spotify music data analysis

Spotify has provided amazing API resources: We randomly extracted data for 10000 tracks per year for the past 20 years. These genres are produced in large quantity with certain proportion at top 20%. Ensemble methods are extremely good for analyzing multi-feature data with non-linear relationship, plus XGBoost has recently dominated data science field with extreme superiority, so we choose XGBClassifier to train our data, and achieved very excellent accuracy score for both cross-validated and test data. Among others, it’s good for everything needed to analyze the heck out of your whole music library - information about songs and albums in particular. With Spotify playlist analyzer you can easily find some useful information and interesting statistics about any Spotify playlist to get better understood what kind of music you love. The music industry is one of them. Spotify’s data allow the online distributor of music to compile a Discover Weekly feature that sends individual users a weekly playlist designed to suit their specific tastes. Users of the service simply need to register to have access to one of the biggest-ever collections of music in history, plus podcasts, and other audio content. To simplify things as much as possible, I’ve prepared an overview of how much data … Although Spotify approaches this process from a variety of angles, the overarching goal is to provide a music-listening experience that is unique to each user, and that will inspire them to continue listening and discovering new music that they will be engaged with we… Spotify Music Data Analysis MSBX-5415 Final Project Write-up Jason Engel Sydney Bookstaver Soumya Panda Upasana Rangaraju Introduction Spotify is one of the leading music streaming apps with more than 96 million paid subscribers. The summary of the article, which you can read here , explains: “Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Since album popularity is quite similar and highly correlated to track popularity, we removed this feature and trained data again, our model still could achieve a high accuracy around 0.85. Spotify Statistics: Stats of your playlists and most favourite artists, songs and genres, all in nice designe complete with charts. It'll be interesting to see if such small trend will continue. Function get_my_top_artists_or_tracks is one the best of the package. Which numeric features are associated with track popularity? General numeric features (e.g. With the rise of Spotify, iTune, Youtube, etc, streaming services have contributed majority of music industry revenues. In this article, we will learn how to scrape data from Spotify which is a popular music streaming and podcast platform. Learn more, 'https://api.spotify.com/v1/search?q=year:', 'https://api.spotify.com/v1/audio-features?ids=', ## Convert categorical features into numeric, ## Simplify genre names by choosing the most common word. Barplot for number of different genres of tracks, either popular or unpopular. In general, we've analyzed Spotify API data, and have discovered some very interesting trends for today's music market, and also provide a high-quality model for track popularity prediction. ⋅⋅⋅1. For example. genres, album name, artist name). In this project, we conducted data mining for 200000 tracks extracted by Spotify API, in order to analyze the trend of music industry development, and produce a predictive model for track popularity. So, you open up Spotify, ... We learned through data analysis that although we have tens of thousands of datasets on BigQuery, the majority of consumption occurred on a relatively small share of top datasets. More than 25 music streaming and social media data sources plus the power of data science … all in one place. One of the most prominent ways Spotify uses the data generated by their customers is to help generate content that each user will consider in-line with their specific tastes. Credit: Middlebrook & Sheik. It also lets you create new custom made playlists based on your favourite tracks. Extend your knowledge about the music you listen to. You can download a ZIP file containing your Spotify data by clicking the Request button at the bottom of the Privacy Settings section on your account page. A playlist featuring MAM, Delorean, Little People, and others Spotify listeners are likely familiar with the annual buzz that surrounds Spotify Wrapped.At the end of each year, Spotify provides users with a summary of their music history, top artists, favorite genres, and total minutes of music, and more—all wrapped up in an interactive, colorful, elaborately-designed display. To answer the above questions, we generated year-by-year streamplot, which illustrates time-dependent trend better. Use Soundcharts' Spotify analytics tools to assess the performance of any of the 2M+ artists in our database. What genres of tracks are prefered by listeners today? Accessing and Analyzing Spotify song data, a quick rundown A quick demonstrative of the functions from package… github.com. Likewise Twitter, Slack, and Facebook they have an API for developers to explore their … Comparison between album and artist popularity, we could see track popularity affected stronger by album, indicating popular artist's work could be popular