The statistic provides data on favorite music genres among consumers in the United States as of July 2018, sorted by age group. According to the source, 52 percent of respondents aged 16 to 19 years old stated that pop music was their favorite music genre, compared to 19 percent of respondents aged 65 or above. Country music in the United States – additional information

In 2012, country music topped the list; 27.6 percent of respondents picked it among their three favorite genres. A year earlier, the result was one percent lower, which allowed classic rock to take the lead. The figures show, however, the genre’s popularity across the United States is unshakeable and it has also been spreading abroad. This could be demonstrated by the international success of (among others) Shania Twain or the second place the Dutch country duo “The Common Linnets” received in the Eurovision Song Contest in 2014, singing “Calm after the storm.”

The genre is also widely popular among American teenagers, earning the second place and 15.3 percent of votes in a survey in August 2012. The first place and more than 18 percent of votes was awarded to pop music, rock scored 13.1 percent and landed in fourth place. Interestingly, Christian music made it to top five with nine percent of votes. The younger generation is also widely represented among country music performers with such prominent names as Taylor Swift (born in 1989), who was the highest paid musician in 2015, and Hunter Hayes (born in 1991).

Country music is also able to attract crowds (and large sums of money) to live performances. Luke Bryan’s tour was the most successful tour in North America in 2016 based on ticket sales as almost 1.43 million tickets were sold for his shows. Fellow country singer, Garth Brooks, came second on the list, selling 1.4 million tickets for his tour in North America in 2016.

This data set was taken from Spotify's Top 50 Playlists (by country). Surprisingly, the Spotify API does not have an endpoint for collecting a track's genres - so what I did was extract each track's URI using Python, get the corresponding Artist URI, and then make a dataframe out of each Artist's genre(s). Lastly, I grouped them by country to see which countries preferred which genres of music.

A couple of things to note about this dataset: 1. An artist may have multiple genres or even 0. My code only takes into account the list of genres generated from the API. In other words, the genre count for each country will NOT equal 50.

1. Many music genres overlap with each other (i.e. electropop = EDM or pop??). For simplicity, I only classified these into one genre based off my own order (you can see it in the code below)

2. There are so many different genres that I couldn't cover every type in my code. For simplicity, I classified these into one genre called "Other". Also keep in mind that this group will include some genres that should belong to other groups because of the genres' unique name (you can see it in the code below).

3. I didn't get every country, only the countries Spotify covered on their Top 50s.

Classification code in Python: df3[~df3['Genres'].str.contains('hip hop|rap|r&b|edm|electronic|house|dubstep|electro|alternative|trance|pop|dance|rock|metal|thrash|emo|latin|reggaeton')] = 'Other' df3.loc[df3['Genres'].str.contains('hip hop|rap|r&b')] = 'Hip hop/Rap/R&b' df3.loc[df3['Genres'].str.contains('edm|electronic|house|dubstep|trance|electro')] = 'EDM' df3.loc[df3['Genres'].str.contains('pop|dance')] = 'Pop' df3.loc[df3['Genres'].str.contains('rock|metal|thrash|emo|alternative')] = 'Rock/Metal' df3.loc[df3['Genres'].str.contains('latin|reggaeton')] = 'Latin/Reggaeton'

According to data gathered in the United States in March 2023, Pop was the most popular genre for Generation Z. ** percent of Gen Z respondents included the genre to be among their favorites. Rap or Hip-Hop was second, being mentioned by a share of ** percent, while Rock concludes the top three, reaching ** percent.

According to a study carried out by Deezer in May 2018, the most popular genre among Americans was rock music, with 56.8 percent of respondents stating that they were currently listening to music within this genre as of the date of survey. Pop and country music were the second and third most popular genres respectively, and 20.2 percent of respondents said they preferred jazz.

The appeal of rock and pop music

The broad appeal of rock and pop music can in part be attributed to how both genres often blend seamlessly into one another and influence other music styles. Heavy rock bands like Led Zeppelin and AC/DC are often more divisive than melodic rock groups like Bon Jovi or Genesis, just like pop music which strays into R&B territory or is better associated with hip hop or EDM. Each have their appeal to fans with different tastes, and the versatility of rock and pop (and music which combines the two) allows such music to reach adults of all ages and backgrounds.

