This skip predictor combines music analytics with machine learning principles to create an in-depth analysis of songs found on the iTunes API. After querying for a song, the program:

🎯Assesses audio features such as tempo, valence, & duration. 🎯Provides detailed breakdowns of factors and their influence on skip probability. 🎯Includes audio previews for immediate listening, as well as links integrating Spotify and iTunes links

🛠️ Tech Stack:

👾Backend: Python with Flask 👾Frontend: HTML5, CSS3, JavaScript 👾APIs: iTunes Search API, Spotify integration 👾Hosting: PythonAnywhere 👾Features: Audio processing, predictive algorithms, responsive design

This project was not only super fun to create, but it was a learning exercise in full-stack development, API integration, and creating meaningful user experiences around music data. It demonstrates how we can use available data to build predictive models that reflect real-world listening behaviors.