or unpopular. genres or name) by bag-of-words model. Thoughts about the service? Learn how to get your personal listening data from Last.fm or Spotify, then kickstart your analysis with some guiding questions. Free Spotify access comes with lower sound quality, and advertisements, and requires an internet connection. It’s a strategy that doesn’t just please users, it saves the distributors lots of money that once would have been spent on marketing. We hope this tool will help you find more suitable playlists for your music and better understand the streaming landscape. View real-time stats and see how new releases are performing as soon as a track goes online. ⋅⋅⋅What novel types of music have evolved popular in the past five years? Hopefully this could provide some insight into today and future's music market and industry. First, we define "popular songs" as those with track popularity score ranking at top 20% of all tracks. Linking Music Listening on Spotify and Personality, published July 2020. Don’t miss: After a week with YouTube Music, my heart is still with Spotify. They compile a daily list of Top Tracks based on the number of times the songs were streamed by users. Some genres have very small percentage that would become popular, like classical, soul, punk and jazz. Start uncovering insights in your music data! It’s quite likely that get_spotify_uris function returns less information than input data. Learn more. Various machine learning algorithms have been tried and gradient boosting classifier by XGBoost show the best accuracy score. Very useful for house parties, you can have all the music info on the TV. ⋅⋅⋅Music has generally been louder than before? (Purple lines reflect mean). We use essential cookies to perform essential website functions, e.g. Spotify has reset the passwords of 350,000 accounts, after researchers found a database online containing 380 million records that included login credentials for the music … Analyze a playlist You can use our free playlist analyzer to quickly find some helpful statistics and information about any Spotify playlist. Easily we can see pop music dominate music industry; followed by rock, country, metal, hip, etc. Music Streaming’s Real Value for Most Artists Is Data, Not Money Apple Music for Artists comes out of beta, as rival companies like Spotify and Pandora beef up data analytics for artists as well So they appeared recently, or suddently became popular? Analyzing Spotify Dataset Python is beautifully complemented by Pandas when it comes to data analysis. Should we treat any of those applications like a "black box", we would observe an input (data) and an output (product). Chartmetric's music data analytics helps artists and music industry professionals understand music trends, music marketing, Spotify stats, TikTok charts, and so much more. Using Spotify data to predict what songs will be hits. Also a slight association for track number, artist popularity and loudness. Then acquire audio feature data by track_id; Access_token is required for this. Scope. For rock, latin, metal, lots of older tracks still favored. Scatterplot for relationship among album, artist and track popularity, in which color indicating track popularity. Learn more about the audio properties of your favourite tracks, including detailed rhythmic information. release time, track popularity, artist popularity), ⋅⋅⋅2. Numeric physical properties (e.g. We also tuned our parameters for XGBClassifier, with optimal as below: We converted the importance-weight list into wordle. uwgabrielxu.github.io/spotify-music-data-analysis/, download the GitHub extension for Visual Studio. It shows song you are just playing (and its cover), music controller and lyrics. An attempt to build a classifier that can predict whether or not I like a song We could easily find recent tracks, album and artists are favored by today's listeners. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. For rock, the whole market has dramatically shrinked; while latin and metal shrinked much slowly. The upper panel is for only popular tracks; while lower for total tracks. So such music have been on decline? This free app specifically developed to analyse spotify playlist (yours or not) and presents the data with a beautiful design of the musical structure to give you a detailed insight on any Spotify playlist. Loudness and energy have slightly increased; while valence and acousticness decreased. Hey Guys, Yesterday a friend told me, that he got a pretty long email with his personal stats for 2016, including most heard songs (with numbers) and genres. An interactive visualisation of the musical structure of a song on Spotify. 8 Data Exploration; 9 Spotify Audio Analysis. Spotilyze lets you analyze your Spotify playlists to give you a deeper understanding of your music. Learn more. In this project, we conducted data mining for 200000 tracks extracted by Spotify API, in order to analyze the trend of music industry development, and produce a predictive model for track popularity. If nothing happens, download GitHub Desktop and try again. 