Rock albums also account for the majority of vinyl album sales in the United States, with pop albums ranking second. However, although the resurgence of vinyl has to a certain extent been reliant on the rock genre, this is not the case when it comes to digital music consumption. Rap and hip hop accounted for 22.8 percent of music video streams in the U.S. in 2018, whereas for rock music videos the share was just 7.1 percent. Rock fared similarly when it came to audio streams, once again losing out to rap and hip hop. Taking such data into consideration, it would seem that rock music fans are generally more drawn to traditional formats and are less inclined to enjoy their music via streaming platforms.

According to a survey conducted in South Korea in 2024, around ** percent of respondents in their teens stated that they had a preference for ballads. This was the lowest figure across all age groups for ballads, making it the most commonly preferred genre. Other commonly popular genres included K-pop and OSTs, though the former had a large age division.

The dataset is a comprehensive collection of 15,150 classic hits from 3,083 artists, spanning a century of music history from 1923 to 2023. This diverse dataset is divided into 19 distinct genres, showcasing the evolution of popular music across different eras and styles. Each track in the dataset is enriched with Spotify audio features, offering detailed insights into the acoustic properties, rhythm, tempo, and other musical characteristics. This makes the dataset not only a valuable resource for exploring trends and comparing genres but also for analyzing the sonic qualities that define classic hits across different time periods and genres.

This dataset contains fictional information about 50,000 songs from various music genres. It includes features such as song popularity, stream count, duration, artists, albums, and languages. The dataset is generated by ChatGPT and does not contain real data. It can be used for creative and educational purposes, such as music analysis, trend forecasting, and song popularity studies.

• song_id: The unique identifier for the song.
• song_title: The title of the song.
• artist: The artist performing the song.
• album: The album where the song is featured.
• genre: The music genre of the song.
• release_date: The release date of the song.
• duration: The duration of the song (in seconds).
• popularity: The popularity score of the song (1-100).
• stream: The total number of streams for the song.
• language: The language of the song.
• explicit_content: Whether the song contains explicit content (e.g., inappropriate language).
• label: The record label that published the song.
• composer: The composer of the song.
• producer: The producer of the song.
• collaboration: Whether the song is a collaboration with other artists.

Spotify Most Popular Songs Dataset 🎵

Overview:

This dataset contains a collection of the most popular songs on Spotify, along with various attributes that can be used for music analysis and recommendation systems. It includes audio features, lyrical details, and general metadata about each track, making it an excellent resource for machine learning, data science, and music analytics projects.

Each song in the dataset includes the following features:

🎧 Audio Features (Extracted from Spotify API):

1. - Danceability – How suitable a track is for dancing (0.0 – 1.0).
2. - Energy – Intensity and activity level of a song (0.0 – 1.0).
3. - Loudness – Overall loudness in decibels (dB).
4. - Speechiness – Presence of spoken words in the track (0.0 – 1.0).
5. - Acousticness – Probability that a track is acoustic (0.0 – 1.0).
6. - Instrumentalness – Predicts if a track is instrumental (0.0 – 1.0).
7. - Liveness – Probability of a live audience (0.0 – 1.0).
8. - Valence – Musical positivity or happiness (0.0 – 1.0).
9. - Tempo – Beats per minute (BPM) of the track.
10. - Key & Mode – Musical key and mode (major/minor).

📝 Lyrics-Based Features:

1. - Lyrics Text – Full lyrics of the song (if available).

🎶 General Song Information:

1. - Track Name – Name of the song.
2. - Artist(s) – Performing artist(s).
3. - Album Name – Album the track belongs to.
4. - Release Year – Year when the song was released.
5. - Genre – Song’s primary genre classification.
6. - Popularity Score – Spotify popularity metric (0 – 1).

