KNeighborsClassifier.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the KNN model
# We'll use k=5, meaning it looks at the 5 nearest neighbors
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

predictions = knn.predict(X_test)

# 5. Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f"KNN Model Accuracy: {accuracy:.2f}")

# Display the confusion matrix
cm = confusion_matrix(y_test, predictions)
sns.heatmap(cm, annot=True, fmt='d', cmap='Greens', 
            xticklabels=iris.target_names, yticklabels=iris.target_names)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('KNN Confusion Matrix (k=5)')
plt.show()