You can find our presentation of this work here.
If the site is up, you'll be able to access it here.
Otherwise, the following is meant to be done in the terminal.
You should have some browser as well as python3 installed.
We recommend Firefox or Google Chrome for the best experience.
- Clone this repository where you want:
git clone https://github.com/com-480-data-visualization/data-visualization-project-2021-vizbrains
cdinto the root directory (containingindex.html) and runpython3 -m http.server; copy the given url- Paste the url in the browser and enjoy!
More information about the audio features we use can be found here.
| Student's name | SCIPER |
|---|---|
| Auguste Baum | 322935 |
| Yanis Berkani | 271348 |
| Clément Petit | 282626 |
As three real music enthusiasts, we've always wanted to learn more and do some scientific research in the music domain. Accordingly, the dataset we chose is a collection of music data from a Kaggle competition.
More precisely, the data consists of music features for about 600 000 tracks and 1.1 million artists from the Spotify streaming service, spanning 100 years (from 1922 to 2021). The data are divided into 2 main datasets:
-
Tracks: name, date of release, popularity, duration, energy, tempo...
-
Artists: name, followers, popularity, genres.
You can find out a lot more details about the features in our
EDA.ipynb notebook (works best on Firefox).
.
├── about.html
├── index.html
├── milestones // Our preliminary analysis and hand-ins
└── viz
├── data // The data we use in our visualisations
├── styles // The stylesheet for our website
├── timeline.js // First visualisation code
├── year_comparator.js // Second visualisation code
└── bar_chart_comparator.js // Third visualisation code
10% of the final grade
Milestone 1 PDF • Milestone 1 MD
10% of the final grade
Milestone 2 PDF • Milestone 2 MD
80% of the final grade