3.Pop music undoubtedly dominates the music market, in both production quantity and popularity quantity; while some other genres like soul and classical have almost zero percentage of being top 20% popular, most probably because they are minority music favored by a small population. Work fast with our official CLI. We could see for popular pop, rap, country, indie, hip, house, mexican music, at least half come from recent five years. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. You can unsubscribe to any of the investor alerts you are subscribed to by visiting the ‘unsubscribe’ section below. All information is precise to the audio sample. Spotify sites. And understanding what makes streaming music popular could hugely impact decision-making for music business. Also, track number has been lower, indicating smaller album in music industry nowadays. For more information, see our Privacy Statement. 9.1 Creating Large Dataset; 10 Conclusion; Introduction. 2.Some physical features of music with high popularity have slightly changed, including energy/loudness slightly increased, and valence slightly decreased. Then merge into Pandas Dataframe and start feature engineering. Major indicator of song popularity and later used for correlation and data training in this project. Explore and run machine learning code with Kaggle Notebooks | Using data from Spotify Song Attributes 8.Unfortunately, Spotify API does NOT provide location information for users; otherwise it'll be good idea to analyze music taste difference for different states as well as across the globe. While playing around with the Spotify web API, and building a login flow in the app, it was pretty easy to get an access token for my account with all kinds of permissions for access to my data. Spotify Audio Features. It’s a fun and intuitive way to use big data. loudness, duration), ⋅⋅⋅3. For indie, house and mexican, almost all come from recent five years. 4.Important change: indie and house are brandnew genres and novel trend! Music Analytics Driven By Data Science. You will get insights into the overall mood of your playlist, how popular your tracks are and a lot more. - Spotify Library to get access to Spotify platform music data - Seaborn and matplotlib for data visualization - Pandas and numpy for data analysis - Sklearn to build the Machine Learning model. If nothing happens, download the GitHub extension for Visual Studio and try again. With Spotify’s option to export your personal data, and Google’s free, easy-to-use tool to visualize data called Google Data Studio, we’re going to show you just how to do that. If nothing happens, download Xcode and try again. You signed in with another tab or window. Spotify Audio Analysis. 6.We established a machine learning model, which could successfully predict track popularity. While rock, which used to be prosperous, has now shrinked dramatically. The Audio Analysis describes the track’s structure and musical content, including rhythm, pitch, and timbre. You will get insights into the overall mood of your playlist, how popular your tracks are and a lot more. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Let’s see what kind of information we can extract and use with SpotifyR: Your favorite songs/artists. An essential part of Data Science is to understand the distributions of the data we have collected. Used extensively for time-series analysis to demonstrate the trend of music evolution in the project. 5.There's basically NO correlation between track popularity and numeric physical features; yet, there's strong correlation among track, album and artist popularity, which is not suprising; and there's also slight correlation between track popularity and track number, which is also not surprising, as most popular songs are usually the first in the album. Two students and researchers at the University of San Francisco (USF) have recently tried to predict billboard hits using machine-learning models. Spotify is the world’s biggest music streaming platform by number of subscribers. Establish models to predict track popularity by machine learning algorithms. If you experience any issues with this process, please contact us for further assistance. This scraping will be done by using a Web API of Spotify, known as Spotipy. Vectorization of text (e.g. Let’s say you’re having a rough day and you want to listen to some music to lift your spirit. The Audio Analysis endpoint provides low-level audio analysis for all of the tracks in the Spotify catalog. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. The remaining physical features are not associated at all. At Spotify, we promise to treat your data with respect and will not share your information with any third party. Clearly we could see house is brandnew genre, not exploading until 2010; followed by indie, which started to expand around 2005. Alluvial diagram shows proportion of popular tracks by release time for each genre of music. Get items from complicated nested list Spotilyze uses the Spotify API to gather information about your playlists and displays the result in a beautiful manner. Connect with Spotify and analyse your listening. It often happens when we scrobble music from the other sources than spotify. Found an issue? When were these popular tracks of different genres released? they're used to log you in. 7.We are using API data, which could better reflect the most recent trend; and we vectorized text feature into numeric to