Diamond Platnumz Spotify Data Analysis Project

Overview

This project analyzes track and album data for Diamond Platnumz, a prominent Tanzanian artist, using the Spotify Web API. The goal is to uncover insights into his music catalog, such as track popularity, audio features, and album release patterns.

Features

• Fetch detailed track and album data for Diamond Platnumz via the Spotify API.
• Analyze audio features (e.g., danceability, energy, tempo) of his songs.
• Identify top-performing tracks based on popularity metrics.
• Visualize trends in audio features and release history.

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Tools & Technologies

• Google Colab: Interactive programming notebook similar to a classic jupyter notebook.
• Spotify Web API: To fetch data on popular tracks, popular albums, and audio features.
• pandas: For data manipulation and analysis.
• matplotlib & seaborn: For creating visualizations.

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Data Pipeline

1. Authentication:

   • Authenticate with the Spotify API using client credentials.

2. Data Retrieval:

   • Fetch all albums and singles by Diamond Platnumz.
   • Retrieve track metadata and audio features for each song.

3. Data Processing:

   • Clean the raw data and structure it into a usable format.
   • Combine track and audio feature data for analysis.

4. Analysis:

   • Explore patterns in track popularity and album releases.
   • Analyze audio features like tempo, valence, and energy.

5. Visualization:

   • Create visualizations to showcase key findings.

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Project Workflow

1. Google Colab Setup:
   Open the Colab notebook directly from the repository or upload it to your Google Drive.

2. Spotify API Setup:

   • Sign up at Spotify for Developers and create an app to get your CLIENT_ID and CLIENT_SECRET.
   • Add the credentials directly into the notebook when prompted or store them securely in a file and load them.

3. Data Extraction and Analysis:

   • Execute the cells sequentially to retrieve data, clean it, and perform analyses.

4. Visualization:

   • Review visual insights directly within the notebook.

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Project Structure

├── notebooks/
│   ├── Diamond_Platnumz_Spotify_Analysis.ipynb  # Main analysis notebook for Colab

├── README.md                 # Project overview
└── requirements.txt          # Python dependencies (for local use)

Example Insights

• Top Tracks by Popularity:
  Discover the top 5 most popular tracks by Diamond Platnumz.

• Audio Features Profile:
  Understand how audio features like danceability, energy, and tempo vary across his songs.

• Release Patterns:
  Visualize the timeline of album and single releases.

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Future Enhancements

• Extend the analysis to compare Diamond Platnumz’s data with similar artists.
• Implement machine learning models to predict track popularity based on audio features.
• Incorporate fan engagement metrics like playlist inclusions or monthly listeners.

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Acknowledgments

• Spotify API: For providing access to a comprehensive dataset.
• The open-source community for tools and libraries.

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License: This project is licensed under the MIT License.