<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data on whois JoeByjo</title><link>https://joebyjo.dev/tags/data/</link><description>Recent content in Data on whois JoeByjo</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 22 Jul 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://joebyjo.dev/tags/data/index.xml" rel="self" type="application/rss+xml"/><item><title>Spotify Analysis</title><link>https://joebyjo.dev/projects/spotifyanalysis/</link><pubDate>Tue, 22 Jul 2025 00:00:00 +0000</pubDate><guid>https://joebyjo.dev/projects/spotifyanalysis/</guid><description>&lt;h1 id="exploring-music-through-data-spotify-audio-feature-analysis"&gt;Exploring Music Through Data: Spotify Audio Feature Analysis&lt;/h1&gt;
&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Music is often described as emotional and subjective, but beneath that subjectivity lies a rich layer of measurable data. Platforms like &lt;strong&gt;Spotify&lt;/strong&gt; analyze tracks using detailed audio features such as &lt;em&gt;danceability&lt;/em&gt;, &lt;em&gt;energy&lt;/em&gt;, and &lt;em&gt;valence&lt;/em&gt;, offering a unique opportunity to study music from a data-driven perspective.&lt;/p&gt;
&lt;p&gt;This project, &lt;em&gt;Spotify Audio Feature Analysis&lt;/em&gt;, was designed to explore how these characteristics vary across genres, popularity levels, and time. By combining publicly available datasets from &lt;strong&gt;Kaggle&lt;/strong&gt; with additional metadata retrieved via the Spotify Web API, the goal was to uncover patterns in how music evolves and what defines different styles of sound.&lt;/p&gt;</description></item></channel></rss>