Containerized end-to-end analytics of Spotify data using Python, dbt, Postgres, and Metabase

Dynamically generated Spotify data to embed in your README or website. Card generation is now 3x faster 🚀🚀🚀

A in depth data analysis and visualization on spotify streaming history.

Classification & Time Series Analysis of Spotify Data using Grid Searched Random Forest & LSTM.

Explore your activity on Spotify with R and "spotifyr": How to analyze and visualize your streaming history and music tastes. Find out how and how much you consume from Spotify, using a copy of your personal data and the "spotifyr" package

The analysis of my Spotify streaming data.

In this repository we will conduct a data collecting trough spotify API, and do some analysis and exploring the data with machine learning approach

Django project based on Spotify API.

Experience a comprehensive exploration of Spotify's musical landscape seamlessly transitioned from Tableau visualizations to SQL analysis. Dive into track inventory, streaming metrics, and sonic trends via interactive dashboards, while leveraging SQL queries for deeper insights into KPIs and cross-platform rankings.

Splits spotify data into files containing chunks

1st Position Winners of the DataCamp "Create the Perfect Party Playlist" Competition 2023 (Umar & Faizan)

A in depth data analysis and visualization on Spotify streaming history.

A simple script that batch scrobbles Spotify Data. Can also scrobble one song.

Min-Max Normalisation of Spotify Data

Computer Vision using Spotify API - Dare Mighty Things Hackaton 2019. Best Use of Audio Visualization Award

#7DaysOfCode challenge about machine learning, proposed by Letícia Pires w/ Alura.

Request your streaming history over the past year from Spotify and then use this app to analyze the JSON file and reveal information about your listening behavior

This project is designed to provide personalized music recommendations for relaxation and meditation. Leveraging ML and data analysis, the system suggests tracks based on user preferences such as tempo, energy, and genre. Join us in enhancing music discovery through advanced algorithms and community-driven contributions.