Spotify Analysis

An exploration of my personal Spotify listening habits

Table of Contents

Description
Installation
Usage
Roadmap
License

Description

Exploring my personal Spotify listening habits. The project has a data pipeline that extracts data from Genius and Spotify api for further analysis. The project stores raw and intermediary data in a s3 data lake to be further processed and loaded.

Installation

1. Clone this repository.

git clone https://github.com/vatdaell/spotify-analysis.git

2. Install all the python packages

pip install -r src/ETL/requirements.txt -r src/ReportGenerator/requirements.txt

3. Set up an AWS account with an s3 bucket.

4. Create a Spotify developer account and create an application

5. Create a Genius Account account and generate a client token

6. Setup a MySQL database for use

7. Create a .env file in the project directory and fill in the details.

S3_BUCKET=bucket_name
SPOTIPY_CLIENT_ID=clientid
SPOTIPY_CLIENT_SECRET=secret
SPOTIPY_REDIRECT_URI=redirect_uri
TABLE_NAME=recent_plays_table_name
GENIUS_ACCESS_TOKEN=genius_access_token
MYSQL_HOST=mysql_host
MYSQL_PORT=port
MYSQL_USER=user
MYSQL_PASS=password
MYSQL_DB=dbname

Usage

To load songs listened to to s3 bucket and load songs data to mysql database along with loading recently played data to mysql database

python src/etl/songs_pipeline.py
python src/etl/recently_played_pipeline.py

Roadmap

Some interesting features I want to implement/analyze in the future

• Use a task scheduler to automate etl tasks
• Extract lyrics of recently listened songs for sentiment analysis
• Recommend similar songs based on listening history
• Link to merch store for top bands
• Analysis of genre of music listened to

License

MIT