Music Data Analysis

Jupyter Notebook

A short data analysis project delving into my personal music streaming data from Apple Music, offering insights into my music listening habits, preferences, and trends over time.

Project Summary:

In this project, I conducted an exploratory data analysis (EDA) on my personal music streaming data from Apple Music to gain insights into my music listening habits and preferences. The main objective was to understand trends in my music streaming behavior, discover favorite artists, genres, and songs, and identify any changes in my musical tastes over time.

I started by describing the variables in the dataset, which included song titles, artists, genres, play counts, and timestamps. After cleaning the data and handling any missing or duplicate entries, I used various data visualizations to explore my music streaming patterns. Through bar charts, pie charts, and heatmaps, I discovered the top artists, most played songs, and peak listening times during the day and week.

The analysis provided valuable insights into my musical journey, highlighting my favorite genres and artists, as well as any shifts in music preferences over time. Overall, this EDA allowed me to better understand my music streaming behavior, and it provided an enjoyable and informative exploration of my personal musical taste.

Data Visualisation Highlights

Click the Jupyter Notebook link located near the top of the page to view all code and data visualisations.