Skip to content

STAT4609 project, in collaboration with @jevrii and @kellycyy. Git commit authors do not necessarily represent the actual author.

Notifications You must be signed in to change notification settings

SOF3/stat4609-project

Folders and files

NameName
Last commit message
Last commit date
Apr 15, 2021
Mar 19, 2021
Apr 1, 2021
Apr 21, 2021
Apr 14, 2021
Apr 14, 2021
Apr 14, 2021
Apr 14, 2021
Apr 26, 2021
Apr 15, 2021
May 5, 2021
Apr 26, 2021
Apr 22, 2021
Apr 21, 2021
Apr 1, 2021
Apr 22, 2021
Apr 1, 2021
Apr 8, 2021
Apr 22, 2021
Apr 8, 2021
Apr 15, 2021
Apr 22, 2021
Apr 15, 2021
Apr 22, 2021
Apr 22, 2021
Mar 28, 2021
Mar 28, 2021
Apr 22, 2021
May 4, 2021
Mar 28, 2021
May 2, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 5, 2021
Apr 15, 2021
May 4, 2021
May 4, 2021
Apr 15, 2021
Apr 15, 2021
Apr 15, 2021
Apr 15, 2021
Apr 15, 2021
Apr 22, 2021
Apr 22, 2021
Apr 22, 2021
Apr 22, 2021
Apr 22, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
May 4, 2021
Apr 8, 2021
Apr 8, 2021
Apr 8, 2021
Apr 8, 2021
Apr 8, 2021
May 4, 2021

Repository files navigation

stat4609-project

This is the course project conducted by @jevrii, @kellycyy and me. The project studies the Netflix Prize dataset, developing three models (kNN, SVD and NCF) to predict ratings.

The project report can be found at report.pdf. Code implementations are found in the following files:

  • kNN: knn_full_submit.ipynb
  • SVD: svd_full_submit.ipynb
  • NCF: ncf_with_full_dataset.ipynb
  • GMF: gmf_with_full_dataset.ipynb

Since this project is only of technical interest of demonstrating different models, we did not try to push the test RMSE to its best. The best result we obtained was only around 0.87 .