Use Cases 🚀:

This dataset is ideal for:

1. - Music Recommendation Systems – Build collaborative or content-based recommenders.
2. - Audio Feature Analysis – Discover trends in song characteristics.
3. - Sentiment Analysis – Study how song lyrics relate to emotions.
4. - Hit Song Prediction – Use machine learning to predict song popularity.
5. - Music Genre Classification – Train classifiers to categorize music.

Acknowledgments:

Data collected using the Spotify API and other sources. If you use this dataset, consider crediting it in your projects!

In 2018, hip-hop and rap music accounted for 21.7 percent of total music consumption in the United States, more than double the percentage of R&B music sales. Other highly popular genres included pop and rock music, whereas just 1.1 percent of all music sold in the U.S. in 2018 was jazz.

Why are some genres more popular than others? 

Whilst music is a highly subjective medium in terms of the listener’s taste and preferences, the top genres in terms of consumption tend not to fluctuate heavily. The catchiness and familiarity of pop music is appealing to a wide range of music fans. Pop songs tend to be easy to listen to and remember, usually feature simple, snappy lyrics to avoid polarizing listeners, making pop overall less divisive than other genres because it is designed to generate mass appeal.

Conversely, religious music by its very nature is a niche genre in that it encompasses, describes or advocates certain beliefs, giving it the equal ability to alienate some listeners while appealing enormously to others, depending on their religious stance.

The hit genre of 2018 was hip-hop and rap, a music style notorious for its tendency to divide listeners. Singer Drake arguably influenced sales within the genre that year, with ‘Scorpion’ topping the list of best-selling albums in the United States based on total streams and ‘Scary Hours’ also making the top ten. Drake came tenth in the list of most successful music tours in North America, with revenue from his live concerts amounting to 79 million U.S. dollars, and second in the ranking was Jay-Z and Beyoncé with 166.4 million dollars in revenue, artists whose music is also strongly aligned with the rap and hip-hop genre.

Other artists in the genre who achieved significant influence in 2018 include Kendrick Lamar, Childish Gambino, Cardi B, Travis Scott and Post Malone, many of whom released songs that year which garnered hundreds of millions of audio streams. The sheer amount of hip-hop and rap music flooding the music industry has had a profound effect on the genre’s popularity, and musicians in the category tend to be prolific songwriters and active social media users. Equally, artists in the genre are arguably passionate about creating music which challenges social norms in a way that rock music has always been famous for.

The Dataset's Purpose: This dataset's goal is to give a complete collection of music facts and lyrics for study and development. It aspires to be a useful resource for a variety of applications such as music analysis, natural language processing, sentiment analysis, recommendation systems, and others. This dataset, which combines song information and lyrics, can help academics, developers, and music fans examine and analyse the link between listeners' preferences and lyrical content.

Dataset Description:

The music dataset contains around 660 songs, each with its own set of characteristics. The following characteristics are included in the dataset:

Name: The title of the song. Lyrics: The lyrics of the song. Singer: The name of the singer or artist who performed the song. Movie: The movie or album associated with the song (if applicable). Genre: The genre or genres to which the song belongs. Rating: The rating or popularity score of the song from Spotify.

The dataset is intended to give a wide variety of songs from various genres, performers, and films. It includes popular songs from numerous ages and places, as well as a wide spectrum of musical styles. The lyrics were obtained from publically accessible services such as Spotify and Soundcloud, and were converted from audio to text using speech recognition algorithms. While every attempt has been taken to assure correctness, please keep in mind that owing to the limits of the data sources and voice recognition algorithms, there may be inaccuracies or missing lyrics encountered upon transcribing.

Use Cases in Research and Development:

This music dataset has several research and development applications. Among the possible applications are:

1. Music Analysis: By analysing the links between song elements such as genre, vocalist, and rating, researchers can acquire insights into the features and patterns of various music genres.
2. Natural Language Processing (NLP): NLP researchers may use the lyrics to create language models, sentiment analysis algorithms, topic modelling approaches, and other text-based music studies.
3. Recommendation Systems: Using the information, developers may create recommendation systems that offer music based on user preferences, lyrics sentiment, or genre similarities.
4. Music Generating Machine Learning Models: The dataset may be used to train machine learning models for generating new lyrics or making music compositions.
5. Music Sentiment Analysis: To get insights into the emotional components of music and its influence on listeners, researchers might analyse the feelings conveyed in song lyrics.
6. Movie Soundtracks Analysis: Researchers can explore the association between song attributes and their use in movie soundtracks by investigating the movie attribute.

