text: # Import libraries
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import confusion_matrix
  from sklearn.tree import DecisionTreeClassifier
  import joblib
  from sklearn import datasets
  
  # Get the dataset
  dataset = datasets.load_iris()
  
  # Split the dataset into features and labels
  X = dataset.data
  y = dataset.target
  
  # Split the dataset into training (80%) and testing (20%) data
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, shuffle = True)
  
  # Build the classifier and make prediction
  classifier = DecisionTreeClassifier()
  classifier.fit(X_train, y_train)
  prediction = classifier.predict(X_test)
  
  # Print the confusion matrix
  print("Confusion Matrix:")
  print(confusion_matrix(y_test, prediction))
  
  # Save the model to disk
  joblib.dump(classifier, 'classifier.joblib')
  
  
  
  
  
  