Write a python program to Implement Decision Tree for a dataset.-Iris1.csv

Python program to implement decision tree algorithm and plot the tree

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn import tree
iris = load_iris()
data = pd.DataFrame(data = iris.data, columns = iris.feature_names)
data['Species'] = iris.target
target = np.unique(iris.target)
target_n = np.unique(iris.target_names)
target_dict = dict(zip(target, target_n))
data['Species'] = data['Species'].replace(target_dict)
x = data.drop(columns = "Species")
y = data["Species"]
names_features = x.columns
target_labels = y.unique()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 93)
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(max_depth = 3, random_state = 93)
dtc.fit(x_train, y_train)
plt.figure(figsize = (30, 10), facecolor = 'b')
Tree = tree.plot_tree(dtc, feature_names = names_features, class_names = target_labels, rounded =
True, filled = True, fontsize = 14)
plt.show()
y_pred = dtc.predict(x_test)