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.

Bu egzersiz

Preprocessing for Machine Learning in Python

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

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

print(wine.head())

Kodu Düzenle ve Çalıştır