Spotify Data: Top 1000 Song Case Study (Data Analysis / Python)

What Does It Take to Hit the Charts

Table of Contents

0. Executive Summary
1. Introduction
2. Collect, Wrangle & Explore
3. Analyze & Visualization: Data Analysis

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Executive Summary

Key Findings

• Mode, Key, and BPM play a significant role in the success of a song.

Mode: Major / Minor

• Major mode is critical for success:
  • Top 10: 70% of songs are Major vs 40% for the last 10 and 55.32% overall.
  • Top 50: 68% are Major vs 54% for the last 50 and 55.32% overall.

Key

• C# is the "golden key":
  • Top 10: 30% of songs are in C# vs 10% for the last 10 and 14.1% overall.
  • Top 50: 24% in C# vs 14% for the last 50.
  • Top 100: 18% in C#.

BPM

• BPM matters, with the best range being between 90 and 110 BPM:
  • Overall average BPM is 122.
  • Top 10: Average BPM is 117, with a high standard deviation (+34).
    • Majority of the Top 10 BPMs are below 110.
    • Minimum BPM for the Top 10 is 90, and the overall minimum is 65.

Energy Score

• A minimum energy level is essential for success:
  • Top 10: Smallest standard deviation, ranging from 78 to 45, with an average of 59.6.
  • Optimal range: 78 to 45.

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Objective & Scope

• Objective:

  • Collect, clean, and analyze the Spotify dataset.
  • Identify key variables of interest.
  • Enable businesses to make data-driven decisions.
  • Share findings and insights.

• Scope:

  • Focus on uncovering patterns in the dataset to understand what drives song success.

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Methodology

Steps Taken

1. Collect Data

   • Dataset used: top-spotify-songs-2023.

2. Wrangle Data

   • Preprocessing, cleaning, transforming, and organizing the data for analysis.

3. Explore Data

   • Use various techniques to explore the dataset.

4. Visualize Data

   • Generate visualizations to uncover insightful discoveries.