Overall, the goal of this music dataset is to provide a rich resource for academics, developers, and music fans to investigate the complicated relationships between song features, lyrics, and numerous research and development applications in the music domain.

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Pop music was the most popular music genre in China. About **** percent of the Chinese music tracks streamed in China were pop music. Electronic music was the second most beloved genre. In general, music produced by the artists from China, Hong Kong, and Taiwan held over **** of the market share.

Skateboard Music Genre Distribution

Genre-Usage by 32 Skateboard Companies (2017)

By data.world's Admin [source]

About this dataset

This folder contains datasets from The Pudding essay The Good, the Rad, and the Gnarly published in June 2018 which provides an in-depth examination of skateboard music genre usage across multiple companies. Not only does this dataset provide insight into trends and patterns in term of genre usage over time, but it also allows users to explore down to the artist level.

The folder contains two files: time_series.tsv and waffle.csv. The former contains data on ingredient lists from 211 chocolate chip cookie recipes alongside their scaled yield, while the latter consists of skateboard company genre usages percents multiplied by 1000 along with associated genres or fake genres used for testing purposes. Both datasets can be used to gain greater understanding into the inner workings of skateboard music taste and trends while still being able to examine particular artists' usage across time and companies if desired

Detailed below are column descriptions for both files:

• time_series.tsv: This file is made up of a number of columns that include 'genre', 'time', 'percentage used (% p)', 'maximum percentage across all genres (% maxp)', 'a peak (p_peak)', and finally a moving average percentage use (p_smooth). Each column is valuable when engaging with this dataset's layerd approach to exploring skateboard music trends over time alongside individual artists growing popularity compared with others in similar styles or even more broad categories such as Hip Hop, Electronic Music etc..

• waffle.csv: This file consists four columns - 'source','value','company','fake genre' - each helping paint a picture about how specific companies utilize various aspects within broader genres like Classic Rock, Indie/Alternative Music etc.. allowing viewers to delve right on down into specifics like exact artist or 80's metallic band etc.. Utilizing this dataset demands attention so as not mixup what particular genre using what company contributing which portion value wise relative overall favorite amongst boardsports enthusiast globally!

  Both these datasets are characterized by their temporal applicability that scale concerts pre-December 2017; hence allowing viewers engage framework bar none! All data available under the MIT License[link]!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In this guide, we will take a look at how you can use this data set to better understand music in the skateboarding community.

Step 1: Exploring the Data Set

The first step is to get familiar with all of the columns contained in this dataset. The following table provides an overview of what is included:

| Header | Description | Data Type | |-------------|------------------------------------------------------------|-----------| | source | Genre of music from broad genre bins | text |
| value | Percentage of associated genres used for corresponding company, multiplied by 1000| number
| company | Skateboard company | text

Using these headers, you can examine which genres are most popular amongst different companies, allowing skaters to draw comparisons between them. This will help skaters form an understanding as to why some companies might enjoy certain music more than others. Additionally, you can track certain trends over time using this dataset - allowing insights into which genres may be becoming more or less popular on each touring team or within each brands video output over time. Finally, if it becomes necessary due to licensing issues or other restrictions one brand places upon its releases or press materials you may ...

Streaming Activity Dataset

4 years of diverse music streaming data across platforms and ages

By Sean Miller [source]

About this dataset

The dataset consists of two main files: Scrobble_Features.csv and My Streaming Activity.csv. The Scrobble_Features.csv file contains detailed information about the music tracks, including genre, duration, popularity, and various audio features. On the other hand, the My Streaming Activity.csv file offers 4 years' worth of music streaming data from multiple platforms.

