🎧📊 Spotify & YouTube Exploratory Data Analysis (EDA) Project

In this project, I applied Exploratory Data Analysis (EDA) using Spotify & Youtube data.

📋 Project Overview

Engagement Metrics Analysis:

• I analyzed the correlation between engagement metrics: views, likes, and comments.
• Findings: As the number of views increases, the number of likes and comments also increases. Likewise, as the number of likes increases, comments also increases as expected.

Audio Metrics Analysis:

• I explored the relationships between audio metrics like danceability, energy, loudness, valance, and instrumentalness.
• Findings:
• Strong positive correlation: There is a strong positive correlation between Energy and Loudness meaning that songs with higher energy tend to be louder or vice versa. There is also a strong positive correlation between Danceability and Valence which means that songs that are suitable for dancing express more positive emotions.
• Strong negative correlation: There is a strong negative correlation between Acousticness and Energy meaning that acoustic songs tend to be less energetic. There is also a strong negative correlation between Acousticness and Loudness meaning that acoustic songs tend to be less loud.r.

Song Distribution Analysis:

• I examined the distribution of songs by album type (album, single, compilation).
• Findings: 72% of songs were released as albums, 24% as singles.

Top 10 Songs on YouTube vs. Spotify:

• Most viewed song on YouTube: "Despacito" (1.5e10 views)
• Most streamed song on Spotify: "Closer" (5e9 streams)

🎯 Summary

This analysis provides valuable insights for artists, music producers, and marketing teams. The relationships between engagement and audio features can help guide strategic decisions.