🧠 AI with Python - 🌳 Train a Decision Tree Classifier on Iris Dataset.

# Import required libraries
from sklearn.datasets import load_iris   # To load Iris dataset
from sklearn.tree import DecisionTreeClassifier, plot_tree  # Model + visualization
from sklearn.model_selection import train_test_split        # For splitting data
from sklearn.metrics import accuracy_score                  # To evaluate model
import matplotlib.pyplot as plt
import pandas as pd

# Load the Iris dataset
iris = load_iris()

# Convert to DataFrame
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

# Display first 5 rows
df.head()

# Features and labels
X = iris.data
y = iris.target

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

print("Training data shape:", X_train.shape)
print("Testing data shape:", X_test.shape)

# Initialize the Decision Tree classifier
dt_clf = DecisionTreeClassifier(random_state=42)

# Fit model
dt_clf.fit(X_train, y_train)

print("Model training complete!")

# Make predictions
y_pred = dt_clf.predict(X_test)

# Evaluate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Decision Tree Accuracy: {accuracy * 100:.2f}%")

# Visualize decision tree
plt.figure(figsize=(12, 8))
plot_tree(dt_clf, feature_names=iris.feature_names, class_names=iris.target_names, filled=True)
plt.show()