plot top 10 predictive features for example churn analysis

churn_plot_feat_imps.py

## Obtain and sort feature importances from fitted model
feature_importances = (rf.feature_importances_)
sorted_idx = feature_importances.argsort()
importance = pd.Series(feature_importances, index=feature_names)

## Plot top 10 most predictive features
plt.figure(figsize=(10,8))
fig = importance.sort_values().tail(10).plot(kind='barh')
fig.set_title('Top 10 Most Predictive Features',
              fontsize=16, fontweight='bold')
plt.xticks(fontsize=12, fontweight='bold')
plt.yticks(fontsize=12, fontweight='bold')
plt.show()