Untitled

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import matplotlib.pyplot as plt
iris = load_iris()
X = iris.data
y = iris.target
df = pd.DataFrame(np.c_[X, y], columns=iris.feature_names + ['target'])
print("Iris Dataset: ")
print(df.head())
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = DecisionTreeClassifier(criterion='entropy', random_state=42)
clf.fit(X_train, y_train)
plt.figure(figsize=(12, 8))
tree.plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
plt.title("Decision Tree Using ID3 Algorithm (Entropy)")
plt.show()
accuracy = clf.score(X_test, y_test)
print(f"Accuracy of the model: {accuracy:.2f}")
new_sample = np.array([[5.0, 3.5, 1.5, 0.2]])
predicted_class = clf.predict(new_sample)
predicted_class_name = iris.target_names[predicted_class][0]
print(f"The predicted class for the new sample {new_sample.flatten()} is: {predicted_class_name}")