Statify

Inspiration

We've all struggled with finding new music to listen to.

What it does

Ever wanted to experiment with a new genre of music but didn't know where to start? Or maybe expand your tastes in a genre you already like? Statify aims to simplify this process by analyzing top songs in a specific genre and generating a Spotify playlist in that genre based off your music interests. All the user needs to do is: visit Statify.com, login to their Spotify account, and select a music genre they are interested in exploring. Statify then analyzes the user's top 100 most played tracks and compares them with that genre's top 1000 songs on Spotify. Statify then creates a playlist for the user based on which songs of that genre fit the user's tastes the most, using a K-Nearest Neighbors Regression machine learning model. Comparison attributes of songs include:

acousticness
danceability
energy
instrumentalness
liveness
loudness
speechiness
valence

The user's top 100 most played tracks were used as a training/test set for the machine learning model. The machine learning model is trained to predict the ordinal ranking of the songs (using the ranking of the user's top 100 most played songs as a basis).

How we built it

Front end: Uses react
Middleware: Flask calls the back-end script and handles the Spotify API calls
Backend: Python script that uses scikit-learn library, running a KNeighborsRegressor machine learning model

Challenges we ran into

Getting the Spotify OAuth running properly
Creating a sizable test dataset for machine learning (Spotify API only allows access to a user's top 100 tracks)
Being limited to the amount of song information we could query for each call (general limit of 50 per call)

Accomplishments that we're proud of

Creating a relatively successful machine learning model given the small size of the test dataset
Deploying a nice looking front-end with a Spotify playlist widget