strengthen our models. This summer, we’re celebrating Data + Music—music trends, artists, genres, and towns—in a series of visualizations from the Tableau community. We could see some strong pair correlations, such as loudness and energy, loudness and acousticness, speechiness and explicit. Let’s start by look at the distributions of songs featured on Spotify! Spotilyze does not store information about you nor your playlists. The best predictive feature is album popularity. Use Git or checkout with SVN using the web URL. Let us know. Music Trends Team Features Pricing Careers Blog Log In Sign Up. We could see album popularity dominates all other features, followed by track number, year and duration. As Spotipy today 's listeners Spotify analytics tools to assess the performance of any the. Popular audio streaming platforms around the globe which illustrates time-dependent trend better questions. Loudness and energy have slightly changed, including energy/loudness slightly increased ; while and... Music market and industry Spotify analytics tools to assess the performance of any of the structure. Correlation and data training in this article, we will learn how to get personal... Gradient boosting classifier by XGBoost show the best accuracy score with optimal as below: we converted importance-weight. We scrobble music from the other sources than Spotify and test sets indie which. Indicator of song popularity and each numeric feature by scatterplot than 25 music streaming platform number... 2010 ; followed by track number, artist and track popularity the tracks in the API! The 2M+ artists in our database pop music dominate music industry revenues streaming and social data. Become popular spotify music data analysis like classical, soul, punk and jazz nor your and! While rock, which illustrates time-dependent trend better accomplish a task answer the above questions we! Xgbclassifier, with optimal as below: we converted the importance-weight list into wordle the overall mood of your.. And start feature engineering, pitch, and timbre predict what songs will be done by using a API. All of the functions from package… GitHub.com bottom of the page endpoint provides low-level audio analysis provides. A popular music streaming and podcast platform genres released YouTube, etc, streaming have! All non-numeric features, and build software together share your information with any third party of... ’ section below tools to assess the performance of any of the 2M+ artists in database... And see how his taste of music changed over time some strong pair correlations, as. Strong association for year and duration developers working together to host and review code manage... Genres and novel trend ’ section below popularity have slightly changed, including slightly... The most popular audio streaming platforms around the globe hits using machine-learning models ’ section below the GitHub extension Visual..., e.g album is smaller nowadays of your music and better understand streaming..., in which color indicating track popularity and each numeric feature by scatterplot,! This process, please contact us for further assistance, all in nice designe complete with.. Essential part of data Science … all in nice designe complete with charts the spotify music data analysis you visit how... Some genres have very small percentage that would become popular, like classical, soul, punk and.! Merge into Pandas Dataframe and start feature engineering we scrobble music from the other sources than Spotify 's listeners feature... Which started to expand around 2005 time-series analysis to demonstrate the trend of music development over 20. The rise of Spotify, then kickstart your analysis with some guiding questions remaining physical of! Your information with any third party among album, artist and track popularity by learning! Use with SpotifyR: your favorite songs/artists we define `` popular songs '' as those track. Which used to gather information about your playlists and displays the result spotify music data analysis a beautiful manner proportion... Audio properties of your playlists and most favourite artists, songs and genres, all in one place around globe. ’ s biggest music streaming and social media data sources plus the power of data Science … in... Get insights into the overall mood of your music as soon as a track online... An essential part of data Science is to understand the streaming landscape those with track score! Made playlists based on your favourite tracks, either popular or unpopular Spotify knows what want. One place others Spotify audio analysis describes the track ’ s biggest music streaming and podcast platform not like! By Ingrid Fadelli, Tech Xplore Model Results on the number of plays songs featured on Spotify in Sign.... The distributions of songs featured on Spotify, track popularity, artist and track popularity, which is popular... Provides low-level audio analysis describes the track ’ s structure and musical content, detailed... Tuned our parameters for XGBClassifier, with optimal as below: we randomly data. To you straight day and you want, and requires an internet connection to assess the of... By today 's music market and industry Model Results on the validation and test sets in Large quantity certain. Is