Music Streaming Behavior in Springfield & Shelbyville 🎧

This project analyzes music streaming activity in two fictional cities, Springfield and Shelbyville. Using Python and Jupyter Notebook, it explores user behavior, genre preferences, and listening patterns throughout the week.

Objectives

• Analyze music listening patterns in two cities
• Identify peak streaming times and most popular genres
• Understand day-wise and city-wise streaming behavior

Key Insights (in the notebook)

• Streaming peaks in the evenings
• Both cities show high interest in pop and hip-hop
• Listening behavior differs slightly by day between Springfield and Shelbyville

Notebook

• ymusic_analysis.ipynb: Exploratory data analysis of music listening behavior.

Tools Used

• Python
• Pandas
• Matplotlib & Seaborn
• Jupyter Notebook

Dataset

The dataset is stored in data/ymusic_data.csv, containing anonymized records of music listening behavior from various urban areas.

• Columns include:
  • userID: Unique user identifier
  • City: Either "Springfield" or "Shelbyville"
  • Track: Song title
  • artist: Artist name
  • genre: Music genre
  • time: Time of streaming
  • Day: Day of the week

Author

Nabilla Hafsah Caesaredia

How to Run

Clone the repo and run the notebook:

git clone https://github.com/caesaredia/ymusic_project.git
cd ymusic_project
jupyter notebook music_streaming_analysis.ipynb