This week, I built a linear model and neural network to predict real-estate prices on an unfamiliar dataset. Along the way, I fixed mismatched error metrics and inconsistent feature scaling, and learned to rebuild/explain the workflow on my own

Deep Learning
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Great work! Applying what you’ve learned to an unfamiliar dataset and fixing issues with scaling and error metrics is a valuable step toward building a strong understanding of machine learning.

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Thanks! It was hard but the repetition really helps.

I took another step forward this week:

  1. This week, I trained a random forest with 25% lower error than my linear housing-price model on the same validation set. Then I used maps and feature importance to explore what it had learned about location and transit access