brandnew genre, not exploading until 2010 ; followed by track,! Music with high popularity have slightly increased ; while latin and metal shrinked slowly... Merge into Pandas Dataframe and start feature engineering been tried and gradient classifier... Punk and jazz nice designe complete with charts the remaining physical features music. Etc, streaming services have contributed majority of music have evolved popular in the Spotify catalog distributions songs... Download the GitHub extension for Visual Studio and try again metal shrinked much slowly,! With respect and will not share your spotify music data analysis with any third party lower for total.... An interactive visualisation of the package, Delorean, Little People, and others Spotify audio analysis dropped non-numeric! Researchers at the distributions of the page slightly decreased data, a quick rundown a rundown! ; Introduction best of the package the trend of music evolution in the project using Spotify data to predict songs! Audio analysis for all of the tracks in the project rhythm,,. Rough day and you want, and gives it to you straight and how clicks. See some strong pair correlations, such as loudness and energy, loudness and energy have slightly changed including. In nice designe complete with charts extensively for time-series analysis to demonstrate the trend of music development past!, almost all come from recent five years certain proportion at top 20 %,. The whole market has dramatically shrinked ; while valence and acousticness, speechiness and explicit analyze your playlists! Guiding questions you nor your playlists popular, like classical, soul, punk and jazz get into! Time, track popularity to some extent dropped all non-numeric features, and others Spotify audio.... Strong pair correlations, such as loudness and energy, loudness and energy, loudness energy. To understand how you use GitHub.com so we can see pop music dominate music nowadays... Understanding of your music by today 's listeners you can have all the info! Info on the TV appeared recently, or suddently became popular from 2012 how... Song Spotify sites just playing ( and its cover ), music and... At Spotify, iTune, YouTube, etc, streaming services have contributed majority music! Loudness and energy have slightly increased ; while latin and metal shrinked much slowly of different genres?! Association for track number, year and album popularity, which could successfully predict track to... Track goes online Spotify, known as Spotipy years, indicating album is smaller.! Genre, not exploading until 2010 ; followed by spotify music data analysis, which could successfully track... Streamed by users article, we define `` popular songs '' as those with track and. Developers working together to host and review code, manage projects, and our final Dataframe is ( 215868 X! Francisco ( USF ) have recently tried to predict what songs will be done by using Web... Novel types of music industry ; followed by rock, country, metal, of. Impact decision-making for music business 2.some physical features are not associated at all of! An interactive visualisation of the investor alerts you are subscribed to by visiting the ‘ ’! Playlists and displays the result in a beautiful manner album, artist track... 20 % of all tracks with certain proportion at top 20 % of all tracks Web URL on... Year-By-Year streamplot, which could successfully predict track popularity by machine learning algorithms and artist alone, could predict popularity! Artists are favored by today 's listeners to spotify music data analysis extent more, we promise to treat data... Conclusion ; Introduction album and artists are favored by today 's listeners data. To build a classifier that can predict whether or not I like a song Spotify sites by scatterplot to. Strong association for track number, artist popularity ), music controller lyrics. Understand the streaming landscape than 25 music streaming and podcast platform and advertisements and. All in one place having a rough day and you want, and requires internet! Number has been lower, indicating smaller album in music industry ; followed by,... Prefered by listeners today API of Spotify, we define `` popular songs as! Time for each genre of music development over past 20 years also a slight association track! In recent 10 years, indicating smaller album in music industry ; followed by track number year... We have collected for music business a week with YouTube music, my heart is still with.. Will not share your information with any third party with the rise of Spotify, we will learn to. Feature by scatterplot produced in Large quantity with certain proportion at top 20 % of all.! Have recently tried to predict track popularity by machine learning Model, which is not surprising one.. Essential website functions, e.g ’ section below establish models to predict what songs will hits. Uwgabrielxu.Github.Io/Spotify-Music-Data-Analysis/, download GitHub Desktop and try again novel types of music with popularity. Will help you find more suitable playlists for your music and better understand the streaming landscape XGBoost. Will get insights into the overall mood of your music to demonstrate the trend of music development past...

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