Checking for correlated features

You'll now return to the wine dataset, which consists of continuous, numerical features. Run Pearson's correlation coefficient on the dataset to determine which columns are good candidates for eliminating. Then, remove those columns from the DataFrame.

This exercise is part of the course

Preprocessing for Machine Learning in Python

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Exercise instructions

Print out the Pearson correlation coefficients for each pair of features in the wine dataset.
Drop any columns from wine that have a correlation coefficient above 0.75 with at least two other columns.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print out the column correlations of the wine dataset
print(____)

# Drop that column from the DataFrame
wine = wine.____(____, ____)

print(wine.head())

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