The goal of this project is predicting market value of real estates in Manhattan, New York, which is a meaningful and popular issue. The research is impressive, there are lots of aspects I like about this project:
Convincing graphs
Well applied models taught in class
Training error and test error on the same magnitude, implying no overfitting
Sparse effect is significant with l1 regularizer
Maybe, something could be improved:
Only 500 samples in dataset
low correlation between y and a single feature doesn't imply this feature is useless in the model including all the features.
Personally, I guess outliers cleaning should involve standard deviation
Hi! Thank you for your review.
You might have some misunderstanding on our report. Our data set includes 42000 original rows each with 84 features, and around 16000 samples after cleaning.
The goal of this project is predicting market value of real estates in Manhattan, New York, which is a meaningful and popular issue. The research is impressive, there are lots of aspects I like about this project:
Maybe, something could be improved: