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.

Deze oefening maakt deel uit van de cursus

Preprocessing for Machine Learning in Python

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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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