Generate png output from decision tree

import pandas as pd

import numpy as np

from sklearn import *

from sklearn.tree import export_graphviz

from mlxtend.plotting import plot_confusion_matrix

import matplotlib.pyplot as plt

df = pd.read_csv("https://raw.githubusercontent.com/abulbasar/data/master/credit-default.csv")

target = "default"

y = np.where(df[target] == 2, 1, 0)

X = df.copy() # features ... label is not included

del X[target]

X = pd.get_dummies(X, drop_first=True) # handle catergorical variables ... one hot encoding

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y

                            , test_size = 0.3, random_state = 1)

pipe = pipeline.Pipeline([

    #("poly", preprocessing.PolynomialFeatures(degree=5, include_bias=False)),

    #("scaler", preprocessing.StandardScaler()),

    ("est", tree.DecisionTreeClassifier(max_depth=3))

])

pipe.fit(X_train, y_train)

y_train_pred = pipe.predict(X_train)

y_test_pred = pipe.predict(X_test)

print("accuracy", metrics.accuracy_score(y_test, y_test_pred),

"\nprecision", metrics.precision_score(y_test, y_test_pred),

"\nrecall", metrics.recall_score(y_test, y_test_pred))

plot_confusion_matrix(metrics.confusion_matrix(y_test, y_test_pred))

est = pipe.steps[-1][-1]

export_graphviz(est, out_file = "tree.dot", feature_names = X.columns, filled=True)