decisiontree.py

import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split 
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.tree import plot_tree
from sklearn import tree
data=load_iris()
X=data.data
y=data.target
print(X.shape,y.shape)


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=10)
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)


y_pred =dtc.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(cm)
print("Classification report - \n", classification_report(y_test,y_pred)) 
fig,axes = plt.subplots(nrows=1, ncols=1, figsize=(4, 4), dpi=200)
tree.plot_tree(dtc, feature_names=data.feature_names,
class_names=data.target_names, filled=True)
plt.show()
fig.savefig("iris_tree.png")


new_data = [[5.1, 3.5, 1.4, 0.2],[6.2, 3.4, 5.4, 2.3]]
predictions = dtc.predict(new_data)
for prediction in predictions:print(f"Predicted class: {prediction}")