feature_selection.py

# Tree-based estimators can be used to compute feature importances, which in turn can be used to discard irrelevant features.
clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')
clf = clf.fit(train, targets)

# Let's have a look at the importance of each feature.
features = pd.DataFrame()
features['feature'] = train.columns
features['importance'] = clf.feature_importances_

# Sorting values by feature importance.
features.sort_values(['importance'],ascending=True, inplace=True)
features.set_index('feature', inplace=True)
features.plot(kind='barh', figsize=(20, 20));

# As you may notice, there is a great importance linked to Title_Mr, Age, Fare, and Sex.