Skip to content

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

License

Notifications You must be signed in to change notification settings

lukebella/SpotifyRegression

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

41 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SpotifyRegression

Open In Colab Python Version from PEP 621 TOML

In this project we develop a prediction system that estimates the popularity of a song. The flow of this work starts with the manipulation of the dataset: ''Spotify Track Dataset''. The training and estimation parts, together with k-fold nested cross validation and kernel functions, are developed from scratch and compared with the scikit-learn operations.

Features

  • Ridge regression
    • On numerical features
    • On all features
  • Kernel ridge regression
  • K-Fold cross validation
    • On Ridge regression
    • On Kernel ridge regression

Developers

About

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

Topics

Resources

License

Stars

Watchers

Forks