Train Data

from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier()
dtree.fit(X_train,y_train)

Predict

predictions = dtree.predict(X_test)

#Print report
from sklearn.metrics import classification_report,confusion_matrix
print(classification_report(y_test,predictions))
print(confusion_matrix(y_test,predictions))

Tree Visualization

from IPython.display import Image  
from sklearn.externals.six import StringIO  
from sklearn.tree import export_graphviz
import pydot 

features = list(df.columns[1:])
features

dot_data = StringIO()  
export_graphviz(dtree, out_file=dot_data,feature_names=features,filled=True,rounded=True)

graph = pydot.graph_from_dot_data(dot_data.getvalue())  
Image(graph[0].create_png())