Key columns in these files include: - Performer: The name of the performer or artist. - Song: The title of the song. - Album: The name of the album that each song belongs to. - spotify_genre: The genre(s) assigned to each song according to Spotify's classification. - spotify_track_preview_url: URLs providing previews for each song on Spotify. - spotify_track_duration_ms: The duration of each song in milliseconds. - spotify_track_popularity: A popularity score indicating how popular each track is on Spotify. - spotify_track_explicit: A boolean value indicating whether or not a track contains explicit content.

Further musical attributes are also included: - danceability: A measure determining how suitable a song is for dancing based on various musical elements. - energy: An indicator measuring the intensity and activity level present in a song's composition. - key: Identifies the key signature (e.g., C major) that each track is performed in - loudness: Reveals how loud or soft a given track is overall in decibels (dB). - mode : Indicates whether a given track is composed in major or minor scale/mode. These attributes aim to provide insights into different aspects of a song's overall composition and impact.

Additionally, this dataset offers information about the timestamps when streaming activities occurred in both Central Time Zone (TimeStamp_Central) and Coordinated Universal Time (UTC) (TimeStamp_UTC).

How to use the dataset

In this guide, we will walk you through how to effectively use this dataset for your analysis or projects. Let's get started!

Understanding the Columns

Before diving into analyzing the data, let's understand the meaning of each column in the dataset:

• Performer: The name of the performer or artist of the song.
• Song: The title of the song.
• spotify_genre: The genre(s) of the song according to Spotify.
• spotify_track_preview_url: The URL of a preview of the song on Spotify.
• spotify_track_duration_ms: The duration of the song in milliseconds.
• spotify_track_popularity: The popularity score of the song on Spotify. (Numeric/Integer)
• spotify_track_explicit: Indicates whether the song contains explicit content. (Boolean)
• danceability: A measure of how suitable a song is for dancing based on a combination of musical elements. (Numeric/Float)
• energy: A measure o fthe intensity and activity level present in a track.(Alternatively it can also represent acoustic as well). (Numeric/Float)
  • 'key'- represents grouping.of songs based on keys found within that specific set pf songs
  • 'loundess' represents how loud or.silent that particular tract is usually defines by Clown Circle Diameter'.(diameter varies with loudness(sound pressure level). -'mode':defines what type/modeis represented(i.e If Major mode denoted by '1',If minor mood is denoted.by value '0') -'Speechiness':Detecting spoken words(actually presence/removal of spoken dialects.song verses). -Acousticness:Probability of track being acoustic,concerted,edt. -instrumentalness-instrumental.also calcylates effectively considering odds and ends ( for example; Intensity of beat.Solo drumming. -'liveness':a sentiment reflecting the probability that a song was performed since the recording being analysed 'valence'-The musical positivity/cheerfulness conveyed by a track.'1'represents most positive ;'0'mostly one(most presumably sad) -tempo:'Rate at which particular beats re occur in.oncluding beats); BPM (

Research Ideas

• Music Recommendation System: This dataset can be used to develop a music recommendation system by analyzing the streaming activity and audio features of different songs. By understanding the preferences and listening habits of users, personalized music recommendations can be generated for individuals or households.
• Genre Analysis and Trends: The dataset provides information about the performer, genre, and popularity of songs. This data can be utilized to analyze trends in music genres over the years, identify popular artists in different genres, and understand the ...

1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

Pop is the most listened to music genre in Germany. Followed by hip-hop music and rock music, these make up the top ***** of the most popular types of music. The ranking is also echoed among those who purchase music CDs, though a rising share of consumers don’t buy CDs at all. In tune with the times While German consumers will still buy music CDs, an increasing share of them is using streaming services to access music. Over a five-year period, the figure grew from **** to ** percent, indicating the changing trends in how listeners consume music and the ongoing rise of digitalization in this area. Spotify was the main source of music consumption in Germany. Festival country Based on various album charts, mainly German- and English-speaking music artists are popular among German listeners. Germany is also known for a variety of international rock festivals, catering to the many subgenres, as well as being one of the leading three countries in Europe hosting major music festivals in general.

