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🎡 Spotify Music Performance Analytics

πŸ“Œ Project Overview

This project analyzes Spotify track performance data using PostgreSQL to uncover insights related to artist popularity, track engagement, and platform performance (Spotify vs YouTube). The analysis focuses on answering business-driven questions and presenting results through a clean, executive-friendly Power BI dashboard.

🎯 Business Objectives

  • Identify top-performing artists and tracks based on engagement
  • Compare Spotify streams vs YouTube views
  • Understand how audio features influence popularity
  • Provide quick, high-impact insights through a minimal, structured dashboard

πŸ› οΈ Tech Stack

  • Database: PostgreSQL
  • Query Language: SQL
  • Visualization: Power BI

πŸ“Š Dashboard Highlights

  • KPI overview of total tracks, artists, streams, and views
  • Top 10 artists and tracks by engagement
  • Platform performance comparison (Spotify vs YouTube)
  • Advanced analysis: Top 5 artists and their top 3 tracks
  • Audio feature impact analysis (energy/danceability vs engagement)

🧠 Key Insights

  • A small number of artists account for a significant share of total engagement
  • Certain tracks perform better on Spotify than YouTube, indicating platform preference
  • High-energy and highly danceable tracks tend to attract higher engagement
  • Top artists consistently have multiple high-performing tracks rather than a single hit

Spotify Dashboard

About

The purpose of this project was to understand how DAX equations work in powerbi and how to use them

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