import numpy as np
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.model_selection import train_test_split
import pydotplus
from IPython.display import Image

# Load the dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train the decision tree classifier
clf = DecisionTreeClassifier(max_depth=3, random_state=42)
clf.fit(X_train, y_train)

# Export the decision tree to DOT format
dot_data = export_graphviz(
    clf, 
    out_file=None, 
    feature_names=iris.feature_names, 
    class_names=iris.target_names, 
    filled=True, 
    rounded=True, 
    special_characters=True
)

# Use pydotplus to create a graph from the DOT data
graph = pydotplus.graph_from_dot_data(dot_data)

# Display the graph in a Jupyter Notebook
Image(graph.create_png())

# Save the graph to a file (optional)
graph.write_png("decision_tree.png")

True

graph

<pydotplus.graphviz.Dot at 0x7fb03495bd70>

from IPython.display import Image
Image(graph.create_png())

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pydotplus
from IPython.display import Image

# Create a custom student dataset
data = {
    "GPA": [3.5, 3.0, 3.8, 3.2, 3.6, 2.8, 3.9, 2.5, 3.1, 3.7],
    "Internship …

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pydotplus
from IPython.display import Image

# Load the Titanic dataset
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
titanic = pd.read_csv(url)

# Preprocess the dataset
# Select features and target variable
features = ['Pclass …