning to help people working in the music industry understand the dynamics of these songs, and identify predictive patterns in making informed decisions by using audio features. The music industry is a thriving and dynamic ecosystem that acts as a dynamic marketplace for music lovers worldwide as well as a haven for artists’ creativity. This industry is evolving faster than ever. From ardent songwriters and performers to record labels, music publishers, internet streaming services, event promoters, and numerous more individuals and organizations, it comprises a diverse range of professions and organizations. The music business is fundamentally motivated by a deep respect for the art of sound and it has a significant impact on the contemporary musical scene.

Music data analysis is a captivating exploration at the intersection of music, technology, and analytics. In an age where vast amounts of musical information are being generated and shared every moment, understanding and deciphering this musical data can unveil profound insights into trends, preferences, and patterns within the world of music. Music data analysis involves harnessing the power of data science and computational methods to unravel the mysteries embedded within musical compositions, user behaviors, and the music industry. Through careful examination and interpretation of this data, not only gain a deeper comprehension of musical genres, artist popularity, and audience engagement, but also optimize music recommendations, enhance music production, and contribute to a more personalized and enriched musical experience for listeners worldwide.

The music industry is a thriving and dynamic ecosystem that acts as a dynamic marketplace for music lovers worldwide as well as a haven for artists’ creativity. This industry is evolving faster than ever. From ardent songwriters and performers to record labels, music publishers, internet streaming services, event promoters, and numerous more individuals and organizations, it comprises a diverse range of professions and organizations. The music business is fundamentally motivated by a deep respect for the art of sound and it has a significant impact on the contemporary musical scene. It is propelled by creativity, technology, business acumen, and a passion for the art of sound. Its evolution is driven by changing consumer preferences, advancements in technology, and the digital transformation of music consumption. Understanding its intricate ecosystem is essential for artists, professionals, and enthusiasts seeking to navigate and contribute to this dynamic world.

The world of music is continuously growing up every year. In 2023, people in the US spend 26.9 hours per week listening to music on average. This amounts to 3 hours 50 minutes of listening to music on average per day. And in a 30-day month, the typical American listens to music for 115 hours and 12 minutes. This is significantly greater than an average person globally. More than two-thirds of adults between the ages of 18 and 34 regularly listen to music which can say that the majority of adults listen to music every day. Moreover, the music industry plays a crucial role in the US. Music streaming represents 84% of total US music industry revenue. A wide range of experts that help singers and musicians with their musical careers are also part of the industry. Those who transmit audio or visual music content are among them, as are talent managers, artists and repertoire managers, business managers, and entertainment attorneys.

In tandem with the captivating exploration of Hit Songs Analysis and the application of machine learning models, this study unfolds as a pioneering venture into the harmonious confluence of art and technology, specifically leveraging the vast reservoirs of Spotify data and Song Lyrics data. Music, as a universal language, echoes through the ages, shaping cultures, resonating emotions, and reflecting societal shifts. With the contemporary surge in data availability, the potential to unravel the intricate dynamics of musical success becomes both an intellectual endeavor and a practical necessity. As we traverse the digital landscape of music consumption, this project aspires not only to dissect the anatomy of hit songs but also to decipher the evolving preferences of listeners within the Spotify ecosystem.

Q&A

Do hit songs always have a positive feeling?
Many of hit songs have positive mood. According to the pie chart, more than half of the hit songs have positive feelings and approximately 49% of hit songs have negative feelings. However, there is not a big difference between the mood of each hit song.
How many clusters can be distinguished from the data?
The data could be separated by 4 cluster.
Which features have the most impact on decision tree model?
Instrumentalness plays an important role in decision tree model. It has the highest score, followed by duration and acousticness.
Which word appears the most in the song lyrics?
The word “know” has the highest frequency appearance in the song lyrics, followed by “like” and come” respectively.
Who has the most number of weeks on the Billboard chart?
Taylor Swift is the artist who has the highest number of appearances on the charts significantly.
What is the best model for hit songs analysis?
Decision Tree model is the best model to do this analysis.
Which genre of songs is the most popular?
Pop is the most popular genre.
What type of music is most likely to be liked by listeners?
Songs contain high energetic songs with high beats and less acoustics and instrumentals with happy mood.
Is there a relationship between the danceability of hit and non-hit songs?
Hit songs have a beat which is slightly easier to dance to compared to the non-hit songs. However, the median showing on the hit songs is not that much higher than the non-hit songs.
What benefits of this analysis?
This analysis can be used by music producers to optimize the production process. Also, streaming platforms can provide highly personalized music recommendations.

Reference

https://headphonesaddict.com/listening-to-music-statistics/
https://headphonesaddict.com/music-streaming-statistics/