Performing Basic Feature Engineering in Keras Reviews

Filip M. · Reviewed about 2 years ago

Mohammad K. · Reviewed about 2 years ago

Yi Y. · Reviewed about 2 years ago

Jonatan A. · Reviewed about 2 years ago

Estelle C. · Reviewed about 2 years ago

Chirag D. · Reviewed about 2 years ago

Florin F. · Reviewed about 2 years ago

mehdi t. · Reviewed about 2 years ago

Truong D. · Reviewed about 2 years ago

Dylan R. · Reviewed about 2 years ago

Federico P. · Reviewed about 2 years ago

poor instructions and no functions description made it really hard to do without looking from the solution

LORENZO S. · Reviewed about 2 years ago

Smilen M. · Reviewed about 2 years ago

Ehsan u. · Reviewed about 2 years ago

ABRIL N. · Reviewed about 2 years ago

Greg G. · Reviewed about 2 years ago

Dakam P. · Reviewed about 2 years ago

Jinay J. · Reviewed about 2 years ago

Aki R. · Reviewed about 2 years ago

Gustavo R. · Reviewed about 2 years ago

Not updated, install scikit-learn, should seed both the lab and solution.

Guang Jun . · Reviewed about 2 years ago

Man L. · Reviewed about 2 years ago

Elvira S. · Reviewed about 2 years ago

Hrutik P. · Reviewed about 2 years ago

This lab was pretty bad to be honest. Many of the parts we're expected to fill in were not really covered (e.g., defining a function within a function for scaling the data seems like an over-complicated way of standardizing the data). It also doesn't follow best ML practices. For instance, you train one model, then load the test dataset to make a prediction. Then after feature engineering, you predict on the test dataset again to compare models. This should not be done. Also, you refer to one of the models as 'linear regression', but I don;t understand why? You create a DNN in Keras and use ReLU activation functions, so how is it linear regression?

Robert S. · Reviewed about 2 years ago