Get the most salient attributes in a decision tree

decision_tree.py

from sklearn.tree import DecisionTreeClassifier
import pandas as pd

# Get the most valuable customers, from step 2
df = pd.read_csv('high_value_customers.csv')

# Churned is our target. Why did they/didn't they churn?
X, y = df.drop('Churned', axis=1), df['Churned']
model = DecisionTreeClassifier()
model.fit(X, y)

# Get features and their importances
features = X.columns
importances = model.feature_importances_

# Sort features by their importance
features_and_importances = zip(features, importances)
features_and_importances = sorted(features_and_importances, key=lambda x: x[1], reverse=True)

# Display the most important features
print('\n'.join(features_and_importances))