feat_imp_load_data.py

# fit RF to plot feature importances
rf_clf.fit(RobustScaler().fit_transform(
  Imputer(strategy="median").fit_transform(X_train)), y_train)

# Plot features importance
importances = rf_clf.feature_importances_
indices = np.argsort(rf_clf.feature_importances_)[::-1]
plt.figure(figsize=(12, 6))
plt.bar(range(1, 25), importances[indices], align="center")
plt.xticks(range(1, 25),
           df.columns[df.columns != "not_fully_paid"][indices],
           rotation=90)
plt.title("Feature Importance", {"fontsize": 16});