Final Report Peer Review

[Linyi9812]

This project aims to predict the sale price of art pieces based on a number of features. This helps to protect sellers and buyers against prices far away from the fair value. I think the project idea itself is very interesting and creative.

Things I like:

• I like how creative you choose the features. For example, your team added the last sale price of the painting and price of the last painting painted by the same artist.

• The interpretation of the clustering part is very interesting.

• The preprocessing part is clever, especially on the part of reducing the number of features that represent auction houses.

Things can be improved:

• Maybe giving more details about the features that you use to make predictions.

• For tuning hyperparameters for tree-based ensemble models, grid search might be a good idea to test more parameters and figure out the best ones.

• Since there are so many features being considered, it might be useful to calculate the feature importance as a byproduct of Decision Trees to get a sense of what factors are most important in determining art prices.