🎵 Spotify Dataset with Genres

📘 Dataset Description

This dataset provides a comprehensive collection of Spotify tracks along with detailed audio features, artist metadata, and genre information.
It’s designed to support a wide range of data analysis, machine learning, and music recommendation projects.

Each record in the dataset represents a unique track on Spotify, enriched with both musical attributes (like tempo, energy, and valence) and artist-level information (including consolidated genre lists).

🎶 Columns Overview

🚀 Possible Use Cases

• 🎧 Music Recommendation Systems — Suggest songs based on genre, mood, or energy.
• 🧠 Machine Learning Models — Predict popularity, classify genres, or cluster songs by audio similarity.
• 📊 Data Analysis & Visualization — Explore trends in music features, release patterns, and artist popularity.
• 🎨 Generative AI Applications — Build AI music mood predictors or content generators using track attributes.

💡 Inspiration

Ever wondered what makes a song popular, energetic, or danceable?
This dataset allows you to explore how audio features correlate with genre, artist style, and listener preferences — a perfect playground for both data scientists and music enthusiasts.

Data on the distribution of streamed music consumption in the United States showed that rock music accounted for ** percent of all on-demand audio and video streams in 2021. The leading genre by streams was, as is almost always the case, R&B and hip-hop. Most streamed artists The demand for streamed audio content is growing year by year, and in 2021, the number of paid music streaming subscribers in the United States exceeded ** million. So which musicians are topping the digital charts? Based on the most recent data, The Weeknd, Justin Bieber, and Ed Sheeran were the top artists on Spotify based on monthly listeners in 2022. The top-10 ranking was visibly dominated by pop singers and bands that year, although Doja Cat and Eminem were the acts that amassed the largest monthly audience within the R&B and hip-hop genre. Keeping it classical A closer look at the distribution of music consumption in the U.S. by genre and format reveals that fans of jazz, rock, and classical music were among the most likely to listen to artists via physical formats. Between ** and ** percent of surveyed respondents stated that they listened to these genres via physical albums, which was above the industry average. One reason for this sense of audio nostalgia could be that these genres are particularly popular with listeners from older, less tech-savvy generations.

About Dataset I'm thrilled to introduce the Ultimate Music Analysis Dataset, a comprehensive collection of 2,000 songs, providing a rich resource for music enthusiasts, data analysts, and researchers delving into the intricacies of musical composition and trends.

🔍 Dataset Features:

🎤 Artist: The name of the artist who performed the song.

🎵 Song: The title of the song.

⏱️ Duration (ms): The length of the song in milliseconds.

🔞 Explicit: Indicates whether the song contains explicit content (True/False).

📅 Year: The release year of the song.

📈 Popularity: A score reflecting the song's popularity.

🕺 Danceability: A measure of how suitable the song is for dancing.

⚡ Energy: A measure of the song's intensity and activity.

🎼 Key: The musical key in which the song is composed.

🔊 Loudness: The overall loudness of the song in decibels.

🎚️ Mode: Indicates the modality (major or minor) of the song.

🗣️ Speechiness: The presence of spoken words in the track.

🎸 Acousticness: A measure of the acoustic sound of the song.

🎹 Instrumentalness: Predicts whether the track contains no vocals.

🎤 Liveness: The probability that the track was performed live.

😊 Valence: The musical positiveness conveyed by the song.

🎧 Tempo: The tempo of the song in beats per minute (BPM).

🎶 Genre: The genre(s) of the song.

Exploring This Dataset Can Help With: Music Analysis: Understanding the characteristics that make songs popular and suitable for different contexts.

Trend Identification: Analyzing how music trends have evolved over the years.

Market Research: Gaining insights into popular genres and artists.

Product Development: Informing the creation of music-related products and services.

Academic Research: Providing a robust data foundation for studies in musicology, data science, and cultural trends.

This dataset is an invaluable resource for anyone looking to explore the diverse and fascinating world of music, offering a detailed look at the elements that contribute to the art and science of song creation and popularity.🚀

Please upvote if you find this helpful! 👍