iris_classifier.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

# Load dataset
iris = load_iris()

X = iris.data
y = iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42
)

# Scale data
scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# KNN model
model = KNeighborsClassifier(n_neighbors=5)

# Train
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

# Results
print("Accuracy:", accuracy_score(y_test, predictions))

print("\nConfusion Matrix:")
print(confusion_matrix(y_test, predictions))

print("\nClassification Report:")
print(classification_report(y_test, predictions))