A Chrome extension for tracking and analyzing streaming data on Spotify. Easily monitor playlist, album, and track streams, export data to Excel.

🎵 Plateforme full-stack d'analytics musicales avec recommandations personnalisées. MongoDB + NestJS + Next.js. Intégration Spotify, aggregation pipelines avancées, visualisations interactives.

Interactive Streamlit dashboard that transforms a prepared listening-history dataset into rich insights: genres, mood/energy trends, discovery habits, device mix, streaks, artist comebacks, and ML-powered 7-day forecasts with confidence bands for platform share, trained offline and visualized directly in the app.

A full-stack web application that transforms music discovery through interactive visualizations, personalized recommendations, and deep artist analytics. Built with the Spotify API, MusicBucket helps users explore new music, track their listening journey, and understand their musical preferences with rich data insights.

Spotify Stats is a privacy-focused web app that lets users explore their Spotify listening habits, including top artists, tracks, and genres. Built with Next.js, and Tailwind CSS, it offers secure authentication via NextAuth.js and integrates Plausible analytics for privacy-conscious tracking.

A Next.js application that lets you explore your Spotify listening history, create playlists based on specific time periods, and visualize your music journey.

🎧 A full-stack music search and analytics platform built with React, Node.js, PostgreSQL, and AWS RDS. Features advanced filtering, interactive visualizations, and RESTful APIs for exploring songs and albums.

AI-powered vinyl cataloging and music discovery platform leveraging BigQuery’s generative AI. Processes mixed-format data to deliver personalized recommendations, collection analytics, and intelligent search. Created for the Kaggle BigQuery AI Challenge to showcase real-world, scalable AI solutions for music lovers.

An interactive Power BI project analyzing multi-year Spotify streaming history to uncover user listening patterns, peak activity times, and music preferences. The dashboard includes YOY growth analysis, heatmaps, top artist/album/track rankings, and quadrant segmentation of songs based on frequency and duration.

SQL analysis on the Chinook (SQLite) dataset: revenue trends, top customers, genres, RFM, cohorts.

Shazam artist discovery scraper

End-to-end data analysis of 8,582 Spotify tracks to uncover what drives track popularity, focusing on artist popularity and followers, album type, track duration, genre, and release timing, and turning these insights into practical recommendations for artists, labels, playlist curators, and streaming platforms

The ultimate self-hosted music analytics suite. Turn your Last.fm scrobbles into a complete, private dashboard.

A full end-to-end machine learning pipeline that predicts Spotify track popularity using audio features and genre encoding. Includes preprocessing, model training, evaluation, and an interactive Streamlit app for real-time predictions and EDA.

This project analyses Spotify track data using linear regression models to explore relationships between audio features and track popularity. It includes Jupyter Notebooks demonstrating simple and multiple linear regression techniques

Utilize advanced machine learning algorithms to predict song popularity and discover top song recommendations across various genres. Dive into interactive visualizations and data-driven insights for a deeper understanding of music trends. Perfect for curating playlists and enhancing listener experiences.

Built SQL queries and aggregations to uncover trends in listening habits, popular genres, and user engagement metrics using Spotify dataset.

Analyze streaming royalty reports with dashboards & KPIs — built with Streamlit.

Business Intelligence project analyzing electronic music trends using Power BI, ETL, and star schema modeling