In this project we develop and implement different regression methods on datasets with a large number of both numerical and categorical features. We will explore linear regression as well as ensemble methods such as Gradient Boosting and Random Forest. We then evaluate each method's advantages along with their limitations such as overfitting.
More description of the project can be found on Final_report.pdf file.
This is a group project done by Gabrielle Belok, Mehrnosh Sarmashghi and Me, Supervised by Professor Prakash Ishwar.