During my data science learning journey, I decided to put my new skills into practice in a real-world context by analyzing my music data.
I collected data from my own Spotify playlists and a friend's playlist, diving deep into the realm of Exploratory Data Analysis (EDA).
Here's the process I followed:
I gathered track information and details from playlists using Spotipy, a library that wraps Spotify's RESTful API.
With Spotipy, I fetched detailed song information, including track name, artist, duration, and audio features.
It’s crucial to obtain your client ID and secret from Spotify Developer's page prior to using Spotipy. You can do this by following the guide at this helpful link.
After collecting the data, I delved into data analysis and visualization. The insights gained revealed intriguing patterns and comparisons.
for more analysis go to Spotify_Data_Analysis.ipynb
Some comparisons among the music taste of myself (MelodicWanderer), my friend (Icecreamy008), and the top hits:
The songs obtained above, along with their audio features, provided the basis for my k-Means clustering model. I used both t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA) to reduce the high-dimensional data into a two-dimensional dataset. To visualize my clustering model, I used the Python packages Seaborn and Matplotlib to showcase the distinction of genres.
You can try this yourself to discover what your music says about you!
For more data practices, it might be a good idea to create personal music recommendation systems.