An analysis on Spotify Tracks

An analysis on a dataset of 160k spotify tracks from 1921-2020. Done as part of Udacity's Data Science Nanodegree, project 1.

Data Source

https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks

Motivation

Music is a big part of many people's lives, mine included. I love listening to songs, and I enjoy the various feelings I get from them. I'm also a huge fan of the artist Adele, and I've been listening soulfully to her heart-wrenching songs since 2013.

With these 2 goals in mind, I decided to analyze the dataset and find the answers to the following questions:

1. Are there any notable trends in recent years' popular songs?

2. Based on the dataset, how well is Adele doing compared to other singers in 2020?

3. What are the key differences between Adele and her top competitors?

Libraries

1. Pandas 
2. Numpy
3. Matplotlib
4. Seaborn

Specific version can be found on requirements.txt

Folder Structure

spotify_analysis
|_data.csv              # Data for analysis
|_requirements.txt      # Libraries and versions used
|_udacity_project.ipynb # IPYNB notebook containing analysis code, graphs, etc.

Usage

1. git clone <repo> #clone repo
2. cd <folder> #cd to where you cloned the repo
3. pip install virtualenv #install venv package (skip if already installed)
4. virtualenv <name> #create venv
5. <name>\Scripts\activate #activate venv
6. pip install -r requirements.txt #install dependencies
7. Run the cells of udacity_project.ipynb #Make sure to change input paths accordingly

Blog Post

https://andrew-william.medium.com/using-data-to-show-why-adele-is-an-amazing-singer-musician-6de886d80e61

Acknowledgements

Kaggle for providing kernels to do analysis on. Spotify for providing documentation for the data. Yamaç Eren Ay for putting together the spotify 160k track dataset.