Spotify Stream Analysis

Project Summary and Key Learnings:

This project involved visualizing the performance of music artists across genres from 2010 to 2019 using detailed datasets from the 'billboard' library and Spotify. The main experiences and insights gained from this project include:

  1. Data Collection and Cleaning: Obtained music data from 'billboard' library and Spotify API using Python. Cleaned and filtered the data to focus on English songs released between 2010 to 2019. Developed data cleaning skills and handling inconsistencies in real-world datasets.
  2. Data Visualization: Created Tableau visualizations to explore correlations between metrics like danceability, energy, tempo, and popularity. Discovered weak correlations between these metrics and popularity, indicating the complexity of factors affecting a song's popularity.
  3. Genre Analysis: Utilized Tableau to analyze genres' impact on metrics like popularity, tempo, etc. Identified pop, dance, and hip-hop as the most popular genres. Learned to visually represent and analyze data trends by genre.
  4. Insights and Conclusion: Discovered that songs with extreme values in metrics are less likely to be popular, suggesting listeners prefer a balanced mix. Recognized the consistency of popular genres and their potential continuation in the next decade. Recognized the need for more diverse country-specific streaming data for comprehensive analysis.

This project provided hands-on experience in data collection, cleaning, visualization, and trend analysis within the music industry, enhancing proficiency in Python, Tableau, and data interpretation.