Spotify Music Trends Analysis

Overview

This project explores Spotify song data using SQL, Python, and Tableau to uncover music trends such as popular genres, song tempos, and audio features.

Dataset

The dataset contains approximately 28680 songs with features like genres, artists, acousticness, danceability, duration, energy, instrumentalness, liveness, loudness, speechiness, tempo, valence, popularity, key, and mode.

Methodology

• SQL: Data cleaning and querying on PostgreSQL to extract insights.
• Python: Data analysis and visualization using pandas, matplotlib, and seaborn.
• Tableau: Interactive dashboard showcasing top genres, popularity trends, tempo distribution, and danceability vs energy scatterplot.

Key Findings

• The top 10 genres include Adult Standards, Album Rock, Classical, Dance-Pop, Alternative Metal, Bebop, Alternative Rock, Classical Performance, Contemporary Country, and Classic Bollywood.
• Average popularity varies significantly by genre.
• Song tempos range roughly from 60-200 BPM.
• Danceability and energy show interesting correlations, helping to identify song mood clusters.

Tableau Dashboard

Explore the interactive dashboard here:
Spotify Music Trends Dashboard

Files in this repo

• spotify_cleaned.csv — Cleaned Spotify dataset CSV
• Spotify_Music_Trends_Exploration.ipynb — Python notebook with analysis and visualizations
• spotify_sql_analysis.sql — SQL queries used for data exploration
• Spotify Music Trends.png — Screenshot of Tableau dashboard

License

This project is licensed under the MIT License.