Plot The Pearson Correlation Matrix

Lesson 6 of 17

It’s very easy to calculate and plot the correlation matrix for the New York TLC dataset, because we can use the CorrelationUtils helper class from the previous lab. The class is completely reusable and will work on any dataset.

Let’s see if our AI agent is smart enough to import code from another project.

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